From bb89a659b39f498bed41a235f78fe58f902c94c6 Mon Sep 17 00:00:00 2001 From: troyyyyy <373071971@qq.com> Date: Tue, 15 Nov 2022 14:23:58 +0800 Subject: [PATCH 001/601] abduction with exhastive search --- abducer/abducer_base.py | 113 +- abducer/kb.py | 142 +- datasets/mnist_add/get_mnist_add.py | 37 + datasets/mnist_add/test_data.txt | 5000 +++++ datasets/mnist_add/train_data.txt | 30000 ++++++++++++++++++++++++++ 5 files changed, 35160 insertions(+), 132 deletions(-) create mode 100644 datasets/mnist_add/get_mnist_add.py create mode 100644 datasets/mnist_add/test_data.txt create mode 100644 datasets/mnist_add/train_data.txt diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 13e1f75..15badf6 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -11,58 +11,60 @@ #================================================================# import abc -from abducer.kb import ClsKB, RegKB -#from kb import ClsKB, RegKB +from kb import add_KB import numpy as np def hamming_dist(A, B): - B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) - -def confidence_dist(A, B): - B = np.array(B) - - #print(A) - A = np.clip(A, 1e-9, 1) - A = np.expand_dims(A, axis=0) - A = A.repeat(axis=0, repeats=(len(B))) - rows = np.array(range(len(B))) - rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) - cols = np.array(range(len(B[0]))) - cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) - return 1 - np.prod(A[rows, cols, B], axis = 1) + return np.sum(np.array(A) != np.array(B)) class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func = "hamming", pred_res_parse = None): + def __init__(self, kb, dist_func = "hamming", pred_res_parse = None, cache = True): self.kb = kb if dist_func == "hamming": - dist_func = hamming_dist - elif dist_func == "confidence": - dist_func = confidence_dist - self.dist_func = dist_func + self.dist_func = hamming_dist if pred_res_parse is None: pred_res_parse = lambda x : x["cls"] self.pred_res_parse = pred_res_parse + + self.cache = cache + self.cache_min_address_num = {} + self.cache_candidates = {} - def abduce(self, data, max_address_num, require_more_address, length = -1): + def abduce(self, data, max_address_num = 3, require_more_address = 0, length = -1): pred_res, ans = data if length == -1: length = len(pred_res) - candidates = self.kb.get_candidates(ans, length) - pred_res = np.array(pred_res) - - cost_list = self.dist_func(pred_res, candidates) - address_num = np.min(cost_list) - threshold = min(address_num + require_more_address, max_address_num) - idxs = np.where(cost_list <= address_num+require_more_address)[0] - - #return [candidates[idx] for idx in idxs], address_num - if len(idxs) > 1: - return None - return [candidates[idx] for idx in idxs][0] + if(self.cache and (tuple(pred_res), ans) in self.cache_min_address_num): + address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), ans)] + require_more_address) + if((tuple(pred_res), ans, address_num) in self.cache_candidates): + print('cached') + return self.cache_candidates[(tuple(pred_res), ans, address_num)] + + + candidates, min_address_num, address_num = self.kb.get_abduce_candidates(pred_res, ans, length, self.dist_func, max_address_num, require_more_address) + + if(self.cache): + self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num + self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates + + return candidates + + # candidates = self.kb.get_candidates(ans, length) + + # cost_list = self.dist_func(pred_res, candidates) + # address_num = np.min(cost_list) + # # threshold = min(address_num + require_more_address, max_address_num) + # idxs = np.where(cost_list <= address_num + require_more_address)[0] + + # return [candidates[idx] for idx in idxs], address_num + + + + # if len(idxs) > 1: + # return None + # return [candidates[idx] for idx in idxs] def batch_abduce(self, Y, C, max_address_num = 3, require_more_address = 0): return [ @@ -71,34 +73,27 @@ class AbducerBase(abc.ABC): ] def __call__(self, Y, C, max_address_num = 3, require_more_address = 0): - return batch_abduce(Y, C, max_address_num, require_more_address) + return self.batch_abduce(Y, C, max_address_num, require_more_address) + + if __name__ == "__main__": - #["1+1", "0+1", "1+0", "2+0"] - X = [[1,3,1], [0,3,1], [1,2,0], [3,2,0]] - Y = [2, 1, 1, 2] - kb = RegKB(X, Y) - + pseudo_label_list = list(range(10)) + kb = add_KB(pseudo_label_list) abd = AbducerBase(kb) - res = abd.abduce(([0,2,0], None), 1, 0) + res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([0, 2, 0], 0.99), 1, 0) + res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 1) print(res) - - A = np.array([[0.5, 0.25, 0.25, 0], [0.3, 0.3, 0.3, 0.1], [0.1, 0.2, 0.3, 0.4]]) - B = [[1, 2, 3], [0, 1, 3]] - res = confidence_dist(A, B) + res = abd.abduce(([1, 1, 1], 4), max_address_num = 1, require_more_address = 1) print(res) - - A = np.array([[0.5, 0.25, 0.25, 0], [0.3, 1.0, 0.3, 0.1], [0.1, 0.2, 0.3, 1.0]]) - B = [[0, 1, 3]] - res = confidence_dist(A, B) + print() + print('Test cache') + res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) print(res) - - kb_str = ['10010001011', '00010001100', '00111101011', '11101000011', '11110011001', '11111010001', '10001010010', '11100100001', '10001001100', '11011010001', '00110000100', '11000000111', '01110111111', '11000101100', '10101011010', '00000110110', '11111110010', '11100101100', '10111001111', '10000101100', '01001011101', '01001110000', '01110001110', '01010010001', '10000100010', '01001011011', '11111111100', '01011101101', '00101110101', '11101001101', '10010110000', '10000000011'] - X = [[int(c) for c in s] for s in kb_str] - kb = RegKB(X, len(X) * [None]) - - abd = AbducerBase(kb) - res = abd.abduce(((1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1), None), 1, 0) + res = abd.abduce(([1, 1, 1], 4), max_address_num = 20, require_more_address = 1) print(res) + # res = abd.abduce(([0, 2, 0], 0.99), 1, 0) + # print(res) + + \ No newline at end of file diff --git a/abducer/kb.py b/abducer/kb.py index d33be07..032731c 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -10,57 +10,104 @@ # #================================================================# -import abc +from abc import ABC, abstractmethod import bisect import copy import numpy as np from collections import defaultdict -class KBBase(abc.ABC): - def __init__(self, X = None, Y = None): +from itertools import product + +class KBBase(ABC): + def __init__(self): pass - def get_candidates(self, key = None, length = None): + @abstractmethod + def get_candidates(self): pass + @abstractmethod def get_all_candidates(self): pass + @abstractmethod + def logic_forward(self, X): + pass + def _length(self, length): if length is None: length = list(self.base.keys()) if type(length) is int: length = [length] return length - + def __len__(self): pass -class ClsKB(KBBase): - def __init__(self, X, Y = None): +class add_KB(KBBase): + def __init__(self, pseudo_label_list, max_len = 5): super().__init__() + self.pseudo_label_list = pseudo_label_list self.base = {} - if X is None: - return - - if Y is None: - Y = [None] * len(X) + X = self.get_X(self.pseudo_label_list, max_len) + Y = self.get_Y(X, self.logic_forward) for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + + def logic_forward(self, nums): + return sum(nums) + + def get_X(self, pseudo_label_list, max_len): + res = [] + assert(max_len >= 2) + for len in range(2, max_len + 1): + res += list(product(pseudo_label_list, repeat = len)) + return res + + def get_Y(self, X, logic_forward): + return [logic_forward(nums) for nums in X] def get_candidates(self, key, length = None): if key is None: return self.get_all_candidates() length = self._length(length) - return sum([self.base[l][key] for l in length], []) def get_all_candidates(self): return sum([sum(v.values(), []) for v in self.base.values()], []) + + def get_abduce_candidates(self, pred_res, key, length, dist_func, max_address_num, require_more_address): + if key is None: + return self.get_all_candidates() + + candidates = [] + all_candidates = list(product(self.pseudo_label_list, repeat = len(pred_res))) + for address_num in range(length + 1): + if(address_num > max_address_num): + print('No candidates found') + return None, None, None + for c in all_candidates: + if(dist_func(c, pred_res) == address_num): + if(self.logic_forward(c) == key): + candidates.append(c) + if(len(candidates) > 0): + min_address_num = address_num + break + + for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): + if(address_num > max_address_num): + return candidates, min_address_num, address_num - 1 + for c in all_candidates: + if(dist_func(c, pred_res) == address_num): + if(self.logic_forward(c) == key): + candidates.append(c) + + return candidates, min_address_num, address_num + def _dict_len(self, dic): return sum(len(c) for c in dic.values()) @@ -68,70 +115,19 @@ class ClsKB(KBBase): def __len__(self): return sum(self._dict_len(v) for v in self.base.values()) -class RegKB(KBBase): - def __init__(self, X, Y = None): - super().__init__() - tmp_dict = {} - for x, y in zip(X, Y): - tmp_dict.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) - self.base = {} - for l in tmp_dict.keys(): - data = sorted(list(zip(tmp_dict[l].keys(), tmp_dict[l].values()))) - X = [x for y, x in data] - Y = [y for y, x in data] - self.base[l] = (X, Y) - - def get_candidates(self, key, length = None): - if key is None: - return self.get_all_candidates() - - length = self._length(length) - - min_err = 999999 - candidates = [] - for l in length: - X, Y = self.base[l] - - idx = bisect.bisect_left(Y, key) - begin = max(0, idx - 1) - end = min(idx + 2, len(X)) - - for idx in range(begin, end): - err = abs(Y[idx] - key) - if abs(err - min_err) < 1e-9: - candidates.extend(X[idx]) - elif err < min_err: - candidates = copy.deepcopy(X[idx]) - min_err = err - return candidates - - def get_all_candidates(self): - return sum([sum(D[0], []) for D in self.base.values()], []) - - def __len__(self): - return sum([sum(len(x) for x in D[0]) for D in self.base.values()]) if __name__ == "__main__": - X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] - Y = [2, 1, 1, 2, 2] - kb = ClsKB(X, Y) - print(len(kb)) - res = kb.get_candidates(2, 5) - print(res) - res = kb.get_candidates(2, 3) - print(res) - res = kb.get_candidates(None) - print(res) - - X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] - Y = [2, 1, 1, 2, 1.5, 1.5] - kb = RegKB(X, Y) - print(len(kb)) - res = kb.get_candidates(1.6) + pseudo_label_list = list(range(10)) + kb = add_KB(pseudo_label_list, max_len = 5) + print('len(kb):', len(kb)) + print() + res = kb.get_candidates(0) print(res) - res = kb.get_candidates(1.6, length = 9) + print() + res = kb.get_candidates(18, length = 2) print(res) - res = kb.get_candidates(None) + print() + res = kb.get_candidates(7, length = 3) print(res) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py new file mode 100644 index 0000000..d05f477 --- /dev/null +++ b/datasets/mnist_add/get_mnist_add.py @@ -0,0 +1,37 @@ +import torch +import torchvision +from torch.utils.data import Dataset +from torchvision.transforms import transforms + +class MNIST_Addition(Dataset): + def __init__(self, dataset, examples): + self.data = list() + self.dataset = dataset + with open(examples) as f: + for line in f: + line = line.strip().split(' ') + self.data.append(tuple([int(i) for i in line])) + + def __len__(self): + return len(self.data) + + def __getitem__(self, index): + i1, i2, l = self.data[index] + return self.dataset[i1][0], self.dataset[i2][0], l + +def get_mnist_add(): + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))]) + train_dataset = MNIST_Addition(torchvision.datasets.MNIST(root='./', train=True, download=True, transform=transform), './train_data.txt') + test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('./', train=False, transform=transform), batch_size=1000, shuffle=True) + X = [] + Y = [] + for i1, i2, l in train_dataset: + X.append([i1, i2]) + Y.append(l) + return X, Y, test_loader + +if __name__ == "__main__": + X, Y, test_loader = get_mnist_add() + print(len(X), len(Y)) + print(X[0][0].shape, X[0][1].shape, Y[0]) + \ No newline at end of file diff --git a/datasets/mnist_add/test_data.txt b/datasets/mnist_add/test_data.txt new file mode 100644 index 0000000..3987383 --- /dev/null +++ b/datasets/mnist_add/test_data.txt @@ -0,0 +1,5000 @@ +5236 2931 11 +7455 7918 13 +7743 9396 6 +8167 8516 9 +3767 674 12 +4307 4808 8 +1567 712 12 +2456 1692 9 +6328 7863 9 +3976 3765 8 +6323 7115 6 +1815 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11 +12881 59581 11 +30137 24034 1 +23138 16823 9 +13316 46312 13 +1053 58346 9 +57520 17503 2 +532 23236 14 +59257 21321 7 +22542 8439 12 +42466 12226 8 +22883 30267 11 +47853 40548 13 +57085 38913 16 +55373 22051 6 +38850 8397 8 +5239 36048 4 +33624 44783 8 +28587 56694 15 +8851 13152 17 +31977 452 7 +57249 41340 6 +10186 44616 17 +11694 41376 0 +53125 53092 12 +2600 14653 11 +33030 3127 12 +26184 7006 9 +10552 7518 15 +5346 59540 14 +58332 58622 14 +23901 53137 16 +30088 8162 1 +47599 34011 8 +15218 40986 10 +49717 6651 4 +58524 47520 10 +22321 57619 11 +18865 41462 7 +6546 43893 11 +9904 37109 11 +23178 18743 7 +56733 11609 14 +13236 26907 6 +2609 37041 14 +40985 18668 11 +42884 43670 12 +6405 37212 10 +17275 38516 9 +11146 15740 15 +19922 23619 13 +37558 16085 5 +25630 59584 8 +1324 39897 5 +32806 543 13 +5234 16099 8 \ No newline at end of file From 146d0eb2c1a222014342646758aea152a3158488 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 14:38:19 +0800 Subject: [PATCH 002/601] Update kb.py add ClsKB and RegKB --- abducer/kb.py | 97 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 97 insertions(+) diff --git a/abducer/kb.py b/abducer/kb.py index 032731c..0ba1479 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -115,7 +115,80 @@ class add_KB(KBBase): def __len__(self): return sum(self._dict_len(v) for v in self.base.values()) +class ClsKB(KBBase): + def __init__(self, X, Y = None): + super().__init__() + self.base = {} + + if X is None: + return + if Y is None: + Y = [None] * len(X) + + for x, y in zip(X, Y): + self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + + def get_candidates(self, key, length = None): + if key is None: + return self.get_all_candidates() + + length = self._length(length) + + return sum([self.base[l][key] for l in length], []) + + def get_all_candidates(self): + return sum([sum(v.values(), []) for v in self.base.values()], []) + + def _dict_len(self, dic): + return sum(len(c) for c in dic.values()) + + def __len__(self): + return sum(self._dict_len(v) for v in self.base.values()) + +class RegKB(KBBase): + def __init__(self, X, Y = None): + super().__init__() + tmp_dict = {} + for x, y in zip(X, Y): + tmp_dict.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + + self.base = {} + for l in tmp_dict.keys(): + data = sorted(list(zip(tmp_dict[l].keys(), tmp_dict[l].values()))) + X = [x for y, x in data] + Y = [y for y, x in data] + self.base[l] = (X, Y) + + def get_candidates(self, key, length = None): + if key is None: + return self.get_all_candidates() + + length = self._length(length) + + min_err = 999999 + candidates = [] + for l in length: + X, Y = self.base[l] + + idx = bisect.bisect_left(Y, key) + begin = max(0, idx - 1) + end = min(idx + 2, len(X)) + + for idx in range(begin, end): + err = abs(Y[idx] - key) + if abs(err - min_err) < 1e-9: + candidates.extend(X[idx]) + elif err < min_err: + candidates = copy.deepcopy(X[idx]) + min_err = err + return candidates + + def get_all_candidates(self): + return sum([sum(D[0], []) for D in self.base.values()], []) + + def __len__(self): + return sum([sum(len(x) for x in D[0]) for D in self.base.values()]) if __name__ == "__main__": pseudo_label_list = list(range(10)) @@ -130,4 +203,28 @@ if __name__ == "__main__": print() res = kb.get_candidates(7, length = 3) print(res) + print() + + X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] + Y = [2, 1, 1, 2, 2] + kb = ClsKB(X, Y) + print(len(kb)) + res = kb.get_candidates(2, 5) + print(res) + res = kb.get_candidates(2, 3) + print(res) + res = kb.get_candidates(None) + print(res) + print() + + X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] + Y = [2, 1, 1, 2, 1.5, 1.5] + kb = RegKB(X, Y) + print(len(kb)) + res = kb.get_candidates(1.6) + print(res) + res = kb.get_candidates(1.6, length = 9) + print(res) + res = kb.get_candidates(None) + print(res) From bc5fd2342f86ed24c3e944f66ea9c64ec358a168 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 14:47:24 +0800 Subject: [PATCH 003/601] Update get_mnist_add.py --- datasets/mnist_add/get_mnist_add.py | 29 ++++++++--------------------- 1 file changed, 8 insertions(+), 21 deletions(-) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py index d05f477..167ee57 100644 --- a/datasets/mnist_add/get_mnist_add.py +++ b/datasets/mnist_add/get_mnist_add.py @@ -3,35 +3,22 @@ import torchvision from torch.utils.data import Dataset from torchvision.transforms import transforms -class MNIST_Addition(Dataset): - def __init__(self, dataset, examples): - self.data = list() - self.dataset = dataset - with open(examples) as f: - for line in f: - line = line.strip().split(' ') - self.data.append(tuple([int(i) for i in line])) - - def __len__(self): - return len(self.data) - - def __getitem__(self, index): - i1, i2, l = self.data[index] - return self.dataset[i1][0], self.dataset[i2][0], l - def get_mnist_add(): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))]) - train_dataset = MNIST_Addition(torchvision.datasets.MNIST(root='./', train=True, download=True, transform=transform), './train_data.txt') + train_dataset = torchvision.datasets.MNIST(root='./', train=True, download=True, transform=transform) test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('./', train=False, transform=transform), batch_size=1000, shuffle=True) + X = [] Y = [] - for i1, i2, l in train_dataset: - X.append([i1, i2]) - Y.append(l) + with open('./train_data.txt') as f: + for line in f: + line = line.strip().split(' ') + X.append((train_dataset[int(line[0])][0], train_dataset[int(line[1])][0])) + Y.append(int(line[2])) + return X, Y, test_loader if __name__ == "__main__": X, Y, test_loader = get_mnist_add() print(len(X), len(Y)) print(X[0][0].shape, X[0][1].shape, Y[0]) - \ No newline at end of file From 523d741c8dd540961fef2d8b495314ea00a6c296 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 14:53:24 +0800 Subject: [PATCH 004/601] Update kb.py --- abducer/kb.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 0ba1479..af4c8ec 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -115,7 +115,7 @@ class add_KB(KBBase): def __len__(self): return sum(self._dict_len(v) for v in self.base.values()) -class ClsKB(KBBase): +class cls_KB(KBBase): def __init__(self, X, Y = None): super().__init__() self.base = {} @@ -128,6 +128,9 @@ class ClsKB(KBBase): for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + + def logic_forward(self): + return None def get_candidates(self, key, length = None): if key is None: @@ -146,7 +149,7 @@ class ClsKB(KBBase): def __len__(self): return sum(self._dict_len(v) for v in self.base.values()) -class RegKB(KBBase): +class reg_KB(KBBase): def __init__(self, X, Y = None): super().__init__() tmp_dict = {} @@ -159,7 +162,10 @@ class RegKB(KBBase): X = [x for y, x in data] Y = [y for y, x in data] self.base[l] = (X, Y) - + + def logic_forward(self): + return None + def get_candidates(self, key, length = None): if key is None: return self.get_all_candidates() @@ -207,7 +213,7 @@ if __name__ == "__main__": X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] Y = [2, 1, 1, 2, 2] - kb = ClsKB(X, Y) + kb = cls_KB(X, Y) print(len(kb)) res = kb.get_candidates(2, 5) print(res) @@ -219,7 +225,7 @@ if __name__ == "__main__": X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] Y = [2, 1, 1, 2, 1.5, 1.5] - kb = RegKB(X, Y) + kb = reg_KB(X, Y) print(len(kb)) res = kb.get_candidates(1.6) print(res) From 196483e23f48c1e4d85b8934adec56bc1ad81d8f Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 15:54:12 +0800 Subject: [PATCH 005/601] Update abducer_base.py --- abducer/abducer_base.py | 28 +++++++++++++++++++++++++--- 1 file changed, 25 insertions(+), 3 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 15badf6..f990013 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -17,6 +17,26 @@ import numpy as np def hamming_dist(A, B): return np.sum(np.array(A) != np.array(B)) +def hamming_dist_kb(A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) + +def confidence_dist_kb(A, B): + B = np.array(B) + + #print(A) + A = np.clip(A, 1e-9, 1) + A = np.expand_dims(A, axis=0) + A = A.repeat(axis=0, repeats=(len(B))) + rows = np.array(range(len(B))) + rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) + cols = np.array(range(len(B[0]))) + cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) + return 1 - np.prod(A[rows, cols, B], axis = 1) + + + class AbducerBase(abc.ABC): def __init__(self, kb, dist_func = "hamming", pred_res_parse = None, cache = True): self.kb = kb @@ -83,17 +103,19 @@ if __name__ == "__main__": abd = AbducerBase(kb) res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) print(res) + print() res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 1) print(res) + print() res = abd.abduce(([1, 1, 1], 4), max_address_num = 1, require_more_address = 1) print(res) print() - print('Test cache') res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([1, 1, 1], 4), max_address_num = 20, require_more_address = 1) + print() + res = abd.abduce(([1, 1, 1], 5), max_address_num = 2, require_more_address = 1) print(res) # res = abd.abduce(([0, 2, 0], 0.99), 1, 0) # print(res) - \ No newline at end of file + From 83dff66d9ff4b8871b8496c9cd75536625bfc070 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 20:04:27 +0800 Subject: [PATCH 006/601] Update kb.py --- abducer/kb.py | 123 +++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 111 insertions(+), 12 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index af4c8ec..c3ddff0 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -17,7 +17,7 @@ import numpy as np from collections import defaultdict -from itertools import product +from itertools import product, combinations class KBBase(ABC): def __init__(self): @@ -80,20 +80,30 @@ class add_KB(KBBase): def get_all_candidates(self): return sum([sum(v.values(), []) for v in self.base.values()], []) - def get_abduce_candidates(self, pred_res, key, length, dist_func, max_address_num, require_more_address): + def get_abduce_candidates(self, pred_res, key, max_address_num, require_more_address): if key is None: return self.get_all_candidates() candidates = [] - all_candidates = list(product(self.pseudo_label_list, repeat = len(pred_res))) - for address_num in range(length + 1): + + for address_num in range(len(pred_res) + 1): if(address_num > max_address_num): print('No candidates found') return None, None, None - for c in all_candidates: - if(dist_func(c, pred_res) == address_num): - if(self.logic_forward(c) == key): - candidates.append(c) + + if(address_num == 0): + if(self.logic_forward(pred_res) == key): + candidates.append(pred_res) + else: + all_address_candidate = list(product(self.pseudo_label_list, repeat = address_num)) + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + for address_idx in address_idx_list: + for c in all_address_candidate: + pred_res_array = np.array(pred_res) + pred_res_array[np.array(address_idx)] = c + if(np.count_nonzero(np.array(c) != np.array(pred_res)[np.array(address_idx)]) == address_num and self.logic_forward(pred_res_array) == key): + candidates.append(pred_res_array) + if(len(candidates) > 0): min_address_num = address_num break @@ -101,10 +111,14 @@ class add_KB(KBBase): for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if(address_num > max_address_num): return candidates, min_address_num, address_num - 1 - for c in all_candidates: - if(dist_func(c, pred_res) == address_num): - if(self.logic_forward(c) == key): - candidates.append(c) + all_candidate = list(product(self.pseudo_label_list, repeat = address_num)) + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + for address_idx in address_idx_list: + for c in all_candidate: + pred_res_array = np.array(pred_res) + pred_res_array[np.array(address_idx)] = c + if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num and self.logic_forward(pred_res_array) == key): + candidates.append(pred_res_array) return candidates, min_address_num, address_num @@ -114,6 +128,91 @@ class add_KB(KBBase): def __len__(self): return sum(self._dict_len(v) for v in self.base.values()) + +# class hwf_KB(KBBase): +# def __init__(self, pseudo_label_list, max_len = 5): +# super().__init__() +# self.pseudo_label_list = pseudo_label_list +# self.base = {} + +# X = self.get_X(self.pseudo_label_list, max_len) +# Y = self.get_Y(X, self.logic_forward) + +# for x, y in zip(X, Y): +# self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + +# def logic_forward(self, nums): +# return sum(nums) + +# def get_X(self, pseudo_label_list, max_len): +# res = [] +# assert(max_len >= 2) +# for len in range(2, max_len + 1): +# res += list(product(pseudo_label_list, repeat = len)) +# return res + +# def get_Y(self, X, logic_forward): +# return [logic_forward(nums) for nums in X] + +# def get_candidates(self, key, length = None): +# if key is None: +# return self.get_all_candidates() + +# length = self._length(length) +# return sum([self.base[l][key] for l in length], []) + +# def get_all_candidates(self): +# return sum([sum(v.values(), []) for v in self.base.values()], []) + +# def get_abduce_candidates(self, pred_res, key, length, dist_func, max_address_num, require_more_address): +# if key is None: +# return self.get_all_candidates() + +# candidates = [] +# # all_candidates = list(product(self.pseudo_label_list, repeat = len(pred_res))) + +# for address_num in range(length + 1): +# if(address_num > max_address_num): +# print('No candidates found') +# return None, None, None + +# if(address_num == 0): +# if(self.logic_forward(pred_res) == key): +# candidates.append(pred_res) +# else: +# all_address_candidate = list(product(self.pseudo_label_list, repeat = address_num)) +# address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) +# for address_idx in address_idx_list: +# for c in all_address_candidate: +# pred_res_array = np.array(pred_res) +# pred_res_array[np.array(address_idx)] = c +# if(np.count_nonzero(np.array(c) != np.array(pred_res)[np.array(address_idx)]) == address_num and self.logic_forward(pred_res_array) == key): +# candidates.append(pred_res_array) + +# if(len(candidates) > 0): +# min_address_num = address_num +# break + +# for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): +# if(address_num > max_address_num): +# return candidates, min_address_num, address_num - 1 +# all_candidate = list(product(self.pseudo_label_list, repeat = address_num)) +# address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) +# for address_idx in address_idx_list: +# for c in all_candidate: +# pred_res_array = np.array(pred_res) +# pred_res_array[np.array(address_idx)] = c +# if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num and self.logic_forward(pred_res_array) == key): +# candidates.append(pred_res_array) + +# return candidates, min_address_num, address_num + + +# def _dict_len(self, dic): +# return sum(len(c) for c in dic.values()) + +# def __len__(self): +# return sum(self._dict_len(v) for v in self.base.values()) class cls_KB(KBBase): def __init__(self, X, Y = None): From 7f07c781d00e8e395077a44588df469baabcaa5d Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 20:24:25 +0800 Subject: [PATCH 007/601] Create kb.py --- abducer/kb.py | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index c3ddff0..9c09765 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -99,10 +99,11 @@ class add_KB(KBBase): address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) for address_idx in address_idx_list: for c in all_address_candidate: - pred_res_array = np.array(pred_res) - pred_res_array[np.array(address_idx)] = c - if(np.count_nonzero(np.array(c) != np.array(pred_res)[np.array(address_idx)]) == address_num and self.logic_forward(pred_res_array) == key): - candidates.append(pred_res_array) + if(np.count_nonzero(np.array(c) != np.array(pred_res)[np.array(address_idx)]) == address_num): + pred_res_array = np.array(pred_res) + pred_res_array[np.array(address_idx)] = c + if(self.logic_forward(pred_res_array) == key): + candidates.append(pred_res_array) if(len(candidates) > 0): min_address_num = address_num @@ -115,10 +116,11 @@ class add_KB(KBBase): address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) for address_idx in address_idx_list: for c in all_candidate: - pred_res_array = np.array(pred_res) - pred_res_array[np.array(address_idx)] = c - if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num and self.logic_forward(pred_res_array) == key): - candidates.append(pred_res_array) + if(np.count_nonzero(np.array(c) != np.array(pred_res)[np.array(address_idx)]) == address_num): + pred_res_array = np.array(pred_res) + pred_res_array[np.array(address_idx)] = c + if(self.logic_forward(pred_res_array) == key): + candidates.append(pred_res_array) return candidates, min_address_num, address_num @@ -310,10 +312,16 @@ if __name__ == "__main__": print(res) print() + pseudo_label_list = list(range(10)) + ['+', '-', '*', '/'] + kb = hwf_KB(pseudo_label_list, max_len = 5) + print('len(kb):', len(kb)) + print() + + X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] Y = [2, 1, 1, 2, 2] kb = cls_KB(X, Y) - print(len(kb)) + print('len(kb):', len(kb)) res = kb.get_candidates(2, 5) print(res) res = kb.get_candidates(2, 3) @@ -325,7 +333,7 @@ if __name__ == "__main__": X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] Y = [2, 1, 1, 2, 1.5, 1.5] kb = reg_KB(X, Y) - print(len(kb)) + print('len(kb):', len(kb)) res = kb.get_candidates(1.6) print(res) res = kb.get_candidates(1.6, length = 9) From db7244d2be0a7b31e7c4cdfb07e3acf7e88b31e6 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 20:25:54 +0800 Subject: [PATCH 008/601] Update kb.py --- abducer/kb.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 9c09765..b60f50e 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -99,8 +99,8 @@ class add_KB(KBBase): address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) for address_idx in address_idx_list: for c in all_address_candidate: - if(np.count_nonzero(np.array(c) != np.array(pred_res)[np.array(address_idx)]) == address_num): - pred_res_array = np.array(pred_res) + pred_res_array = np.array(pred_res) + if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num): pred_res_array[np.array(address_idx)] = c if(self.logic_forward(pred_res_array) == key): candidates.append(pred_res_array) @@ -116,8 +116,8 @@ class add_KB(KBBase): address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) for address_idx in address_idx_list: for c in all_candidate: - if(np.count_nonzero(np.array(c) != np.array(pred_res)[np.array(address_idx)]) == address_num): - pred_res_array = np.array(pred_res) + pred_res_array = np.array(pred_res) + if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num): pred_res_array[np.array(address_idx)] = c if(self.logic_forward(pred_res_array) == key): candidates.append(pred_res_array) From 5beb068ee1c3d9f902fcefc27a1dcf93402b6b28 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 21:23:00 +0800 Subject: [PATCH 009/601] Update kb.py --- abducer/kb.py | 156 +++++++++++++++----------------------------------- 1 file changed, 46 insertions(+), 110 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index b60f50e..95e9692 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -16,8 +16,7 @@ import copy import numpy as np from collections import defaultdict - -from itertools import product, combinations +from itertools import product class KBBase(ABC): def __init__(self): @@ -46,16 +45,17 @@ class KBBase(ABC): pass class add_KB(KBBase): - def __init__(self, pseudo_label_list, max_len = 5): + def __init__(self, pseudo_label_list, kb_max_len = -1): super().__init__() self.pseudo_label_list = pseudo_label_list self.base = {} - - X = self.get_X(self.pseudo_label_list, max_len) - Y = self.get_Y(X, self.logic_forward) + self.kb_max_len = kb_max_len + if(self.kb_max_len > 0): + X = self.get_X(self.pseudo_label_list, self.kb_max_len) + Y = self.get_Y(X, self.logic_forward) - for x, y in zip(X, Y): - self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + for x, y in zip(X, Y): + self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) def logic_forward(self, nums): return sum(nums) @@ -71,60 +71,20 @@ class add_KB(KBBase): return [logic_forward(nums) for nums in X] def get_candidates(self, key, length = None): + if(self.base == {}): + return [] + if key is None: return self.get_all_candidates() length = self._length(length) + if(self.kb_max_len < min(length)): + return [] return sum([self.base[l][key] for l in length], []) def get_all_candidates(self): return sum([sum(v.values(), []) for v in self.base.values()], []) - def get_abduce_candidates(self, pred_res, key, max_address_num, require_more_address): - if key is None: - return self.get_all_candidates() - - candidates = [] - - for address_num in range(len(pred_res) + 1): - if(address_num > max_address_num): - print('No candidates found') - return None, None, None - - if(address_num == 0): - if(self.logic_forward(pred_res) == key): - candidates.append(pred_res) - else: - all_address_candidate = list(product(self.pseudo_label_list, repeat = address_num)) - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) - for address_idx in address_idx_list: - for c in all_address_candidate: - pred_res_array = np.array(pred_res) - if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num): - pred_res_array[np.array(address_idx)] = c - if(self.logic_forward(pred_res_array) == key): - candidates.append(pred_res_array) - - if(len(candidates) > 0): - min_address_num = address_num - break - - for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): - if(address_num > max_address_num): - return candidates, min_address_num, address_num - 1 - all_candidate = list(product(self.pseudo_label_list, repeat = address_num)) - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) - for address_idx in address_idx_list: - for c in all_candidate: - pred_res_array = np.array(pred_res) - if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num): - pred_res_array[np.array(address_idx)] = c - if(self.logic_forward(pred_res_array) == key): - candidates.append(pred_res_array) - - return candidates, min_address_num, address_num - - def _dict_len(self, dic): return sum(len(c) for c in dic.values()) @@ -132,16 +92,17 @@ class add_KB(KBBase): return sum(self._dict_len(v) for v in self.base.values()) # class hwf_KB(KBBase): -# def __init__(self, pseudo_label_list, max_len = 5): +# def __init__(self, pseudo_label_list, kb_max_len = -1): # super().__init__() # self.pseudo_label_list = pseudo_label_list # self.base = {} - -# X = self.get_X(self.pseudo_label_list, max_len) -# Y = self.get_Y(X, self.logic_forward) +# self.kb_max_len = kb_max_len +# if(self.kb_max_len > 0): +# X = self.get_X(self.pseudo_label_list, self.kb_max_len) +# Y = self.get_Y(X, self.logic_forward) -# for x, y in zip(X, Y): -# self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) +# for x, y in zip(X, Y): +# self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) # def logic_forward(self, nums): # return sum(nums) @@ -157,65 +118,26 @@ class add_KB(KBBase): # return [logic_forward(nums) for nums in X] # def get_candidates(self, key, length = None): +# if(self.base == {}): +# return [] + # if key is None: # return self.get_all_candidates() # length = self._length(length) +# if(self.kb_max_len < min(length)): +# return [] # return sum([self.base[l][key] for l in length], []) # def get_all_candidates(self): # return sum([sum(v.values(), []) for v in self.base.values()], []) - -# def get_abduce_candidates(self, pred_res, key, length, dist_func, max_address_num, require_more_address): -# if key is None: -# return self.get_all_candidates() - -# candidates = [] -# # all_candidates = list(product(self.pseudo_label_list, repeat = len(pred_res))) - -# for address_num in range(length + 1): -# if(address_num > max_address_num): -# print('No candidates found') -# return None, None, None - -# if(address_num == 0): -# if(self.logic_forward(pred_res) == key): -# candidates.append(pred_res) -# else: -# all_address_candidate = list(product(self.pseudo_label_list, repeat = address_num)) -# address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) -# for address_idx in address_idx_list: -# for c in all_address_candidate: -# pred_res_array = np.array(pred_res) -# pred_res_array[np.array(address_idx)] = c -# if(np.count_nonzero(np.array(c) != np.array(pred_res)[np.array(address_idx)]) == address_num and self.logic_forward(pred_res_array) == key): -# candidates.append(pred_res_array) - -# if(len(candidates) > 0): -# min_address_num = address_num -# break - -# for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): -# if(address_num > max_address_num): -# return candidates, min_address_num, address_num - 1 -# all_candidate = list(product(self.pseudo_label_list, repeat = address_num)) -# address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) -# for address_idx in address_idx_list: -# for c in all_candidate: -# pred_res_array = np.array(pred_res) -# pred_res_array[np.array(address_idx)] = c -# if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num and self.logic_forward(pred_res_array) == key): -# candidates.append(pred_res_array) - -# return candidates, min_address_num, address_num - # def _dict_len(self, dic): # return sum(len(c) for c in dic.values()) # def __len__(self): # return sum(self._dict_len(v) for v in self.base.values()) - + class cls_KB(KBBase): def __init__(self, X, Y = None): super().__init__() @@ -298,25 +220,39 @@ class reg_KB(KBBase): return sum([sum(len(x) for x in D[0]) for D in self.base.values()]) if __name__ == "__main__": + # With ground KB pseudo_label_list = list(range(10)) - kb = add_KB(pseudo_label_list, max_len = 5) + kb = add_KB(pseudo_label_list, kb_max_len = 5) print('len(kb):', len(kb)) - print() res = kb.get_candidates(0) print(res) - print() res = kb.get_candidates(18, length = 2) print(res) - print() + res = kb.get_candidates(18, length = 8) + print(res) res = kb.get_candidates(7, length = 3) print(res) print() - pseudo_label_list = list(range(10)) + ['+', '-', '*', '/'] - kb = hwf_KB(pseudo_label_list, max_len = 5) + # Without ground KB + pseudo_label_list = list(range(10)) + kb = add_KB(pseudo_label_list) print('len(kb):', len(kb)) + res = kb.get_candidates(0) + print(res) + res = kb.get_candidates(18, length = 2) + print(res) + res = kb.get_candidates(18, length = 8) + print(res) + res = kb.get_candidates(7, length = 3) + print(res) print() + # pseudo_label_list = list(range(10)) + ['+', '-', '*', '/'] + # kb = hwf_KB(pseudo_label_list, max_len = 5) + # print('len(kb):', len(kb)) + # print() + X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] Y = [2, 1, 1, 2, 2] From 7edd652eea43fb43824db61b7bf1929fc0b12779 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 21:23:15 +0800 Subject: [PATCH 010/601] Update abducer_base.py --- abducer/abducer_base.py | 77 +++++++++++++++++++++++++++++++++-------- 1 file changed, 62 insertions(+), 15 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index f990013..b1c661b 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -14,15 +14,14 @@ import abc from kb import add_KB import numpy as np -def hamming_dist(A, B): - return np.sum(np.array(A) != np.array(B)) +from itertools import product, combinations -def hamming_dist_kb(A, B): +def hamming_dist(A, B): B = np.array(B) A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) return np.sum(A != B, axis = 1) -def confidence_dist_kb(A, B): +def confidence_dist(A, B): B = np.array(B) #print(A) @@ -41,7 +40,10 @@ class AbducerBase(abc.ABC): def __init__(self, kb, dist_func = "hamming", pred_res_parse = None, cache = True): self.kb = kb if dist_func == "hamming": - self.dist_func = hamming_dist + dist_func = hamming_dist + elif dist_func == "confidence": + dist_func = confidence_dist + self.dist_func = dist_func if pred_res_parse is None: pred_res_parse = lambda x : x["cls"] self.pred_res_parse = pred_res_parse @@ -50,6 +52,7 @@ class AbducerBase(abc.ABC): self.cache_min_address_num = {} self.cache_candidates = {} + def abduce(self, data, max_address_num = 3, require_more_address = 0, length = -1): pred_res, ans = data @@ -62,8 +65,16 @@ class AbducerBase(abc.ABC): print('cached') return self.cache_candidates[(tuple(pred_res), ans, address_num)] - - candidates, min_address_num, address_num = self.kb.get_abduce_candidates(pred_res, ans, length, self.dist_func, max_address_num, require_more_address) + if(self.kb.base != {}): + all_candidates = self.kb.get_candidates(ans, length) + cost_list = self.dist_func(pred_res, all_candidates) + min_address_num = np.min(cost_list) + address_num = min(max_address_num, min_address_num + require_more_address) + idxs = np.where(cost_list <= address_num)[0] + candidates = [all_candidates[idx] for idx in idxs] + + else: + candidates, min_address_num, address_num = self.get_abduce_candidates(pred_res, ans, max_address_num, require_more_address) if(self.cache): self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num @@ -71,21 +82,57 @@ class AbducerBase(abc.ABC): return candidates - # candidates = self.kb.get_candidates(ans, length) - # cost_list = self.dist_func(pred_res, candidates) - # address_num = np.min(cost_list) - # # threshold = min(address_num + require_more_address, max_address_num) - # idxs = np.where(cost_list <= address_num + require_more_address)[0] - - # return [candidates[idx] for idx in idxs], address_num # if len(idxs) > 1: # return None # return [candidates[idx] for idx in idxs] + + def address(self, address_num, pred_res, key): + new_candidates = [] + all_address_candidate = list(product(self.kb.pseudo_label_list, repeat = address_num)) + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + for address_idx in address_idx_list: + for c in all_address_candidate: + pred_res_array = np.array(pred_res) + if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num): + pred_res_array[np.array(address_idx)] = c + if(self.kb.logic_forward(pred_res_array) == key): + new_candidates.append(pred_res_array) + return new_candidates, address_num + + def get_abduce_candidates(self, pred_res, key, max_address_num, require_more_address): + + candidates = [] + for address_num in range(len(pred_res) + 1): + if(address_num > max_address_num): + print('No candidates found') + return None, None, None + + if(address_num == 0): + if(self.kb.logic_forward(pred_res) == key): + candidates.append(pred_res) + else: + new_candidates, address_num = self.address(address_num, pred_res, key) + candidates += new_candidates + + if(len(candidates) > 0): + min_address_num = address_num + break + + for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): + if(address_num > max_address_num): + return candidates, min_address_num, address_num - 1 + new_candidates, address_num = self.address(address_num, pred_res, key) + candidates += new_candidates + + return candidates, min_address_num, address_num + + + def batch_abduce(self, Y, C, max_address_num = 3, require_more_address = 0): return [ self.abduce((y, c), max_address_num, require_more_address)\ @@ -101,7 +148,7 @@ if __name__ == "__main__": pseudo_label_list = list(range(10)) kb = add_KB(pseudo_label_list) abd = AbducerBase(kb) - res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 1) print(res) print() res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 1) From 0883e27fc197609601fcf5a06da8ca17a1c773eb Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 15 Nov 2022 22:50:07 +0800 Subject: [PATCH 011/601] Update abducer_base.py --- abducer/abducer_base.py | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index b1c661b..38cbf64 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -45,7 +45,10 @@ class AbducerBase(abc.ABC): dist_func = confidence_dist self.dist_func = dist_func if pred_res_parse is None: - pred_res_parse = lambda x : x["cls"] + if(dist_func == "hamming"): + pred_res_parse = lambda x : x["cls"] + elif dist_func == "confidence": + pred_res_parse = lambda x : x[" "] self.pred_res_parse = pred_res_parse self.cache = cache @@ -75,20 +78,18 @@ class AbducerBase(abc.ABC): else: candidates, min_address_num, address_num = self.get_abduce_candidates(pred_res, ans, max_address_num, require_more_address) + cost_list = self.dist_func(pred_res, candidates) if(self.cache): self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates - - return candidates - + cost_list = self.dist_func(pred_res, candidates) + min_address_num = np.min(cost_list) + idxs = np.where(cost_list == min_address_num)[0] + candidates = [candidates[idx] for idx in idxs] - - - # if len(idxs) > 1: - # return None - # return [candidates[idx] for idx in idxs] + return candidates[0] def address(self, address_num, pred_res, key): new_candidates = [] @@ -99,7 +100,7 @@ class AbducerBase(abc.ABC): pred_res_array = np.array(pred_res) if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num): pred_res_array[np.array(address_idx)] = c - if(self.kb.logic_forward(pred_res_array) == key): + if(abs(self.kb.logic_forward(pred_res_array) - key) <= 1e-3): new_candidates.append(pred_res_array) return new_candidates, address_num @@ -113,7 +114,7 @@ class AbducerBase(abc.ABC): return None, None, None if(address_num == 0): - if(self.kb.logic_forward(pred_res) == key): + if(abs(self.kb.logic_forward(pred_res) - key) <= 1e-3): candidates.append(pred_res) else: new_candidates, address_num = self.address(address_num, pred_res, key) @@ -148,7 +149,7 @@ if __name__ == "__main__": pseudo_label_list = list(range(10)) kb = add_KB(pseudo_label_list) abd = AbducerBase(kb) - res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 1) + res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) print(res) print() res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 1) From 7f29b79aeeb7982410fbc9aafc7e3b090524c7ce Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Tue, 15 Nov 2022 23:25:17 +0800 Subject: [PATCH 012/601] modify wabl_models.py and basic_model.py --- models/basic_model.py | 139 +++++++++++++++++++++++------------------- models/lenet5.py | 2 +- models/wabl_models.py | 62 ++++++------------- 3 files changed, 94 insertions(+), 109 deletions(-) diff --git a/models/basic_model.py b/models/basic_model.py index c54bb22..47aeab9 100644 --- a/models/basic_model.py +++ b/models/basic_model.py @@ -18,8 +18,6 @@ from torch.autograd import Variable from torch.utils.data import Dataset import torchvision -import utils.utils as mutils - import os from multiprocessing import Pool @@ -121,6 +119,7 @@ class FakeRecorder(): from torch.nn import init from torch import nn + def weigth_init(m): if isinstance(m, nn.Conv2d): init.xavier_uniform_(m.weight.data) @@ -137,18 +136,23 @@ class BasicModel(): model, criterion, optimizer, - converter, device, params, sign_list, + transform = None, + target_transform=None, + collate_fn = None, + pretrained = False, recorder = None): self.model = model.to(device) - self.model.apply(weigth_init) + self.criterion = criterion self.optimizer = optimizer - self.converter = converter + self.transform = transform + self.target_transform = target_transform self.device = device + sign_list = sorted(list(set(sign_list))) self.mapping = dict(zip(sign_list, list(range(len(sign_list))))) self.remapping = dict(zip(list(range(len(sign_list))), sign_list)) @@ -157,8 +161,15 @@ class BasicModel(): recorder = FakeRecorder() self.recorder = recorder + if pretrained: + # the paths of model, optimizer should be included in params + self.load(params.load_dir) + else: + self.model.apply(weigth_init) + self.save_interval = params.saveInterval self.params = params + self.collate_fn = collate_fn pass def _fit(self, data_loader, n_epoch, stop_loss): @@ -171,7 +182,10 @@ class BasicModel(): recorder.print(f"{epoch}/{n_epoch} model training loss is {loss_value}") if loss_value < min_loss: min_loss = loss_value - if loss_value < stop_loss: + if epoch > 0 and self.save_interval is not None and epoch % self.save_interval == 0: + assert hasattr(self.params, 'save_dir') + self.save(self.params.save_dir) + if stop_loss is not None and loss_value < stop_loss: break recorder.print("Model fitted, minimal loss is ", min_loss) return loss_value @@ -181,30 +195,32 @@ class BasicModel(): def fit(self, data_loader = None, X = None, - y = None, - n_epoch = 100, - stop_loss = 0.001): + y = None): if data_loader is None: params = self.params + collate_fn = self.collate_fn + transform = self.transform + target_transform = self.target_transform + Y = self.str2ints(y) - train_dataset = XYDataset(X, Y) + train_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) sampler = None data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=params.batchSize, \ shuffle=True, sampler=sampler, num_workers=int(params.workers), \ - collate_fn=alignCollate(imgH=params.imgH, imgW=params.imgW, keep_ratio=params.keep_ratio)) - return self._fit(data_loader, n_epoch, stop_loss) + collate_fn=collate_fn) + return self._fit(data_loader, params.n_epoch, params.stop_loss) def train_epoch(self, data_loader): - loss_avg = mutils.averager() + # loss_avg = mutils.averager() + self.model.train() + loss_value = 0 for i, data in enumerate(data_loader): X = data[0] Y = data[1] - cost = self.train_batch(X, Y) - loss_avg.add(cost) - - loss_value = float(loss_avg.val()) - loss_avg.reset() + loss = self.train_batch(X, Y) + loss_value += loss.item() + return loss_value def train_batch(self, X, Y): @@ -212,17 +228,10 @@ class BasicModel(): model = self.model criterion = self.criterion optimizer = self.optimizer - converter = self.converter device = self.device - - # set training mode - for p in model.parameters(): - p.requires_grad = True - model.train() # init training status - torch.autograd.set_detect_anomaly(True) - optimizer.zero_grad() + # torch.autograd.set_detect_anomaly(True) # model predict X = X.to(device) @@ -233,41 +242,39 @@ class BasicModel(): loss = criterion(pred_Y, Y) # back propagation and optimize + optimizer.zero_grad() loss.backward() optimizer.step() return loss def _predict(self, data_loader): model = self.model - criterion = self.criterion - converter = self.converter - params = self.params device = self.device - - for p in model.parameters(): - p.requires_grad = False model.eval() - - n_correct = 0 - - results = [] - for i, data in enumerate(data_loader): - X = data[0].to(device) - pred_Y = model(X) - results.append(pred_Y) + + with torch.no_grad(): + results = [] + for i, data in enumerate(data_loader): + X = data[0].to(device) + pred_Y = model(X) + results.append(pred_Y) return torch.cat(results, axis=0) def predict(self, data_loader = None, X = None, print_prefix = ""): - params = self.params if data_loader is None: + params = self.params + collate_fn = self.collate_fn + transform = self.transform + target_transform = self.target_transform + Y = [0] * len(X) - val_dataset = XYDataset(X, Y) + val_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) sampler = None data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=params.batchSize, \ shuffle=False, sampler=sampler, num_workers=int(params.workers), \ - collate_fn=alignCollate(imgH=params.imgH, imgW=params.imgW, keep_ratio=params.keep_ratio)) + collate_fn=collate_fn) recorder = self.recorder recorder.print('Start Predict ', print_prefix) @@ -275,14 +282,18 @@ class BasicModel(): return [self.remapping[int(y)] for y in Y] def predict_proba(self, data_loader = None, X = None, print_prefix = ""): - params = self.params if data_loader is None: + params = self.params + collate_fn = self.collate_fn + transform = self.transform + target_transform = self.target_transform + Y = [0] * len(X) - val_dataset = XYDataset(X, Y) + val_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) sampler = None data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=params.batchSize, \ shuffle=False, sampler=sampler, num_workers=int(params.workers), \ - collate_fn=alignCollate(imgH=params.imgH, imgW=params.imgW, keep_ratio=params.keep_ratio)) + collate_fn=collate_fn) recorder = self.recorder recorder.print('Start Predict ', print_prefix) @@ -292,45 +303,45 @@ class BasicModel(): model = self.model criterion = self.criterion recorder = self.recorder - converter = self.converter - params = self.params device = self.device recorder.print('Start val ', print_prefix) - - for p in model.parameters(): - p.requires_grad = False model.eval() n_correct = 0 pred_num = 0 - loss_avg = mutils.averager() - for i, data in enumerate(data_loader): - X = data[0].to(device) - Y = data[1].to(device) + loss_value = 0 + with torch.no_grad(): + for i, data in enumerate(data_loader): + X = data[0].to(device) + Y = data[1].to(device) - pred_Y = model(X) + pred_Y = model(X) - correct_num = sum(Y == pred_Y.argmax(axis=1)) - loss = criterion(pred_Y, Y) - loss_avg.add(loss) + correct_num = sum(Y == pred_Y.argmax(axis=1)) + loss = criterion(pred_Y, Y) + loss_value += loss.item() n_correct += correct_num pred_num += len(X) accuracy = float(n_correct) / float(pred_num) - recorder.print('[%s] Val loss: %f, accuray: %f' % (print_prefix, loss_avg.val(), accuracy)) + recorder.print('[%s] Val loss: %f, accuray: %f' % (print_prefix, loss_value, accuracy)) return accuracy def val(self, data_loader = None, X = None, y = None, print_prefix = ""): - params = self.params if data_loader is None: - y = self.str2ints(y) - val_dataset = XYDataset(X, y) + params = self.params + collate_fn = self.collate_fn + transform = self.transform + target_transform = self.target_transform + + Y = self.str2ints(y) + val_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) sampler = None data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=params.batchSize, \ shuffle=True, sampler=sampler, num_workers=int(params.workers), \ - collate_fn=alignCollate(imgH=params.imgH, imgW=params.imgW, keep_ratio=params.keep_ratio)) + collate_fn=collate_fn) return self._val(data_loader, print_prefix) def score(self, data_loader = None, X = None, y = None, print_prefix = ""): diff --git a/models/lenet5.py b/models/lenet5.py index 676f553..9d5b054 100644 --- a/models/lenet5.py +++ b/models/lenet5.py @@ -34,7 +34,7 @@ class LeNet5(nn.Module): self.fc1 = nn.Linear(256, 120) self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 13) + self.fc3 = nn.Linear(84, 10) def forward(self, x): '''å‰å‘传播函数''' diff --git a/models/wabl_models.py b/models/wabl_models.py index 4b27418..15fe6e6 100644 --- a/models/wabl_models.py +++ b/models/wabl_models.py @@ -21,6 +21,7 @@ from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF +from models.basic_model import BasicModel import pickle as pk import random @@ -36,7 +37,6 @@ def merge_data(X): ret_X = list(chain(*X)) return ret_X, ret_mark - def reshape_data(Y, marks): begin_mark = 0 ret_Y = [] @@ -58,57 +58,26 @@ class WABLBasicModel: pass def predict(self, X): - if self.share: - data_X, marks = merge_data(X) - prob = self.cls_list[0].predict_proba(X = data_X) - cls = np.array(prob).argmax(axis = 1) + data_X, marks = merge_data(X) + prob = self.cls_list[0].predict_proba(X = data_X) + cls = np.array(prob).argmax(axis = 1) - prob = reshape_data(prob, marks) - cls = reshape_data(cls, marks) - else: - cls_result = [] - prob_result = [] - for i in range(self.code_len): - data_X = get_part_data(X, i) - tmp_prob = self.cls_list[i].predict_proba(X = data_X) - cls_result.append(np.array(tmp_prob).argmax(axis = 1)) - prob_result.append(tmp_prob) - - cls = list(zip(*cls_result)) - prob = list(zip(*prob_result)) + prob = reshape_data(prob, marks) + cls = reshape_data(cls, marks) return {"cls" : cls, "prob" : prob} def valid(self, X, Y): - if self.share: - data_X, _ = merge_data(X) - data_Y, _ = merge_data(Y) - score = self.cls_list[0].score(X = data_X, y = data_Y) - return score, [score] - else: - score_list = [] - for i in range(self.code_len): - data_X = get_part_data(X, i) - data_Y = get_part_data(Y, i) - score_list.append(self.cls_list[i].score(data_X, data_Y)) - - return sum(score_list) / len(score_list), score_list + data_X, _ = merge_data(X) + data_Y, _ = merge_data(Y) + score = self.cls_list[0].score(X = data_X, y = data_Y) + return score, [score] def train(self, X, Y): #self.label_lists = [] - if self.share: - data_X, _ = merge_data(X) - data_Y, _ = merge_data(Y) - self.cls_list[0].fit(X = data_X, y = data_Y) - else: - for i in range(self.code_len): - data_X = get_part_data(X, i) - data_Y = get_part_data(Y, i) - self.cls_list[i].fit(data_X, data_Y) - - def _set_label_lists(self, label_lists): - label_lists = [sorted(list(set(label_list))) for label_list in label_lists] - self.label_lists = label_lists + data_X, _ = merge_data(X) + data_Y, _ = merge_data(Y) + self.cls_list[0].fit(X = data_X, y = data_Y) class DecisionTree(WABLBasicModel): def __init__(self, code_len, label_lists, share = False): @@ -169,6 +138,11 @@ class CNN(WABLBasicModel): self.cls_list[i].fit(data_X, data_Y) #self.label_lists.append(sorted(list(set(data_Y)))) +class MyModel(WABLBasicModel): + def __init__(self, base_model): + + self.cls_list = [] + self.cls_list.append(base_model) if __name__ == "__main__": #data_path = "utils/hamming_data/generated_data/hamming_7_3_0.20.pk" From 6ffdf44c351c31ea0692688f0efdb0eafb9b92f1 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 10:16:13 +0800 Subject: [PATCH 013/601] Update kb.py --- abducer/kb.py | 158 ++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 115 insertions(+), 43 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 95e9692..6007a5b 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -91,52 +91,120 @@ class add_KB(KBBase): def __len__(self): return sum(self._dict_len(v) for v in self.base.values()) -# class hwf_KB(KBBase): -# def __init__(self, pseudo_label_list, kb_max_len = -1): -# super().__init__() -# self.pseudo_label_list = pseudo_label_list -# self.base = {} -# self.kb_max_len = kb_max_len -# if(self.kb_max_len > 0): -# X = self.get_X(self.pseudo_label_list, self.kb_max_len) -# Y = self.get_Y(X, self.logic_forward) - -# for x, y in zip(X, Y): -# self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) +class hwf_KB(KBBase): + def __init__(self, pseudo_label_list, kb_max_len = -1): + super().__init__() + self.pseudo_label_list = pseudo_label_list + self.base = {} + self.kb_max_len = kb_max_len + if(self.kb_max_len > 0): + X = self.get_X(self.pseudo_label_list, self.kb_max_len) + Y = self.get_Y(X, self.logic_forward) + + for x, y in zip(X, Y): + self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) -# def logic_forward(self, nums): -# return sum(nums) + def calculate(self, formula): + stack = [] + postfix = [] + priority = {'+': 0, '-': 0, + '*': 1, '/': 1} + skip_flag = 0 + for i in range(len(formula)): + if formula[i] == '-': + if i == 0: + formula.insert(0, 0) + for i in range(len(formula)): + if skip_flag: + skip_flag -= 1 + continue + char = formula[i] + if char in priority.keys(): + while stack and (priority[char] <= priority[stack[-1]]): + postfix.append(stack.pop()) + stack.append(char) + else: + num = char + while (i + 1) < len(formula): + if formula[i + 1] not in priority.keys(): + skip_flag += 1 + num = num * 10 + formula[i + 1] + i += 1 + else: + break + postfix.append(num) + while stack: + postfix.append(stack.pop()) + + for i in postfix: + if i in priority.keys(): + num2 = stack.pop() + num1 = stack.pop() + if i == '+': + res = num1 + num2 + elif i == '-': + res = num1 - num2 + elif i == '*': + res = num1 * num2 + elif i == '/': + if(num2 == 0): + return np.inf + res = num1 / num2 + stack.append(res) + else: + stack.append(i) + return round(stack[0], 2) -# def get_X(self, pseudo_label_list, max_len): -# res = [] -# assert(max_len >= 2) -# for len in range(2, max_len + 1): -# res += list(product(pseudo_label_list, repeat = len)) -# return res - -# def get_Y(self, X, logic_forward): -# return [logic_forward(nums) for nums in X] - -# def get_candidates(self, key, length = None): -# if(self.base == {}): -# return [] + def valid_formula(self, formula): + symbol_idx_list = [] + first_minus_flag = 0 + for idx, c in enumerate(formula): + if(idx == 0 and c == '-'): + first_minus_flag = 1 + continue + if(c in ['+', '-', '*', '/']): + if(idx - 1 in symbol_idx_list or (idx == 1 and first_minus_flag == 1)): + return False + symbol_idx_list.append(idx) + if(0 in symbol_idx_list or len(formula) - 1 in symbol_idx_list): + return False + return True + + def logic_forward(self, formula): + if(self.valid_formula(formula) == False): + return np.inf + return self.calculate(list(formula)) -# if key is None: -# return self.get_all_candidates() + def get_X(self, pseudo_label_list, max_len): + res = [] + assert(max_len >= 2) + for len in range(2, max_len + 1): + res += list(product(pseudo_label_list, repeat = len)) + return res -# length = self._length(length) -# if(self.kb_max_len < min(length)): -# return [] -# return sum([self.base[l][key] for l in length], []) + def get_Y(self, X, logic_forward): + return [logic_forward(formula) for formula in X] + + def get_candidates(self, key, length = None): + if(self.base == {}): + return [] + + if key is None: + return self.get_all_candidates() + + length = self._length(length) + if(self.kb_max_len < min(length)): + return [] + return sum([self.base[l][key] for l in length], []) -# def get_all_candidates(self): -# return sum([sum(v.values(), []) for v in self.base.values()], []) + def get_all_candidates(self): + return sum([sum(v.values(), []) for v in self.base.values()], []) -# def _dict_len(self, dic): -# return sum(len(c) for c in dic.values()) + def _dict_len(self, dic): + return sum(len(c) for c in dic.values()) -# def __len__(self): -# return sum(self._dict_len(v) for v in self.base.values()) + def __len__(self): + return sum(self._dict_len(v) for v in self.base.values()) class cls_KB(KBBase): def __init__(self, X, Y = None): @@ -248,10 +316,14 @@ if __name__ == "__main__": print(res) print() - # pseudo_label_list = list(range(10)) + ['+', '-', '*', '/'] - # kb = hwf_KB(pseudo_label_list, max_len = 5) - # print('len(kb):', len(kb)) - # print() + pseudo_label_list = list(range(10)) + ['+', '-', '*', '/'] + kb = hwf_KB(pseudo_label_list, kb_max_len = 5) + print('len(kb):', len(kb)) + res = kb.get_candidates(1, length = 3) + print(res) + res = kb.get_candidates(3.67, length = 5) + print(res) + print() X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] From 821c79f44dd4b427b18b1deb8ce029032554c413 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 10:44:30 +0800 Subject: [PATCH 014/601] Update kb.py --- abducer/kb.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 6007a5b..5d25a46 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -124,11 +124,11 @@ class hwf_KB(KBBase): postfix.append(stack.pop()) stack.append(char) else: - num = char + num = int(char) while (i + 1) < len(formula): if formula[i + 1] not in priority.keys(): skip_flag += 1 - num = num * 10 + formula[i + 1] + num = num * 10 + int(formula[i + 1]) i += 1 else: break @@ -316,7 +316,7 @@ if __name__ == "__main__": print(res) print() - pseudo_label_list = list(range(10)) + ['+', '-', '*', '/'] + pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '+', '-', '*', '/'] kb = hwf_KB(pseudo_label_list, kb_max_len = 5) print('len(kb):', len(kb)) res = kb.get_candidates(1, length = 3) From 73bb3674e205cdf1e0c0b9a7a1304ee267333a39 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 10:44:46 +0800 Subject: [PATCH 015/601] Update abducer_base.py --- abducer/abducer_base.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 38cbf64..37c4bf8 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -11,7 +11,7 @@ #================================================================# import abc -from kb import add_KB +from kb import add_KB, hwf_KB import numpy as np from itertools import product, combinations @@ -100,7 +100,7 @@ class AbducerBase(abc.ABC): pred_res_array = np.array(pred_res) if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num): pred_res_array[np.array(address_idx)] = c - if(abs(self.kb.logic_forward(pred_res_array) - key) <= 1e-3): + if(self.kb.logic_forward(pred_res_array) == key): new_candidates.append(pred_res_array) return new_candidates, address_num @@ -151,19 +151,21 @@ if __name__ == "__main__": abd = AbducerBase(kb) res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) print(res) - print() res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 1) print(res) - print() res = abd.abduce(([1, 1, 1], 4), max_address_num = 1, require_more_address = 1) print(res) - print() res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) print(res) - print() res = abd.abduce(([1, 1, 1], 5), max_address_num = 2, require_more_address = 1) print(res) - # res = abd.abduce(([0, 2, 0], 0.99), 1, 0) - # print(res) - + print() + pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '+', '-', '*', '/'] + kb = hwf_KB(pseudo_label_list) + abd = AbducerBase(kb) + res = abd.abduce((['5', '+', '2'], 3), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce((['5', '+', '2'], 1.67), max_address_num = 2, require_more_address = 0) + print(res) + print() From 5efe40fc637780c7d96c58dcbdcfc19e52097bc7 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 10:59:13 +0800 Subject: [PATCH 016/601] Update abducer_base.py --- abducer/abducer_base.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 37c4bf8..576cce4 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -55,6 +55,11 @@ class AbducerBase(abc.ABC): self.cache_min_address_num = {} self.cache_candidates = {} + def get_min_cost_candidate(self, pred_res, candidates): + cost_list = self.dist_func(pred_res, candidates) + min_address_num = np.min(cost_list) + idxs = np.where(cost_list == min_address_num)[0] + return [candidates[idx] for idx in idxs][0] def abduce(self, data, max_address_num = 3, require_more_address = 0, length = -1): pred_res, ans = data @@ -78,18 +83,13 @@ class AbducerBase(abc.ABC): else: candidates, min_address_num, address_num = self.get_abduce_candidates(pred_res, ans, max_address_num, require_more_address) - cost_list = self.dist_func(pred_res, candidates) if(self.cache): self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates - - cost_list = self.dist_func(pred_res, candidates) - min_address_num = np.min(cost_list) - idxs = np.where(cost_list == min_address_num)[0] - candidates = [candidates[idx] for idx in idxs] - - return candidates[0] + + candidates = self.get_min_cost_candidate(pred_res, candidates) + return candidates def address(self, address_num, pred_res, key): new_candidates = [] @@ -168,4 +168,6 @@ if __name__ == "__main__": print(res) res = abd.abduce((['5', '+', '2'], 1.67), max_address_num = 2, require_more_address = 0) print(res) + res = abd.abduce((['5', '+', '3'], 0.33), max_address_num = 3, require_more_address = 3) + print(res) print() From 8b5a2ce2a53cbb2bda24817266ded05434927f7d Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 13:07:03 +0800 Subject: [PATCH 017/601] Update get_mnist_add.py --- datasets/mnist_add/get_mnist_add.py | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py index 167ee57..0c9a273 100644 --- a/datasets/mnist_add/get_mnist_add.py +++ b/datasets/mnist_add/get_mnist_add.py @@ -5,20 +5,28 @@ from torchvision.transforms import transforms def get_mnist_add(): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))]) - train_dataset = torchvision.datasets.MNIST(root='./', train=True, download=True, transform=transform) - test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('./', train=False, transform=transform), batch_size=1000, shuffle=True) + img_dataset = torchvision.datasets.MNIST(root='./', train=True, download=True, transform=transform) - X = [] - Y = [] + train_X = [] + train_Y = [] with open('./train_data.txt') as f: for line in f: line = line.strip().split(' ') - X.append((train_dataset[int(line[0])][0], train_dataset[int(line[1])][0])) - Y.append(int(line[2])) + train_X.append((img_dataset[int(line[0])][0], img_dataset[int(line[1])][0])) + train_Y.append(int(line[2])) - return X, Y, test_loader + test_X = [] + test_Y = [] + with open('./test_data.txt') as f: + for line in f: + line = line.strip().split(' ') + test_X.append((img_dataset[int(line[0])][0], img_dataset[int(line[1])][0])) + test_Y.append(int(line[2])) + + return train_X, train_Y, test_X, test_Y if __name__ == "__main__": - X, Y, test_loader = get_mnist_add() - print(len(X), len(Y)) - print(X[0][0].shape, X[0][1].shape, Y[0]) + train_X, train_Y, test_X, test_Y = get_mnist_add() + print(len(train_X), len(test_X)) + print(train_X[0][0].shape, train_X[0][1].shape, train_Y[0]) + From 4fa2f8594555aa6091b56f9b73a270583839acae Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 13:09:47 +0800 Subject: [PATCH 018/601] Update get_mnist_add.py --- datasets/mnist_add/get_mnist_add.py | 28 ++++++++++++---------------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py index 0c9a273..183f9a5 100644 --- a/datasets/mnist_add/get_mnist_add.py +++ b/datasets/mnist_add/get_mnist_add.py @@ -3,25 +3,22 @@ import torchvision from torch.utils.data import Dataset from torchvision.transforms import transforms +def get_data(file, img_dataset): + X = [] + Y = [] + with open(file) as f: + for line in f: + line = line.strip().split(' ') + X.append((img_dataset[int(line[0])][0], img_dataset[int(line[1])][0])) + Y.append(int(line[2])) + return X, Y + def get_mnist_add(): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))]) img_dataset = torchvision.datasets.MNIST(root='./', train=True, download=True, transform=transform) - train_X = [] - train_Y = [] - with open('./train_data.txt') as f: - for line in f: - line = line.strip().split(' ') - train_X.append((img_dataset[int(line[0])][0], img_dataset[int(line[1])][0])) - train_Y.append(int(line[2])) - - test_X = [] - test_Y = [] - with open('./test_data.txt') as f: - for line in f: - line = line.strip().split(' ') - test_X.append((img_dataset[int(line[0])][0], img_dataset[int(line[1])][0])) - test_Y.append(int(line[2])) + train_X, train_Y = get_data('./train_data.txt', img_dataset) + test_X, test_Y = get_data('./test_data.txt', img_dataset) return train_X, train_Y, test_X, test_Y @@ -29,4 +26,3 @@ if __name__ == "__main__": train_X, train_Y, test_X, test_Y = get_mnist_add() print(len(train_X), len(test_X)) print(train_X[0][0].shape, train_X[0][1].shape, train_Y[0]) - From f9812e319958b99ff2185fe88f4797119bac7ccf Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 13:22:54 +0800 Subject: [PATCH 019/601] Update get_mnist_add.py --- datasets/mnist_add/get_mnist_add.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py index 183f9a5..eb75e47 100644 --- a/datasets/mnist_add/get_mnist_add.py +++ b/datasets/mnist_add/get_mnist_add.py @@ -9,7 +9,7 @@ def get_data(file, img_dataset): with open(file) as f: for line in f: line = line.strip().split(' ') - X.append((img_dataset[int(line[0])][0], img_dataset[int(line[1])][0])) + X.append([img_dataset[int(line[0])][0], img_dataset[int(line[1])][0]]) Y.append(int(line[2])) return X, Y @@ -26,3 +26,4 @@ if __name__ == "__main__": train_X, train_Y, test_X, test_Y = get_mnist_add() print(len(train_X), len(test_X)) print(train_X[0][0].shape, train_X[0][1].shape, train_Y[0]) + From 05ae1517b9eb8c3c5c13f3ca570052f05c7879c6 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 13:40:08 +0800 Subject: [PATCH 020/601] Create get_hwf.py --- datasets/hwf/get_hwf.py | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 datasets/hwf/get_hwf.py diff --git a/datasets/hwf/get_hwf.py b/datasets/hwf/get_hwf.py new file mode 100644 index 0000000..98eea5e --- /dev/null +++ b/datasets/hwf/get_hwf.py @@ -0,0 +1,36 @@ +import json +from PIL import Image +from torchvision.transforms import transforms + +img_transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.5,), (1,)) + ]) + +def get_data(file): + X = [] + Y = [] + img_dir = './data/Handwritten_Math_Symbols/' + with open(file) as f: + data = json.load(f) + for idx in range(len(data)): + imgs = [] + for img_path in data[idx]['img_paths']: + img = Image.open(img_dir + img_path).convert('L') + img = img_transform(img) + imgs.append(img) + X.append(imgs) + Y.append(data[idx]['res']) + return X, Y + +def get_hwf(): + train_X, train_Y = get_data('./data/expr_train.json') + test_X, test_Y = get_data('./data/expr_test.json') + + return train_X, train_Y, test_X, test_Y + +if __name__ == "__main__": + train_X, train_Y, test_X, test_Y = get_hwf() + print(len(train_X), len(test_X)) + print(len(train_X[0]), train_X[0][0].shape, train_Y[0]) + From eaeaba3e721fe93920133418155703b41d203c82 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 16 Nov 2022 13:42:36 +0800 Subject: [PATCH 021/601] Create README.md --- datasets/hwf/README.md | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 datasets/hwf/README.md diff --git a/datasets/hwf/README.md b/datasets/hwf/README.md new file mode 100644 index 0000000..b8f3047 --- /dev/null +++ b/datasets/hwf/README.md @@ -0,0 +1,4 @@ +Download the Handwritten Formula Recognition dataset from [google drive](https://drive.google.com/file/d/1G07kw-wK-rqbg_85tuB7FNfA49q8lvoy/view?usp=sharing) to this folder and unzip it: +``` +unzip HWF.zip +``` From 3616608aaf61f6cb03e2f2cb55145f27844c0fee Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:24:50 +0800 Subject: [PATCH 022/601] Update get_mnist_add.py --- datasets/mnist_add/get_mnist_add.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py index eb75e47..fcada50 100644 --- a/datasets/mnist_add/get_mnist_add.py +++ b/datasets/mnist_add/get_mnist_add.py @@ -15,10 +15,10 @@ def get_data(file, img_dataset): def get_mnist_add(): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))]) - img_dataset = torchvision.datasets.MNIST(root='./', train=True, download=True, transform=transform) + img_dataset = torchvision.datasets.MNIST(root='./datasets/mnist_add/', train=True, download=True, transform=transform) - train_X, train_Y = get_data('./train_data.txt', img_dataset) - test_X, test_Y = get_data('./test_data.txt', img_dataset) + train_X, train_Y = get_data('./datasets/mnist_add/train_data.txt', img_dataset) + test_X, test_Y = get_data('./datasets/mnist_add/test_data.txt', img_dataset) return train_X, train_Y, test_X, test_Y From 22de038d343487e81600fd2ba1a36c722590e6f6 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:26:15 +0800 Subject: [PATCH 023/601] Update get_hwf.py --- datasets/hwf/get_hwf.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/datasets/hwf/get_hwf.py b/datasets/hwf/get_hwf.py index 98eea5e..cfd1666 100644 --- a/datasets/hwf/get_hwf.py +++ b/datasets/hwf/get_hwf.py @@ -7,10 +7,10 @@ img_transform = transforms.Compose([ transforms.Normalize((0.5,), (1,)) ]) -def get_data(file): +def get_data(file, precision_num = 2): X = [] Y = [] - img_dir = './data/Handwritten_Math_Symbols/' + img_dir = './datasets/hwf/data/Handwritten_Math_Symbols/' with open(file) as f: data = json.load(f) for idx in range(len(data)): @@ -20,12 +20,12 @@ def get_data(file): img = img_transform(img) imgs.append(img) X.append(imgs) - Y.append(data[idx]['res']) + Y.append(round(data[idx]['res'], precision_num)) return X, Y -def get_hwf(): - train_X, train_Y = get_data('./data/expr_train.json') - test_X, test_Y = get_data('./data/expr_test.json') +def get_hwf(precision_num = 2): + train_X, train_Y = get_data('./datasets/hwf/data/expr_train.json', precision_num) + test_X, test_Y = get_data('./datasets/hwf/data/expr_test.json', precision_num) return train_X, train_Y, test_X, test_Y From f6f92a5f083adfc420a806fab565ae8d07cf788b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:29:45 +0800 Subject: [PATCH 024/601] Update kb.py --- abducer/kb.py | 23 ++++++++++------------- 1 file changed, 10 insertions(+), 13 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 5d25a46..12772e5 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -45,9 +45,9 @@ class KBBase(ABC): pass class add_KB(KBBase): - def __init__(self, pseudo_label_list, kb_max_len = -1): + def __init__(self, kb_max_len = -1): super().__init__() - self.pseudo_label_list = pseudo_label_list + self.pseudo_label_list = list(range(10)) self.base = {} self.kb_max_len = kb_max_len if(self.kb_max_len > 0): @@ -92,9 +92,9 @@ class add_KB(KBBase): return sum(self._dict_len(v) for v in self.base.values()) class hwf_KB(KBBase): - def __init__(self, pseudo_label_list, kb_max_len = -1): + def __init__(self, kb_max_len = -1): super().__init__() - self.pseudo_label_list = pseudo_label_list + self.pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'] self.base = {} self.kb_max_len = kb_max_len if(self.kb_max_len > 0): @@ -157,13 +157,13 @@ class hwf_KB(KBBase): def valid_formula(self, formula): symbol_idx_list = [] - first_minus_flag = 0 for idx, c in enumerate(formula): if(idx == 0 and c == '-'): - first_minus_flag = 1 + if(len(formula) == 1 or formula[1] in ['+', '-', '*', '/']): + return False continue if(c in ['+', '-', '*', '/']): - if(idx - 1 in symbol_idx_list or (idx == 1 and first_minus_flag == 1)): + if(idx - 1 in symbol_idx_list): return False symbol_idx_list.append(idx) if(0 in symbol_idx_list or len(formula) - 1 in symbol_idx_list): @@ -289,8 +289,7 @@ class reg_KB(KBBase): if __name__ == "__main__": # With ground KB - pseudo_label_list = list(range(10)) - kb = add_KB(pseudo_label_list, kb_max_len = 5) + kb = add_KB(kb_max_len = 5) print('len(kb):', len(kb)) res = kb.get_candidates(0) print(res) @@ -303,8 +302,7 @@ if __name__ == "__main__": print() # Without ground KB - pseudo_label_list = list(range(10)) - kb = add_KB(pseudo_label_list) + kb = add_KB() print('len(kb):', len(kb)) res = kb.get_candidates(0) print(res) @@ -316,8 +314,7 @@ if __name__ == "__main__": print(res) print() - pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '+', '-', '*', '/'] - kb = hwf_KB(pseudo_label_list, kb_max_len = 5) + kb = hwf_KB(kb_max_len = 5) print('len(kb):', len(kb)) res = kb.get_candidates(1, length = 3) print(res) From 70fa5cf56a941fe9508a89e1a3f43cb76bc92727 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:31:18 +0800 Subject: [PATCH 025/601] Update abducer_base.py --- abducer/abducer_base.py | 52 +++++++++++++++++++++-------------------- 1 file changed, 27 insertions(+), 25 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 576cce4..1168ea6 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -11,7 +11,8 @@ #================================================================# import abc -from kb import add_KB, hwf_KB +# from kb import add_KB, hwf_KB +from abducer.kb import add_KB, hwf_KB import numpy as np from itertools import product, combinations @@ -37,18 +38,19 @@ def confidence_dist(A, B): class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func = "hamming", pred_res_parse = None, cache = True): + def __init__(self, kb, dist_func = 'hamming', pred_res_parse = None, cache = True): self.kb = kb - if dist_func == "hamming": - dist_func = hamming_dist - elif dist_func == "confidence": - dist_func = confidence_dist - self.dist_func = dist_func + + if(dist_func == 'hamming'): + self.dist_func = hamming_dist + elif(dist_func == 'confidence'): + self.dist_func = confidence_dist + if pred_res_parse is None: - if(dist_func == "hamming"): + if(dist_func == 'hamming'): pred_res_parse = lambda x : x["cls"] - elif dist_func == "confidence": - pred_res_parse = lambda x : x[" "] + elif dist_func == 'confidence': + pred_res_parse = lambda x : x["prob"] self.pred_res_parse = pred_res_parse self.cache = cache @@ -61,20 +63,24 @@ class AbducerBase(abc.ABC): idxs = np.where(cost_list == min_address_num)[0] return [candidates[idx] for idx in idxs][0] - def abduce(self, data, max_address_num = 3, require_more_address = 0, length = -1): + def abduce(self, data, max_address_num = -1, require_more_address = 0): pred_res, ans = data + pred_res = [self.kb.pseudo_label_list[sym] for sym in pred_res] + - if length == -1: - length = len(pred_res) + if max_address_num == -1: + max_address_num = len(pred_res) if(self.cache and (tuple(pred_res), ans) in self.cache_min_address_num): address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), ans)] + require_more_address) if((tuple(pred_res), ans, address_num) in self.cache_candidates): - print('cached') - return self.cache_candidates[(tuple(pred_res), ans, address_num)] + # print('cached') + candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] + candidates = self.get_min_cost_candidate(pred_res, candidates) + return candidates if(self.kb.base != {}): - all_candidates = self.kb.get_candidates(ans, length) + all_candidates = self.kb.get_candidates(ans, len(pred_res)) cost_list = self.dist_func(pred_res, all_candidates) min_address_num = np.min(cost_list) address_num = min(max_address_num, min_address_num + require_more_address) @@ -107,7 +113,6 @@ class AbducerBase(abc.ABC): def get_abduce_candidates(self, pred_res, key, max_address_num, require_more_address): candidates = [] - for address_num in range(len(pred_res) + 1): if(address_num > max_address_num): print('No candidates found') @@ -132,22 +137,20 @@ class AbducerBase(abc.ABC): return candidates, min_address_num, address_num - - - def batch_abduce(self, Y, C, max_address_num = 3, require_more_address = 0): + + def batch_abduce(self, Y, C, max_address_num = -1, require_more_address = 0): return [ self.abduce((y, c), max_address_num, require_more_address)\ for y, c in zip(self.pred_res_parse(Y), C) ] - def __call__(self, Y, C, max_address_num = 3, require_more_address = 0): + def __call__(self, Y, C, max_address_num = -1, require_more_address = 0): return self.batch_abduce(Y, C, max_address_num, require_more_address) if __name__ == "__main__": - pseudo_label_list = list(range(10)) - kb = add_KB(pseudo_label_list) + kb = add_KB() abd = AbducerBase(kb) res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) print(res) @@ -161,8 +164,7 @@ if __name__ == "__main__": print(res) print() - pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '+', '-', '*', '/'] - kb = hwf_KB(pseudo_label_list) + kb = hwf_KB() abd = AbducerBase(kb) res = abd.abduce((['5', '+', '2'], 3), max_address_num = 2, require_more_address = 0) print(res) From c0fd799c5ddba479a214a597b6a9f9b11b00be2d Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:31:48 +0800 Subject: [PATCH 026/601] Update basic_model.py --- models/basic_model.py | 134 ++++++------------------------------------ 1 file changed, 17 insertions(+), 117 deletions(-) diff --git a/models/basic_model.py b/models/basic_model.py index 47aeab9..3885067 100644 --- a/models/basic_model.py +++ b/models/basic_model.py @@ -14,47 +14,11 @@ import sys sys.path.append("..") import torch -from torch.autograd import Variable from torch.utils.data import Dataset -import torchvision import os from multiprocessing import Pool -import random -import torch -from torch.utils.data import Dataset -from torch.utils.data import sampler -import torchvision.transforms as transforms -import six -import sys -from PIL import Image -import numpy as np -import collections - -class resizeNormalize(object): - - def __init__(self, size, interpolation=Image.BILINEAR): - self.size = size - self.interpolation = interpolation - self.toTensor = transforms.ToTensor() - self.transform = transforms.Compose([ - #transforms.ToPILImage(), - #transforms.RandomHorizontalFlip(), - #transforms.RandomVerticalFlip(), - #transforms.RandomRotation(30), - #transforms.RandomAffine(30), - transforms.ToTensor(), - - ]) - - def __call__(self, img): - #img = img.resize(self.size, self.interpolation) - #img = self.toTensor(img) - img = self.transform(img) - img.sub_(0.5).div_(0.5) - return img - class XYDataset(Dataset): def __init__(self, X, Y, transform=None, target_transform=None): self.X = X @@ -80,36 +44,6 @@ class XYDataset(Dataset): return (img, label, index) -class alignCollate(object): - - def __init__(self, imgH=32, imgW=100, keep_ratio=False, min_ratio=1): - self.imgH = imgH - self.imgW = imgW - self.keep_ratio = keep_ratio - self.min_ratio = min_ratio - - def __call__(self, batch): - images, labels, img_keys = zip(*batch) - - imgH = self.imgH - imgW = self.imgW - if self.keep_ratio: - ratios = [] - for image in images: - w, h = image.shape[:2] - ratios.append(w / float(h)) - ratios.sort() - max_ratio = ratios[-1] - imgW = int(np.floor(max_ratio * imgH)) - imgW = max(imgH * self.min_ratio, imgW) # assure imgH >= imgW - - transform = resizeNormalize((imgW, imgH)) - images = [transform(image) for image in images] - images = torch.cat([t.unsqueeze(0) for t in images], 0) - labels = torch.LongTensor(labels) - - return images, labels, img_keys - class FakeRecorder(): def __init__(self): pass @@ -117,20 +51,6 @@ class FakeRecorder(): def print(self, *x): pass -from torch.nn import init -from torch import nn - -def weigth_init(m): - if isinstance(m, nn.Conv2d): - init.xavier_uniform_(m.weight.data) - init.constant_(m.bias.data,0.1) - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1) - m.bias.data.zero_() - elif isinstance(m, nn.Linear): - m.weight.data.normal_(0,0.01) - m.bias.data.zero_() - class BasicModel(): def __init__(self, model, @@ -142,7 +62,6 @@ class BasicModel(): transform = None, target_transform=None, collate_fn = None, - pretrained = False, recorder = None): self.model = model.to(device) @@ -153,7 +72,7 @@ class BasicModel(): self.target_transform = target_transform self.device = device - sign_list = sorted(list(set(sign_list))) + self.sign_list = sorted(list(set(sign_list))) self.mapping = dict(zip(sign_list, list(range(len(sign_list))))) self.remapping = dict(zip(list(range(len(sign_list))), sign_list)) @@ -161,12 +80,6 @@ class BasicModel(): recorder = FakeRecorder() self.recorder = recorder - if pretrained: - # the paths of model, optimizer should be included in params - self.load(params.load_dir) - else: - self.model.apply(weigth_init) - self.save_interval = params.saveInterval self.params = params self.collate_fn = collate_fn @@ -211,41 +124,28 @@ class BasicModel(): return self._fit(data_loader, params.n_epoch, params.stop_loss) def train_epoch(self, data_loader): - # loss_avg = mutils.averager() - self.model.train() - - loss_value = 0 - for i, data in enumerate(data_loader): - X = data[0] - Y = data[1] - loss = self.train_batch(X, Y) - loss_value += loss.item() - - return loss_value - - def train_batch(self, X, Y): - #cpu_images, cpu_texts, _ = data model = self.model criterion = self.criterion optimizer = self.optimizer device = self.device - - # init training status - # torch.autograd.set_detect_anomaly(True) + + model.train() + + loss_value = 0 + for _, data in enumerate(data_loader): + X = data[0].to(device) + Y = data[1].to(device) + pred_Y = model(X) - # model predict - X = X.to(device) - Y = Y.to(device) - pred_Y = model(X) + loss = criterion(pred_Y, Y) - # calculate loss - loss = criterion(pred_Y, Y) + optimizer.zero_grad() + loss.backward() + optimizer.step() - # back propagation and optimize - optimizer.zero_grad() - loss.backward() - optimizer.step() - return loss + loss_value += loss.item() + + return loss_value def _predict(self, data_loader): model = self.model @@ -297,7 +197,7 @@ class BasicModel(): recorder = self.recorder recorder.print('Start Predict ', print_prefix) - return torch.softmax(self._predict(data_loader), axis=1) + return torch.softmax(self._predict(data_loader), axis=1).cpu() def _val(self, data_loader, print_prefix): model = self.model From baa8a366d20e32bc7ddaef89cb0b827e9e04adb7 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:34:13 +0800 Subject: [PATCH 027/601] Create example.py --- example.py | 85 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 example.py diff --git a/example.py b/example.py new file mode 100644 index 0000000..405e2bd --- /dev/null +++ b/example.py @@ -0,0 +1,85 @@ +# coding: utf-8 +#================================================================# +# Copyright (C) 2021 Freecss All rights reserved. +# +# File Name ：share_example.py +# Author ：freecss +# Email ：karlfreecss@gmail.com +# Created Date ：2021/06/07 +# Description ： +# +#================================================================# + +from utils.plog import logger +from models.wabl_models import DecisionTree, KNN +import pickle as pk +import numpy as np +import time +import framework +import utils.plog as plog +import torch.nn as nn +import torch + +from models.lenet5 import LeNet5 +from models.basic_model import BasicModel +from models.wabl_models import MyModel + +from multiprocessing import Pool +import os +from datasets.data_generator import generate_data_via_codes, code_generator +from collections import defaultdict +from abducer.abducer_base import AbducerBase +from abducer.kb import add_KB, hwf_KB +from datasets.mnist_add.get_mnist_add import get_mnist_add +from datasets.hwf.get_hwf import get_hwf + +class Params: + imgH = 45 + imgW = 45 + keep_ratio = True + saveInterval = 10 + batchSize = 16 + workers = 16 + n_epoch = 10 + stop_loss = None + +def run_test(): + + result_dir = 'results' + + recorder_file_path = f"{result_dir}/1116.pk"# + + # words = code_generator(code_len, code_num, letter_num) + kb = add_KB() + abducer = AbducerBase(kb) + + recorder = logger() + recorder.set_savefile("test.log") + + + train_X, train_Y, test_X, test_Y = get_mnist_add() + # train_X, train_Y, test_X, test_Y = get_hwf() + + + recorder = plog.ResultRecorder() + cls = LeNet5() + + criterion = nn.CrossEntropyLoss(size_average=True) + optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + sign_list = list(range(10)) + base_model = BasicModel(cls, criterion, optimizer, device, Params(), sign_list, recorder=recorder) + model = MyModel(base_model) + + res = framework.train(model, abducer, train_X, train_Y, logic_forward = kb.logic_forward, sample_num = 10000, verbose = 1) + print(res) + + + recorder.dump(open(recorder_file_path, "wb")) + return True + +if __name__ == "__main__": + os.system("mkdir results") + + run_test() + From dc1b669ab225d40711b2c1988fa37297399f36eb Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:37:01 +0800 Subject: [PATCH 028/601] Update framework.py --- framework.py | 161 ++++++++++++++++++++++++++++++++------------------- 1 file changed, 102 insertions(+), 59 deletions(-) diff --git a/framework.py b/framework.py index b41a334..27c2dba 100644 --- a/framework.py +++ b/framework.py @@ -17,17 +17,17 @@ import numpy as np from utils.plog import INFO, DEBUG, clocker @clocker -def block_sample(X_bak, Y_bak, C_bak, sample_num, epoch_idx): +def block_sample(X_bak, C_bak, sample_num, epoch_idx): part_num = (len(X_bak) // sample_num) if part_num == 0: part_num = 1 seg_idx = epoch_idx % part_num INFO("seg_idx:", seg_idx, ", part num:", part_num, ", data num:", len(X_bak)) X = X_bak[sample_num * seg_idx: sample_num * (seg_idx + 1)] - Y = Y_bak[sample_num * seg_idx: sample_num * (seg_idx + 1)] + # Y = Y_bak[sample_num * seg_idx: sample_num * (seg_idx + 1)] C = C_bak[sample_num * seg_idx: sample_num * (seg_idx + 1)] - return X, Y, C + return X, C def get_taglist(self, Y): tmp = [[str(x) for x in label] for label in Y] @@ -35,49 +35,58 @@ def get_taglist(self, Y): return tmp @clocker -def result_statistics(pseudo_Y, Y, abduced_Y): - - abd_err_num = 0 - abd_char_num = 0 - abd_char_acc = 0 - abd_failed = 0 - word_err_num = 0 +def result_statistics(C, pseudo_Y, logic_forward): + abl_acc = 0 + for tidx, (c, pseudo_y) in enumerate(zip(C, pseudo_Y)): + if(logic_forward(pseudo_y) == c): + abl_acc += 1 + - ori_char_num = 0 - ori_char_acc = 0 + return abl_acc / len(C) - for tidx, (pseudo_y, y, abduced_y) in enumerate(zip(pseudo_Y, Y, abduced_Y)): - pseudo_y = pseudo_y - if sum(abduced_y != y) != 0: - abd_err_num += 1 - if abduced_y is not None: - abd_char_num += len(y) - abd_char_acc += sum(abduced_y == y) - else: - abd_failed += 1 +# def result_statistics(pseudo_Y, Y, abduced_Y): - ori_char_num += len(pseudo_y) - ori_char_acc += sum(pseudo_y == y) +# abd_err_num = 0 +# abd_char_num = 0 +# abd_char_acc = 0 +# abd_failed = 0 +# word_err_num = 0 + +# ori_char_num = 0 +# ori_char_acc = 0 + +# for tidx, (pseudo_y, y, abduced_y) in enumerate(zip(pseudo_Y, Y, abduced_Y)): +# pseudo_y = pseudo_y +# if sum(abduced_y != y) != 0: +# abd_err_num += 1 +# if abduced_y is not None: +# abd_char_num += len(y) +# abd_char_acc += sum(abduced_y == y) +# else: +# abd_failed += 1 + +# ori_char_num += len(pseudo_y) +# ori_char_acc += sum(pseudo_y == y) - if abduced_y is not None and sum(y != pseudo_y) == 0 and sum(pseudo_y != abduced_y) > 0: - INFO(pseudo_y, y, abduced_y) - pk.dump((pseudo_y, y, abduced_y), open("bug.pk", "wb")) +# if abduced_y is not None and sum(y != pseudo_y) == 0 and sum(pseudo_y != abduced_y) > 0: +# INFO(pseudo_y, y, abduced_y) +# pk.dump((pseudo_y, y, abduced_y), open("bug.pk", "wb")) - if sum(pseudo_y != y) != 0: - word_err_num += 1 +# if sum(pseudo_y != y) != 0: +# word_err_num += 1 - INFO("") - INFO("Abd word level accuracy:", 1 - word_err_num / len(pseudo_Y)) - INFO("Abd char level accuracy:", abd_char_acc / abd_char_num) - INFO("Ori char level accuracy:", ori_char_acc / ori_char_num) - INFO("") +# INFO("") +# INFO("Abd word level accuracy:", 1 - word_err_num / len(pseudo_Y)) +# INFO("Abd char level accuracy:", abd_char_acc / abd_char_num) +# INFO("Ori char level accuracy:", ori_char_acc / ori_char_num) +# INFO("") - result = {"total_word" : len(pseudo_Y), "accuracy_word" : len(pseudo_Y) - word_err_num, - "total_abd_char": abd_char_num, "accuracy_abd_char" : abd_char_acc, - "total_ori_char": ori_char_num, "accuracy_ori_char" : ori_char_acc, - "total_abd_failed": abd_failed} +# result = {"total_word" : len(pseudo_Y), "accuracy_word" : len(pseudo_Y) - word_err_num, +# "total_abd_char": abd_char_num, "accuracy_abd_char" : abd_char_acc, +# "total_ori_char": ori_char_num, "accuracy_ori_char" : ori_char_acc, +# "total_abd_failed": abd_failed} - return result +# return result @clocker def filter_data(X, abduced_Y): @@ -106,7 +115,7 @@ def is_all_sublabel_exist(labels, std_label_list): def pretrain(model, X, Y): pass -def train(model, abducer, X, Y, C = None, epochs = 10, sample_num = -1, verbose = -1, check_sublabel = True): +def train(model, abducer, X, C, logic_forward, epochs = 10, sample_num = -1, verbose = -1): # Set default parameters if sample_num == -1: sample_num = len(X) @@ -114,42 +123,76 @@ def train(model, abducer, X, Y, C = None, epochs = 10, sample_num = -1, verbose if verbose < 1: verbose = epochs - if C is None: - C = [None] * len(X) - - # Set function running time recorder - valid_func = clocker(model.valid) predict_func = clocker(model.predict) train_func = clocker(model.train) abduce_func = clocker(abducer.batch_abduce) X_bak = X - Y_bak = Y C_bak = C - + epochs = 50 # Abductive learning train process - res = {} for epoch_idx in range(epochs): - X, Y, C = block_sample(X_bak, Y_bak, C_bak, sample_num, epoch_idx) + X, C = block_sample(X_bak, C_bak, sample_num, epoch_idx) preds_res = predict_func(X) + + abl_acc = result_statistics(C, preds_res['cls'], logic_forward) + print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc) + abduced_Y = abduce_func(preds_res, C) - finetune_X, finetune_Y = filter_data(X, abduced_Y) - score, score_list = valid_func(X, Y) - if ((epoch_idx + 1) % verbose == 0) or (epoch_idx == epochs - 1): - res = result_statistics(preds_res["cls"], Y, abduced_Y) - INFO(res) - - if check_sublabel and (not is_all_sublabel_exist(finetune_Y, model.label_lists)): - INFO("There is some sub label missing", len(finetune_Y)) - break + + finetune_X, finetune_Y = filter_data(X, abduced_Y) + if len(finetune_X) > 0: train_func(finetune_X, finetune_Y)#, n_epoch = 10) else: INFO("lack of data, all abduced failed", len(finetune_X)) - return res - #return ret + return abl_acc + +# def train(model, abducer, X, Y, C = None, epochs = 10, sample_num = -1, verbose = -1, check_sublabel = True): +# # Set default parameters +# if sample_num == -1: +# sample_num = len(X) + +# if verbose < 1: +# verbose = epochs + +# if C is None: +# C = [None] * len(X) + +# # Set function running time recorder +# valid_func = clocker(model.valid) +# predict_func = clocker(model.predict) +# train_func = clocker(model.train) + +# abduce_func = clocker(abducer.batch_abduce) + +# X_bak = X +# Y_bak = Y +# C_bak = C + +# # Abductive learning train process +# res = {} +# for epoch_idx in range(epochs): +# X, Y, C = block_sample(X_bak, Y_bak, C_bak, sample_num, epoch_idx) +# preds_res = predict_func(X) +# abduced_Y = abduce_func(preds_res, C) +# finetune_X, finetune_Y = filter_data(X, abduced_Y) +# score, score_list = valid_func(X, Y) +# if ((epoch_idx + 1) % verbose == 0) or (epoch_idx == epochs - 1): +# res = result_statistics(preds_res["cls"], Y, abduced_Y) +# INFO(res) + +# if check_sublabel and (not is_all_sublabel_exist(finetune_Y, model.label_lists)): +# INFO("There is some sub label missing", len(finetune_Y)) +# break + +# if len(finetune_X) > 0: +# train_func(finetune_X, finetune_Y)#, n_epoch = 10) +# else: +# INFO("lack of data, all abduced failed", len(finetune_X)) +# return res if __name__ == "__main__": pass From 9f8589d0cbe157f372fd8dad676c01be61c78b77 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:50:11 +0800 Subject: [PATCH 029/601] Update example.py --- example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example.py b/example.py index 405e2bd..7eb8b03 100644 --- a/example.py +++ b/example.py @@ -71,7 +71,7 @@ def run_test(): base_model = BasicModel(cls, criterion, optimizer, device, Params(), sign_list, recorder=recorder) model = MyModel(base_model) - res = framework.train(model, abducer, train_X, train_Y, logic_forward = kb.logic_forward, sample_num = 10000, verbose = 1) + res = framework.train(model, abducer, train_X, train_Y, sample_num = 10000, verbose = 1) print(res) From d42db9a52e81c642f933af1252959b24d200bc64 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:50:34 +0800 Subject: [PATCH 030/601] Update framework.py --- framework.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/framework.py b/framework.py index 27c2dba..92a20b5 100644 --- a/framework.py +++ b/framework.py @@ -115,7 +115,7 @@ def is_all_sublabel_exist(labels, std_label_list): def pretrain(model, X, Y): pass -def train(model, abducer, X, C, logic_forward, epochs = 10, sample_num = -1, verbose = -1): +def train(model, abducer, X, C, epochs = 10, sample_num = -1, verbose = -1): # Set default parameters if sample_num == -1: sample_num = len(X) @@ -136,7 +136,7 @@ def train(model, abducer, X, C, logic_forward, epochs = 10, sample_num = -1, ver X, C = block_sample(X_bak, C_bak, sample_num, epoch_idx) preds_res = predict_func(X) - abl_acc = result_statistics(C, preds_res['cls'], logic_forward) + abl_acc = result_statistics(C, preds_res['cls'], abducer.kb.logic_forward) print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc) abduced_Y = abduce_func(preds_res, C) From 9980102b015e4852ad54b4e032024a77510a57b8 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 17 Nov 2022 16:28:03 +0800 Subject: [PATCH 031/601] Update abducer_base.py --- abducer/abducer_base.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 1168ea6..23019b8 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -65,8 +65,6 @@ class AbducerBase(abc.ABC): def abduce(self, data, max_address_num = -1, require_more_address = 0): pred_res, ans = data - pred_res = [self.kb.pseudo_label_list[sym] for sym in pred_res] - if max_address_num == -1: max_address_num = len(pred_res) From 7505852a58f9b381a9308c33aefd83ca1bf4c498 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Thu, 17 Nov 2022 21:34:11 +0800 Subject: [PATCH 032/601] update basic_model.py --- models/basic_model.py | 24 +++++++----------------- 1 file changed, 7 insertions(+), 17 deletions(-) diff --git a/models/basic_model.py b/models/basic_model.py index 3885067..e251ec4 100644 --- a/models/basic_model.py +++ b/models/basic_model.py @@ -58,7 +58,6 @@ class BasicModel(): optimizer, device, params, - sign_list, transform = None, target_transform=None, collate_fn = None, @@ -72,10 +71,6 @@ class BasicModel(): self.target_transform = target_transform self.device = device - self.sign_list = sorted(list(set(sign_list))) - self.mapping = dict(zip(sign_list, list(range(len(sign_list))))) - self.remapping = dict(zip(list(range(len(sign_list))), sign_list)) - if recorder is None: recorder = FakeRecorder() self.recorder = recorder @@ -89,7 +84,7 @@ class BasicModel(): recorder = self.recorder recorder.print("model fitting") - min_loss = 999999999 + min_loss = 999999999 for epoch in range(n_epoch): loss_value = self.train_epoch(data_loader) recorder.print(f"{epoch}/{n_epoch} model training loss is {loss_value}") @@ -103,9 +98,6 @@ class BasicModel(): recorder.print("Model fitted, minimal loss is ", min_loss) return loss_value - def str2ints(self, Y): - return [self.mapping[y] for y in Y] - def fit(self, data_loader = None, X = None, y = None): @@ -115,8 +107,7 @@ class BasicModel(): transform = self.transform target_transform = self.target_transform - Y = self.str2ints(y) - train_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) + train_dataset = XYDataset(X, y, transform=transform, target_transform=target_transform) sampler = None data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=params.batchSize, \ shuffle=True, sampler=sampler, num_workers=int(params.workers), \ @@ -155,7 +146,7 @@ class BasicModel(): with torch.no_grad(): results = [] - for i, data in enumerate(data_loader): + for _, data in enumerate(data_loader): X = data[0].to(device) pred_Y = model(X) results.append(pred_Y) @@ -179,7 +170,7 @@ class BasicModel(): recorder = self.recorder recorder.print('Start Predict ', print_prefix) Y = self._predict(data_loader).argmax(axis=1) - return [self.remapping[int(y)] for y in Y] + return [int(y) for y in Y] def predict_proba(self, data_loader = None, X = None, print_prefix = ""): if data_loader is None: @@ -197,7 +188,7 @@ class BasicModel(): recorder = self.recorder recorder.print('Start Predict ', print_prefix) - return torch.softmax(self._predict(data_loader), axis=1).cpu() + return torch.softmax(self._predict(data_loader), axis=1).cpu().numpy() def _val(self, data_loader, print_prefix): model = self.model @@ -212,7 +203,7 @@ class BasicModel(): pred_num = 0 loss_value = 0 with torch.no_grad(): - for i, data in enumerate(data_loader): + for _, data in enumerate(data_loader): X = data[0].to(device) Y = data[1].to(device) @@ -236,8 +227,7 @@ class BasicModel(): transform = self.transform target_transform = self.target_transform - Y = self.str2ints(y) - val_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) + val_dataset = XYDataset(X, y, transform=transform, target_transform=target_transform) sampler = None data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=params.batchSize, \ shuffle=True, sampler=sampler, num_workers=int(params.workers), \ From a61ee9cd54d55f2ee3b74e27f7cd21655234f8c9 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Thu, 17 Nov 2022 21:42:14 +0800 Subject: [PATCH 033/601] update lenet5.py --- models/lenet5.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/models/lenet5.py b/models/lenet5.py index 9d5b054..56c1ca6 100644 --- a/models/lenet5.py +++ b/models/lenet5.py @@ -20,21 +20,24 @@ from torch import nn from torch.nn import functional as F from torch.autograd import Variable import torchvision.transforms as transforms +import numpy as np from models.basic_model import BasicModel - import utils.plog as plog class LeNet5(nn.Module): - def __init__(self): + def __init__(self, num_classes=10, image_size=(28, 28)): super().__init__() self.conv1 = nn.Conv2d(1, 6, 3, padding=1) self.conv2 = nn.Conv2d(6, 16, 3) self.conv3 = nn.Conv2d(16, 16, 3) - self.fc1 = nn.Linear(256, 120) + feature_map_size = ((np.array(image_size) // 2 - 2) // 2 - 2) + num_features = 16 * feature_map_size[0] * feature_map_size[1] + + self.fc1 = nn.Linear(num_features, 120) self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 10) + self.fc3 = nn.Linear(84, num_classes) def forward(self, x): '''å‰å‘传播函数''' From b521dd1cb049666381bf9390737c4b3fbb57236d Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Thu, 17 Nov 2022 21:43:53 +0800 Subject: [PATCH 034/601] update wabl_models.py --- models/wabl_models.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/models/wabl_models.py b/models/wabl_models.py index 15fe6e6..5d44719 100644 --- a/models/wabl_models.py +++ b/models/wabl_models.py @@ -48,19 +48,17 @@ def reshape_data(Y, marks): class WABLBasicModel: - """ - label_lists 的目标在于为å„个符å·è®¾ç½®ç¼–å·ï¼Œæ— è®ºæ–¹æ³•æ˜¯ç»™å‡ºå­—典形å¼çš„概率还是给出listå½¢å¼çš„，都å¯ä»¥é€šè¿‡è¿™ç§æ–¹å¼è§£å†³. - åŽç»­å¯èƒ½ä¼šè€ƒè™‘更加完善的措施，é™ä½Žè¿™éƒ¨åˆ†çš„å¤æ‚度 - 当模型共享的时候，label_lists 之间的元素也是共享的 - """ - def __init__(self): - pass + def __init__(self, pseudo_label_list): + self.pseudo_label_list = pseudo_label_list + self.mapping = dict(zip(pseudo_label_list, list(range(len(pseudo_label_list))))) + self.remapping = dict(zip(list(range(len(pseudo_label_list))), pseudo_label_list)) def predict(self, X): data_X, marks = merge_data(X) prob = self.cls_list[0].predict_proba(X = data_X) - cls = np.array(prob).argmax(axis = 1) + _cls = prob.argmax(axis = 1) + cls = list(map(lambda x : self.remapping[x], _cls)) prob = reshape_data(prob, marks) cls = reshape_data(cls, marks) @@ -69,14 +67,16 @@ class WABLBasicModel: def valid(self, X, Y): data_X, _ = merge_data(X) - data_Y, _ = merge_data(Y) + _data_Y, _ = merge_data(Y) + data_Y = list(map(lambda y : self.mapping[y], _data_Y)) score = self.cls_list[0].score(X = data_X, y = data_Y) return score, [score] def train(self, X, Y): #self.label_lists = [] data_X, _ = merge_data(X) - data_Y, _ = merge_data(Y) + _data_Y, _ = merge_data(Y) + data_Y = list(map(lambda y : self.mapping[y], _data_Y)) self.cls_list[0].fit(X = data_X, y = data_Y) class DecisionTree(WABLBasicModel): @@ -139,8 +139,8 @@ class CNN(WABLBasicModel): #self.label_lists.append(sorted(list(set(data_Y)))) class MyModel(WABLBasicModel): - def __init__(self, base_model): - + def __init__(self, base_model, pseudo_label_list): + super(MyModel, self).__init__(pseudo_label_list) self.cls_list = [] self.cls_list.append(base_model) From 06247730c9e3c6fb2d85bac6245f17ee99e8bded Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 18 Nov 2022 12:58:52 +0800 Subject: [PATCH 035/601] Update example.py --- example.py | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/example.py b/example.py index 7eb8b03..65789e2 100644 --- a/example.py +++ b/example.py @@ -11,8 +11,6 @@ #================================================================# from utils.plog import logger -from models.wabl_models import DecisionTree, KNN -import pickle as pk import numpy as np import time import framework @@ -26,8 +24,6 @@ from models.wabl_models import MyModel from multiprocessing import Pool import os -from datasets.data_generator import generate_data_via_codes, code_generator -from collections import defaultdict from abducer.abducer_base import AbducerBase from abducer.kb import add_KB, hwf_KB from datasets.mnist_add.get_mnist_add import get_mnist_add @@ -49,27 +45,27 @@ def run_test(): recorder_file_path = f"{result_dir}/1116.pk"# - # words = code_generator(code_len, code_num, letter_num) - kb = add_KB() + # kb = add_KB() + kb = hwf_KB() abducer = AbducerBase(kb) recorder = logger() recorder.set_savefile("test.log") - train_X, train_Y, test_X, test_Y = get_mnist_add() - # train_X, train_Y, test_X, test_Y = get_hwf() + # train_X, train_Y, test_X, test_Y = get_mnist_add() + train_X, train_Y, test_X, test_Y = get_hwf() recorder = plog.ResultRecorder() - cls = LeNet5() + cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_X[0][0].shape[1:])) criterion = nn.CrossEntropyLoss(size_average=True) optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - sign_list = list(range(10)) - base_model = BasicModel(cls, criterion, optimizer, device, Params(), sign_list, recorder=recorder) - model = MyModel(base_model) + + base_model = BasicModel(cls, criterion, optimizer, device, Params(), recorder=recorder) + model = MyModel(base_model, kb.pseudo_label_list) res = framework.train(model, abducer, train_X, train_Y, sample_num = 10000, verbose = 1) print(res) From dfaeb9f8d575515933b52943751153ec2761d831 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 18 Nov 2022 15:38:41 +0800 Subject: [PATCH 036/601] Update get_mnist_add.py --- datasets/mnist_add/get_mnist_add.py | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py index fcada50..1af834a 100644 --- a/datasets/mnist_add/get_mnist_add.py +++ b/datasets/mnist_add/get_mnist_add.py @@ -3,27 +3,39 @@ import torchvision from torch.utils.data import Dataset from torchvision.transforms import transforms -def get_data(file, img_dataset): +def get_data(file, img_dataset, get_pseudo_label): X = [] + if(get_pseudo_label): + Z = [] Y = [] with open(file) as f: for line in f: line = line.strip().split(' ') X.append([img_dataset[int(line[0])][0], img_dataset[int(line[1])][0]]) + if(get_pseudo_label): + Z.append([img_dataset[int(line[0])][1], img_dataset[int(line[1])][1]]) Y.append(int(line[2])) - return X, Y + + if(get_pseudo_label): + return X, Z, Y + else: + return X, None, Y -def get_mnist_add(): +def get_mnist_add(train = True, get_pseudo_label = False): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))]) img_dataset = torchvision.datasets.MNIST(root='./datasets/mnist_add/', train=True, download=True, transform=transform) - train_X, train_Y = get_data('./datasets/mnist_add/train_data.txt', img_dataset) - test_X, test_Y = get_data('./datasets/mnist_add/test_data.txt', img_dataset) + if(train): + file = './datasets/mnist_add/train_data.txt' + else: + file = './datasets/mnist_add/test_data.txt' + + return get_data(file, img_dataset, get_pseudo_label) - return train_X, train_Y, test_X, test_Y if __name__ == "__main__": - train_X, train_Y, test_X, test_Y = get_mnist_add() + train_X, train_Y = get_mnist_add(train = True) + test_X, test_Y = get_mnist_add(train = False) print(len(train_X), len(test_X)) print(train_X[0][0].shape, train_X[0][1].shape, train_Y[0]) From 649513bb79f76179124750934927dc5706ec853c Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 18 Nov 2022 15:39:03 +0800 Subject: [PATCH 037/601] Update get_hwf.py --- datasets/hwf/get_hwf.py | 33 ++++++++++++++++++++++++--------- 1 file changed, 24 insertions(+), 9 deletions(-) diff --git a/datasets/hwf/get_hwf.py b/datasets/hwf/get_hwf.py index cfd1666..f94f1a5 100644 --- a/datasets/hwf/get_hwf.py +++ b/datasets/hwf/get_hwf.py @@ -7,30 +7,45 @@ img_transform = transforms.Compose([ transforms.Normalize((0.5,), (1,)) ]) -def get_data(file, precision_num = 2): +def get_data(file, get_pseudo_label, precision_num = 2): X = [] + if(get_pseudo_label): + Z = [] Y = [] img_dir = './datasets/hwf/data/Handwritten_Math_Symbols/' with open(file) as f: data = json.load(f) for idx in range(len(data)): imgs = [] + imgs_pseudo_label = [] for img_path in data[idx]['img_paths']: img = Image.open(img_dir + img_path).convert('L') img = img_transform(img) imgs.append(img) - X.append(imgs) - Y.append(round(data[idx]['res'], precision_num)) - return X, Y + if(get_pseudo_label): + imgs_pseudo_label.append(img_path.split('/')[0]) + if(len(imgs) == 3): + X.append(imgs) + if(get_pseudo_label): + Z.append(imgs_pseudo_label) + Y.append(round(data[idx]['res'], precision_num)) + + if(get_pseudo_label): + return X, Z, Y + else: + return X, None, Y -def get_hwf(precision_num = 2): - train_X, train_Y = get_data('./datasets/hwf/data/expr_train.json', precision_num) - test_X, test_Y = get_data('./datasets/hwf/data/expr_test.json', precision_num) +def get_hwf(train = True, get_pseudo_label = False, precision_num = 2): + if(train): + file = './datasets/hwf/data/expr_train.json' + else: + file = './datasets/hwf/data/expr_test.json' - return train_X, train_Y, test_X, test_Y + return get_data(file, get_pseudo_label, precision_num) if __name__ == "__main__": - train_X, train_Y, test_X, test_Y = get_hwf() + train_X, train_Y = get_hwf(train = True) + test_X, test_Y = get_hwf(train = False) print(len(train_X), len(test_X)) print(len(train_X[0]), train_X[0][0].shape, train_Y[0]) From 35bdb8d01fe02bbfc49f58df7ceffedd65744093 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 18 Nov 2022 15:52:01 +0800 Subject: [PATCH 038/601] Update framework.py --- framework.py | 83 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 48 insertions(+), 35 deletions(-) diff --git a/framework.py b/framework.py index 92a20b5..5479316 100644 --- a/framework.py +++ b/framework.py @@ -17,33 +17,41 @@ import numpy as np from utils.plog import INFO, DEBUG, clocker @clocker -def block_sample(X_bak, C_bak, sample_num, epoch_idx): - part_num = (len(X_bak) // sample_num) +def block_sample(X, Z, Y, sample_num, epoch_idx): + part_num = (len(X) // sample_num) if part_num == 0: part_num = 1 seg_idx = epoch_idx % part_num INFO("seg_idx:", seg_idx, ", part num:", part_num, ", data num:", len(X_bak)) - X = X_bak[sample_num * seg_idx: sample_num * (seg_idx + 1)] - # Y = Y_bak[sample_num * seg_idx: sample_num * (seg_idx + 1)] - C = C_bak[sample_num * seg_idx: sample_num * (seg_idx + 1)] + X = X[sample_num * seg_idx: sample_num * (seg_idx + 1)] + Z = Z[sample_num * seg_idx: sample_num * (seg_idx + 1)] + Y = Y[sample_num * seg_idx: sample_num * (seg_idx + 1)] - return X, C + return X, Z, Y -def get_taglist(self, Y): - tmp = [[str(x) for x in label] for label in Y] +def get_taglist(self, Z): + tmp = [[str(x) for x in label] for label in Z] tmp = sorted(list(set(tmp))) return tmp -@clocker -def result_statistics(C, pseudo_Y, logic_forward): +def get_abl_acc(Y, pseudo_Z, logic_forward): abl_acc = 0 - for tidx, (c, pseudo_y) in enumerate(zip(C, pseudo_Y)): - if(logic_forward(pseudo_y) == c): - abl_acc += 1 - + for y, pseudo_z in zip(Y, pseudo_Z): + if(logic_forward(pseudo_z) == y): + abl_acc += 1 + return abl_acc / len(Y) + +def get_char_acc(Z, pseudo_Z): + char_acc = 0 + char_num = 0 + for z, pseudo_z in zip(Z, pseudo_Z): + char_num += len(z) + for zidx in range(len(z)): + if(z[zidx] == pseudo_z[zidx]): + char_acc += 1 + return char_acc / char_num + - return abl_acc / len(C) - # def result_statistics(pseudo_Y, Y, abduced_Y): # abd_err_num = 0 @@ -89,14 +97,14 @@ def result_statistics(C, pseudo_Y, logic_forward): # return result @clocker -def filter_data(X, abduced_Y): - finetune_Y = [] +def filter_data(X, abduced_Z): + finetune_Z = [] finetune_X = [] - for abduced_x, abduced_y in zip(X, abduced_Y): - if abduced_y is not None: + for abduced_x, abduced_z in zip(X, abduced_Z): + if abduced_z is not None: finetune_X.append(abduced_x) - finetune_Y.append(abduced_y) - return finetune_X, finetune_Y + finetune_Z.append(abduced_z) + return finetune_X, finetune_Z @clocker def is_all_sublabel_exist(labels, std_label_list): @@ -112,42 +120,47 @@ def is_all_sublabel_exist(labels, std_label_list): return False return True -def pretrain(model, X, Y): +def pretrain(model, X, Z): pass -def train(model, abducer, X, C, epochs = 10, sample_num = -1, verbose = -1): +def train(model, abducer, X, Z, Y, epochs = 10, sample_num = -1, verbose = -1): # Set default parameters if sample_num == -1: sample_num = len(X) if verbose < 1: verbose = epochs + + char_acc_flag = 1 + if Z == None: + char_acc_flag = 0 + Z = [None] * len(X) predict_func = clocker(model.predict) train_func = clocker(model.train) abduce_func = clocker(abducer.batch_abduce) - X_bak = X - C_bak = C epochs = 50 + # Abductive learning train process for epoch_idx in range(epochs): - X, C = block_sample(X_bak, C_bak, sample_num, epoch_idx) + X, Z, Y = block_sample(X, Z, Y, sample_num, epoch_idx) preds_res = predict_func(X) - - abl_acc = result_statistics(C, preds_res['cls'], abducer.kb.logic_forward) - print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc) - - abduced_Y = abduce_func(preds_res, C) - + abduced_Z = abduce_func(preds_res, Y) - finetune_X, finetune_Y = filter_data(X, abduced_Y) + abl_acc = get_abl_acc(Y, preds_res['cls'], abducer.kb.logic_forward) + if(not char_acc_flag): + ori_char_acc = get_char_acc(Z, preds_res['cls']) + abd_char_acc = get_char_acc(abduced_Z, preds_res['cls']) + print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc, ' ori_char_acc:', ori_char_acc, ' abd_char_acc:', abd_char_acc) + finetune_X, finetune_Z = filter_data(X, abduced_Z) if len(finetune_X) > 0: - train_func(finetune_X, finetune_Y)#, n_epoch = 10) + train_func(finetune_X, finetune_Z) else: INFO("lack of data, all abduced failed", len(finetune_X)) + return abl_acc # def train(model, abducer, X, Y, C = None, epochs = 10, sample_num = -1, verbose = -1, check_sublabel = True): From b037edaf0b0eb82ad1d6c57e8ef50663474211ab Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 18 Nov 2022 16:03:17 +0800 Subject: [PATCH 039/601] Update framework.py --- framework.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/framework.py b/framework.py index 5479316..a37483b 100644 --- a/framework.py +++ b/framework.py @@ -22,7 +22,7 @@ def block_sample(X, Z, Y, sample_num, epoch_idx): if part_num == 0: part_num = 1 seg_idx = epoch_idx % part_num - INFO("seg_idx:", seg_idx, ", part num:", part_num, ", data num:", len(X_bak)) + INFO("seg_idx:", seg_idx, ", part num:", part_num, ", data num:", len(X)) X = X[sample_num * seg_idx: sample_num * (seg_idx + 1)] Z = Z[sample_num * seg_idx: sample_num * (seg_idx + 1)] Y = Y[sample_num * seg_idx: sample_num * (seg_idx + 1)] @@ -150,10 +150,12 @@ def train(model, abducer, X, Z, Y, epochs = 10, sample_num = -1, verbose = -1): abduced_Z = abduce_func(preds_res, Y) abl_acc = get_abl_acc(Y, preds_res['cls'], abducer.kb.logic_forward) - if(not char_acc_flag): + if(char_acc_flag): ori_char_acc = get_char_acc(Z, preds_res['cls']) abd_char_acc = get_char_acc(abduced_Z, preds_res['cls']) - print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc, ' ori_char_acc:', ori_char_acc, ' abd_char_acc:', abd_char_acc) + print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc, ' ori_char_acc:', ori_char_acc, ' abd_char_acc:', abd_char_acc) + else: + print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc) finetune_X, finetune_Z = filter_data(X, abduced_Z) if len(finetune_X) > 0: From a8f33df3a306a4dbd98b6a290d9f23e3b75e28de Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 18 Nov 2022 16:03:54 +0800 Subject: [PATCH 040/601] Update example.py --- example.py | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/example.py b/example.py index 65789e2..14d2108 100644 --- a/example.py +++ b/example.py @@ -30,8 +30,8 @@ from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf class Params: - imgH = 45 - imgW = 45 + imgH = 28 + imgW = 28 keep_ratio = True saveInterval = 10 batchSize = 16 @@ -45,17 +45,15 @@ def run_test(): recorder_file_path = f"{result_dir}/1116.pk"# - # kb = add_KB() - kb = hwf_KB() - abducer = AbducerBase(kb) + kb = add_KB() + # kb = hwf_KB() + abducer = AbducerBase(kb, 2) recorder = logger() recorder.set_savefile("test.log") - - # train_X, train_Y, test_X, test_Y = get_mnist_add() - train_X, train_Y, test_X, test_Y = get_hwf() - + train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) + test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) recorder = plog.ResultRecorder() cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_X[0][0].shape[1:])) @@ -67,7 +65,7 @@ def run_test(): base_model = BasicModel(cls, criterion, optimizer, device, Params(), recorder=recorder) model = MyModel(base_model, kb.pseudo_label_list) - res = framework.train(model, abducer, train_X, train_Y, sample_num = 10000, verbose = 1) + res = framework.train(model, abducer, train_X, train_Z, train_Y, sample_num = 10000, verbose = 1) print(res) From 24dcf02b33a822b0bd3a8bf3379ec7c22a5c1382 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sat, 19 Nov 2022 09:51:37 +0800 Subject: [PATCH 041/601] Update kb.py --- abducer/kb.py | 229 +++++++++++++++----------------------------------- 1 file changed, 70 insertions(+), 159 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 12772e5..a448a83 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -44,12 +44,14 @@ class KBBase(ABC): def __len__(self): pass -class add_KB(KBBase): - def __init__(self, kb_max_len = -1): + +class ClsKB(KBBase): + def __init__(self, pseudo_label_list, kb_max_len = -1): super().__init__() - self.pseudo_label_list = list(range(10)) + self.pseudo_label_list = pseudo_label_list self.base = {} self.kb_max_len = kb_max_len + if(self.kb_max_len > 0): X = self.get_X(self.pseudo_label_list, self.kb_max_len) Y = self.get_Y(X, self.logic_forward) @@ -57,9 +59,6 @@ class add_KB(KBBase): for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) - def logic_forward(self, nums): - return sum(nums) - def get_X(self, pseudo_label_list, max_len): res = [] assert(max_len >= 2) @@ -69,6 +68,9 @@ class add_KB(KBBase): def get_Y(self, X, logic_forward): return [logic_forward(nums) for nums in X] + + def logic_forward(self): + return None def get_candidates(self, key, length = None): if(self.base == {}): @@ -76,7 +78,7 @@ class add_KB(KBBase): if key is None: return self.get_all_candidates() - + length = self._length(length) if(self.kb_max_len < min(length)): return [] @@ -84,163 +86,72 @@ class add_KB(KBBase): def get_all_candidates(self): return sum([sum(v.values(), []) for v in self.base.values()], []) - + def _dict_len(self, dic): return sum(len(c) for c in dic.values()) def __len__(self): return sum(self._dict_len(v) for v in self.base.values()) - -class hwf_KB(KBBase): + + + +class add_KB(ClsKB): def __init__(self, kb_max_len = -1): - super().__init__() - self.pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'] - self.base = {} - self.kb_max_len = kb_max_len - if(self.kb_max_len > 0): - X = self.get_X(self.pseudo_label_list, self.kb_max_len) - Y = self.get_Y(X, self.logic_forward) + self.pseudo_label_list = list(range(10)) + super().__init__(self.pseudo_label_list, kb_max_len) + + def logic_forward(self, nums): + return sum(nums) - for x, y in zip(X, Y): - self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + def get_candidates(self, key, length = None): + return super().get_candidates(key, length) - def calculate(self, formula): - stack = [] - postfix = [] - priority = {'+': 0, '-': 0, - '*': 1, '/': 1} - skip_flag = 0 - for i in range(len(formula)): - if formula[i] == '-': - if i == 0: - formula.insert(0, 0) - for i in range(len(formula)): - if skip_flag: - skip_flag -= 1 - continue - char = formula[i] - if char in priority.keys(): - while stack and (priority[char] <= priority[stack[-1]]): - postfix.append(stack.pop()) - stack.append(char) - else: - num = int(char) - while (i + 1) < len(formula): - if formula[i + 1] not in priority.keys(): - skip_flag += 1 - num = num * 10 + int(formula[i + 1]) - i += 1 - else: - break - postfix.append(num) - while stack: - postfix.append(stack.pop()) + def get_all_candidates(self): + return super().get_all_candidates() + + def _dict_len(self, dic): + return super()._dict_len(dic) - for i in postfix: - if i in priority.keys(): - num2 = stack.pop() - num1 = stack.pop() - if i == '+': - res = num1 + num2 - elif i == '-': - res = num1 - num2 - elif i == '*': - res = num1 * num2 - elif i == '/': - if(num2 == 0): - return np.inf - res = num1 / num2 - stack.append(res) - else: - stack.append(i) - return round(stack[0], 2) + def __len__(self): + return super().__len__() +class hwf_KB(ClsKB): + def __init__(self, kb_max_len = -1): + self.pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'] + super().__init__(self.pseudo_label_list, kb_max_len) + def valid_formula(self, formula): - symbol_idx_list = [] - for idx, c in enumerate(formula): - if(idx == 0 and c == '-'): - if(len(formula) == 1 or formula[1] in ['+', '-', '*', '/']): - return False - continue - if(c in ['+', '-', '*', '/']): - if(idx - 1 in symbol_idx_list): - return False - symbol_idx_list.append(idx) - if(0 in symbol_idx_list or len(formula) - 1 in symbol_idx_list): + if(len(formula) % 2 == 0): return False + for i in range(len(formula)): + if(i % 2 == 0 and formula[i] not in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']): + return False + if(i % 2 != 0 and formula[i] not in ['+', '-', '*', '/']): + return False return True def logic_forward(self, formula): if(self.valid_formula(formula) == False): return np.inf - return self.calculate(list(formula)) + try: + return eval(''.join(formula)) + except ZeroDivisionError: + return np.inf - def get_X(self, pseudo_label_list, max_len): - res = [] - assert(max_len >= 2) - for len in range(2, max_len + 1): - res += list(product(pseudo_label_list, repeat = len)) - return res - - def get_Y(self, X, logic_forward): - return [logic_forward(formula) for formula in X] - def get_candidates(self, key, length = None): - if(self.base == {}): - return [] - - if key is None: - return self.get_all_candidates() - - length = self._length(length) - if(self.kb_max_len < min(length)): - return [] - return sum([self.base[l][key] for l in length], []) + return super().get_candidates(key, length) def get_all_candidates(self): - return sum([sum(v.values(), []) for v in self.base.values()], []) - - def _dict_len(self, dic): - return sum(len(c) for c in dic.values()) - - def __len__(self): - return sum(self._dict_len(v) for v in self.base.values()) - -class cls_KB(KBBase): - def __init__(self, X, Y = None): - super().__init__() - self.base = {} - - if X is None: - return - - if Y is None: - Y = [None] * len(X) - - for x, y in zip(X, Y): - self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + return super().get_all_candidates() - def logic_forward(self): - return None - - def get_candidates(self, key, length = None): - if key is None: - return self.get_all_candidates() - - length = self._length(length) - - return sum([self.base[l][key] for l in length], []) - - def get_all_candidates(self): - return sum([sum(v.values(), []) for v in self.base.values()], []) - def _dict_len(self, dic): - return sum(len(c) for c in dic.values()) + return super()._dict_len(dic) def __len__(self): - return sum(self._dict_len(v) for v in self.base.values()) + return super().__len__() + -class reg_KB(KBBase): +class RegKB(KBBase): def __init__(self, X, Y = None): super().__init__() tmp_dict = {} @@ -323,26 +234,26 @@ if __name__ == "__main__": print() - X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] - Y = [2, 1, 1, 2, 2] - kb = cls_KB(X, Y) - print('len(kb):', len(kb)) - res = kb.get_candidates(2, 5) - print(res) - res = kb.get_candidates(2, 3) - print(res) - res = kb.get_candidates(None) - print(res) - print() + # X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] + # Y = [2, 1, 1, 2, 2] + # kb = ClsKB(X, Y) + # print('len(kb):', len(kb)) + # res = kb.get_candidates(2, 5) + # print(res) + # res = kb.get_candidates(2, 3) + # print(res) + # res = kb.get_candidates(None) + # print(res) + # print() - X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] - Y = [2, 1, 1, 2, 1.5, 1.5] - kb = reg_KB(X, Y) - print('len(kb):', len(kb)) - res = kb.get_candidates(1.6) - print(res) - res = kb.get_candidates(1.6, length = 9) - print(res) - res = kb.get_candidates(None) - print(res) + # X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] + # Y = [2, 1, 1, 2, 1.5, 1.5] + # kb = RegKB(X, Y) + # print('len(kb):', len(kb)) + # res = kb.get_candidates(1.6) + # print(res) + # res = kb.get_candidates(1.6, length = 9) + # print(res) + # res = kb.get_candidates(None) + # print(res) From 5224c86035d29cbf769aade41b7f0db510671172 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sat, 19 Nov 2022 10:09:36 +0800 Subject: [PATCH 042/601] Update kb.py --- abducer/kb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abducer/kb.py b/abducer/kb.py index a448a83..85c7519 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -134,7 +134,7 @@ class hwf_KB(ClsKB): if(self.valid_formula(formula) == False): return np.inf try: - return eval(''.join(formula)) + return round(eval(''.join(formula)), 2) except ZeroDivisionError: return np.inf From cf33651a7ade6cf1f68698a7ece95cc96ce72cc3 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sat, 19 Nov 2022 10:25:51 +0800 Subject: [PATCH 043/601] Update abducer_base.py --- abducer/abducer_base.py | 51 +++++++++++++++++++---------------------- 1 file changed, 23 insertions(+), 28 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 23019b8..af9fdc8 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -38,34 +38,29 @@ def confidence_dist(A, B): class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func = 'hamming', pred_res_parse = None, cache = True): + def __init__(self, kb, dist_func = 'confidence', cache = True): self.kb = kb - - if(dist_func == 'hamming'): - self.dist_func = hamming_dist - elif(dist_func == 'confidence'): - self.dist_func = confidence_dist - - if pred_res_parse is None: - if(dist_func == 'hamming'): - pred_res_parse = lambda x : x["cls"] - elif dist_func == 'confidence': - pred_res_parse = lambda x : x["prob"] - self.pred_res_parse = pred_res_parse - + assert(dist_func == 'hamming' or dist_func == 'confidence') + self.dist_func = dist_func self.cache = cache self.cache_min_address_num = {} self.cache_candidates = {} - def get_min_cost_candidate(self, pred_res, candidates): - cost_list = self.dist_func(pred_res, candidates) + def get_cost_list(self, pred_res, pred_res_prob, candidates): + if(self.dist_func == 'hamming'): + return hamming_dist(pred_res, candidates) + elif(self.dist_func == 'confidence'): + return confidence_dist(pred_res_prob, candidates) + + def get_min_cost_candidate(self, pred_res, pred_res_prob, candidates): + cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) min_address_num = np.min(cost_list) idxs = np.where(cost_list == min_address_num)[0] return [candidates[idx] for idx in idxs][0] def abduce(self, data, max_address_num = -1, require_more_address = 0): - pred_res, ans = data - + pred_res, pred_res_prob, ans = data + if max_address_num == -1: max_address_num = len(pred_res) @@ -74,12 +69,12 @@ class AbducerBase(abc.ABC): if((tuple(pred_res), ans, address_num) in self.cache_candidates): # print('cached') candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] - candidates = self.get_min_cost_candidate(pred_res, candidates) + candidates = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) return candidates if(self.kb.base != {}): all_candidates = self.kb.get_candidates(ans, len(pred_res)) - cost_list = self.dist_func(pred_res, all_candidates) + cost_list = self.get_cost_list(pred_res, pred_res_prob, all_candidates) min_address_num = np.min(cost_list) address_num = min(max_address_num, min_address_num + require_more_address) idxs = np.where(cost_list <= address_num)[0] @@ -92,7 +87,7 @@ class AbducerBase(abc.ABC): self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates - candidates = self.get_min_cost_candidate(pred_res, candidates) + candidates = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) return candidates def address(self, address_num, pred_res, key): @@ -136,18 +131,18 @@ class AbducerBase(abc.ABC): return candidates, min_address_num, address_num - def batch_abduce(self, Y, C, max_address_num = -1, require_more_address = 0): + def batch_abduce(self, Z, Y, max_address_num = -1, require_more_address = 0): return [ - self.abduce((y, c), max_address_num, require_more_address)\ - for y, c in zip(self.pred_res_parse(Y), C) + self.abduce((z, prob, y), max_address_num, require_more_address)\ + for z, prob, y in zip(Z['cls'], Z['prob'], Y) ] - def __call__(self, Y, C, max_address_num = -1, require_more_address = 0): - return self.batch_abduce(Y, C, max_address_num, require_more_address) + def __call__(self, Z, Y, max_address_num = -1, require_more_address = 0): + return self.batch_abduce(Z, Y, max_address_num, require_more_address) -if __name__ == "__main__": +if __name__ == '__main__': kb = add_KB() abd = AbducerBase(kb) res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) @@ -166,7 +161,7 @@ if __name__ == "__main__": abd = AbducerBase(kb) res = abd.abduce((['5', '+', '2'], 3), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce((['5', '+', '2'], 1.67), max_address_num = 2, require_more_address = 0) + res = abd.abduce((['5', '+', '2'], 1.67), max_address_num = 3, require_more_address = 0) print(res) res = abd.abduce((['5', '+', '3'], 0.33), max_address_num = 3, require_more_address = 3) print(res) From 36304c98dc8af74cf8c7ab51fbc8d9ddf18cb03a Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sat, 19 Nov 2022 10:42:26 +0800 Subject: [PATCH 044/601] Update abducer_base.py --- abducer/abducer_base.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index af9fdc8..c94bca4 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -144,25 +144,25 @@ class AbducerBase(abc.ABC): if __name__ == '__main__': kb = add_KB() - abd = AbducerBase(kb) - res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) + abd = AbducerBase(kb, 'hamming') + res = abd.abduce(([1, 1, 1], None, 4), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 1) + res = abd.abduce(([1, 1, 1], None, 4), max_address_num = 2, require_more_address = 1) print(res) - res = abd.abduce(([1, 1, 1], 4), max_address_num = 1, require_more_address = 1) + res = abd.abduce(([1, 1, 1], None, 4), max_address_num = 1, require_more_address = 1) print(res) - res = abd.abduce(([1, 1, 1], 4), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1, 1], None, 4), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([1, 1, 1], 5), max_address_num = 2, require_more_address = 1) + res = abd.abduce(([1, 1, 1], None, 5), max_address_num = 2, require_more_address = 1) print(res) print() kb = hwf_KB() abd = AbducerBase(kb) - res = abd.abduce((['5', '+', '2'], 3), max_address_num = 2, require_more_address = 0) + res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce((['5', '+', '2'], 1.67), max_address_num = 3, require_more_address = 0) + res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num = 3, require_more_address = 0) print(res) - res = abd.abduce((['5', '+', '3'], 0.33), max_address_num = 3, require_more_address = 3) + res = abd.abduce((['5', '+', '3'], None, 0.33), max_address_num = 3, require_more_address = 3) print(res) print() From 65e3b93912bc436d0138cadbe99549f67d43c5d5 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sat, 19 Nov 2022 13:56:47 +0800 Subject: [PATCH 045/601] Update example.py --- example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example.py b/example.py index 14d2108..1a20b28 100644 --- a/example.py +++ b/example.py @@ -36,7 +36,7 @@ class Params: saveInterval = 10 batchSize = 16 workers = 16 - n_epoch = 10 + n_epoch = 1 stop_loss = None def run_test(): From 90c5703b57477805a9f4de700128e26cba57ecf6 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Sat, 19 Nov 2022 23:58:27 +0800 Subject: [PATCH 046/601] update basic_model.py and wabl_models.py --- models/basic_model.py | 48 ++++++++++++++++++++++++------------------- models/wabl_models.py | 11 ++++------ 2 files changed, 31 insertions(+), 28 deletions(-) diff --git a/models/basic_model.py b/models/basic_model.py index e251ec4..86fbea5 100644 --- a/models/basic_model.py +++ b/models/basic_model.py @@ -42,7 +42,7 @@ class XYDataset(Dataset): if self.target_transform is not None: label = self.target_transform(label) - return (img, label, index) + return (img, label) class FakeRecorder(): def __init__(self): @@ -57,14 +57,24 @@ class BasicModel(): criterion, optimizer, device, - params, + batch_size = 1, + num_epochs = 10, + stop_loss = 0.01, + num_workers = 0, + save_interval = None, + save_dir = None, transform = None, - target_transform=None, + target_transform = None, collate_fn = None, recorder = None): self.model = model.to(device) + self.batch_size = batch_size + self.num_epochs = num_epochs + self.stop_loss = stop_loss + self.num_workers = num_workers + self.criterion = criterion self.optimizer = optimizer self.transform = transform @@ -75,8 +85,8 @@ class BasicModel(): recorder = FakeRecorder() self.recorder = recorder - self.save_interval = params.saveInterval - self.params = params + self.save_interval = save_interval + self.save_dir = save_dir self.collate_fn = collate_fn pass @@ -91,9 +101,9 @@ class BasicModel(): if loss_value < min_loss: min_loss = loss_value if epoch > 0 and self.save_interval is not None and epoch % self.save_interval == 0: - assert hasattr(self.params, 'save_dir') - self.save(self.params.save_dir) - if stop_loss is not None and loss_value < stop_loss: + assert self.save_dir is not None + self.save(self.save_dir) + if stop_loss is not None and loss_value < stop_loss: break recorder.print("Model fitted, minimal loss is ", min_loss) return loss_value @@ -102,17 +112,16 @@ class BasicModel(): X = None, y = None): if data_loader is None: - params = self.params collate_fn = self.collate_fn transform = self.transform target_transform = self.target_transform train_dataset = XYDataset(X, y, transform=transform, target_transform=target_transform) sampler = None - data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=params.batchSize, \ - shuffle=True, sampler=sampler, num_workers=int(params.workers), \ + data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size, \ + shuffle=True, sampler=sampler, num_workers=int(self.num_workers), \ collate_fn=collate_fn) - return self._fit(data_loader, params.n_epoch, params.stop_loss) + return self._fit(data_loader, self.num_epochs, self.stop_loss) def train_epoch(self, data_loader): model = self.model @@ -155,7 +164,6 @@ class BasicModel(): def predict(self, data_loader = None, X = None, print_prefix = ""): if data_loader is None: - params = self.params collate_fn = self.collate_fn transform = self.transform target_transform = self.target_transform @@ -163,8 +171,8 @@ class BasicModel(): Y = [0] * len(X) val_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) sampler = None - data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=params.batchSize, \ - shuffle=False, sampler=sampler, num_workers=int(params.workers), \ + data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ + shuffle=False, sampler=sampler, num_workers=int(self.num_workers), \ collate_fn=collate_fn) recorder = self.recorder @@ -174,7 +182,6 @@ class BasicModel(): def predict_proba(self, data_loader = None, X = None, print_prefix = ""): if data_loader is None: - params = self.params collate_fn = self.collate_fn transform = self.transform target_transform = self.target_transform @@ -182,8 +189,8 @@ class BasicModel(): Y = [0] * len(X) val_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) sampler = None - data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=params.batchSize, \ - shuffle=False, sampler=sampler, num_workers=int(params.workers), \ + data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ + shuffle=False, sampler=sampler, num_workers=int(self.num_workers), \ collate_fn=collate_fn) recorder = self.recorder @@ -222,15 +229,14 @@ class BasicModel(): def val(self, data_loader = None, X = None, y = None, print_prefix = ""): if data_loader is None: - params = self.params collate_fn = self.collate_fn transform = self.transform target_transform = self.target_transform val_dataset = XYDataset(X, y, transform=transform, target_transform=target_transform) sampler = None - data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=params.batchSize, \ - shuffle=True, sampler=sampler, num_workers=int(params.workers), \ + data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ + shuffle=True, sampler=sampler, num_workers=int(self.num_workers), \ collate_fn=collate_fn) return self._val(data_loader, print_prefix) diff --git a/models/wabl_models.py b/models/wabl_models.py index 5d44719..b854039 100644 --- a/models/wabl_models.py +++ b/models/wabl_models.py @@ -49,7 +49,10 @@ def reshape_data(Y, marks): class WABLBasicModel: - def __init__(self, pseudo_label_list): + def __init__(self, base_model, pseudo_label_list): + self.cls_list = [] + self.cls_list.append(base_model) + self.pseudo_label_list = pseudo_label_list self.mapping = dict(zip(pseudo_label_list, list(range(len(pseudo_label_list))))) self.remapping = dict(zip(list(range(len(pseudo_label_list))), pseudo_label_list)) @@ -138,12 +141,6 @@ class CNN(WABLBasicModel): self.cls_list[i].fit(data_X, data_Y) #self.label_lists.append(sorted(list(set(data_Y)))) -class MyModel(WABLBasicModel): - def __init__(self, base_model, pseudo_label_list): - super(MyModel, self).__init__(pseudo_label_list) - self.cls_list = [] - self.cls_list.append(base_model) - if __name__ == "__main__": #data_path = "utils/hamming_data/generated_data/hamming_7_3_0.20.pk" data_path = "datasets/generated_data/0_code_7_2_0.00.pk" From 40a3889c68dd4472007ea96d193eec52711098d6 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sun, 20 Nov 2022 22:01:50 +0800 Subject: [PATCH 047/601] Update abducer_base.py --- abducer/abducer_base.py | 51 ++++++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index c94bca4..bbf00fa 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -17,23 +17,7 @@ import numpy as np from itertools import product, combinations -def hamming_dist(A, B): - B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) -def confidence_dist(A, B): - B = np.array(B) - - #print(A) - A = np.clip(A, 1e-9, 1) - A = np.expand_dims(A, axis=0) - A = A.repeat(axis=0, repeats=(len(B))) - rows = np.array(range(len(B))) - rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) - cols = np.array(range(len(B[0]))) - cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) - return 1 - np.prod(A[rows, cols, B], axis = 1) @@ -46,11 +30,31 @@ class AbducerBase(abc.ABC): self.cache_min_address_num = {} self.cache_candidates = {} + def hamming_dist(self, A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) + + def confidence_dist(self, A, B): + mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) + B = [list(map(lambda x : mapping[x], b)) for b in B] + + B = np.array(B) + A = np.clip(A, 1e-9, 1) + A = np.expand_dims(A, axis=0) + A = A.repeat(axis=0, repeats=(len(B))) + rows = np.array(range(len(B))) + rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) + cols = np.array(range(len(B[0]))) + cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) + return 1 - np.prod(A[rows, cols, B], axis = 1) + + def get_cost_list(self, pred_res, pred_res_prob, candidates): if(self.dist_func == 'hamming'): - return hamming_dist(pred_res, candidates) + return self.hamming_dist(pred_res, candidates) elif(self.dist_func == 'confidence'): - return confidence_dist(pred_res_prob, candidates) + return self.confidence_dist(pred_res_prob, candidates) def get_min_cost_candidate(self, pred_res, pred_res_prob, candidates): cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) @@ -60,7 +64,6 @@ class AbducerBase(abc.ABC): def abduce(self, data, max_address_num = -1, require_more_address = 0): pred_res, pred_res_prob, ans = data - if max_address_num == -1: max_address_num = len(pred_res) @@ -69,12 +72,12 @@ class AbducerBase(abc.ABC): if((tuple(pred_res), ans, address_num) in self.cache_candidates): # print('cached') candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] - candidates = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) - return candidates + candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + return candidate if(self.kb.base != {}): all_candidates = self.kb.get_candidates(ans, len(pred_res)) - cost_list = self.get_cost_list(pred_res, pred_res_prob, all_candidates) + cost_list = self.hamming_dist(pred_res, all_candidates) min_address_num = np.min(cost_list) address_num = min(max_address_num, min_address_num + require_more_address) idxs = np.where(cost_list <= address_num)[0] @@ -87,8 +90,8 @@ class AbducerBase(abc.ABC): self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates - candidates = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) - return candidates + candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + return candidate def address(self, address_num, pred_res, key): new_candidates = [] From fad85240a2223c205b524c2701e317231f4ff694 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sun, 20 Nov 2022 22:06:49 +0800 Subject: [PATCH 048/601] Update kb.py --- abducer/kb.py | 62 +++++++++++++++++---------------------------------- 1 file changed, 21 insertions(+), 41 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 85c7519..54a367e 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -46,23 +46,21 @@ class KBBase(ABC): class ClsKB(KBBase): - def __init__(self, pseudo_label_list, kb_max_len = -1): + def __init__(self, pseudo_label_list, len_list = None): super().__init__() self.pseudo_label_list = pseudo_label_list self.base = {} - self.kb_max_len = kb_max_len + self.len_list = len_list - if(self.kb_max_len > 0): - X = self.get_X(self.pseudo_label_list, self.kb_max_len) + if(self.len_list != None): + X = self.get_X(self.pseudo_label_list, self.len_list) Y = self.get_Y(X, self.logic_forward) - for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) - def get_X(self, pseudo_label_list, max_len): + def get_X(self, pseudo_label_list, len_list): res = [] - assert(max_len >= 2) - for len in range(2, max_len + 1): + for len in len_list: res += list(product(pseudo_label_list, repeat = len)) return res @@ -80,7 +78,7 @@ class ClsKB(KBBase): return self.get_all_candidates() length = self._length(length) - if(self.kb_max_len < min(length)): + if(max(self.len_list) < min(length)): return [] return sum([self.base[l][key] for l in length], []) @@ -96,29 +94,18 @@ class ClsKB(KBBase): class add_KB(ClsKB): - def __init__(self, kb_max_len = -1): + def __init__(self, len_list = None): self.pseudo_label_list = list(range(10)) - super().__init__(self.pseudo_label_list, kb_max_len) + super().__init__(self.pseudo_label_list, len_list) def logic_forward(self, nums): return sum(nums) - - def get_candidates(self, key, length = None): - return super().get_candidates(key, length) - - def get_all_candidates(self): - return super().get_all_candidates() - def _dict_len(self, dic): - return super()._dict_len(dic) - - def __len__(self): - return super().__len__() class hwf_KB(ClsKB): - def __init__(self, kb_max_len = -1): + def __init__(self, len_list = None): self.pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'] - super().__init__(self.pseudo_label_list, kb_max_len) + super().__init__(self.pseudo_label_list, len_list) def valid_formula(self, formula): if(len(formula) % 2 == 0): @@ -137,18 +124,6 @@ class hwf_KB(ClsKB): return round(eval(''.join(formula)), 2) except ZeroDivisionError: return np.inf - - def get_candidates(self, key, length = None): - return super().get_candidates(key, length) - - def get_all_candidates(self): - return super().get_all_candidates() - - def _dict_len(self, dic): - return super()._dict_len(dic) - - def __len__(self): - return super().__len__() class RegKB(KBBase): @@ -198,17 +173,18 @@ class RegKB(KBBase): def __len__(self): return sum([sum(len(x) for x in D[0]) for D in self.base.values()]) +import time if __name__ == "__main__": # With ground KB - kb = add_KB(kb_max_len = 5) + kb = add_KB(len_list = [2]) print('len(kb):', len(kb)) res = kb.get_candidates(0) print(res) - res = kb.get_candidates(18, length = 2) + res = kb.get_candidates(18) print(res) - res = kb.get_candidates(18, length = 8) + res = kb.get_candidates(18) print(res) - res = kb.get_candidates(7, length = 3) + res = kb.get_candidates(7) print(res) print() @@ -225,8 +201,12 @@ if __name__ == "__main__": print(res) print() - kb = hwf_KB(kb_max_len = 5) + start = time.time() + kb = hwf_KB(len_list = [1, 3, 5, 7]) + print(time.time() - start) print('len(kb):', len(kb)) + res = kb.get_candidates(2, length = 1) + print(res) res = kb.get_candidates(1, length = 3) print(res) res = kb.get_candidates(3.67, length = 5) From 1a38478bda69a51e23136a04eaada3fb439e93e1 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 09:30:30 +0800 Subject: [PATCH 049/601] Update kb.py --- abducer/kb.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 54a367e..a934ad8 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -77,9 +77,9 @@ class ClsKB(KBBase): if key is None: return self.get_all_candidates() - length = self._length(length) - if(max(self.len_list) < min(length)): + if (type(length) is int and length not in self.len_list): return [] + length = self._length(length) return sum([self.base[l][key] for l in length], []) def get_all_candidates(self): @@ -94,7 +94,7 @@ class ClsKB(KBBase): class add_KB(ClsKB): - def __init__(self, len_list = None): + def __init__(self, len_list = [2]): self.pseudo_label_list = list(range(10)) super().__init__(self.pseudo_label_list, len_list) @@ -103,7 +103,7 @@ class add_KB(ClsKB): class hwf_KB(ClsKB): - def __init__(self, len_list = None): + def __init__(self, len_list = [1, 3, 5, 7]): self.pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'] super().__init__(self.pseudo_label_list, len_list) From 15eda317bcf53bf2ecb15ca6096baceed0de1593 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 09:46:31 +0800 Subject: [PATCH 050/601] Update kb.py --- abducer/kb.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index a934ad8..98ea9bc 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -19,7 +19,7 @@ from collections import defaultdict from itertools import product class KBBase(ABC): - def __init__(self): + def __init__(self, GKB_flag = False): pass @abstractmethod @@ -46,13 +46,13 @@ class KBBase(ABC): class ClsKB(KBBase): - def __init__(self, pseudo_label_list, len_list = None): + def __init__(self, GKB_flag = False, pseudo_label_list = None, len_list = None): super().__init__() self.pseudo_label_list = pseudo_label_list self.base = {} self.len_list = len_list - if(self.len_list != None): + if GKB_flag: X = self.get_X(self.pseudo_label_list, self.len_list) Y = self.get_Y(X, self.logic_forward) for x, y in zip(X, Y): @@ -94,18 +94,18 @@ class ClsKB(KBBase): class add_KB(ClsKB): - def __init__(self, len_list = [2]): + def __init__(self, GKB_flag = False, len_list = [2]): self.pseudo_label_list = list(range(10)) - super().__init__(self.pseudo_label_list, len_list) + super().__init__(GKB_flag, self.pseudo_label_list, len_list) def logic_forward(self, nums): return sum(nums) class hwf_KB(ClsKB): - def __init__(self, len_list = [1, 3, 5, 7]): + def __init__(self, GKB_flag = False, len_list = [1, 3, 5, 7]): self.pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'] - super().__init__(self.pseudo_label_list, len_list) + super().__init__(GKB_flag, self.pseudo_label_list, len_list) def valid_formula(self, formula): if(len(formula) % 2 == 0): @@ -127,7 +127,7 @@ class hwf_KB(ClsKB): class RegKB(KBBase): - def __init__(self, X, Y = None): + def __init__(self, GKB_flag = False, X = None, Y = None): super().__init__() tmp_dict = {} for x, y in zip(X, Y): @@ -176,7 +176,7 @@ class RegKB(KBBase): import time if __name__ == "__main__": # With ground KB - kb = add_KB(len_list = [2]) + kb = add_KB(GKB_flag = True) print('len(kb):', len(kb)) res = kb.get_candidates(0) print(res) @@ -202,7 +202,7 @@ if __name__ == "__main__": print() start = time.time() - kb = hwf_KB(len_list = [1, 3, 5, 7]) + kb = hwf_KB(GKB_flag = True) print(time.time() - start) print('len(kb):', len(kb)) res = kb.get_candidates(2, length = 1) From bc8a72c21dad1289b307e1ee49787cc3762fa15f Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Mon, 21 Nov 2022 14:47:42 +0800 Subject: [PATCH 051/601] update readme --- README => readme.md | 9 +++++++++ 1 file changed, 9 insertions(+) rename README => readme.md (66%) diff --git a/README b/readme.md similarity index 66% rename from README rename to readme.md index 0aa2a11..1a9ebf1 100644 --- a/README +++ b/readme.md @@ -22,3 +22,12 @@ python share_example.py ## NOTICE They can only be used for academic purpose. For other purposes, please contact with LAMDA Group(www.lamda.nju.edu.cn). +## To do list + +- Improve speed and accuracy +- Add comparison with DeepProbLog, NGS,... (Accuracy and Speed) +- Add Inference/Abduction example with FOL engine (e.g., Prolog) +- Add zoopt optimization +- Rearrange structure and make it a python package +- Documents + From 0242e44c09f7e46532bc87c362a5141c36fb8ff7 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 14:56:35 +0800 Subject: [PATCH 052/601] Update kb.py --- abducer/kb.py | 49 +++++++++++++++++++++++++++++++++---------------- 1 file changed, 33 insertions(+), 16 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 98ea9bc..c206f62 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -19,7 +19,7 @@ from collections import defaultdict from itertools import product class KBBase(ABC): - def __init__(self, GKB_flag = False): + def __init__(self): pass @abstractmethod @@ -31,7 +31,11 @@ class KBBase(ABC): pass @abstractmethod - def logic_forward(self, X): + def logic_forward(self): + pass + + @abstractmethod + def valid_candidate(self): pass def _length(self, length): @@ -48,27 +52,34 @@ class KBBase(ABC): class ClsKB(KBBase): def __init__(self, GKB_flag = False, pseudo_label_list = None, len_list = None): super().__init__() + self.GKB_flag = GKB_flag self.pseudo_label_list = pseudo_label_list self.base = {} self.len_list = len_list if GKB_flag: - X = self.get_X(self.pseudo_label_list, self.len_list) - Y = self.get_Y(X, self.logic_forward) + X, Y = self.get_GKB(self.pseudo_label_list, self.len_list) for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) - def get_X(self, pseudo_label_list, len_list): - res = [] + def get_GKB(self, pseudo_label_list, len_list): + all_X = [] for len in len_list: - res += list(product(pseudo_label_list, repeat = len)) - return res - - def get_Y(self, X, logic_forward): - return [logic_forward(nums) for nums in X] + all_X += list(product(pseudo_label_list, repeat = len)) + + X = [] + Y = [] + for x in all_X: + if self.valid_candidate(x): + X.append(x) + Y.append(self.logic_forward(x)) + return X, Y + + def valid_candidate(self): + pass def logic_forward(self): - return None + pass def get_candidates(self, key, length = None): if(self.base == {}): @@ -92,11 +103,13 @@ class ClsKB(KBBase): return sum(self._dict_len(v) for v in self.base.values()) - class add_KB(ClsKB): def __init__(self, GKB_flag = False, len_list = [2]): self.pseudo_label_list = list(range(10)) super().__init__(GKB_flag, self.pseudo_label_list, len_list) + + def valid_candidate(self, x): + return True def logic_forward(self, nums): return sum(nums) @@ -107,7 +120,7 @@ class hwf_KB(ClsKB): self.pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'] super().__init__(GKB_flag, self.pseudo_label_list, len_list) - def valid_formula(self, formula): + def valid_candidate(self, formula): if(len(formula) % 2 == 0): return False for i in range(len(formula)): @@ -118,7 +131,7 @@ class hwf_KB(ClsKB): return True def logic_forward(self, formula): - if(self.valid_formula(formula) == False): + if(self.valid_candidate(formula) == False): return np.inf try: return round(eval(''.join(formula)), 2) @@ -140,8 +153,12 @@ class RegKB(KBBase): Y = [y for y, x in data] self.base[l] = (X, Y) + def valid_candidate(self): + pass + def logic_forward(self): - return None + pass + def get_candidates(self, key, length = None): if key is None: From d544ccd3c12fefcbaebf0cee867409c48a290348 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 14:56:54 +0800 Subject: [PATCH 053/601] Update abducer_base.py --- abducer/abducer_base.py | 55 ++++++++++++++++++++++------------------- 1 file changed, 30 insertions(+), 25 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index bbf00fa..6de9987 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -11,16 +11,12 @@ #================================================================# import abc -# from kb import add_KB, hwf_KB -from abducer.kb import add_KB, hwf_KB +from kb import add_KB, hwf_KB +# from abducer.kb import add_KB, hwf_KB import numpy as np from itertools import product, combinations - - - - class AbducerBase(abc.ABC): def __init__(self, kb, dist_func = 'confidence', cache = True): self.kb = kb @@ -57,10 +53,15 @@ class AbducerBase(abc.ABC): return self.confidence_dist(pred_res_prob, candidates) def get_min_cost_candidate(self, pred_res, pred_res_prob, candidates): - cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) - min_address_num = np.min(cost_list) - idxs = np.where(cost_list == min_address_num)[0] - return [candidates[idx] for idx in idxs][0] + if(len(candidates) == 0): + return [] + elif(len(candidates) == 1): + return candidates[0] + else: + cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) + min_address_num = np.min(cost_list) + idxs = np.where(cost_list == min_address_num)[0] + return [candidates[idx] for idx in idxs][0] def abduce(self, data, max_address_num = -1, require_more_address = 0): pred_res, pred_res_prob, ans = data @@ -75,13 +76,16 @@ class AbducerBase(abc.ABC): candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) return candidate - if(self.kb.base != {}): + if self.kb.GKB_flag: all_candidates = self.kb.get_candidates(ans, len(pred_res)) - cost_list = self.hamming_dist(pred_res, all_candidates) - min_address_num = np.min(cost_list) - address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(cost_list <= address_num)[0] - candidates = [all_candidates[idx] for idx in idxs] + if(len(all_candidates) == 0): + return [] + else: + cost_list = self.hamming_dist(pred_res, all_candidates) + min_address_num = np.min(cost_list) + address_num = min(max_address_num, min_address_num + require_more_address) + idxs = np.where(cost_list <= address_num)[0] + candidates = [all_candidates[idx] for idx in idxs] else: candidates, min_address_num, address_num = self.get_abduce_candidates(pred_res, ans, max_address_num, require_more_address) @@ -111,8 +115,7 @@ class AbducerBase(abc.ABC): candidates = [] for address_num in range(len(pred_res) + 1): if(address_num > max_address_num): - print('No candidates found') - return None, None, None + return [], None, None if(address_num == 0): if(abs(self.kb.logic_forward(pred_res) - key) <= 1e-3): @@ -146,24 +149,26 @@ class AbducerBase(abc.ABC): if __name__ == '__main__': - kb = add_KB() + kb = add_KB(GKB_flag = True) abd = AbducerBase(kb, 'hamming') - res = abd.abduce(([1, 1, 1], None, 4), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], None, 4), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([1, 1, 1], None, 4), max_address_num = 2, require_more_address = 1) + res = abd.abduce(([1, 1], None, 4), max_address_num = 2, require_more_address = 1) print(res) - res = abd.abduce(([1, 1, 1], None, 4), max_address_num = 1, require_more_address = 1) + res = abd.abduce(([1, 1], None, 4), max_address_num = 1, require_more_address = 1) print(res) - res = abd.abduce(([1, 1, 1], None, 4), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], None, 4), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([1, 1, 1], None, 5), max_address_num = 2, require_more_address = 1) + res = abd.abduce(([1, 1], None, 5), max_address_num = 2, require_more_address = 1) print(res) print() kb = hwf_KB() - abd = AbducerBase(kb) + abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) print(res) + res = abd.abduce((['5', '+', '2'], None, 3.09), max_address_num = 2, require_more_address = 0) + print(res) res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num = 3, require_more_address = 0) print(res) res = abd.abduce((['5', '+', '3'], None, 0.33), max_address_num = 3, require_more_address = 3) From 4a1de111f96d8876f6bd43a69a7658da14cb9221 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 15:00:58 +0800 Subject: [PATCH 054/601] Update kb.py --- abducer/kb.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index c206f62..499d9bb 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -60,7 +60,7 @@ class ClsKB(KBBase): if GKB_flag: X, Y = self.get_GKB(self.pseudo_label_list, self.len_list) for x, y in zip(X, Y): - self.base.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + self.base.setdefault(len(x), defaultdict(list))[y].append(x) def get_GKB(self, pseudo_label_list, len_list): all_X = [] @@ -104,9 +104,10 @@ class ClsKB(KBBase): class add_KB(ClsKB): - def __init__(self, GKB_flag = False, len_list = [2]): - self.pseudo_label_list = list(range(10)) - super().__init__(GKB_flag, self.pseudo_label_list, len_list) + def __init__(self, GKB_flag = False, \ + pseudo_label_list = list(range(10)), \ + len_list = [2]): + super().__init__(GKB_flag, pseudo_label_list, len_list) def valid_candidate(self, x): return True @@ -116,9 +117,10 @@ class add_KB(ClsKB): class hwf_KB(ClsKB): - def __init__(self, GKB_flag = False, len_list = [1, 3, 5, 7]): - self.pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'] - super().__init__(GKB_flag, self.pseudo_label_list, len_list) + def __init__(self, GKB_flag = False, \ + pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'], \ + len_list = [1, 3, 5, 7]): + super().__init__(GKB_flag, pseudo_label_list, len_list) def valid_candidate(self, formula): if(len(formula) % 2 == 0): From 6c76700be1a4cf98bd32ebad90883da57fcc1b63 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 15:03:10 +0800 Subject: [PATCH 055/601] Update abducer_base.py --- abducer/abducer_base.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 6de9987..a0af59d 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -155,10 +155,6 @@ if __name__ == '__main__': print(res) res = abd.abduce(([1, 1], None, 4), max_address_num = 2, require_more_address = 1) print(res) - res = abd.abduce(([1, 1], None, 4), max_address_num = 1, require_more_address = 1) - print(res) - res = abd.abduce(([1, 1], None, 4), max_address_num = 2, require_more_address = 0) - print(res) res = abd.abduce(([1, 1], None, 5), max_address_num = 2, require_more_address = 1) print(res) print() From 15231012669e0fffe8e684e01aa9bf92d2834e7c Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Mon, 21 Nov 2022 15:23:26 +0800 Subject: [PATCH 056/601] update basic model --- models/basic_model.py | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/models/basic_model.py b/models/basic_model.py index 86fbea5..182e1e5 100644 --- a/models/basic_model.py +++ b/models/basic_model.py @@ -131,21 +131,19 @@ class BasicModel(): model.train() - loss_value = 0 - for _, data in enumerate(data_loader): - X = data[0].to(device) - Y = data[1].to(device) - pred_Y = model(X) - - loss = criterion(pred_Y, Y) + total_loss, total_num = 0.0, 0 + for data, target in data_loader: + data, target = data.to(device), target.to(device) + out = model(data) + loss = criterion(out, target) optimizer.zero_grad() loss.backward() optimizer.step() - loss_value += loss.item() + total_loss += loss.item() * data.size(0) - return loss_value + return total_loss / total_num def _predict(self, data_loader): model = self.model @@ -155,9 +153,9 @@ class BasicModel(): with torch.no_grad(): results = [] - for _, data in enumerate(data_loader): - X = data[0].to(device) - pred_Y = model(X) + for data, _ in data_loader: + data = data.to(device) + pred_Y = model(data) results.append(pred_Y) return torch.cat(results, axis=0) From 41be49c68ec1c7eb414fc3839815b61305acbaf4 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 15:55:58 +0800 Subject: [PATCH 057/601] Update abducer_base.py --- abducer/abducer_base.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index a0af59d..b26c88a 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -47,15 +47,15 @@ class AbducerBase(abc.ABC): def get_cost_list(self, pred_res, pred_res_prob, candidates): - if(self.dist_func == 'hamming'): + if self.dist_func == 'hamming': return self.hamming_dist(pred_res, candidates) - elif(self.dist_func == 'confidence'): + elif self.dist_func == 'confidence': return self.confidence_dist(pred_res_prob, candidates) def get_min_cost_candidate(self, pred_res, pred_res_prob, candidates): - if(len(candidates) == 0): + if len(candidates) == 0: return [] - elif(len(candidates) == 1): + elif len(candidates) == 1: return candidates[0] else: cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) @@ -68,9 +68,9 @@ class AbducerBase(abc.ABC): if max_address_num == -1: max_address_num = len(pred_res) - if(self.cache and (tuple(pred_res), ans) in self.cache_min_address_num): + if self.cache and (tuple(pred_res), ans) in self.cache_min_address_num: address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), ans)] + require_more_address) - if((tuple(pred_res), ans, address_num) in self.cache_candidates): + if (tuple(pred_res), ans, address_num) in self.cache_candidates: # print('cached') candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) @@ -78,7 +78,7 @@ class AbducerBase(abc.ABC): if self.kb.GKB_flag: all_candidates = self.kb.get_candidates(ans, len(pred_res)) - if(len(all_candidates) == 0): + if len(all_candidates) == 0: return [] else: cost_list = self.hamming_dist(pred_res, all_candidates) @@ -90,7 +90,7 @@ class AbducerBase(abc.ABC): else: candidates, min_address_num, address_num = self.get_abduce_candidates(pred_res, ans, max_address_num, require_more_address) - if(self.cache): + if self.cache: self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates @@ -104,9 +104,9 @@ class AbducerBase(abc.ABC): for address_idx in address_idx_list: for c in all_address_candidate: pred_res_array = np.array(pred_res) - if(np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num): + if np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num: pred_res_array[np.array(address_idx)] = c - if(self.kb.logic_forward(pred_res_array) == key): + if self.kb.logic_forward(pred_res_array) == key: new_candidates.append(pred_res_array) return new_candidates, address_num @@ -114,22 +114,22 @@ class AbducerBase(abc.ABC): candidates = [] for address_num in range(len(pred_res) + 1): - if(address_num > max_address_num): + if address_num > max_address_num: return [], None, None - if(address_num == 0): - if(abs(self.kb.logic_forward(pred_res) - key) <= 1e-3): + if address_num == 0: + if abs(self.kb.logic_forward(pred_res) - key) <= 1e-3: candidates.append(pred_res) else: new_candidates, address_num = self.address(address_num, pred_res, key) candidates += new_candidates - if(len(candidates) > 0): + if len(candidates) > 0: min_address_num = address_num break for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): - if(address_num > max_address_num): + if address_num > max_address_num: return candidates, min_address_num, address_num - 1 new_candidates, address_num = self.address(address_num, pred_res, key) candidates += new_candidates From 6925860422ea8f5134c9389dca78af4ad3edaa2a Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 15:56:09 +0800 Subject: [PATCH 058/601] Update kb.py --- abducer/kb.py | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 499d9bb..7fa4b3f 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -54,10 +54,10 @@ class ClsKB(KBBase): super().__init__() self.GKB_flag = GKB_flag self.pseudo_label_list = pseudo_label_list - self.base = {} self.len_list = len_list if GKB_flag: + self.base = {} X, Y = self.get_GKB(self.pseudo_label_list, self.len_list) for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(x) @@ -82,7 +82,7 @@ class ClsKB(KBBase): pass def get_candidates(self, key, length = None): - if(self.base == {}): + if not self.GKB_flag or self.base == {}: return [] if key is None: @@ -94,13 +94,22 @@ class ClsKB(KBBase): return sum([self.base[l][key] for l in length], []) def get_all_candidates(self): - return sum([sum(v.values(), []) for v in self.base.values()], []) + if not self.GKB_flag or self.base == {}: + return [] + else: + return sum([sum(v.values(), []) for v in self.base.values()], []) def _dict_len(self, dic): - return sum(len(c) for c in dic.values()) + if not self.GKB_flag: + return 0 + else: + return sum(len(c) for c in dic.values()) def __len__(self): - return sum(self._dict_len(v) for v in self.base.values()) + if not self.GKB_flag: + return 0 + else: + return sum(self._dict_len(v) for v in self.base.values()) class add_KB(ClsKB): @@ -123,17 +132,17 @@ class hwf_KB(ClsKB): super().__init__(GKB_flag, pseudo_label_list, len_list) def valid_candidate(self, formula): - if(len(formula) % 2 == 0): + if len(formula) % 2 == 0: return False for i in range(len(formula)): - if(i % 2 == 0 and formula[i] not in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']): + if i % 2 == 0 and formula[i] not in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']: return False - if(i % 2 != 0 and formula[i] not in ['+', '-', '*', '/']): + if i % 2 != 0 and formula[i] not in ['+', '-', '*', '/']: return False return True def logic_forward(self, formula): - if(self.valid_candidate(formula) == False): + if not self.valid_candidate(formula): return np.inf try: return round(eval(''.join(formula)), 2) From 5c6cbd45c74c2099ca5fbc7dc9c88a49bdf19077 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 21 Nov 2022 16:50:11 +0800 Subject: [PATCH 059/601] Update kb.py --- abducer/kb.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 7fa4b3f..8c3f49a 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -127,7 +127,7 @@ class add_KB(ClsKB): class hwf_KB(ClsKB): def __init__(self, GKB_flag = False, \ - pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', '*', '/'], \ + pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], \ len_list = [1, 3, 5, 7]): super().__init__(GKB_flag, pseudo_label_list, len_list) @@ -137,7 +137,7 @@ class hwf_KB(ClsKB): for i in range(len(formula)): if i % 2 == 0 and formula[i] not in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']: return False - if i % 2 != 0 and formula[i] not in ['+', '-', '*', '/']: + if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: return False return True @@ -145,6 +145,8 @@ class hwf_KB(ClsKB): if not self.valid_candidate(formula): return np.inf try: + mapping = {'0':'0', '1':'1', '2':'2', '3':'3', '4':'4', '5':'5', '6':'6', '7':'7', '8':'8', '9':'9', '+':'+', '-':'-', 'times':'*', 'div':'/'} + formula = [mapping[f] for f in formula] return round(eval(''.join(formula)), 2) except ZeroDivisionError: return np.inf From 06830d5a10713ff78ff7763bbfb417e62acc7a32 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Mon, 21 Nov 2022 17:01:03 +0800 Subject: [PATCH 060/601] update basic_model.py --- models/basic_model.py | 21 ++++++--------------- 1 file changed, 6 insertions(+), 15 deletions(-) diff --git a/models/basic_model.py b/models/basic_model.py index 86fbea5..669474f 100644 --- a/models/basic_model.py +++ b/models/basic_model.py @@ -20,13 +20,12 @@ import os from multiprocessing import Pool class XYDataset(Dataset): - def __init__(self, X, Y, transform=None, target_transform=None): + def __init__(self, X, Y, transform=None): self.X = X - self.Y = Y + self.Y = torch.LongTensor(Y) self.n_sample = len(X) self.transform = transform - self.target_transform = target_transform def __len__(self): return len(self.X) @@ -39,8 +38,6 @@ class XYDataset(Dataset): img = self.transform(img) label = self.Y[index] - if self.target_transform is not None: - label = self.target_transform(label) return (img, label) @@ -64,7 +61,6 @@ class BasicModel(): save_interval = None, save_dir = None, transform = None, - target_transform = None, collate_fn = None, recorder = None): @@ -78,7 +74,6 @@ class BasicModel(): self.criterion = criterion self.optimizer = optimizer self.transform = transform - self.target_transform = target_transform self.device = device if recorder is None: @@ -114,9 +109,8 @@ class BasicModel(): if data_loader is None: collate_fn = self.collate_fn transform = self.transform - target_transform = self.target_transform - train_dataset = XYDataset(X, y, transform=transform, target_transform=target_transform) + train_dataset = XYDataset(X, y, transform=transform) sampler = None data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size, \ shuffle=True, sampler=sampler, num_workers=int(self.num_workers), \ @@ -166,10 +160,9 @@ class BasicModel(): if data_loader is None: collate_fn = self.collate_fn transform = self.transform - target_transform = self.target_transform Y = [0] * len(X) - val_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) + val_dataset = XYDataset(X, Y, transform=transform) sampler = None data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ shuffle=False, sampler=sampler, num_workers=int(self.num_workers), \ @@ -184,10 +177,9 @@ class BasicModel(): if data_loader is None: collate_fn = self.collate_fn transform = self.transform - target_transform = self.target_transform Y = [0] * len(X) - val_dataset = XYDataset(X, Y, transform=transform, target_transform=target_transform) + val_dataset = XYDataset(X, Y, transform=transform) sampler = None data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ shuffle=False, sampler=sampler, num_workers=int(self.num_workers), \ @@ -231,9 +223,8 @@ class BasicModel(): if data_loader is None: collate_fn = self.collate_fn transform = self.transform - target_transform = self.target_transform - val_dataset = XYDataset(X, y, transform=transform, target_transform=target_transform) + val_dataset = XYDataset(X, y, transform=transform) sampler = None data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ shuffle=True, sampler=sampler, num_workers=int(self.num_workers), \ From 5d47158adb0d8e5269113313be54fef84edf19d0 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Mon, 21 Nov 2022 17:02:46 +0800 Subject: [PATCH 061/601] update example.py and polg.py, add .gitignore --- .gitignore | 3 +++ example.py | 34 ++++++---------------------------- utils/plog.py | 40 ++++++++++++++++------------------------ 3 files changed, 25 insertions(+), 52 deletions(-) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8ebaf8d --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +*.pyc +/results +raw/ \ No newline at end of file diff --git a/example.py b/example.py index 1a20b28..d63185c 100644 --- a/example.py +++ b/example.py @@ -11,16 +11,13 @@ #================================================================# from utils.plog import logger -import numpy as np -import time import framework -import utils.plog as plog import torch.nn as nn import torch from models.lenet5 import LeNet5 from models.basic_model import BasicModel -from models.wabl_models import MyModel +from models.wabl_models import WABLBasicModel from multiprocessing import Pool import os @@ -29,51 +26,32 @@ from abducer.kb import add_KB, hwf_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf -class Params: - imgH = 28 - imgW = 28 - keep_ratio = True - saveInterval = 10 - batchSize = 16 - workers = 16 - n_epoch = 1 - stop_loss = None - def run_test(): - - result_dir = 'results' - - recorder_file_path = f"{result_dir}/1116.pk"# kb = add_KB() # kb = hwf_KB() - abducer = AbducerBase(kb, 2) + abducer = AbducerBase(kb) recorder = logger() - recorder.set_savefile("test.log") train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) - recorder = plog.ResultRecorder() cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_X[0][0].shape[1:])) - criterion = nn.CrossEntropyLoss(size_average=True) + criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - base_model = BasicModel(cls, criterion, optimizer, device, Params(), recorder=recorder) - model = MyModel(base_model, kb.pseudo_label_list) + base_model = BasicModel(cls, criterion, optimizer, device, recorder=recorder) + model = WABLBasicModel(base_model, kb.pseudo_label_list) res = framework.train(model, abducer, train_X, train_Z, train_Y, sample_num = 10000, verbose = 1) print(res) - - recorder.dump(open(recorder_file_path, "wb")) + recorder.dump() return True if __name__ == "__main__": - os.system("mkdir results") - run_test() diff --git a/utils/plog.py b/utils/plog.py index 941dc11..4aee019 100644 --- a/utils/plog.py +++ b/utils/plog.py @@ -16,51 +16,42 @@ import pickle as pk import os import functools -log_name = "default_log.txt" -logging.basicConfig(level=logging.INFO, - filename=log_name, - filemode='a', - format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s') - global recorder recorder = None -def mkdir(dirpath): - if not os.path.exists(dirpath): - os.makedirs(dirpath) - class ResultRecorder: - def __init__(self, pk_dir = None, pk_filepath = None): - self.set_savefile(pk_dir, pk_filepath) - + def __init__(self, pk_dir = 'results', pk_filepath = None): self.result = {} + + logging.basicConfig(level=logging.DEBUG, filemode='a') + + self.set_savefile(pk_dir, pk_filepath) + logging.info("===========================================================") - logging.info("============= Result Recorder Version: 0.02 ===============") + logging.info("============= Result Recorder Version: 0.03 ===============") logging.info("===========================================================\n") pass def set_savefile(self, pk_dir = None, pk_filepath = None): if pk_dir is None: - pk_dir = "result" - mkdir(pk_dir) + pk_dir = "results" + + if not os.path.exists(pk_dir): + os.makedirs(pk_dir) if pk_filepath is None: local_time = time.strftime("%Y%m%d_%H_%M_%S", time.localtime()) pk_filepath = os.path.join(pk_dir, local_time + ".pk") - - self.save_file = open(pk_filepath, "wb") - logger = logging.getLogger() - logger.handlers[0].stream.close() - logger.removeHandler(logger.handlers[0]) + self.save_file = pk_filepath filename = os.path.join(pk_dir, local_time + ".txt") file_handler = logging.FileHandler(filename) file_handler.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s') file_handler.setFormatter(formatter) - logger.addHandler(file_handler) + logging.getLogger().addHandler(file_handler) def print(self, *argv, screen = False): info = "" @@ -99,7 +90,8 @@ class ResultRecorder: def dump(self, save_file = None): if save_file is None: save_file = self.save_file - pk.dump(self.result, save_file) + with open(save_file, 'wb') as f: + pk.dump(self.result, f) def clock(self, func): @functools.wraps(func) @@ -111,7 +103,7 @@ class ResultRecorder: name = func.__name__ # arg_str = ','.join(repr(arg) for arg in args) # context = f"{name}: ({arg_str})=>({result}), cost {elapsed}s" - context = f"{name}: ()=>(), cost {elapsed}s" + context = f"{name}: cost {elapsed}s" self.write_kv("func:", context) return result From c681e3cb0a521d10cec905dee8ad78ca1dd88939 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Mon, 21 Nov 2022 22:42:07 +0800 Subject: [PATCH 062/601] update basic_model.py and related part in other files --- abducer/abducer_base.py | 3 +- example.py | 4 +- framework.py | 10 ++- models/basic_model.py | 135 ++++++++++++++++++---------------------- utils/plog.py | 36 +++++------ 5 files changed, 84 insertions(+), 104 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index b26c88a..54380ec 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -11,8 +11,7 @@ #================================================================# import abc -from kb import add_KB, hwf_KB -# from abducer.kb import add_KB, hwf_KB +from abducer.kb import add_KB, hwf_KB import numpy as np from itertools import product, combinations diff --git a/example.py b/example.py index d63185c..dad33f7 100644 --- a/example.py +++ b/example.py @@ -43,11 +43,11 @@ def run_test(): optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - base_model = BasicModel(cls, criterion, optimizer, device, recorder=recorder) + base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, num_epochs=1, recorder=recorder) model = WABLBasicModel(base_model, kb.pseudo_label_list) res = framework.train(model, abducer, train_X, train_Z, train_Y, sample_num = 10000, verbose = 1) - print(res) + recorder.print("abl_acc is ", res) recorder.dump() return True diff --git a/framework.py b/framework.py index a37483b..4f84694 100644 --- a/framework.py +++ b/framework.py @@ -123,7 +123,7 @@ def is_all_sublabel_exist(labels, std_label_list): def pretrain(model, X, Z): pass -def train(model, abducer, X, Z, Y, epochs = 10, sample_num = -1, verbose = -1): +def train(model, abducer, X, Z, Y, epochs = 5, sample_num = -1, verbose = -1): # Set default parameters if sample_num == -1: sample_num = len(X) @@ -138,10 +138,7 @@ def train(model, abducer, X, Z, Y, epochs = 10, sample_num = -1, verbose = -1): predict_func = clocker(model.predict) train_func = clocker(model.train) - abduce_func = clocker(abducer.batch_abduce) - - epochs = 50 # Abductive learning train process for epoch_idx in range(epochs): @@ -153,12 +150,13 @@ def train(model, abducer, X, Z, Y, epochs = 10, sample_num = -1, verbose = -1): if(char_acc_flag): ori_char_acc = get_char_acc(Z, preds_res['cls']) abd_char_acc = get_char_acc(abduced_Z, preds_res['cls']) - print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc, ' ori_char_acc:', ori_char_acc, ' abd_char_acc:', abd_char_acc) + INFO('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc, ' ori_char_acc:', ori_char_acc, ' abd_char_acc:', abd_char_acc) else: - print('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc) + INFO('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc) finetune_X, finetune_Z = filter_data(X, abduced_Z) if len(finetune_X) > 0: + # model.valid(finetune_X, finetune_Z) train_func(finetune_X, finetune_Z) else: INFO("lack of data, all abduced failed", len(finetune_X)) diff --git a/models/basic_model.py b/models/basic_model.py index 00efd30..11063a8 100644 --- a/models/basic_model.py +++ b/models/basic_model.py @@ -55,7 +55,7 @@ class BasicModel(): optimizer, device, batch_size = 1, - num_epochs = 10, + num_epochs = 1, stop_loss = 0.01, num_workers = 0, save_interval = None, @@ -89,15 +89,15 @@ class BasicModel(): recorder = self.recorder recorder.print("model fitting") - min_loss = 999999999 + min_loss = 1e10 for epoch in range(n_epoch): loss_value = self.train_epoch(data_loader) recorder.print(f"{epoch}/{n_epoch} model training loss is {loss_value}") - if loss_value < min_loss: + if min_loss < 0 or loss_value < min_loss: min_loss = loss_value - if epoch > 0 and self.save_interval is not None and epoch % self.save_interval == 0: + if self.save_interval is not None and (epoch + 1) % self.save_interval == 0: assert self.save_dir is not None - self.save(self.save_dir) + self.save(epoch + 1, self.save_dir) if stop_loss is not None and loss_value < stop_loss: break recorder.print("Model fitted, minimal loss is ", min_loss) @@ -107,14 +107,7 @@ class BasicModel(): X = None, y = None): if data_loader is None: - collate_fn = self.collate_fn - transform = self.transform - - train_dataset = XYDataset(X, y, transform=transform) - sampler = None - data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size, \ - shuffle=True, sampler=sampler, num_workers=int(self.num_workers), \ - collate_fn=collate_fn) + data_loader = self._data_loader(X, y) return self._fit(data_loader, self.num_epochs, self.stop_loss) def train_epoch(self, data_loader): @@ -136,6 +129,7 @@ class BasicModel(): optimizer.step() total_loss += loss.item() * data.size(0) + total_num += data.size(0) return total_loss / total_num @@ -149,107 +143,98 @@ class BasicModel(): results = [] for data, _ in data_loader: data = data.to(device) - pred_Y = model(data) - results.append(pred_Y) + out = model(data) + results.append(out) return torch.cat(results, axis=0) def predict(self, data_loader = None, X = None, print_prefix = ""): - if data_loader is None: - collate_fn = self.collate_fn - transform = self.transform - - Y = [0] * len(X) - val_dataset = XYDataset(X, Y, transform=transform) - sampler = None - data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ - shuffle=False, sampler=sampler, num_workers=int(self.num_workers), \ - collate_fn=collate_fn) - recorder = self.recorder - recorder.print('Start Predict ', print_prefix) - Y = self._predict(data_loader).argmax(axis=1) - return [int(y) for y in Y] + recorder.print('Start Predict Class ', print_prefix) - def predict_proba(self, data_loader = None, X = None, print_prefix = ""): if data_loader is None: - collate_fn = self.collate_fn - transform = self.transform - - Y = [0] * len(X) - val_dataset = XYDataset(X, Y, transform=transform) - sampler = None - data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ - shuffle=False, sampler=sampler, num_workers=int(self.num_workers), \ - collate_fn=collate_fn) + data_loader = self._data_loader(X) + return self._predict(data_loader).argmax(axis=1).cpu().numpy() + def predict_proba(self, data_loader = None, X = None, print_prefix = ""): recorder = self.recorder - recorder.print('Start Predict ', print_prefix) - return torch.softmax(self._predict(data_loader), axis=1).cpu().numpy() + recorder.print('Start Predict Probability ', print_prefix) - def _val(self, data_loader, print_prefix): + if data_loader is None: + data_loader = self._data_loader(X) + return self._predict(data_loader).softmax(axis=1).cpu().numpy() + + def _val(self, data_loader): model = self.model criterion = self.criterion - recorder = self.recorder device = self.device - recorder.print('Start val ', print_prefix) model.eval() - - n_correct = 0 - pred_num = 0 - loss_value = 0 + + total_correct_num, total_num, total_loss = 0, 0, 0.0 + with torch.no_grad(): - for _, data in enumerate(data_loader): - X = data[0].to(device) - Y = data[1].to(device) + for data, target in data_loader: + data, target = data.to(device), target.to(device) - pred_Y = model(X) + out = model(data) - correct_num = sum(Y == pred_Y.argmax(axis=1)) - loss = criterion(pred_Y, Y) - loss_value += loss.item() + correct_num = sum(target == out.argmax(axis=1)).item() + loss = criterion(out, target) + total_loss += loss.item() * data.size(0) - n_correct += correct_num - pred_num += len(X) + total_correct_num += correct_num + total_num += data.size(0) + + mean_loss = total_loss / total_num + accuracy = total_correct_num / total_num - accuracy = float(n_correct) / float(pred_num) - recorder.print('[%s] Val loss: %f, accuray: %f' % (print_prefix, loss_value, accuracy)) - return accuracy + return mean_loss, accuracy def val(self, data_loader = None, X = None, y = None, print_prefix = ""): - if data_loader is None: - collate_fn = self.collate_fn - transform = self.transform + recorder = self.recorder + recorder.print('Start val ', print_prefix) - val_dataset = XYDataset(X, y, transform=transform) - sampler = None - data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size, \ - shuffle=True, sampler=sampler, num_workers=int(self.num_workers), \ - collate_fn=collate_fn) - return self._val(data_loader, print_prefix) + if data_loader is None: + data_loader = self._data_loader(X, y) + mean_loss, accuracy = self._val(data_loader) + recorder.print('[%s] Val loss: %f, accuray: %f' % (print_prefix, mean_loss, accuracy)) + return accuracy def score(self, data_loader = None, X = None, y = None, print_prefix = ""): return self.val(data_loader, X, y, print_prefix) - def save(self, save_dir): + def _data_loader(self, X, y = None): + collate_fn = self.collate_fn + transform = self.transform + + if y is None: + y = [0] * len(X) + dataset = XYDataset(X, y, transform=transform) + sampler = None + data_loader = torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, \ + shuffle=False, sampler=sampler, num_workers=int(self.num_workers), \ + collate_fn=collate_fn) + return data_loader + + def save(self, epoch_id, save_dir): recorder = self.recorder if not os.path.exists(save_dir): os.mkdir(save_dir) recorder.print("Saving model and opter") - save_path = os.path.join(save_dir, "net.pth") + save_path = os.path.join(save_dir, str(epoch_id) + "_net.pth") torch.save(self.model.state_dict(), save_path) - save_path = os.path.join(save_dir, "opt.pth") + save_path = os.path.join(save_dir, str(epoch_id) + "_opt.pth") torch.save(self.optimizer.state_dict(), save_path) - def load(self, load_dir): + def load(self, epoch_id, load_dir): recorder = self.recorder recorder.print("Loading model and opter") - load_path = os.path.join(load_dir, "net.pth") + load_path = os.path.join(load_dir, str(epoch_id) + "_net.pth") self.model.load_state_dict(torch.load(load_path)) - load_path = os.path.join(load_dir, "opt.pth") + load_path = os.path.join(load_dir, str(epoch_id) + "_opt.pth") self.optimizer.load_state_dict(torch.load(load_path)) if __name__ == "__main__": diff --git a/utils/plog.py b/utils/plog.py index 4aee019..178b8b7 100644 --- a/utils/plog.py +++ b/utils/plog.py @@ -20,12 +20,11 @@ global recorder recorder = None class ResultRecorder: - def __init__(self, pk_dir = 'results', pk_filepath = None): - self.result = {} - + def __init__(self): logging.basicConfig(level=logging.DEBUG, filemode='a') - self.set_savefile(pk_dir, pk_filepath) + self.result = {} + self.set_savefile() logging.info("===========================================================") logging.info("============= Result Recorder Version: 0.03 ===============") @@ -33,20 +32,19 @@ class ResultRecorder: pass - def set_savefile(self, pk_dir = None, pk_filepath = None): - if pk_dir is None: - pk_dir = "results" - - if not os.path.exists(pk_dir): - os.makedirs(pk_dir) + def set_savefile(self): + local_time = time.strftime("%Y%m%d_%H_%M_%S", time.localtime()) - if pk_filepath is None: - local_time = time.strftime("%Y%m%d_%H_%M_%S", time.localtime()) - pk_filepath = os.path.join(pk_dir, local_time + ".pk") + save_dir = os.path.join("results", local_time) + save_file_path = os.path.join(save_dir, "result.pk") - self.save_file = pk_filepath + self.save_dir = save_dir + self.save_file_path = save_file_path + + if not os.path.exists(save_dir): + os.makedirs(save_dir) - filename = os.path.join(pk_dir, local_time + ".txt") + filename = os.path.join(save_dir, "log.txt") file_handler = logging.FileHandler(filename) file_handler.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s') @@ -87,10 +85,10 @@ class ResultRecorder: #self.print_result(label + ":" + str(data)) self.store_kv(label, data) - def dump(self, save_file = None): - if save_file is None: - save_file = self.save_file - with open(save_file, 'wb') as f: + def dump(self, save_file_path = None): + if save_file_path is None: + save_file_path = self.save_file_path + with open(save_file_path, 'wb') as f: pk.dump(self.result, f) def clock(self, func): From f9ea562a3f86fa39493f4be31c50debdac7e8c21 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 10:26:39 +0800 Subject: [PATCH 063/601] Update abducer_base.py --- abducer/abducer_base.py | 1 + 1 file changed, 1 insertion(+) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 54380ec..284ebe4 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -11,6 +11,7 @@ #================================================================# import abc +# from kb import add_KB, hwf_KB from abducer.kb import add_KB, hwf_KB import numpy as np From 778ae172dfe1972a8d5fa75e149aff0ea2d530e0 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 10:27:15 +0800 Subject: [PATCH 064/601] Update get_mnist_add.py --- datasets/mnist_add/get_mnist_add.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py index 1af834a..871e503 100644 --- a/datasets/mnist_add/get_mnist_add.py +++ b/datasets/mnist_add/get_mnist_add.py @@ -23,9 +23,9 @@ def get_data(file, img_dataset, get_pseudo_label): def get_mnist_add(train = True, get_pseudo_label = False): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))]) - img_dataset = torchvision.datasets.MNIST(root='./datasets/mnist_add/', train=True, download=True, transform=transform) + img_dataset = torchvision.datasets.MNIST(root='./datasets/mnist_add/', train=train, download=True, transform=transform) - if(train): + if train: file = './datasets/mnist_add/train_data.txt' else: file = './datasets/mnist_add/test_data.txt' From af00136661a327c031af7080e23615dceaa533a1 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 10:27:49 +0800 Subject: [PATCH 065/601] Update get_hwf.py --- datasets/hwf/get_hwf.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/datasets/hwf/get_hwf.py b/datasets/hwf/get_hwf.py index f94f1a5..543e0d5 100644 --- a/datasets/hwf/get_hwf.py +++ b/datasets/hwf/get_hwf.py @@ -24,11 +24,10 @@ def get_data(file, get_pseudo_label, precision_num = 2): imgs.append(img) if(get_pseudo_label): imgs_pseudo_label.append(img_path.split('/')[0]) - if(len(imgs) == 3): - X.append(imgs) - if(get_pseudo_label): - Z.append(imgs_pseudo_label) - Y.append(round(data[idx]['res'], precision_num)) + X.append(imgs) + if(get_pseudo_label): + Z.append(imgs_pseudo_label) + Y.append(round(data[idx]['res'], precision_num)) if(get_pseudo_label): return X, Z, Y From 740df944c1c09aca25aa50997c453601e6e63070 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 10:28:30 +0800 Subject: [PATCH 066/601] Update get_hwf.py --- datasets/hwf/get_hwf.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/datasets/hwf/get_hwf.py b/datasets/hwf/get_hwf.py index 543e0d5..084ed16 100644 --- a/datasets/hwf/get_hwf.py +++ b/datasets/hwf/get_hwf.py @@ -22,14 +22,14 @@ def get_data(file, get_pseudo_label, precision_num = 2): img = Image.open(img_dir + img_path).convert('L') img = img_transform(img) imgs.append(img) - if(get_pseudo_label): + if get_pseudo_label: imgs_pseudo_label.append(img_path.split('/')[0]) X.append(imgs) - if(get_pseudo_label): + if get_pseudo_label: Z.append(imgs_pseudo_label) Y.append(round(data[idx]['res'], precision_num)) - if(get_pseudo_label): + if get_pseudo_label: return X, Z, Y else: return X, None, Y From 1258d83edc3538376474b31781284cf585ee3c14 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 10:29:05 +0800 Subject: [PATCH 067/601] Update get_hwf.py --- datasets/hwf/get_hwf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datasets/hwf/get_hwf.py b/datasets/hwf/get_hwf.py index 084ed16..b478a15 100644 --- a/datasets/hwf/get_hwf.py +++ b/datasets/hwf/get_hwf.py @@ -9,7 +9,7 @@ img_transform = transforms.Compose([ def get_data(file, get_pseudo_label, precision_num = 2): X = [] - if(get_pseudo_label): + if get_pseudo_label: Z = [] Y = [] img_dir = './datasets/hwf/data/Handwritten_Math_Symbols/' From 44b140c1fd7c5d158c0d818f5943083c688369e9 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 10:29:29 +0800 Subject: [PATCH 068/601] Update get_mnist_add.py --- datasets/mnist_add/get_mnist_add.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/datasets/mnist_add/get_mnist_add.py b/datasets/mnist_add/get_mnist_add.py index 871e503..d00c6ce 100644 --- a/datasets/mnist_add/get_mnist_add.py +++ b/datasets/mnist_add/get_mnist_add.py @@ -5,18 +5,18 @@ from torchvision.transforms import transforms def get_data(file, img_dataset, get_pseudo_label): X = [] - if(get_pseudo_label): + if get_pseudo_label: Z = [] Y = [] with open(file) as f: for line in f: line = line.strip().split(' ') X.append([img_dataset[int(line[0])][0], img_dataset[int(line[1])][0]]) - if(get_pseudo_label): + if get_pseudo_label: Z.append([img_dataset[int(line[0])][1], img_dataset[int(line[1])][1]]) Y.append(int(line[2])) - if(get_pseudo_label): + if get_pseudo_label: return X, Z, Y else: return X, None, Y From ef0298e91a44c1a9502eb50407b2c9d8987f805f Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 12:34:37 +0800 Subject: [PATCH 069/601] Update kb.py --- abducer/kb.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 8c3f49a..e987d15 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -34,10 +34,6 @@ class KBBase(ABC): def logic_forward(self): pass - @abstractmethod - def valid_candidate(self): - pass - def _length(self, length): if length is None: length = list(self.base.keys()) @@ -57,6 +53,7 @@ class ClsKB(KBBase): self.len_list = len_list if GKB_flag: + # self.base = np.load('abducer/hwf.npy', allow_pickle=True).item() self.base = {} X, Y = self.get_GKB(self.pseudo_label_list, self.len_list) for x, y in zip(X, Y): @@ -70,14 +67,12 @@ class ClsKB(KBBase): X = [] Y = [] for x in all_X: - if self.valid_candidate(x): + y = self.logic_forward(x) + if y != np.inf: X.append(x) - Y.append(self.logic_forward(x)) + Y.append(y) return X, Y - def valid_candidate(self): - pass - def logic_forward(self): pass @@ -88,7 +83,7 @@ class ClsKB(KBBase): if key is None: return self.get_all_candidates() - if (type(length) is int and length not in self.len_list): + if type(length) is int and length not in self.len_list: return [] length = self._length(length) return sum([self.base[l][key] for l in length], []) @@ -117,9 +112,6 @@ class add_KB(ClsKB): pseudo_label_list = list(range(10)), \ len_list = [2]): super().__init__(GKB_flag, pseudo_label_list, len_list) - - def valid_candidate(self, x): - return True def logic_forward(self, nums): return sum(nums) From f78c69ae105bf92fda477c13b88de5e76249f0d0 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 13:48:50 +0800 Subject: [PATCH 070/601] Create lenet5.py --- models/lenet5.py | 60 ++++++++++++++++++------------------------------ 1 file changed, 22 insertions(+), 38 deletions(-) diff --git a/models/lenet5.py b/models/lenet5.py index 56c1ca6..1016d2c 100644 --- a/models/lenet5.py +++ b/models/lenet5.py @@ -52,48 +52,32 @@ class LeNet5(nn.Module): return x def num_flat_features(self, x): - #x.size()返回值为(256, 16, 5, 5)，size的值为(16, 5, 5)，256是batch_size - size = x.size()[1:] #x.size返回的是一个元组，size表示截å–元组中第二个开始的数字 + size = x.size()[1:] num_features = 1 for s in size: num_features *= s return num_features -class Params: - imgH = 28 - imgW = 28 - keep_ratio = True - saveInterval = 10 - batchSize = 16 - num_workers = 16 -def get_data(): #æ•°æ®é¢„å¤„ç† - transform = transforms.Compose([transforms.ToTensor(), - transforms.Normalize((0.5), (0.5))]) - #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) - #训练集 - train_set = torchvision.datasets.MNIST(root='data/', train=True, transform=transform, download=True) - train_loader = torch.utils.data.DataLoader(train_set, batch_size=1024, shuffle=True, num_workers = 16) - #测试集 - test_set = torchvision.datasets.MNIST(root='data/', train=False, transform=transform, download=True) - test_loader = torch.utils.data.DataLoader(test_set, batch_size = 1024, shuffle = False, num_workers = 16) - classes = ('plane','car','bird','cat','deer','dog','frog','horse','ship','truck') - - return train_loader, test_loader, classes - -if __name__ == "__main__": - recorder = plog.ResultRecorder() - cls = LeNet5() - criterion = nn.CrossEntropyLoss(size_average=True) - optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - model = BasicModel(cls, criterion, optimizer, None, device, Params(), recorder) - - train_loader, test_loader, classes = get_data() - - #model.val(test_loader, print_prefix = "before training") - model.fit(train_loader, n_epoch = 100) - model.val(test_loader, print_prefix = "after trained") - res = model.predict(test_loader, print_prefix = "predict") - print(res.argmax(axis=1)[:10]) +class SymbolNet(nn.Module): + def __init__(self, num_classes=14): + super(SymbolNet, self).__init__() + self.conv1 = nn.Conv2d(1, 32, 3, stride = 1, padding = 1) + self.conv2 = nn.Conv2d(32, 64, 3, stride = 1, padding = 1) + self.dropout1 = nn.Dropout2d(0.25) + self.dropout2 = nn.Dropout2d(0.5) + self.fc1 = nn.Linear(30976, 128) + self.fc2 = nn.Linear(128, num_classes) + def forward(self, x): + x = self.conv1(x) + x = F.relu(x) + x = self.conv2(x) + x = F.max_pool2d(x, 2) + x = self.dropout1(x) + x = torch.flatten(x, 1) + x = self.fc1(x) + x = F.relu(x) + x = self.dropout2(x) + x = self.fc2(x) + return x From d9313561724357ff0596aafd78fbab85891101ec Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Tue, 22 Nov 2022 15:50:37 +0800 Subject: [PATCH 071/601] fix bug in plog.py --- utils/plog.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/utils/plog.py b/utils/plog.py index 178b8b7..9a3ab05 100644 --- a/utils/plog.py +++ b/utils/plog.py @@ -37,9 +37,10 @@ class ResultRecorder: save_dir = os.path.join("results", local_time) save_file_path = os.path.join(save_dir, "result.pk") + save_file = open(save_file_path, "wb") self.save_dir = save_dir - self.save_file_path = save_file_path + self.save_file = save_file if not os.path.exists(save_dir): os.makedirs(save_dir) @@ -85,11 +86,10 @@ class ResultRecorder: #self.print_result(label + ":" + str(data)) self.store_kv(label, data) - def dump(self, save_file_path = None): - if save_file_path is None: - save_file_path = self.save_file_path - with open(save_file_path, 'wb') as f: - pk.dump(self.result, f) + def dump(self, save_file = None): + if save_file is None: + save_file = self.save_file + pk.dump(self.result, save_file) def clock(self, func): @functools.wraps(func) From 6806a3cfbf76dab3d556204be8b93a4f1f69ed8e Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Tue, 22 Nov 2022 16:11:08 +0800 Subject: [PATCH 072/601] fix bug in plog.py --- utils/plog.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/utils/plog.py b/utils/plog.py index 9a3ab05..0b8f5b5 100644 --- a/utils/plog.py +++ b/utils/plog.py @@ -36,15 +36,14 @@ class ResultRecorder: local_time = time.strftime("%Y%m%d_%H_%M_%S", time.localtime()) save_dir = os.path.join("results", local_time) + if not os.path.exists(save_dir): + os.makedirs(save_dir) save_file_path = os.path.join(save_dir, "result.pk") save_file = open(save_file_path, "wb") self.save_dir = save_dir self.save_file = save_file - if not os.path.exists(save_dir): - os.makedirs(save_dir) - filename = os.path.join(save_dir, "log.txt") file_handler = logging.FileHandler(filename) file_handler.setLevel(logging.DEBUG) From 4677d2651f9b0c589a42b7caa34b3ce19fb84185 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 18:50:22 +0800 Subject: [PATCH 073/601] Update framework.py --- framework.py | 129 ++++++--------------------------------------------- 1 file changed, 14 insertions(+), 115 deletions(-) diff --git a/framework.py b/framework.py index 4f84694..7f0debf 100644 --- a/framework.py +++ b/framework.py @@ -16,7 +16,6 @@ import numpy as np from utils.plog import INFO, DEBUG, clocker -@clocker def block_sample(X, Z, Y, sample_num, epoch_idx): part_num = (len(X) // sample_num) if part_num == 0: @@ -34,96 +33,40 @@ def get_taglist(self, Z): tmp = sorted(list(set(tmp))) return tmp -def get_abl_acc(Y, pseudo_Z, logic_forward): +def get_abl_acc(pred_Z, Y, logic_forward): abl_acc = 0 - for y, pseudo_z in zip(Y, pseudo_Z): - if(logic_forward(pseudo_z) == y): + for pred_z, y in zip(pred_Z, Y): + if(logic_forward(pred_z) == y): abl_acc += 1 return abl_acc / len(Y) -def get_char_acc(Z, pseudo_Z): +def get_char_acc(Z, pred_Z): char_acc = 0 char_num = 0 - for z, pseudo_z in zip(Z, pseudo_Z): + for pred_z, z in zip(pred_Z, Z): char_num += len(z) for zidx in range(len(z)): - if(z[zidx] == pseudo_z[zidx]): + if(pred_z[zidx] == z[zidx]): char_acc += 1 return char_acc / char_num - -# def result_statistics(pseudo_Y, Y, abduced_Y): - -# abd_err_num = 0 -# abd_char_num = 0 -# abd_char_acc = 0 -# abd_failed = 0 -# word_err_num = 0 - -# ori_char_num = 0 -# ori_char_acc = 0 - -# for tidx, (pseudo_y, y, abduced_y) in enumerate(zip(pseudo_Y, Y, abduced_Y)): -# pseudo_y = pseudo_y -# if sum(abduced_y != y) != 0: -# abd_err_num += 1 -# if abduced_y is not None: -# abd_char_num += len(y) -# abd_char_acc += sum(abduced_y == y) -# else: -# abd_failed += 1 - -# ori_char_num += len(pseudo_y) -# ori_char_acc += sum(pseudo_y == y) - -# if abduced_y is not None and sum(y != pseudo_y) == 0 and sum(pseudo_y != abduced_y) > 0: -# INFO(pseudo_y, y, abduced_y) -# pk.dump((pseudo_y, y, abduced_y), open("bug.pk", "wb")) -# if sum(pseudo_y != y) != 0: -# word_err_num += 1 - -# INFO("") -# INFO("Abd word level accuracy:", 1 - word_err_num / len(pseudo_Y)) -# INFO("Abd char level accuracy:", abd_char_acc / abd_char_num) -# INFO("Ori char level accuracy:", ori_char_acc / ori_char_num) -# INFO("") - -# result = {"total_word" : len(pseudo_Y), "accuracy_word" : len(pseudo_Y) - word_err_num, -# "total_abd_char": abd_char_num, "accuracy_abd_char" : abd_char_acc, -# "total_ori_char": ori_char_num, "accuracy_ori_char" : ori_char_acc, -# "total_abd_failed": abd_failed} - -# return result - -@clocker def filter_data(X, abduced_Z): finetune_Z = [] finetune_X = [] for abduced_x, abduced_z in zip(X, abduced_Z): - if abduced_z is not None: + if abduced_z is not []: finetune_X.append(abduced_x) finetune_Z.append(abduced_z) return finetune_X, finetune_Z -@clocker -def is_all_sublabel_exist(labels, std_label_list): - if not labels: - return False - - labels = np.array(labels).T - for idx, (std_label, label) in enumerate(zip(std_label_list, labels)): - std_num = len(set(std_label)) - sublabel_num = len(set(label)) - if std_num != sublabel_num: - INFO(f"sublabel {idx} should have {std_num} class, but data only have {sublabel_num} class", screen=True) - return False - return True - def pretrain(model, X, Z): pass -def train(model, abducer, X, Z, Y, epochs = 5, sample_num = -1, verbose = -1): +def train(model, abducer, train_data, test_data, epochs = 5, sample_num = -1, verbose = -1): + X, Z, Y = train_data + test_X, test_Z, test_Y = test_data + # Set default parameters if sample_num == -1: sample_num = len(X) @@ -146,10 +89,10 @@ def train(model, abducer, X, Z, Y, epochs = 5, sample_num = -1, verbose = -1): preds_res = predict_func(X) abduced_Z = abduce_func(preds_res, Y) - abl_acc = get_abl_acc(Y, preds_res['cls'], abducer.kb.logic_forward) + abl_acc = get_abl_acc(preds_res['cls'], Y, abducer.kb.logic_forward) if(char_acc_flag): - ori_char_acc = get_char_acc(Z, preds_res['cls']) - abd_char_acc = get_char_acc(abduced_Z, preds_res['cls']) + ori_char_acc = get_char_acc(preds_res['cls'], Z) + abd_char_acc = get_char_acc(preds_res['cls'], abduced_Z) INFO('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc, ' ori_char_acc:', ori_char_acc, ' abd_char_acc:', abd_char_acc) else: INFO('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc) @@ -163,49 +106,5 @@ def train(model, abducer, X, Z, Y, epochs = 5, sample_num = -1, verbose = -1): return abl_acc -# def train(model, abducer, X, Y, C = None, epochs = 10, sample_num = -1, verbose = -1, check_sublabel = True): -# # Set default parameters -# if sample_num == -1: -# sample_num = len(X) - -# if verbose < 1: -# verbose = epochs - -# if C is None: -# C = [None] * len(X) - -# # Set function running time recorder -# valid_func = clocker(model.valid) -# predict_func = clocker(model.predict) -# train_func = clocker(model.train) - -# abduce_func = clocker(abducer.batch_abduce) - -# X_bak = X -# Y_bak = Y -# C_bak = C - -# # Abductive learning train process -# res = {} -# for epoch_idx in range(epochs): -# X, Y, C = block_sample(X_bak, Y_bak, C_bak, sample_num, epoch_idx) -# preds_res = predict_func(X) -# abduced_Y = abduce_func(preds_res, C) -# finetune_X, finetune_Y = filter_data(X, abduced_Y) -# score, score_list = valid_func(X, Y) -# if ((epoch_idx + 1) % verbose == 0) or (epoch_idx == epochs - 1): -# res = result_statistics(preds_res["cls"], Y, abduced_Y) -# INFO(res) - -# if check_sublabel and (not is_all_sublabel_exist(finetune_Y, model.label_lists)): -# INFO("There is some sub label missing", len(finetune_Y)) -# break - -# if len(finetune_X) > 0: -# train_func(finetune_X, finetune_Y)#, n_epoch = 10) -# else: -# INFO("lack of data, all abduced failed", len(finetune_X)) -# return res - if __name__ == "__main__": pass From acbaa9f2e4fb63410d09c17153b0fb1efaf771bb Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 19:04:15 +0800 Subject: [PATCH 074/601] Update kb.py --- abducer/kb.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index e987d15..b21f97a 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -119,7 +119,7 @@ class add_KB(ClsKB): class hwf_KB(ClsKB): def __init__(self, GKB_flag = False, \ - pseudo_label_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], \ + pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], \ len_list = [1, 3, 5, 7]): super().__init__(GKB_flag, pseudo_label_list, len_list) @@ -127,7 +127,7 @@ class hwf_KB(ClsKB): if len(formula) % 2 == 0: return False for i in range(len(formula)): - if i % 2 == 0 and formula[i] not in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']: + if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']: return False if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: return False @@ -136,12 +136,9 @@ class hwf_KB(ClsKB): def logic_forward(self, formula): if not self.valid_candidate(formula): return np.inf - try: - mapping = {'0':'0', '1':'1', '2':'2', '3':'3', '4':'4', '5':'5', '6':'6', '7':'7', '8':'8', '9':'9', '+':'+', '-':'-', 'times':'*', 'div':'/'} - formula = [mapping[f] for f in formula] - return round(eval(''.join(formula)), 2) - except ZeroDivisionError: - return np.inf + mapping = {'1':'1', '2':'2', '3':'3', '4':'4', '5':'5', '6':'6', '7':'7', '8':'8', '9':'9', '+':'+', '-':'-', 'times':'*', 'div':'/'} + formula = [mapping[f] for f in formula] + return round(eval(''.join(formula)), 2) class RegKB(KBBase): From 469407e8ed9849a172047430490a80b93c3ed417 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 22:13:30 +0800 Subject: [PATCH 075/601] Update framework.py --- framework.py | 55 +++++++++++++++++++++++++--------------------------- 1 file changed, 26 insertions(+), 29 deletions(-) diff --git a/framework.py b/framework.py index 7f0debf..7e36b1b 100644 --- a/framework.py +++ b/framework.py @@ -28,28 +28,27 @@ def block_sample(X, Z, Y, sample_num, epoch_idx): return X, Z, Y -def get_taglist(self, Z): - tmp = [[str(x) for x in label] for label in Z] - tmp = sorted(list(set(tmp))) - return tmp - -def get_abl_acc(pred_Z, Y, logic_forward): - abl_acc = 0 +def result_statistics(pred_Z, Z, Y, logic_forward, char_acc_flag): + result = {} + if char_acc_flag: + char_acc_num = 0 + char_num = 0 + for pred_z, z in zip(pred_Z, Z): + char_num += len(z) + for zidx in range(len(z)): + if(pred_z[zidx] == z[zidx]): + char_acc_num += 1 + char_acc = char_acc_num / char_num + result["Character level accuracy"] = char_acc + + abl_acc_num = 0 for pred_z, y in zip(pred_Z, Y): if(logic_forward(pred_z) == y): - abl_acc += 1 - return abl_acc / len(Y) - -def get_char_acc(Z, pred_Z): - char_acc = 0 - char_num = 0 - for pred_z, z in zip(pred_Z, Z): - char_num += len(z) - for zidx in range(len(z)): - if(pred_z[zidx] == z[zidx]): - char_acc += 1 - return char_acc / char_num - + abl_acc_num += 1 + abl_acc = abl_acc_num / len(Y) + result["ABL accuracy"] = abl_acc + + return result def filter_data(X, abduced_Z): finetune_Z = [] @@ -63,7 +62,7 @@ def filter_data(X, abduced_Z): def pretrain(model, X, Z): pass -def train(model, abducer, train_data, test_data, epochs = 5, sample_num = -1, verbose = -1): +def train(model, abducer, train_data, test_data, epochs = 50, sample_num = -1, verbose = -1): X, Z, Y = train_data test_X, test_Z, test_Y = test_data @@ -89,13 +88,9 @@ def train(model, abducer, train_data, test_data, epochs = 5, sample_num = -1, ve preds_res = predict_func(X) abduced_Z = abduce_func(preds_res, Y) - abl_acc = get_abl_acc(preds_res['cls'], Y, abducer.kb.logic_forward) - if(char_acc_flag): - ori_char_acc = get_char_acc(preds_res['cls'], Z) - abd_char_acc = get_char_acc(preds_res['cls'], abduced_Z) - INFO('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc, ' ori_char_acc:', ori_char_acc, ' abd_char_acc:', abd_char_acc) - else: - INFO('epoch_idx:', epoch_idx, ' abl_acc:', abl_acc) + if ((epoch_idx + 1) % verbose == 0) or (epoch_idx == epochs - 1): + res = result_statistics(preds_res['cls'], Z, Y, abducer.kb.logic_forward, char_acc_flag) + INFO('epoch: ', epoch_idx + 1, ' ', res) finetune_X, finetune_Z = filter_data(X, abduced_Z) if len(finetune_X) > 0: @@ -104,7 +99,9 @@ def train(model, abducer, train_data, test_data, epochs = 5, sample_num = -1, ve else: INFO("lack of data, all abduced failed", len(finetune_X)) - return abl_acc + return res if __name__ == "__main__": pass + + From d252128d0f3703e0b56bf99d555d82e60346123e Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 22 Nov 2022 22:18:22 +0800 Subject: [PATCH 076/601] Update example.py --- example.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/example.py b/example.py index dad33f7..7ceaeff 100644 --- a/example.py +++ b/example.py @@ -15,7 +15,7 @@ import framework import torch.nn as nn import torch -from models.lenet5 import LeNet5 +from models.lenet5 import LeNet5, SymbolNet from models.basic_model import BasicModel from models.wabl_models import WABLBasicModel @@ -28,16 +28,20 @@ from datasets.hwf.get_hwf import get_hwf def run_test(): - kb = add_KB() - # kb = hwf_KB() + # kb = add_KB(True) + kb = hwf_KB(True) abducer = AbducerBase(kb) recorder = logger() - train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) - test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) + # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) + # test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) + + train_data = get_hwf(train = True, get_pseudo_label = True) + test_data = get_hwf(train = False, get_pseudo_label = True) - cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_X[0][0].shape[1:])) + # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) + cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) @@ -46,12 +50,11 @@ def run_test(): base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, num_epochs=1, recorder=recorder) model = WABLBasicModel(base_model, kb.pseudo_label_list) - res = framework.train(model, abducer, train_X, train_Z, train_Y, sample_num = 10000, verbose = 1) - recorder.print("abl_acc is ", res) + res = framework.train(model, abducer, train_data, test_data, sample_num = 10000, verbose = 1) + recorder.print(res) recorder.dump() return True if __name__ == "__main__": run_test() - From c3a7112204ff48903a232591781beb5531ec50c5 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 23 Nov 2022 10:06:05 +0800 Subject: [PATCH 077/601] Update abducer_base.py --- abducer/abducer_base.py | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 284ebe4..83826a6 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -10,12 +10,15 @@ # #================================================================# +import sys +sys.path.append("..") + import abc -# from kb import add_KB, hwf_KB from abducer.kb import add_KB, hwf_KB import numpy as np from itertools import product, combinations +import time class AbducerBase(abc.ABC): def __init__(self, kb, dist_func = 'confidence', cache = True): @@ -23,8 +26,10 @@ class AbducerBase(abc.ABC): assert(dist_func == 'hamming' or dist_func == 'confidence') self.dist_func = dist_func self.cache = cache - self.cache_min_address_num = {} - self.cache_candidates = {} + + if self.cache: + self.cache_min_address_num = {} + self.cache_candidates = {} def hamming_dist(self, A, B): B = np.array(B) @@ -79,7 +84,9 @@ class AbducerBase(abc.ABC): if self.kb.GKB_flag: all_candidates = self.kb.get_candidates(ans, len(pred_res)) if len(all_candidates) == 0: - return [] + candidates = [] + min_address_num = 0 + address_num = 0 else: cost_list = self.hamming_dist(pred_res, all_candidates) min_address_num = np.min(cost_list) @@ -108,30 +115,30 @@ class AbducerBase(abc.ABC): pred_res_array[np.array(address_idx)] = c if self.kb.logic_forward(pred_res_array) == key: new_candidates.append(pred_res_array) - return new_candidates, address_num + return new_candidates def get_abduce_candidates(self, pred_res, key, max_address_num, require_more_address): - candidates = [] + print(pred_res) for address_num in range(len(pred_res) + 1): - if address_num > max_address_num: - return [], None, None - if address_num == 0: if abs(self.kb.logic_forward(pred_res) - key) <= 1e-3: candidates.append(pred_res) else: - new_candidates, address_num = self.address(address_num, pred_res, key) + new_candidates = self.address(address_num, pred_res, key) candidates += new_candidates if len(candidates) > 0: min_address_num = address_num break + + if address_num >= max_address_num: + return [], 0, 0 for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if address_num > max_address_num: return candidates, min_address_num, address_num - 1 - new_candidates, address_num = self.address(address_num, pred_res, key) + new_candidates = self.address(address_num, pred_res, key) candidates += new_candidates return candidates, min_address_num, address_num @@ -163,7 +170,7 @@ if __name__ == '__main__': abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce((['5', '+', '2'], None, 3.09), max_address_num = 2, require_more_address = 0) + res = abd.abduce((['5', '+', '2'], None, 64), max_address_num = 3, require_more_address = 0) print(res) res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num = 3, require_more_address = 0) print(res) From 18fc1be9fa2b25da7690182f675757bbfc78345b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 23 Nov 2022 13:40:47 +0800 Subject: [PATCH 078/601] Speed up get_abduce_candidates --- abducer/abducer_base.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 83826a6..63a3c73 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -110,11 +110,13 @@ class AbducerBase(abc.ABC): address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) for address_idx in address_idx_list: for c in all_address_candidate: - pred_res_array = np.array(pred_res) - if np.count_nonzero(np.array(c) != pred_res_array[np.array(address_idx)]) == address_num: - pred_res_array[np.array(address_idx)] = c - if self.kb.logic_forward(pred_res_array) == key: - new_candidates.append(pred_res_array) + address_list = [pred_res[i] for i in address_idx] + if(sum([address_list[i] == c[i] for i in range(address_num)]) == 0): + candidate = pred_res.copy() + for i, idx in enumerate(address_idx): + candidate[idx] = c[i] + if self.kb.logic_forward(candidate) == key: + new_candidates.append(candidate) return new_candidates def get_abduce_candidates(self, pred_res, key, max_address_num, require_more_address): From 66149d92f78b4c33301907170cb11407401019c8 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 23 Nov 2022 13:57:39 +0800 Subject: [PATCH 079/601] Update framework.py --- framework.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/framework.py b/framework.py index 7e36b1b..ed013b7 100644 --- a/framework.py +++ b/framework.py @@ -63,7 +63,7 @@ def pretrain(model, X, Z): pass def train(model, abducer, train_data, test_data, epochs = 50, sample_num = -1, verbose = -1): - X, Z, Y = train_data + train_X, train_Z, train_Y = train_data test_X, test_Z, test_Y = test_data # Set default parameters @@ -74,9 +74,9 @@ def train(model, abducer, train_data, test_data, epochs = 50, sample_num = -1, v verbose = epochs char_acc_flag = 1 - if Z == None: + if train_Z == None: char_acc_flag = 0 - Z = [None] * len(X) + train_Z = [None] * len(X) predict_func = clocker(model.predict) train_func = clocker(model.train) @@ -84,7 +84,7 @@ def train(model, abducer, train_data, test_data, epochs = 50, sample_num = -1, v # Abductive learning train process for epoch_idx in range(epochs): - X, Z, Y = block_sample(X, Z, Y, sample_num, epoch_idx) + X, Z, Y = block_sample(train_X, train_Z, train_Y, sample_num, epoch_idx) preds_res = predict_func(X) abduced_Z = abduce_func(preds_res, Y) From 5f940cbda93220aa56216376cbc208c388f895a6 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 24 Nov 2022 17:42:16 +0800 Subject: [PATCH 080/601] Update kb.py --- abducer/kb.py | 327 +++++++++++++++++--------------------------------- 1 file changed, 108 insertions(+), 219 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index b21f97a..f26b363 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -1,8 +1,8 @@ # coding: utf-8 #================================================================# -# Copyright (C) 2021 LAMDA All rights reserved. +# Copyright (C) 2021 Freecss All rights reserved. # -# File Name ：kb.py +# File Name ：abducer_base.py # Author ：freecss # Email ：karlfreecss@gmail.com # Created Date ：2021/06/03 @@ -10,249 +10,138 @@ # #================================================================# -from abc import ABC, abstractmethod -import bisect -import copy -import numpy as np - -from collections import defaultdict -from itertools import product - -class KBBase(ABC): - def __init__(self): - pass - - @abstractmethod - def get_candidates(self): - pass - - @abstractmethod - def get_all_candidates(self): - pass - - @abstractmethod - def logic_forward(self): - pass - - def _length(self, length): - if length is None: - length = list(self.base.keys()) - if type(length) is int: - length = [length] - return length - - def __len__(self): - pass +import sys +sys.path.append("..") +import abc +from abducer.kb import add_KB, hwf_KB, add_prolog_KB +import numpy as np -class ClsKB(KBBase): - def __init__(self, GKB_flag = False, pseudo_label_list = None, len_list = None): - super().__init__() - self.GKB_flag = GKB_flag - self.pseudo_label_list = pseudo_label_list - self.len_list = len_list - - if GKB_flag: - # self.base = np.load('abducer/hwf.npy', allow_pickle=True).item() - self.base = {} - X, Y = self.get_GKB(self.pseudo_label_list, self.len_list) - for x, y in zip(X, Y): - self.base.setdefault(len(x), defaultdict(list))[y].append(x) - - def get_GKB(self, pseudo_label_list, len_list): - all_X = [] - for len in len_list: - all_X += list(product(pseudo_label_list, repeat = len)) - - X = [] - Y = [] - for x in all_X: - y = self.logic_forward(x) - if y != np.inf: - X.append(x) - Y.append(y) - return X, Y - - def logic_forward(self): - pass +from itertools import product, combinations +import time - def get_candidates(self, key, length = None): - if not self.GKB_flag or self.base == {}: - return [] +class AbducerBase(abc.ABC): + def __init__(self, kb, dist_func = 'confidence', cache = True): + self.kb = kb + assert(dist_func == 'hamming' or dist_func == 'confidence') + self.dist_func = dist_func + self.cache = cache - if key is None: - return self.get_all_candidates() - - if type(length) is int and length not in self.len_list: - return [] - length = self._length(length) - return sum([self.base[l][key] for l in length], []) - - def get_all_candidates(self): - if not self.GKB_flag or self.base == {}: + if self.cache: + self.cache_min_address_num = {} + self.cache_candidates = {} + + def hamming_dist(self, A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) + + def confidence_dist(self, A, B): + mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) + B = [list(map(lambda x : mapping[x], b)) for b in B] + + B = np.array(B) + A = np.clip(A, 1e-9, 1) + A = np.expand_dims(A, axis=0) + A = A.repeat(axis=0, repeats=(len(B))) + rows = np.array(range(len(B))) + rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) + cols = np.array(range(len(B[0]))) + cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) + return 1 - np.prod(A[rows, cols, B], axis = 1) + + + def get_cost_list(self, pred_res, pred_res_prob, candidates): + if self.dist_func == 'hamming': + return self.hamming_dist(pred_res, candidates) + elif self.dist_func == 'confidence': + return self.confidence_dist(pred_res_prob, candidates) + + def get_min_cost_candidate(self, pred_res, pred_res_prob, candidates): + if len(candidates) == 0: return [] + elif len(candidates) == 1: + return candidates[0] else: - return sum([sum(v.values(), []) for v in self.base.values()], []) - - def _dict_len(self, dic): - if not self.GKB_flag: - return 0 - else: - return sum(len(c) for c in dic.values()) - - def __len__(self): - if not self.GKB_flag: - return 0 + cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) + min_address_num = np.min(cost_list) + idxs = np.where(cost_list == min_address_num)[0] + return [candidates[idx] for idx in idxs][0] + + def filter_all_candidates(self, pred_res, all_candidates, max_address_num, require_more_address): + if len(all_candidates) == 0: + candidates = [] + min_address_num = 0 + address_num = 0 else: - return sum(self._dict_len(v) for v in self.base.values()) - - -class add_KB(ClsKB): - def __init__(self, GKB_flag = False, \ - pseudo_label_list = list(range(10)), \ - len_list = [2]): - super().__init__(GKB_flag, pseudo_label_list, len_list) - - def logic_forward(self, nums): - return sum(nums) - - -class hwf_KB(ClsKB): - def __init__(self, GKB_flag = False, \ - pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], \ - len_list = [1, 3, 5, 7]): - super().__init__(GKB_flag, pseudo_label_list, len_list) + cost_list = self.hamming_dist(pred_res, all_candidates) + min_address_num = np.min(cost_list) + address_num = min(max_address_num, min_address_num + require_more_address) + idxs = np.where(cost_list <= address_num)[0] + candidates = [all_candidates[idx] for idx in idxs] + return candidates, min_address_num, address_num + + def abduce(self, data, max_address_num = -1, require_more_address = 0): + pred_res, pred_res_prob, ans = data + + if self.cache and (tuple(pred_res), ans) in self.cache_min_address_num: + address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), ans)] + require_more_address) + if (tuple(pred_res), ans, address_num) in self.cache_candidates: + candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] + candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + return candidate - def valid_candidate(self, formula): - if len(formula) % 2 == 0: - return False - for i in range(len(formula)): - if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']: - return False - if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: - return False - return True - - def logic_forward(self, formula): - if not self.valid_candidate(formula): - return np.inf - mapping = {'1':'1', '2':'2', '3':'3', '4':'4', '5':'5', '6':'6', '7':'7', '8':'8', '9':'9', '+':'+', '-':'-', 'times':'*', 'div':'/'} - formula = [mapping[f] for f in formula] - return round(eval(''.join(formula)), 2) - - -class RegKB(KBBase): - def __init__(self, GKB_flag = False, X = None, Y = None): - super().__init__() - tmp_dict = {} - for x, y in zip(X, Y): - tmp_dict.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) - - self.base = {} - for l in tmp_dict.keys(): - data = sorted(list(zip(tmp_dict[l].keys(), tmp_dict[l].values()))) - X = [x for y, x in data] - Y = [y for y, x in data] - self.base[l] = (X, Y) + candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, ans, max_address_num, require_more_address) + + if self.cache: + self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num + self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates - def valid_candidate(self): - pass - - def logic_forward(self): - pass + candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + return candidate - - def get_candidates(self, key, length = None): - if key is None: - return self.get_all_candidates() - - length = self._length(length) - - min_err = 999999 - candidates = [] - for l in length: - X, Y = self.base[l] + + def batch_abduce(self, Z, Y, max_address_num = -1, require_more_address = 0): + return [ + self.abduce((z, prob, y), max_address_num, require_more_address)\ + for z, prob, y in zip(Z['cls'], Z['prob'], Y) + ] - idx = bisect.bisect_left(Y, key) - begin = max(0, idx - 1) - end = min(idx + 2, len(X)) - - for idx in range(begin, end): - err = abs(Y[idx] - key) - if abs(err - min_err) < 1e-9: - candidates.extend(X[idx]) - elif err < min_err: - candidates = copy.deepcopy(X[idx]) - min_err = err - return candidates - - def get_all_candidates(self): - return sum([sum(D[0], []) for D in self.base.values()], []) - - def __len__(self): - return sum([sum(len(x) for x in D[0]) for D in self.base.values()]) + def __call__(self, Z, Y, max_address_num = -1, require_more_address = 0): + return self.batch_abduce(Z, Y, max_address_num, require_more_address) + -import time -if __name__ == "__main__": - # With ground KB +if __name__ == '__main__': kb = add_KB(GKB_flag = True) - print('len(kb):', len(kb)) - res = kb.get_candidates(0) + abd = AbducerBase(kb, 'hamming') + res = abd.abduce(([1, 1], None, 17), max_address_num = 2, require_more_address = 0) print(res) - res = kb.get_candidates(18) + res = abd.abduce(([1, 1], None, 17), max_address_num = 1, require_more_address = 0) print(res) - res = kb.get_candidates(18) - print(res) - res = kb.get_candidates(7) + res = abd.abduce(([1, 1], None, 20), max_address_num = 2, require_more_address = 0) print(res) print() - # Without ground KB - kb = add_KB() - print('len(kb):', len(kb)) - res = kb.get_candidates(0) - print(res) - res = kb.get_candidates(18, length = 2) + kb = add_prolog_KB() + abd = AbducerBase(kb, 'hamming') + res = abd.abduce(([1, 1], None, 17), max_address_num = 2, require_more_address = 0) print(res) - res = kb.get_candidates(18, length = 8) + res = abd.abduce(([1, 1], None, 17), max_address_num = 1, require_more_address = 0) print(res) - res = kb.get_candidates(7, length = 3) + res = abd.abduce(([1, 1], None, 20), max_address_num = 2, require_more_address = 0) print(res) print() - start = time.time() - kb = hwf_KB(GKB_flag = True) - print(time.time() - start) - print('len(kb):', len(kb)) - res = kb.get_candidates(2, length = 1) + kb = hwf_KB(len_list = [1, 3, 5]) + abd = AbducerBase(kb, 'hamming') + res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce((['5', '+', '2'], None, 64), max_address_num = 3, require_more_address = 0) print(res) - res = kb.get_candidates(1, length = 3) + res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num = 3, require_more_address = 0) print(res) - res = kb.get_candidates(3.67, length = 5) + res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num = 5, require_more_address = 3) print(res) print() - # X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] - # Y = [2, 1, 1, 2, 2] - # kb = ClsKB(X, Y) - # print('len(kb):', len(kb)) - # res = kb.get_candidates(2, 5) - # print(res) - # res = kb.get_candidates(2, 3) - # print(res) - # res = kb.get_candidates(None) - # print(res) - # print() - - # X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] - # Y = [2, 1, 1, 2, 1.5, 1.5] - # kb = RegKB(X, Y) - # print('len(kb):', len(kb)) - # res = kb.get_candidates(1.6) - # print(res) - # res = kb.get_candidates(1.6, length = 9) - # print(res) - # res = kb.get_candidates(None) - # print(res) - From abb7fb4a1eb0927a2e96abcf8d6baa91e08a9ac1 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 24 Nov 2022 17:42:27 +0800 Subject: [PATCH 081/601] Update kb.py --- abducer/kb.py | 403 ++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 310 insertions(+), 93 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index f26b363..2600f8b 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -1,8 +1,8 @@ # coding: utf-8 #================================================================# -# Copyright (C) 2021 Freecss All rights reserved. +# Copyright (C) 2021 LAMDA All rights reserved. # -# File Name ：abducer_base.py +# File Name ：kb.py # Author ：freecss # Email ：karlfreecss@gmail.com # Created Date ：2021/06/03 @@ -10,63 +10,27 @@ # #================================================================# -import sys -sys.path.append("..") - -import abc -from abducer.kb import add_KB, hwf_KB, add_prolog_KB +from abc import ABC, abstractmethod +import bisect +import copy import numpy as np +from collections import defaultdict from itertools import product, combinations -import time -class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func = 'confidence', cache = True): - self.kb = kb - assert(dist_func == 'hamming' or dist_func == 'confidence') - self.dist_func = dist_func - self.cache = cache - - if self.cache: - self.cache_min_address_num = {} - self.cache_candidates = {} +import pyswip - def hamming_dist(self, A, B): - B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) +class KBBase(ABC): + def __init__(self): + pass - def confidence_dist(self, A, B): - mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) - B = [list(map(lambda x : mapping[x], b)) for b in B] + @abstractmethod + def logic_forward(self): + pass - B = np.array(B) - A = np.clip(A, 1e-9, 1) - A = np.expand_dims(A, axis=0) - A = A.repeat(axis=0, repeats=(len(B))) - rows = np.array(range(len(B))) - rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) - cols = np.array(range(len(B[0]))) - cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) - return 1 - np.prod(A[rows, cols, B], axis = 1) - - - def get_cost_list(self, pred_res, pred_res_prob, candidates): - if self.dist_func == 'hamming': - return self.hamming_dist(pred_res, candidates) - elif self.dist_func == 'confidence': - return self.confidence_dist(pred_res_prob, candidates) - - def get_min_cost_candidate(self, pred_res, pred_res_prob, candidates): - if len(candidates) == 0: - return [] - elif len(candidates) == 1: - return candidates[0] - else: - cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) - min_address_num = np.min(cost_list) - idxs = np.where(cost_list == min_address_num)[0] - return [candidates[idx] for idx in idxs][0] + @abstractmethod + def abduce_candidates(self): + pass def filter_all_candidates(self, pred_res, all_candidates, max_address_num, require_more_address): if len(all_candidates) == 0: @@ -80,68 +44,321 @@ class AbducerBase(abc.ABC): idxs = np.where(cost_list <= address_num)[0] candidates = [all_candidates[idx] for idx in idxs] return candidates, min_address_num, address_num + + def hamming_dist(self, A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) + + def __len__(self): + pass + +class ClsKB(KBBase): + def __init__(self, GKB_flag = False, pseudo_label_list = None, len_list = None): + super().__init__() + self.GKB_flag = GKB_flag + self.pseudo_label_list = pseudo_label_list + self.len_list = len_list + self.prolog_flag = False + + if GKB_flag: + # self.base = np.load('abducer/hwf.npy', allow_pickle=True).item() + self.base = {} + X, Y = self.get_GKB(self.pseudo_label_list, self.len_list) + for x, y in zip(X, Y): + self.base.setdefault(len(x), defaultdict(list))[y].append(x) + else: + self.all_address_candidate_dict = {} + for address_num in range(1, max(self.len_list) + 1): + self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat = address_num)) + + def get_GKB(self, pseudo_label_list, len_list): + all_X = [] + for len in len_list: + all_X += list(product(pseudo_label_list, repeat = len)) + + X = [] + Y = [] + for x in all_X: + y = self.logic_forward(x) + if y != np.inf: + X.append(x) + Y.append(y) + return X, Y + + def logic_forward(self): + pass + + def abduce_candidates(self, pred_res, key, max_address_num = -1, require_more_address = 0): + if max_address_num == -1: + max_address_num = len(pred_res) + if self.GKB_flag: + all_candidates = self.get_candidates_GKB(key, len(pred_res)) + return self.filter_all_candidates(pred_res, all_candidates, max_address_num, require_more_address) + else: + return self.abduction(pred_res, key, max_address_num, require_more_address) - def abduce(self, data, max_address_num = -1, require_more_address = 0): - pred_res, pred_res_prob, ans = data - if self.cache and (tuple(pred_res), ans) in self.cache_min_address_num: - address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), ans)] + require_more_address) - if (tuple(pred_res), ans, address_num) in self.cache_candidates: - candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] - candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) - return candidate + + def get_candidates_GKB(self, key, length = None): + if self.base == {}: + return [] - candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, ans, max_address_num, require_more_address) + if key is None: + return self.get_all_candidates() + + if length is None: + length = list(self.base.keys()) + elif type(length) is int and length not in self.len_list: + return [] + else: + length = [length] + + return sum([self.base[l][key] for l in length], []) + + def get_all_candidates(self): + if self.base == {}: + return [] + else: + return sum([sum(v.values(), []) for v in self.base.values()], []) + + + + + + def abduction(self, pred_res, key, max_address_num, require_more_address): + candidates = [] + for address_num in range(len(pred_res) + 1): + if address_num == 0: + if abs(self.logic_forward(pred_res) - key) <= 1e-3: + candidates.append(pred_res) + else: + new_candidates = self.address(address_num, pred_res, key) + candidates += new_candidates + + if len(candidates) > 0: + min_address_num = address_num + break + + if address_num >= max_address_num: + return [], 0, 0 - if self.cache: - self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num - self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates + for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): + if address_num > max_address_num: + return candidates, min_address_num, address_num - 1 + new_candidates = self.address(address_num, pred_res, key) + candidates += new_candidates - candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) - return candidate + return candidates, min_address_num, address_num + + def address(self, address_num, pred_res, key): + new_candidates = [] + all_address_candidate = self.all_address_candidate_dict[address_num] + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + for address_idx in address_idx_list: + for c in all_address_candidate: + address_list = [pred_res[i] for i in address_idx] + if(sum([address_list[i] == c[i] for i in range(address_num)]) == 0): + candidate = pred_res.copy() + for i, idx in enumerate(address_idx): + candidate[idx] = c[i] + if self.logic_forward(candidate) == key: + new_candidates.append(candidate) + return new_candidates + - - def batch_abduce(self, Z, Y, max_address_num = -1, require_more_address = 0): - return [ - self.abduce((z, prob, y), max_address_num, require_more_address)\ - for z, prob, y in zip(Z['cls'], Z['prob'], Y) - ] - def __call__(self, Z, Y, max_address_num = -1, require_more_address = 0): - return self.batch_abduce(Z, Y, max_address_num, require_more_address) + def _dict_len(self, dic): + if not self.GKB_flag: + return 0 + else: + return sum(len(c) for c in dic.values()) + + def __len__(self): + if not self.GKB_flag: + return 0 + else: + return sum(self._dict_len(v) for v in self.base.values()) + + +class add_KB(ClsKB): + def __init__(self, GKB_flag = False, \ + pseudo_label_list = list(range(10)), \ + len_list = [2]): + super().__init__(GKB_flag, pseudo_label_list, len_list) + + def logic_forward(self, nums): + return sum(nums) + +class hwf_KB(ClsKB): + def __init__(self, GKB_flag = False, \ + pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], \ + len_list = [1, 3, 5, 7]): + super().__init__(GKB_flag, pseudo_label_list, len_list) + + def valid_candidate(self, formula): + if len(formula) % 2 == 0: + return False + for i in range(len(formula)): + if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']: + return False + if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: + return False + return True + + def logic_forward(self, formula): + if not self.valid_candidate(formula): + return np.inf + mapping = {'1':'1', '2':'2', '3':'3', '4':'4', '5':'5', '6':'6', '7':'7', '8':'8', '9':'9', '+':'+', '-':'-', 'times':'*', 'div':'/'} + formula = [mapping[f] for f in formula] + return round(eval(''.join(formula)), 2) + + + + + +class prolog_KB(KBBase): + def __init__(self, pseudo_label_list): + super().__init__() + self.pseudo_label_list = pseudo_label_list + self.prolog = pyswip.Prolog() + for i in self.pseudo_label_list: + self.prolog.assertz("pseudo_label(%s)" % i) + self.prolog_flag = True + + def logic_forward(self): + pass + + def abduce_candidates(self, pred_res, key, max_address_num = -1, require_more_address = 0): + if max_address_num == -1: + max_address_num = len(pred_res) + all_candidates = self.get_candidates_prolog(key) + return self.filter_all_candidates(pred_res, all_candidates, max_address_num, require_more_address) + + +class add_prolog_KB(prolog_KB): + def __init__(self, pseudo_label_list = list(range(10))): + super().__init__(pseudo_label_list) + self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") + + def logic_forward(self, nums): + return list(self.prolog.query("addition(%s, %s, Res)." %(nums[0], nums[1])))[0]['Res'] + + def get_candidates_prolog(self, key): + return [(z['Z1'], z['Z2']) for z in list(self.prolog.query("addition(Z1, Z2, %s)." % key))] + + +class RegKB(KBBase): + def __init__(self, GKB_flag = False, X = None, Y = None): + super().__init__() + tmp_dict = {} + for x, y in zip(X, Y): + tmp_dict.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) + + self.base = {} + for l in tmp_dict.keys(): + data = sorted(list(zip(tmp_dict[l].keys(), tmp_dict[l].values()))) + X = [x for y, x in data] + Y = [y for y, x in data] + self.base[l] = (X, Y) + + def valid_candidate(self): + pass + + def logic_forward(self): + pass + + def abduce_candidates(self, key, length = None): + if key is None: + return self.get_all_candidates() + + length = self._length(length) + + min_err = 999999 + candidates = [] + for l in length: + X, Y = self.base[l] -if __name__ == '__main__': + idx = bisect.bisect_left(Y, key) + begin = max(0, idx - 1) + end = min(idx + 2, len(X)) + + for idx in range(begin, end): + err = abs(Y[idx] - key) + if abs(err - min_err) < 1e-9: + candidates.extend(X[idx]) + elif err < min_err: + candidates = copy.deepcopy(X[idx]) + min_err = err + return candidates + + def get_all_candidates(self): + return sum([sum(D[0], []) for D in self.base.values()], []) + + def __len__(self): + return sum([sum(len(x) for x in D[0]) for D in self.base.values()]) + +import time +if __name__ == "__main__": + # With ground KB kb = add_KB(GKB_flag = True) - abd = AbducerBase(kb, 'hamming') - res = abd.abduce(([1, 1], None, 17), max_address_num = 2, require_more_address = 0) + print('len(kb):', len(kb)) + res = kb.get_candidates_GKB(0) print(res) - res = abd.abduce(([1, 1], None, 17), max_address_num = 1, require_more_address = 0) + res = kb.get_candidates_GKB(18) print(res) - res = abd.abduce(([1, 1], None, 20), max_address_num = 2, require_more_address = 0) + res = kb.get_candidates_GKB(18) + print(res) + res = kb.get_candidates_GKB(16) print(res) print() - kb = add_prolog_KB() - abd = AbducerBase(kb, 'hamming') - res = abd.abduce(([1, 1], None, 17), max_address_num = 2, require_more_address = 0) - print(res) - res = abd.abduce(([1, 1], None, 17), max_address_num = 1, require_more_address = 0) - print(res) - res = abd.abduce(([1, 1], None, 20), max_address_num = 2, require_more_address = 0) - print(res) + # Without ground KB + kb = add_KB() + print('len(kb):', len(kb)) print() - kb = hwf_KB(len_list = [1, 3, 5]) - abd = AbducerBase(kb, 'hamming') - res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) + # Prolog + kb = add_prolog_KB() + print(kb.logic_forward([3, 4])) + res = kb.get_candidates_prolog(16) print(res) - res = abd.abduce((['5', '+', '2'], None, 64), max_address_num = 3, require_more_address = 0) + + start = time.time() + kb = hwf_KB(GKB_flag = True, len_list = [1, 3, 5]) + print(time.time() - start) + print('len(kb):', len(kb)) + res = kb.get_candidates_GKB(2, length = 1) print(res) - res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num = 3, require_more_address = 0) + res = kb.get_candidates_GKB(1, length = 3) print(res) - res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num = 5, require_more_address = 3) + res = kb.get_candidates_GKB(3.67, length = 5) print(res) print() + + # X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] + # Y = [2, 1, 1, 2, 2] + # kb = ClsKB(X, Y) + # print('len(kb):', len(kb)) + # res = kb.get_candidates(2, 5) + # print(res) + # res = kb.get_candidates(2, 3) + # print(res) + # res = kb.get_candidates(None) + # print(res) + # print() + + # X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] + # Y = [2, 1, 1, 2, 1.5, 1.5] + # kb = RegKB(X, Y) + # print('len(kb):', len(kb)) + # res = kb.get_candidates(1.6) + # print(res) + # res = kb.get_candidates(1.6, length = 9) + # print(res) + # res = kb.get_candidates(None) + # print(res) + From 15e4427702179edb463e65fcedabd00e484986cb Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 24 Nov 2022 17:42:43 +0800 Subject: [PATCH 082/601] Update abducer_base.py --- abducer/abducer_base.py | 102 ++++++++++++++-------------------------- 1 file changed, 34 insertions(+), 68 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 63a3c73..f26b363 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -14,7 +14,7 @@ import sys sys.path.append("..") import abc -from abducer.kb import add_KB, hwf_KB +from abducer.kb import add_KB, hwf_KB, add_prolog_KB import numpy as np from itertools import product, combinations @@ -67,35 +67,31 @@ class AbducerBase(abc.ABC): min_address_num = np.min(cost_list) idxs = np.where(cost_list == min_address_num)[0] return [candidates[idx] for idx in idxs][0] + + def filter_all_candidates(self, pred_res, all_candidates, max_address_num, require_more_address): + if len(all_candidates) == 0: + candidates = [] + min_address_num = 0 + address_num = 0 + else: + cost_list = self.hamming_dist(pred_res, all_candidates) + min_address_num = np.min(cost_list) + address_num = min(max_address_num, min_address_num + require_more_address) + idxs = np.where(cost_list <= address_num)[0] + candidates = [all_candidates[idx] for idx in idxs] + return candidates, min_address_num, address_num def abduce(self, data, max_address_num = -1, require_more_address = 0): pred_res, pred_res_prob, ans = data - if max_address_num == -1: - max_address_num = len(pred_res) if self.cache and (tuple(pred_res), ans) in self.cache_min_address_num: address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), ans)] + require_more_address) if (tuple(pred_res), ans, address_num) in self.cache_candidates: - # print('cached') candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) return candidate - - if self.kb.GKB_flag: - all_candidates = self.kb.get_candidates(ans, len(pred_res)) - if len(all_candidates) == 0: - candidates = [] - min_address_num = 0 - address_num = 0 - else: - cost_list = self.hamming_dist(pred_res, all_candidates) - min_address_num = np.min(cost_list) - address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(cost_list <= address_num)[0] - candidates = [all_candidates[idx] for idx in idxs] - - else: - candidates, min_address_num, address_num = self.get_abduce_candidates(pred_res, ans, max_address_num, require_more_address) + + candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, ans, max_address_num, require_more_address) if self.cache: self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num @@ -104,47 +100,6 @@ class AbducerBase(abc.ABC): candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) return candidate - def address(self, address_num, pred_res, key): - new_candidates = [] - all_address_candidate = list(product(self.kb.pseudo_label_list, repeat = address_num)) - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) - for address_idx in address_idx_list: - for c in all_address_candidate: - address_list = [pred_res[i] for i in address_idx] - if(sum([address_list[i] == c[i] for i in range(address_num)]) == 0): - candidate = pred_res.copy() - for i, idx in enumerate(address_idx): - candidate[idx] = c[i] - if self.kb.logic_forward(candidate) == key: - new_candidates.append(candidate) - return new_candidates - - def get_abduce_candidates(self, pred_res, key, max_address_num, require_more_address): - candidates = [] - print(pred_res) - for address_num in range(len(pred_res) + 1): - if address_num == 0: - if abs(self.kb.logic_forward(pred_res) - key) <= 1e-3: - candidates.append(pred_res) - else: - new_candidates = self.address(address_num, pred_res, key) - candidates += new_candidates - - if len(candidates) > 0: - min_address_num = address_num - break - - if address_num >= max_address_num: - return [], 0, 0 - - for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): - if address_num > max_address_num: - return candidates, min_address_num, address_num - 1 - new_candidates = self.address(address_num, pred_res, key) - candidates += new_candidates - - return candidates, min_address_num, address_num - def batch_abduce(self, Z, Y, max_address_num = -1, require_more_address = 0): return [ @@ -154,21 +109,30 @@ class AbducerBase(abc.ABC): def __call__(self, Z, Y, max_address_num = -1, require_more_address = 0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) - - + if __name__ == '__main__': kb = add_KB(GKB_flag = True) abd = AbducerBase(kb, 'hamming') - res = abd.abduce(([1, 1], None, 4), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], None, 17), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([1, 1], None, 4), max_address_num = 2, require_more_address = 1) + res = abd.abduce(([1, 1], None, 17), max_address_num = 1, require_more_address = 0) print(res) - res = abd.abduce(([1, 1], None, 5), max_address_num = 2, require_more_address = 1) + res = abd.abduce(([1, 1], None, 20), max_address_num = 2, require_more_address = 0) print(res) print() - kb = hwf_KB() + kb = add_prolog_KB() + abd = AbducerBase(kb, 'hamming') + res = abd.abduce(([1, 1], None, 17), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], None, 17), max_address_num = 1, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], None, 20), max_address_num = 2, require_more_address = 0) + print(res) + print() + + kb = hwf_KB(len_list = [1, 3, 5]) abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) print(res) @@ -176,6 +140,8 @@ if __name__ == '__main__': print(res) res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num = 3, require_more_address = 0) print(res) - res = abd.abduce((['5', '+', '3'], None, 0.33), max_address_num = 3, require_more_address = 3) + res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num = 5, require_more_address = 3) print(res) print() + + From 2c2f38d60b445ca830771d3e5a0eba080bd1ecd8 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 24 Nov 2022 17:48:52 +0800 Subject: [PATCH 083/601] Update framework.py --- framework.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/framework.py b/framework.py index ed013b7..2a1fdad 100644 --- a/framework.py +++ b/framework.py @@ -68,7 +68,7 @@ def train(model, abducer, train_data, test_data, epochs = 50, sample_num = -1, v # Set default parameters if sample_num == -1: - sample_num = len(X) + sample_num = len(train_X) if verbose < 1: verbose = epochs From cdb8bc2067b8c3cce5f24a587d341bb002e6e56f Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 24 Nov 2022 21:42:12 +0800 Subject: [PATCH 084/601] Update kb.py --- abducer/kb.py | 170 +++++++++++++++++++------------------------------- 1 file changed, 63 insertions(+), 107 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 2600f8b..63c4959 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -32,23 +32,32 @@ class KBBase(ABC): def abduce_candidates(self): pass - def filter_all_candidates(self, pred_res, all_candidates, max_address_num, require_more_address): - if len(all_candidates) == 0: - candidates = [] - min_address_num = 0 - address_num = 0 - else: - cost_list = self.hamming_dist(pred_res, all_candidates) - min_address_num = np.min(cost_list) - address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(cost_list <= address_num)[0] - candidates = [all_candidates[idx] for idx in idxs] + + def abduction(self, pred_res, key, max_address_num, require_more_address): + candidates = [] + for address_num in range(len(pred_res) + 1): + if address_num == 0: + if abs(self.logic_forward(pred_res) - key) <= 1e-3: + candidates.append(pred_res) + else: + new_candidates = self.address(address_num, pred_res, key) + candidates += new_candidates + + if len(candidates) > 0: + min_address_num = address_num + break + + if address_num >= max_address_num: + return [], 0, 0 + + for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): + if address_num > max_address_num: + return candidates, min_address_num, address_num - 1 + new_candidates = self.address(address_num, pred_res, key) + candidates += new_candidates + return candidates, min_address_num, address_num - def hamming_dist(self, A, B): - B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) def __len__(self): pass @@ -90,66 +99,38 @@ class ClsKB(KBBase): pass def abduce_candidates(self, pred_res, key, max_address_num = -1, require_more_address = 0): - if max_address_num == -1: - max_address_num = len(pred_res) if self.GKB_flag: - all_candidates = self.get_candidates_GKB(key, len(pred_res)) - return self.filter_all_candidates(pred_res, all_candidates, max_address_num, require_more_address) + return self.abduce_from_GKB(pred_res, key, max_address_num, require_more_address) else: return self.abduction(pred_res, key, max_address_num, require_more_address) - def get_candidates_GKB(self, key, length = None): - if self.base == {}: + def abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): + if self.base == {} or len(pred_res) not in self.len_list: return [] - if key is None: - return self.get_all_candidates() + all_candidates = self.base[len(pred_res)][key] - if length is None: - length = list(self.base.keys()) - elif type(length) is int and length not in self.len_list: - return [] + if len(all_candidates) == 0: + candidates = [] + min_address_num = 0 + address_num = 0 else: - length = [length] - - return sum([self.base[l][key] for l in length], []) + cost_list = self.hamming_dist(pred_res, all_candidates) + min_address_num = np.min(cost_list) + address_num = min(max_address_num, min_address_num + require_more_address) + idxs = np.where(cost_list <= address_num)[0] + candidates = [all_candidates[idx] for idx in idxs] + + return candidates, min_address_num, address_num - def get_all_candidates(self): - if self.base == {}: - return [] - else: - return sum([sum(v.values(), []) for v in self.base.values()], []) + def hamming_dist(self, A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) - - - - - def abduction(self, pred_res, key, max_address_num, require_more_address): - candidates = [] - for address_num in range(len(pred_res) + 1): - if address_num == 0: - if abs(self.logic_forward(pred_res) - key) <= 1e-3: - candidates.append(pred_res) - else: - new_candidates = self.address(address_num, pred_res, key) - candidates += new_candidates - - if len(candidates) > 0: - min_address_num = address_num - break - - if address_num >= max_address_num: - return [], 0, 0 - - for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): - if address_num > max_address_num: - return candidates, min_address_num, address_num - 1 - new_candidates = self.address(address_num, pred_res, key) - candidates += new_candidates - return candidates, min_address_num, address_num def address(self, address_num, pred_res, key): new_candidates = [] @@ -214,9 +195,6 @@ class hwf_KB(ClsKB): return round(eval(''.join(formula)), 2) - - - class prolog_KB(KBBase): def __init__(self, pseudo_label_list): super().__init__() @@ -224,16 +202,29 @@ class prolog_KB(KBBase): self.prolog = pyswip.Prolog() for i in self.pseudo_label_list: self.prolog.assertz("pseudo_label(%s)" % i) - self.prolog_flag = True def logic_forward(self): pass - def abduce_candidates(self, pred_res, key, max_address_num = -1, require_more_address = 0): - if max_address_num == -1: - max_address_num = len(pred_res) - all_candidates = self.get_candidates_prolog(key) - return self.filter_all_candidates(pred_res, all_candidates, max_address_num, require_more_address) + def abduce_candidates(self, pred_res, key, max_address_num, require_more_address): + return self.abduction(pred_res, key, max_address_num, require_more_address) + + def address(self, address_num, pred_res, key): + new_candidates = [] + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + for address_idx in address_idx_list: + query_string = "addition(" + for idx, i in enumerate(pred_res): + tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' + query_string += tmp + query_string += "%s)." + abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string % key))] + for c in abduce_c: + candidate = pred_res.copy() + for i, idx in enumerate(address_idx): + candidate[idx] = c[i] + new_candidates.append(candidate) + return new_candidates class add_prolog_KB(prolog_KB): @@ -301,42 +292,7 @@ class RegKB(KBBase): import time if __name__ == "__main__": - # With ground KB - kb = add_KB(GKB_flag = True) - print('len(kb):', len(kb)) - res = kb.get_candidates_GKB(0) - print(res) - res = kb.get_candidates_GKB(18) - print(res) - res = kb.get_candidates_GKB(18) - print(res) - res = kb.get_candidates_GKB(16) - print(res) - print() - - # Without ground KB - kb = add_KB() - print('len(kb):', len(kb)) - print() - - # Prolog - kb = add_prolog_KB() - print(kb.logic_forward([3, 4])) - res = kb.get_candidates_prolog(16) - print(res) - - start = time.time() - kb = hwf_KB(GKB_flag = True, len_list = [1, 3, 5]) - print(time.time() - start) - print('len(kb):', len(kb)) - res = kb.get_candidates_GKB(2, length = 1) - print(res) - res = kb.get_candidates_GKB(1, length = 3) - print(res) - res = kb.get_candidates_GKB(3.67, length = 5) - print(res) - print() - + pass # X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] From 20146ee8dd710dbf475d2a9250b44394d92a0436 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 24 Nov 2022 21:42:27 +0800 Subject: [PATCH 085/601] Update abducer_base.py --- abducer/abducer_base.py | 40 ++++++++++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 14 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index f26b363..93810c1 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -17,7 +17,6 @@ import abc from abducer.kb import add_KB, hwf_KB, add_prolog_KB import numpy as np -from itertools import product, combinations import time class AbducerBase(abc.ABC): @@ -26,7 +25,7 @@ class AbducerBase(abc.ABC): assert(dist_func == 'hamming' or dist_func == 'confidence') self.dist_func = dist_func self.cache = cache - + if self.cache: self.cache_min_address_num = {} self.cache_candidates = {} @@ -83,21 +82,23 @@ class AbducerBase(abc.ABC): def abduce(self, data, max_address_num = -1, require_more_address = 0): pred_res, pred_res_prob, ans = data + if max_address_num == -1: + max_address_num = len(pred_res) if self.cache and (tuple(pred_res), ans) in self.cache_min_address_num: address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), ans)] + require_more_address) if (tuple(pred_res), ans, address_num) in self.cache_candidates: candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] - candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) - return candidate + return self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, ans, max_address_num, require_more_address) + if self.cache: self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates - - candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + + candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) return candidate @@ -112,23 +113,34 @@ class AbducerBase(abc.ABC): if __name__ == '__main__': + prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0],[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] + prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0],[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] + kb = add_KB(GKB_flag = True) - abd = AbducerBase(kb, 'hamming') - res = abd.abduce(([1, 1], None, 17), max_address_num = 2, require_more_address = 0) + abd = AbducerBase(kb, 'confidence') + res = abd.abduce(([1, 1], prob1, 8), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], prob2, 8), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], prob1, 17), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([1, 1], None, 17), max_address_num = 1, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 17), max_address_num = 1, require_more_address = 0) print(res) - res = abd.abduce(([1, 1], None, 20), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 20), max_address_num = 2, require_more_address = 0) print(res) print() kb = add_prolog_KB() - abd = AbducerBase(kb, 'hamming') - res = abd.abduce(([1, 1], None, 17), max_address_num = 2, require_more_address = 0) + abd = AbducerBase(kb, 'confidence') + res = abd.abduce(([1, 1], prob1, 8), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], prob2, 8), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], prob1, 17), max_address_num = 2, require_more_address = 0) print(res) - res = abd.abduce(([1, 1], None, 17), max_address_num = 1, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 17), max_address_num = 1, require_more_address = 0) print(res) - res = abd.abduce(([1, 1], None, 20), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 20), max_address_num = 2, require_more_address = 0) print(res) print() From 880b2a1800dc9f13ded22cc18395a317dcb5cad5 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 24 Nov 2022 21:44:12 +0800 Subject: [PATCH 086/601] Update kb.py --- abducer/kb.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 63c4959..78ebb10 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -234,9 +234,6 @@ class add_prolog_KB(prolog_KB): def logic_forward(self, nums): return list(self.prolog.query("addition(%s, %s, Res)." %(nums[0], nums[1])))[0]['Res'] - - def get_candidates_prolog(self, key): - return [(z['Z1'], z['Z2']) for z in list(self.prolog.query("addition(Z1, Z2, %s)." % key))] class RegKB(KBBase): From 986e38b3a24072a510f5c56ee3773030f841a846 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 25 Nov 2022 09:54:04 +0800 Subject: [PATCH 087/601] Update kb.py --- abducer/kb.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 78ebb10..2415d6f 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -213,11 +213,7 @@ class prolog_KB(KBBase): new_candidates = [] address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) for address_idx in address_idx_list: - query_string = "addition(" - for idx, i in enumerate(pred_res): - tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' - query_string += tmp - query_string += "%s)." + query_string = self.get_query_string(pred_res, address_idx) abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string % key))] for c in abduce_c: candidate = pred_res.copy() @@ -234,6 +230,15 @@ class add_prolog_KB(prolog_KB): def logic_forward(self, nums): return list(self.prolog.query("addition(%s, %s, Res)." %(nums[0], nums[1])))[0]['Res'] + + def get_query_string(self, pred_res, address_idx): + query_string = "addition(" + for idx, i in enumerate(pred_res): + tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' + query_string += tmp + query_string += "%s)." + return query_string + class RegKB(KBBase): From c6cd213dc8f9b24585970e244544a1da6b140073 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 25 Nov 2022 12:26:50 +0800 Subject: [PATCH 088/601] Update kb.py --- abducer/kb.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 2415d6f..0fdfb04 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -132,18 +132,17 @@ class ClsKB(KBBase): - def address(self, address_num, pred_res, key): + def address(self, address_num, pred_res, key): new_candidates = [] all_address_candidate = self.all_address_candidate_dict[address_num] address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) for address_idx in address_idx_list: for c in all_address_candidate: - address_list = [pred_res[i] for i in address_idx] - if(sum([address_list[i] == c[i] for i in range(address_num)]) == 0): - candidate = pred_res.copy() - for i, idx in enumerate(address_idx): - candidate[idx] = c[i] - if self.logic_forward(candidate) == key: + candidate = pred_res.copy() + for i, idx in enumerate(address_idx): + candidate[idx] = c[i] + if self.logic_forward(candidate) == key: + if(sum(pred_res[idx] != candidate[idx] for idx in range(len(pred_res))) == address_num): new_candidates.append(candidate) return new_candidates @@ -219,7 +218,8 @@ class prolog_KB(KBBase): candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - new_candidates.append(candidate) + if(sum(pred_res[idx] != candidate[idx] for idx in range(len(pred_res))) == address_num): + new_candidates.append(candidate) return new_candidates From c74e7c4b9f240c761cfc964ecc5579cb0a2da771 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 25 Nov 2022 12:32:05 +0800 Subject: [PATCH 089/601] Update kb.py --- abducer/kb.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 0fdfb04..9ff7652 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -142,7 +142,7 @@ class ClsKB(KBBase): for i, idx in enumerate(address_idx): candidate[idx] = c[i] if self.logic_forward(candidate) == key: - if(sum(pred_res[idx] != candidate[idx] for idx in range(len(pred_res))) == address_num): + if sum(pred_res[idx] != candidate[idx] for idx in range(len(pred_res))) == address_num: new_candidates.append(candidate) return new_candidates @@ -218,7 +218,7 @@ class prolog_KB(KBBase): candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - if(sum(pred_res[idx] != candidate[idx] for idx in range(len(pred_res))) == address_num): + if sum(pred_res[idx] != candidate[idx] for idx in range(len(pred_res))) == address_num: new_candidates.append(candidate) return new_candidates From 3b8f77e4ff91aa52390a7853d90adcb3cde14981 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 5 Dec 2022 16:33:36 +0800 Subject: [PATCH 090/601] Add zoopt option --- abducer/kb.py | 103 +++++++++++++++++++++++++++++++++----------------- 1 file changed, 69 insertions(+), 34 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 9ff7652..7ca8a25 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -20,6 +20,7 @@ from itertools import product, combinations import pyswip + class KBBase(ABC): def __init__(self): pass @@ -31,10 +32,20 @@ class KBBase(ABC): @abstractmethod def abduce_candidates(self): pass - + + + def address(self, address_num, pred_res, key): + new_candidates = [] + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + + for address_idx in address_idx_list: + candidates = self.address_by_idx(pred_res, key, address_idx) + new_candidates += candidates + return new_candidates def abduction(self, pred_res, key, max_address_num, require_more_address): candidates = [] + for address_num in range(len(pred_res) + 1): if address_num == 0: if abs(self.logic_forward(pred_res) - key) <= 1e-3: @@ -106,6 +117,11 @@ class ClsKB(KBBase): + def hamming_dist(self, A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) + def abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): if self.base == {} or len(pred_res) not in self.len_list: return [] @@ -124,28 +140,18 @@ class ClsKB(KBBase): candidates = [all_candidates[idx] for idx in idxs] return candidates, min_address_num, address_num - - def hamming_dist(self, A, B): - B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) - - def address(self, address_num, pred_res, key): - new_candidates = [] - all_address_candidate = self.all_address_candidate_dict[address_num] - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) - for address_idx in address_idx_list: - for c in all_address_candidate: - candidate = pred_res.copy() - for i, idx in enumerate(address_idx): - candidate[idx] = c[i] - if self.logic_forward(candidate) == key: - if sum(pred_res[idx] != candidate[idx] for idx in range(len(pred_res))) == address_num: - new_candidates.append(candidate) - return new_candidates - + def address_by_idx(self, pred_res, key, address_idx): + candidates = [] + abduce_c = self.all_address_candidate_dict[len(address_idx)] + for c in abduce_c: + candidate = pred_res.copy() + for i, idx in enumerate(address_idx): + candidate[idx] = c[i] + if self.logic_forward(candidate) == key: + candidates.append(candidate) + return candidates def _dict_len(self, dic): @@ -208,19 +214,32 @@ class prolog_KB(KBBase): def abduce_candidates(self, pred_res, key, max_address_num, require_more_address): return self.abduction(pred_res, key, max_address_num, require_more_address) - def address(self, address_num, pred_res, key): - new_candidates = [] - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) - for address_idx in address_idx_list: - query_string = self.get_query_string(pred_res, address_idx) - abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string % key))] - for c in abduce_c: - candidate = pred_res.copy() - for i, idx in enumerate(address_idx): - candidate[idx] = c[i] - if sum(pred_res[idx] != candidate[idx] for idx in range(len(pred_res))) == address_num: - new_candidates.append(candidate) - return new_candidates + + + def address_by_idx(self, pred_res, key, address_idx, verbose=True): + candidates = [] + query_string = self.get_query_string(pred_res, address_idx) + abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string % key))] + for c in abduce_c: + candidate = pred_res.copy() + for i, idx in enumerate(address_idx): + candidate[idx] = c[i] + candidates.append(candidate) + return candidates + + def address_by_idx2(self, pred_res, key, address_idx): + candidates = [] + query_string = self.get_query_string(pred_res, address_idx) + abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string % key))] + # print('abduce_c:', abduce_c) + for c in abduce_c: + candidate = pred_res.copy() + for i, idx in enumerate(address_idx): + candidate[idx] = c[i] + candidates.append(candidate) + return candidates + + class add_prolog_KB(prolog_KB): @@ -240,6 +259,22 @@ class add_prolog_KB(prolog_KB): return query_string +class hed_prolog_KB(prolog_KB): + def __init__(self, pseudo_label_list = list(range(10))): + super().__init__(pseudo_label_list) + self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") + + def logic_forward(self, nums): + return list(self.prolog.query("addition(%s, %s, Res)." %(nums[0], nums[1])))[0]['Res'] + + def get_query_string(self, pred_res, address_idx): + query_string = "addition(" + for idx, i in enumerate(pred_res): + tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' + query_string += tmp + query_string += "%s)." + return query_string + class RegKB(KBBase): def __init__(self, GKB_flag = False, X = None, Y = None): From 4a217b71b7adc13abc8a3318c4c6df88dc6d2448 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 5 Dec 2022 16:33:53 +0800 Subject: [PATCH 091/601] Add zoopt option --- abducer/abducer_base.py | 95 ++++++++++++++++++++++++++++------------- 1 file changed, 66 insertions(+), 29 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 93810c1..ce4601b 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -16,19 +16,22 @@ sys.path.append("..") import abc from abducer.kb import add_KB, hwf_KB, add_prolog_KB import numpy as np +from zoopt import Dimension, Objective, Parameter, Opt import time class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func = 'confidence', cache = True): + def __init__(self, kb, dist_func = 'confidence', zoopt = False, cache = True): self.kb = kb assert(dist_func == 'hamming' or dist_func == 'confidence') self.dist_func = dist_func self.cache = cache - + self.zoopt = zoopt + if self.cache: self.cache_min_address_num = {} self.cache_candidates = {} + def hamming_dist(self, A, B): B = np.array(B) @@ -66,38 +69,58 @@ class AbducerBase(abc.ABC): min_address_num = np.min(cost_list) idxs = np.where(cost_list == min_address_num)[0] return [candidates[idx] for idx in idxs][0] + + + + def zoopt_address_score(self, pred_res, key, address_idx): + candidates = self.kb.address_by_idx2(pred_res, key, address_idx) + return 0 if len(candidates) > 0 else 1 - def filter_all_candidates(self, pred_res, all_candidates, max_address_num, require_more_address): - if len(all_candidates) == 0: - candidates = [] - min_address_num = 0 - address_num = 0 - else: - cost_list = self.hamming_dist(pred_res, all_candidates) - min_address_num = np.min(cost_list) - address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(cost_list <= address_num)[0] - candidates = [all_candidates[idx] for idx in idxs] - return candidates, min_address_num, address_num + def constraint_address_num(self, solution, max_address_num): + x = solution.get_x() + return max_address_num - x.sum() + + def zoopt_get_address_idx(self, pred_res, key, max_address_num): + dimension = Dimension(size=len(pred_res), + regs=[[0, 1]] * len(pred_res), + tys=[False] * len(pred_res)) + objective = Objective(lambda sol: self.zoopt_address_score(pred_res, key, [idx for idx, i in enumerate(sol.get_x()) if i != 0]), + dim=dimension, + constraint=lambda sol: self.constraint_address_num(sol, max_address_num)) + parameter = Parameter(budget=100 * dimension.get_size(), autoset=True) + solution = Opt.min(objective, parameter).get_x() + + address_idx = [idx for idx, i in enumerate(solution) if i != 0] + address_num = solution.sum() + + return address_idx, address_num + + + def abduce(self, data, max_address_num = -1, require_more_address = 0): - pred_res, pred_res_prob, ans = data + pred_res, pred_res_prob, key = data if max_address_num == -1: max_address_num = len(pred_res) - - if self.cache and (tuple(pred_res), ans) in self.cache_min_address_num: - address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), ans)] + require_more_address) - if (tuple(pred_res), ans, address_num) in self.cache_candidates: - candidates = self.cache_candidates[(tuple(pred_res), ans, address_num)] + + if self.cache and (tuple(pred_res), key) in self.cache_min_address_num: + address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) + if (tuple(pred_res), key, address_num) in self.cache_candidates: + candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] return self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) - candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, ans, max_address_num, require_more_address) - - + if self.zoopt: + address_idx, address_num = self.zoopt_get_address_idx(pred_res, key, max_address_num) + candidates = self.kb.address_by_idx(pred_res, key, address_idx) + min_address_num = address_num + else: + candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, key, max_address_num, require_more_address) + + if self.cache: - self.cache_min_address_num[(tuple(pred_res), ans)] = min_address_num - self.cache_candidates[(tuple(pred_res), ans, address_num)] = candidates - + self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num + self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates + candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) return candidate @@ -113,8 +136,8 @@ class AbducerBase(abc.ABC): if __name__ == '__main__': - prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0],[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] - prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0],[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] + prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] + prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] kb = add_KB(GKB_flag = True) abd = AbducerBase(kb, 'confidence') @@ -144,6 +167,20 @@ if __name__ == '__main__': print(res) print() + kb = add_prolog_KB() + abd = AbducerBase(kb, 'confidence', zoopt = True) + res = abd.abduce(([1, 1], prob1, 8), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], prob2, 8), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], prob1, 17), max_address_num = 2, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], prob1, 17), max_address_num = 1, require_more_address = 0) + print(res) + res = abd.abduce(([1, 1], prob1, 20), max_address_num = 2, require_more_address = 0) + print(res) + print() + kb = hwf_KB(len_list = [1, 3, 5]) abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) @@ -156,4 +193,4 @@ if __name__ == '__main__': print(res) print() - + From 6d247b41fb8c667885ee7069b0c4380aa2e7392b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 5 Dec 2022 16:35:11 +0800 Subject: [PATCH 092/601] Update abducer_base.py --- abducer/abducer_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index ce4601b..0a7f6cb 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -73,7 +73,7 @@ class AbducerBase(abc.ABC): def zoopt_address_score(self, pred_res, key, address_idx): - candidates = self.kb.address_by_idx2(pred_res, key, address_idx) + candidates = self.kb.address_by_idx(pred_res, key, address_idx) return 0 if len(candidates) > 0 else 1 def constraint_address_num(self, solution, max_address_num): From 19c6c346b86e2b073d3e2424f42650ce3a54ea12 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 5 Dec 2022 16:35:45 +0800 Subject: [PATCH 093/601] Add zoopt option --- abducer/kb.py | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 7ca8a25..4a0c9a0 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -226,20 +226,7 @@ class prolog_KB(KBBase): candidate[idx] = c[i] candidates.append(candidate) return candidates - - def address_by_idx2(self, pred_res, key, address_idx): - candidates = [] - query_string = self.get_query_string(pred_res, address_idx) - abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string % key))] - # print('abduce_c:', abduce_c) - for c in abduce_c: - candidate = pred_res.copy() - for i, idx in enumerate(address_idx): - candidate[idx] = c[i] - candidates.append(candidate) - return candidates - - + class add_prolog_KB(prolog_KB): From 279cb7a703aac1278b6aecb8c0b23eb35d2f11a2 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 5 Dec 2022 16:36:08 +0800 Subject: [PATCH 094/601] Add zoopt option --- abducer/abducer_base.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 0a7f6cb..d0baf1f 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -51,8 +51,7 @@ class AbducerBase(abc.ABC): cols = np.array(range(len(B[0]))) cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) return 1 - np.prod(A[rows, cols, B], axis = 1) - - + def get_cost_list(self, pred_res, pred_res_prob, candidates): if self.dist_func == 'hamming': return self.hamming_dist(pred_res, candidates) From 0f1307dd10636e8f48b50c018d1d652acec72435 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 5 Dec 2022 16:40:55 +0800 Subject: [PATCH 095/601] Add zoopt option --- abducer/kb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abducer/kb.py b/abducer/kb.py index 4a0c9a0..db2bf6f 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -89,7 +89,7 @@ class ClsKB(KBBase): self.base.setdefault(len(x), defaultdict(list))[y].append(x) else: self.all_address_candidate_dict = {} - for address_num in range(1, max(self.len_list) + 1): + for address_num in range(max(self.len_list) + 1): self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat = address_num)) def get_GKB(self, pseudo_label_list, len_list): From c4ab9843d307fe4dd2d8279e941794b5d9f348ac Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 5 Dec 2022 16:52:17 +0800 Subject: [PATCH 096/601] Add zoopt option --- abducer/abducer_base.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index d0baf1f..e2072ec 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -106,21 +106,26 @@ class AbducerBase(abc.ABC): address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) if (tuple(pred_res), key, address_num) in self.cache_candidates: candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] - return self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + if self.zoopt: + return candidates[0] + else: + return self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) if self.zoopt: address_idx, address_num = self.zoopt_get_address_idx(pred_res, key, max_address_num) candidates = self.kb.address_by_idx(pred_res, key, address_idx) min_address_num = address_num + candidate = candidates[0] else: candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, key, max_address_num, require_more_address) + candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) if self.cache: self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates - candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + return candidate @@ -138,7 +143,7 @@ if __name__ == '__main__': prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] - kb = add_KB(GKB_flag = True) + kb = add_KB() abd = AbducerBase(kb, 'confidence') res = abd.abduce(([1, 1], prob1, 8), max_address_num = 2, require_more_address = 0) print(res) @@ -192,4 +197,3 @@ if __name__ == '__main__': print(res) print() - From 97daae2839fccc65be2c84b7481c22207eaa6d33 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 5 Dec 2022 20:47:32 +0800 Subject: [PATCH 097/601] Update readme.md --- readme.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/readme.md b/readme.md index 1a9ebf1..e3726a9 100644 --- a/readme.md +++ b/readme.md @@ -24,10 +24,10 @@ They can only be used for academic purpose. For other purposes, please contact w ## To do list -- Improve speed and accuracy -- Add comparison with DeepProbLog, NGS,... (Accuracy and Speed) -- Add Inference/Abduction example with FOL engine (e.g., Prolog) -- Add zoopt optimization -- Rearrange structure and make it a python package -- Documents +- [ ] Improve speed and accuracy +- [ ] Add comparison with DeepProbLog, NGS,... (Accuracy and Speed) +- [x] Add Inference/Abduction example with FOL engine (e.g., Prolog) +- [x] Add zoopt optimization +- [ ] Rearrange structure and make it a python package +- [ ] Documents From c4c2f7e030ca5cd794facf2aa0e039a619e504e1 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 12:51:45 +0800 Subject: [PATCH 098/601] Add kb and abducer for HED --- abducer/kb.py | 133 +++++++++++++++++++++++++++++++++++++------------- 1 file changed, 98 insertions(+), 35 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index db2bf6f..9ae8efd 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -22,7 +22,7 @@ import pyswip class KBBase(ABC): - def __init__(self): + def __init__(self, pseudo_label_list = None): pass @abstractmethod @@ -34,16 +34,16 @@ class KBBase(ABC): pass - def address(self, address_num, pred_res, key): + def address(self, address_num, pred_res, key, multiple_predictions = False): new_candidates = [] address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) for address_idx in address_idx_list: - candidates = self.address_by_idx(pred_res, key, address_idx) + candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) new_candidates += candidates return new_candidates - def abduction(self, pred_res, key, max_address_num, require_more_address): + def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions = False): candidates = [] for address_num in range(len(pred_res) + 1): @@ -51,7 +51,7 @@ class KBBase(ABC): if abs(self.logic_forward(pred_res) - key) <= 1e-3: candidates.append(pred_res) else: - new_candidates = self.address(address_num, pred_res, key) + new_candidates = self.address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates if len(candidates) > 0: @@ -64,12 +64,30 @@ class KBBase(ABC): for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if address_num > max_address_num: return candidates, min_address_num, address_num - 1 - new_candidates = self.address(address_num, pred_res, key) + new_candidates = self.address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates return candidates, min_address_num, address_num + # for multiple predictions, modify from `learn_add.py` + def flatten(self, l): + return [item for sublist in l for item in sublist] + + # for multiple predictions, modify from `learn_add.py` + def reform_ids(self, flatten_pred_res, save_pred_res): + re = [] + i = 0 + for e in save_pred_res: + j = 0 + ids = [] + while j < len(e): + ids.append(flatten_pred_res[i + j]) + j += 1 + re.append(ids) + i = i + j + return re + def __len__(self): pass @@ -82,7 +100,6 @@ class ClsKB(KBBase): self.prolog_flag = False if GKB_flag: - # self.base = np.load('abducer/hwf.npy', allow_pickle=True).item() self.base = {} X, Y = self.get_GKB(self.pseudo_label_list, self.len_list) for x, y in zip(X, Y): @@ -109,11 +126,11 @@ class ClsKB(KBBase): def logic_forward(self): pass - def abduce_candidates(self, pred_res, key, max_address_num = -1, require_more_address = 0): + def abduce_candidates(self, pred_res, key, max_address_num = -1, require_more_address = 0, multiple_predictions = False): if self.GKB_flag: return self.abduce_from_GKB(pred_res, key, max_address_num, require_more_address) else: - return self.abduction(pred_res, key, max_address_num, require_more_address) + return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) @@ -142,14 +159,23 @@ class ClsKB(KBBase): return candidates, min_address_num, address_num - def address_by_idx(self, pred_res, key, address_idx): + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions = False): candidates = [] abduce_c = self.all_address_candidate_dict[len(address_idx)] + + if multiple_predictions: + save_pred_res = pred_res + pred_res = self.flatten(pred_res) + for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - if self.logic_forward(candidate) == key: + + if multiple_predictions: + candidate = self.reform_ids(candidate, save_pred_res) + + if self.logic_forward(candidate) == key: candidates.append(candidate) return candidates @@ -176,7 +202,7 @@ class add_KB(ClsKB): def logic_forward(self, nums): return sum(nums) -class hwf_KB(ClsKB): +class HWF_KB(ClsKB): def __init__(self, GKB_flag = False, \ pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], \ len_list = [1, 3, 5, 7]): @@ -205,62 +231,99 @@ class prolog_KB(KBBase): super().__init__() self.pseudo_label_list = pseudo_label_list self.prolog = pyswip.Prolog() - for i in self.pseudo_label_list: - self.prolog.assertz("pseudo_label(%s)" % i) + def logic_forward(self): pass - def abduce_candidates(self, pred_res, key, max_address_num, require_more_address): - return self.abduction(pred_res, key, max_address_num, require_more_address) + def abduce_candidates(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) - def address_by_idx(self, pred_res, key, address_idx, verbose=True): + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions = False): candidates = [] - query_string = self.get_query_string(pred_res, address_idx) - abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string % key))] + + if not multiple_predictions: + query_string = self.get_query_string(pred_res, key, address_idx) + else: + query_string = self.get_query_string_need_flatten(pred_res, key, address_idx) + + if multiple_predictions: + save_pred_res = pred_res + pred_res = self.flatten(pred_res) + + abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string))] for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] + + if multiple_predictions: + candidate = self.reform_ids(candidate, save_pred_res) + candidates.append(candidate) - return candidates - + return candidates class add_prolog_KB(prolog_KB): def __init__(self, pseudo_label_list = list(range(10))): super().__init__(pseudo_label_list) + for i in self.pseudo_label_list: + self.prolog.assertz("pseudo_label(%s)" % i) self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") def logic_forward(self, nums): return list(self.prolog.query("addition(%s, %s, Res)." %(nums[0], nums[1])))[0]['Res'] - def get_query_string(self, pred_res, address_idx): + def get_query_string(self, pred_res, key, address_idx): query_string = "addition(" for idx, i in enumerate(pred_res): tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' query_string += tmp - query_string += "%s)." + query_string += "%s)." % key return query_string -class hed_prolog_KB(prolog_KB): - def __init__(self, pseudo_label_list = list(range(10))): +class HED_prolog_KB(prolog_KB): + def __init__(self, pseudo_label_list = [0, 1, '+', '=']): super().__init__(pseudo_label_list) - self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") + self.prolog.consult('../datasets/hed/learn_add.pl') - def logic_forward(self, nums): - return list(self.prolog.query("addition(%s, %s, Res)." %(nums[0], nums[1])))[0]['Res'] + # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` + def logic_forward(self, exs): + return len(list(self.prolog.query("abduce_consistent_insts(%s)." % exs))) != 0 - def get_query_string(self, pred_res, address_idx): - query_string = "addition(" - for idx, i in enumerate(pred_res): - tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' - query_string += tmp - query_string += "%s)." - return query_string + + def get_query_string_need_flatten(self, pred_res, key, address_idx): + # flatten + flatten_pred_res = self.flatten(pred_res) + # add variables for prolog + for idx in range(len(flatten_pred_res)): + if idx in address_idx: + flatten_pred_res[idx] = 'X' + str(idx) + # unflatten + new_pred_res = self.reform_ids(flatten_pred_res, pred_res) + + query_string = "abduce_consistent_insts(%s)." % new_pred_res + return query_string.replace("'", "").replace("+", "'+'").replace("=", "'='") + + + + def consist_rule(self, exs, rules): + rule_str = "%s" % rules + rule_str = rule_str.replace("'", "") + return len(list(self.prolog.query("consistent_inst_feature(%s, %s)." %(exs, rule_str)))) != 0 + + def abduce_rules(self, pred_res): + prolog_rules = list(self.prolog.query("consistent_inst_feature(%s, X)." % pred_res))[0]['X'] + rules = [] + for rule in prolog_rules: + rules.append(rule.value) + return rules + + # def consist_rules(self, pred_res, rules): + class RegKB(KBBase): From 2587ac2f7e4d64b2539c10d97ff04927970a0e26 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 12:52:14 +0800 Subject: [PATCH 099/601] Add kb and abducer for HED --- abducer/abducer_base.py | 80 +++++++++++++++++++++++++++++------------ 1 file changed, 57 insertions(+), 23 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index e2072ec..8fde5a1 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -14,19 +14,20 @@ import sys sys.path.append("..") import abc -from abducer.kb import add_KB, hwf_KB, add_prolog_KB +from abducer.kb import add_KB, HWF_KB, add_prolog_KB, HED_prolog_KB import numpy as np from zoopt import Dimension, Objective, Parameter, Opt import time class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func = 'confidence', zoopt = False, cache = True): + def __init__(self, kb, dist_func = 'confidence', zoopt = False, multiple_predictions = False, cache = True): self.kb = kb assert(dist_func == 'hamming' or dist_func == 'confidence') self.dist_func = dist_func - self.cache = cache self.zoopt = zoopt + self.multiple_predictions = multiple_predictions + self.cache = cache if self.cache: self.cache_min_address_num = {} @@ -58,10 +59,10 @@ class AbducerBase(abc.ABC): elif self.dist_func == 'confidence': return self.confidence_dist(pred_res_prob, candidates) - def get_min_cost_candidate(self, pred_res, pred_res_prob, candidates): + def get_one_candidate(self, pred_res, pred_res_prob, candidates): if len(candidates) == 0: return [] - elif len(candidates) == 1: + elif len(candidates) == 1 or self.zoopt: return candidates[0] else: cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) @@ -71,8 +72,19 @@ class AbducerBase(abc.ABC): + + # for multiple_prediction + def flatten(self, l): + if self.multiple_predictions: + return [item for sublist in l for item in sublist] + else: + return l + + + + # for zoopt def zoopt_address_score(self, pred_res, key, address_idx): - candidates = self.kb.address_by_idx(pred_res, key, address_idx) + candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) return 0 if len(candidates) > 0 else 1 def constraint_address_num(self, solution, max_address_num): @@ -80,9 +92,9 @@ class AbducerBase(abc.ABC): return max_address_num - x.sum() def zoopt_get_address_idx(self, pred_res, key, max_address_num): - dimension = Dimension(size=len(pred_res), - regs=[[0, 1]] * len(pred_res), - tys=[False] * len(pred_res)) + dimension = Dimension(size=len(self.flatten(pred_res)), + regs=[[0, 1]] * len(self.flatten(pred_res)), + tys=[False] * len(self.flatten(pred_res))) objective = Objective(lambda sol: self.zoopt_address_score(pred_res, key, [idx for idx, i in enumerate(sol.get_x()) if i != 0]), dim=dimension, constraint=lambda sol: self.constraint_address_num(sol, max_address_num)) @@ -90,7 +102,7 @@ class AbducerBase(abc.ABC): solution = Opt.min(objective, parameter).get_x() address_idx = [idx for idx, i in enumerate(solution) if i != 0] - address_num = solution.sum() + address_num = int(solution.sum()) return address_idx, address_num @@ -102,32 +114,34 @@ class AbducerBase(abc.ABC): if max_address_num == -1: max_address_num = len(pred_res) - if self.cache and (tuple(pred_res), key) in self.cache_min_address_num: - address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) - if (tuple(pred_res), key, address_num) in self.cache_candidates: - candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] + if self.cache and (tuple(self.flatten(pred_res)), key) in self.cache_min_address_num: + address_num = min(max_address_num, self.cache_min_address_num[(tuple(self.flatten(pred_res)), key)] + require_more_address) + if (tuple(self.flatten(pred_res)), key, address_num) in self.cache_candidates: + candidates = self.cache_candidates[(tuple(self.flatten(pred_res)), key, address_num)] if self.zoopt: return candidates[0] else: - return self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + return self.get_one_candidate(pred_res, pred_res_prob, candidates) if self.zoopt: address_idx, address_num = self.zoopt_get_address_idx(pred_res, key, max_address_num) - candidates = self.kb.address_by_idx(pred_res, key, address_idx) + candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) min_address_num = address_num - candidate = candidates[0] else: - candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, key, max_address_num, require_more_address) - candidate = self.get_min_cost_candidate(pred_res, pred_res_prob, candidates) + candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, key, max_address_num, require_more_address, self.multiple_predictions) + + candidate = self.get_one_candidate(pred_res, pred_res_prob, candidates) if self.cache: - self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num - self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates + self.cache_min_address_num[(tuple(self.flatten(pred_res)), key)] = min_address_num + self.cache_candidates[(tuple(self.flatten(pred_res)), key, address_num)] = candidates - return candidate + def abduce_rules(self, pred_res): + return self.kb.abduce_rules(pred_res) + def batch_abduce(self, Z, Y, max_address_num = -1, require_more_address = 0): return [ @@ -185,7 +199,7 @@ if __name__ == '__main__': print(res) print() - kb = hwf_KB(len_list = [1, 3, 5]) + kb = HWF_KB(len_list = [1, 3, 5]) abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) print(res) @@ -197,3 +211,23 @@ if __name__ == '__main__': print(res) print() + kb = HED_prolog_KB() + abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) + consist_re = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] + consist_re2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules + inconsist_re = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] + rules = ['my_op([0], [0], [1, 1])', 'my_op([1], [1], [0])', 'my_op([1], [0], [0])'] + + print(kb.logic_forward(consist_re), kb.logic_forward(inconsist_re)) + print(kb.consist_rule(consist_re, rules), kb.consist_rule(consist_re2, rules)) + print() + + res = abd.abduce((consist_re, None, None)) + print(res) + res = abd.abduce((inconsist_re, None, None)) + print(res) + print() + + abduced_rules = abd.abduce_rules(consist_re) + print(abduced_rules) + From 734a81834a197e7fe2181d2dbc4bd2847f99f866 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 12:56:27 +0800 Subject: [PATCH 100/601] Add kb and abducer for HED --- abducer/abducer_base.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 8fde5a1..322f200 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -213,21 +213,21 @@ if __name__ == '__main__': kb = HED_prolog_KB() abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) - consist_re = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] - consist_re2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules - inconsist_re = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] + consist_exs = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] + consist_exs2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules + inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] rules = ['my_op([0], [0], [1, 1])', 'my_op([1], [1], [0])', 'my_op([1], [0], [0])'] - print(kb.logic_forward(consist_re), kb.logic_forward(inconsist_re)) - print(kb.consist_rule(consist_re, rules), kb.consist_rule(consist_re2, rules)) + print(kb.logic_forward(consist_exs), kb.logic_forward(inconsist_exs)) + print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) print() - res = abd.abduce((consist_re, None, None)) + res = abd.abduce((consist_exs, None, None)) print(res) - res = abd.abduce((inconsist_re, None, None)) + res = abd.abduce((inconsist_exs, None, None)) print(res) print() - abduced_rules = abd.abduce_rules(consist_re) + abduced_rules = abd.abduce_rules(consist_exs) print(abduced_rules) From 22b11315a1af851440fd858a28867fae2e961f51 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 13:26:22 +0800 Subject: [PATCH 101/601] Update kb.py --- abducer/kb.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/abducer/kb.py b/abducer/kb.py index 9ae8efd..4bcf448 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -43,12 +43,18 @@ class KBBase(ABC): new_candidates += candidates return new_candidates + def correct_result(self, pred_res, key): + if type(key) == int: + return abs(self.logic_forward(pred_res) - key) <= 1e-3 + else: + return self.logic_forward(pred_res) == key + def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions = False): candidates = [] for address_num in range(len(pred_res) + 1): if address_num == 0: - if abs(self.logic_forward(pred_res) - key) <= 1e-3: + if self.correct_result(pred_res, key): candidates.append(pred_res) else: new_candidates = self.address(address_num, pred_res, key, multiple_predictions) From 4ed250ea19a01fd808308a29559d8a0970af2325 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 13:27:08 +0800 Subject: [PATCH 102/601] Update abducer_base.py --- abducer/abducer_base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 322f200..3dd4822 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -222,9 +222,9 @@ if __name__ == '__main__': print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) print() - res = abd.abduce((consist_exs, None, None)) + res = abd.abduce((consist_exs, True, None)) print(res) - res = abd.abduce((inconsist_exs, None, None)) + res = abd.abduce((inconsist_exs, True, None)) print(res) print() From 6b1ca5d9d9bd0f64dda7a5cdc02d03100caac437 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 13:35:42 +0800 Subject: [PATCH 103/601] Fix bugs for non-zoopt option --- abducer/abducer_base.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 3dd4822..5060529 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -212,7 +212,7 @@ if __name__ == '__main__': print() kb = HED_prolog_KB() - abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) + abd = AbducerBase(kb, zoopt=False, multiple_predictions=True) consist_exs = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] consist_exs2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] @@ -222,9 +222,9 @@ if __name__ == '__main__': print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) print() - res = abd.abduce((consist_exs, True, None)) + res = abd.abduce((consist_exs, None, True)) print(res) - res = abd.abduce((inconsist_exs, True, None)) + res = abd.abduce((inconsist_exs, None, True)) print(res) print() From 01ac0b8dd1fce2e15c4af97a53c5892f7f7a4d4b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 13:36:00 +0800 Subject: [PATCH 104/601] Fix bugs for non-zoopt option --- abducer/kb.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 4bcf448..7ade7f9 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -36,7 +36,10 @@ class KBBase(ABC): def address(self, address_num, pred_res, key, multiple_predictions = False): new_candidates = [] - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + if not multiple_predictions: + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + else: + address_idx_list = list(combinations(list(range(len(self.flatten(pred_res)))), address_num)) for address_idx in address_idx_list: candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) @@ -44,10 +47,10 @@ class KBBase(ABC): return new_candidates def correct_result(self, pred_res, key): - if type(key) == int: + if type(key) != bool: return abs(self.logic_forward(pred_res) - key) <= 1e-3 else: - return self.logic_forward(pred_res) == key + return self.logic_forward(pred_res) def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions = False): candidates = [] From 3bd27a45ba23a15ee57328297f17fbbe48874078 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 18:49:07 +0800 Subject: [PATCH 105/601] Update abducer_base.py --- abducer/abducer_base.py | 119 +++++++++++++++++++--------------------- 1 file changed, 56 insertions(+), 63 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 5060529..e087377 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -11,12 +11,14 @@ #================================================================# import sys +sys.path.append(".") sys.path.append("..") import abc -from abducer.kb import add_KB, HWF_KB, add_prolog_KB, HED_prolog_KB +from abducer.kb import * import numpy as np from zoopt import Dimension, Objective, Parameter, Opt +from utils.utils import _confidence_dist, _flatten, _hamming_dist import time @@ -32,72 +34,44 @@ class AbducerBase(abc.ABC): if self.cache: self.cache_min_address_num = {} self.cache_candidates = {} - - - def hamming_dist(self, A, B): - B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) - def confidence_dist(self, A, B): - mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) - B = [list(map(lambda x : mapping[x], b)) for b in B] - - B = np.array(B) - A = np.clip(A, 1e-9, 1) - A = np.expand_dims(A, axis=0) - A = A.repeat(axis=0, repeats=(len(B))) - rows = np.array(range(len(B))) - rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) - cols = np.array(range(len(B[0]))) - cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) - return 1 - np.prod(A[rows, cols, B], axis = 1) - def get_cost_list(self, pred_res, pred_res_prob, candidates): + def _get_cost_list(self, pred_res, pred_res_prob, candidates): if self.dist_func == 'hamming': - return self.hamming_dist(pred_res, candidates) + return _hamming_dist(pred_res, candidates) elif self.dist_func == 'confidence': - return self.confidence_dist(pred_res_prob, candidates) + mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) + return _confidence_dist(pred_res_prob, [list(map(lambda x : mapping[x], c)) for c in candidates]) - def get_one_candidate(self, pred_res, pred_res_prob, candidates): + def _get_one_candidate(self, pred_res, pred_res_prob, candidates): if len(candidates) == 0: return [] elif len(candidates) == 1 or self.zoopt: return candidates[0] else: - cost_list = self.get_cost_list(pred_res, pred_res_prob, candidates) + cost_list = self._get_cost_list(pred_res, pred_res_prob, candidates) min_address_num = np.min(cost_list) idxs = np.where(cost_list == min_address_num)[0] return [candidates[idx] for idx in idxs][0] - - - - # for multiple_prediction - def flatten(self, l): - if self.multiple_predictions: - return [item for sublist in l for item in sublist] - else: - return l - - # for zoopt - def zoopt_address_score(self, pred_res, key, address_idx): + def _zoopt_address_score(self, pred_res, key, address_idx): candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) return 0 if len(candidates) > 0 else 1 - def constraint_address_num(self, solution, max_address_num): + def _constrain_address_num(self, solution, max_address_num): x = solution.get_x() return max_address_num - x.sum() - def zoopt_get_address_idx(self, pred_res, key, max_address_num): - dimension = Dimension(size=len(self.flatten(pred_res)), - regs=[[0, 1]] * len(self.flatten(pred_res)), - tys=[False] * len(self.flatten(pred_res))) - objective = Objective(lambda sol: self.zoopt_address_score(pred_res, key, [idx for idx, i in enumerate(sol.get_x()) if i != 0]), + def _zoopt_get_address_idx(self, pred_res, key, max_address_num): + length = len(_flatten(pred_res)) + dimension = Dimension(size=length, + regs=[[0, 1]] * length, + tys=[False] * length) + objective = Objective(lambda sol: self._zoopt_address_score(pred_res, key, [idx for idx, i in enumerate(sol.get_x()) if i != 0]), dim=dimension, - constraint=lambda sol: self.constraint_address_num(sol, max_address_num)) + constraint=lambda sol: self._constrain_address_num(sol, max_address_num)) parameter = Parameter(budget=100 * dimension.get_size(), autoset=True) solution = Opt.min(objective, parameter).get_x() @@ -107,35 +81,51 @@ class AbducerBase(abc.ABC): return address_idx, address_num + def _get_cache(self, data, max_address_num, require_more_address): + pred_res, pred_res_prob, key = data + if self.multiple_predictions: + pred_res = _flatten(pred_res) + key = tuple(key) + if (tuple(pred_res), key) in self.cache_min_address_num: + address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) + if (tuple(pred_res), key, address_num) in self.cache_candidates: + candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] + if self.zoopt: + return candidates[0] + else: + return self._get_one_candidate(pred_res, pred_res_prob, candidates) + return None + + def _set_cache(self, pred_res, key, min_address_num, address_num, candidates): + if self.multiple_predictions: + pred_res = _flatten(pred_res) + key = tuple(key) + self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num + self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates def abduce(self, data, max_address_num = -1, require_more_address = 0): pred_res, pred_res_prob, key = data if max_address_num == -1: - max_address_num = len(pred_res) - - if self.cache and (tuple(self.flatten(pred_res)), key) in self.cache_min_address_num: - address_num = min(max_address_num, self.cache_min_address_num[(tuple(self.flatten(pred_res)), key)] + require_more_address) - if (tuple(self.flatten(pred_res)), key, address_num) in self.cache_candidates: - candidates = self.cache_candidates[(tuple(self.flatten(pred_res)), key, address_num)] - if self.zoopt: - return candidates[0] - else: - return self.get_one_candidate(pred_res, pred_res_prob, candidates) + max_address_num = len(_flatten(pred_res)) + if self.cache: + candidate = self._get_cache(data, max_address_num, require_more_address) + if candidate is not None: + return candidate + if self.zoopt: - address_idx, address_num = self.zoopt_get_address_idx(pred_res, key, max_address_num) + address_idx, address_num = self._zoopt_get_address_idx(pred_res, key, max_address_num) candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) min_address_num = address_num else: candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, key, max_address_num, require_more_address, self.multiple_predictions) - candidate = self.get_one_candidate(pred_res, pred_res_prob, candidates) + candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) if self.cache: - self.cache_min_address_num[(tuple(self.flatten(pred_res)), key)] = min_address_num - self.cache_candidates[(tuple(self.flatten(pred_res)), key, address_num)] = candidates + self._set_cache(pred_res, key, min_address_num, address_num, candidates) return candidate @@ -144,10 +134,13 @@ class AbducerBase(abc.ABC): def batch_abduce(self, Z, Y, max_address_num = -1, require_more_address = 0): - return [ + if self.multiple_predictions: + return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) + else: + return [ self.abduce((z, prob, y), max_address_num, require_more_address)\ for z, prob, y in zip(Z['cls'], Z['prob'], Y) - ] + ] def __call__(self, Z, Y, max_address_num = -1, require_more_address = 0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) @@ -212,7 +205,7 @@ if __name__ == '__main__': print() kb = HED_prolog_KB() - abd = AbducerBase(kb, zoopt=False, multiple_predictions=True) + abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) consist_exs = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] consist_exs2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] @@ -222,9 +215,9 @@ if __name__ == '__main__': print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) print() - res = abd.abduce((consist_exs, None, True)) + res = abd.abduce((consist_exs, None, [1] * len(consist_exs))) print(res) - res = abd.abduce((inconsist_exs, None, True)) + res = abd.abduce((inconsist_exs, None, [1] * len(consist_exs))) print(res) print() From d4ef2ca8d9730e03a0d5307977e273ff226f021f Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 18:49:23 +0800 Subject: [PATCH 106/601] Update kb.py --- abducer/kb.py | 72 ++++++++++++++++++--------------------------------- 1 file changed, 25 insertions(+), 47 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 7ade7f9..f44ffec 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -15,8 +15,12 @@ import bisect import copy import numpy as np +import sys +sys.path.append("..") + from collections import defaultdict from itertools import product, combinations +from utils.utils import _flatten, _reform_ids, _hamming_dist import pyswip @@ -39,25 +43,21 @@ class KBBase(ABC): if not multiple_predictions: address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) else: - address_idx_list = list(combinations(list(range(len(self.flatten(pred_res)))), address_num)) + address_idx_list = list(combinations(list(range(len(_flatten(pred_res)))), address_num)) for address_idx in address_idx_list: candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) new_candidates += candidates return new_candidates - def correct_result(self, pred_res, key): - if type(key) != bool: - return abs(self.logic_forward(pred_res) - key) <= 1e-3 - else: - return self.logic_forward(pred_res) + def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions = False): candidates = [] for address_num in range(len(pred_res) + 1): if address_num == 0: - if self.correct_result(pred_res, key): + if abs(self.logic_forward(pred_res) - key) <= 1e-3: candidates.append(pred_res) else: new_candidates = self.address(address_num, pred_res, key, multiple_predictions) @@ -79,23 +79,7 @@ class KBBase(ABC): return candidates, min_address_num, address_num - # for multiple predictions, modify from `learn_add.py` - def flatten(self, l): - return [item for sublist in l for item in sublist] - - # for multiple predictions, modify from `learn_add.py` - def reform_ids(self, flatten_pred_res, save_pred_res): - re = [] - i = 0 - for e in save_pred_res: - j = 0 - ids = [] - while j < len(e): - ids.append(flatten_pred_res[i + j]) - j += 1 - re.append(ids) - i = i + j - return re + def __len__(self): pass @@ -110,7 +94,7 @@ class ClsKB(KBBase): if GKB_flag: self.base = {} - X, Y = self.get_GKB(self.pseudo_label_list, self.len_list) + X, Y = self._get_GKB(self.pseudo_label_list, self.len_list) for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(x) else: @@ -118,7 +102,7 @@ class ClsKB(KBBase): for address_num in range(max(self.len_list) + 1): self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat = address_num)) - def get_GKB(self, pseudo_label_list, len_list): + def _get_GKB(self, pseudo_label_list, len_list): all_X = [] for len in len_list: all_X += list(product(pseudo_label_list, repeat = len)) @@ -142,12 +126,6 @@ class ClsKB(KBBase): return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) - - def hamming_dist(self, A, B): - B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) - def abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): if self.base == {} or len(pred_res) not in self.len_list: return [] @@ -159,7 +137,7 @@ class ClsKB(KBBase): min_address_num = 0 address_num = 0 else: - cost_list = self.hamming_dist(pred_res, all_candidates) + cost_list = _hamming_dist(pred_res, all_candidates) min_address_num = np.min(cost_list) address_num = min(max_address_num, min_address_num + require_more_address) idxs = np.where(cost_list <= address_num)[0] @@ -174,7 +152,7 @@ class ClsKB(KBBase): if multiple_predictions: save_pred_res = pred_res - pred_res = self.flatten(pred_res) + pred_res = _flatten(pred_res) for c in abduce_c: candidate = pred_res.copy() @@ -182,7 +160,7 @@ class ClsKB(KBBase): candidate[idx] = c[i] if multiple_predictions: - candidate = self.reform_ids(candidate, save_pred_res) + candidate = _reform_ids(candidate, save_pred_res) if self.logic_forward(candidate) == key: candidates.append(candidate) @@ -252,15 +230,15 @@ class prolog_KB(KBBase): def address_by_idx(self, pred_res, key, address_idx, multiple_predictions = False): candidates = [] - + # print(address_idx) if not multiple_predictions: query_string = self.get_query_string(pred_res, key, address_idx) else: - query_string = self.get_query_string_need_flatten(pred_res, key, address_idx) + query_string = self.get_query_string_need__flatten(pred_res, key, address_idx) if multiple_predictions: save_pred_res = pred_res - pred_res = self.flatten(pred_res) + pred_res = _flatten(pred_res) abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string))] for c in abduce_c: @@ -269,7 +247,7 @@ class prolog_KB(KBBase): candidate[idx] = c[i] if multiple_predictions: - candidate = self.reform_ids(candidate, save_pred_res) + candidate = _reform_ids(candidate, save_pred_res) candidates.append(candidate) return candidates @@ -297,22 +275,22 @@ class add_prolog_KB(prolog_KB): class HED_prolog_KB(prolog_KB): def __init__(self, pseudo_label_list = [0, 1, '+', '=']): super().__init__(pseudo_label_list) - self.prolog.consult('../datasets/hed/learn_add.pl') + self.prolog.consult('./datasets/hed/learn_add.pl') # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` def logic_forward(self, exs): return len(list(self.prolog.query("abduce_consistent_insts(%s)." % exs))) != 0 - def get_query_string_need_flatten(self, pred_res, key, address_idx): - # flatten - flatten_pred_res = self.flatten(pred_res) + def get_query_string_need__flatten(self, pred_res, key, address_idx): + # _flatten + _flatten_pred_res = _flatten(pred_res) # add variables for prolog - for idx in range(len(flatten_pred_res)): + for idx in range(len(_flatten_pred_res)): if idx in address_idx: - flatten_pred_res[idx] = 'X' + str(idx) - # unflatten - new_pred_res = self.reform_ids(flatten_pred_res, pred_res) + _flatten_pred_res[idx] = 'X' + str(idx) + # un_flatten + new_pred_res = _reform_ids(_flatten_pred_res, pred_res) query_string = "abduce_consistent_insts(%s)." % new_pred_res return query_string.replace("'", "").replace("+", "'+'").replace("=", "'='") From 9ffb1707c0c4cc478db0438cc00215c3587d977b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 18:49:53 +0800 Subject: [PATCH 107/601] Create utils.py --- utils/utils.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 utils/utils.py diff --git a/utils/utils.py b/utils/utils.py new file mode 100644 index 0000000..742dc03 --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,35 @@ +import numpy as np + +# for multiple predictions, modify from `learn_add.py` +def _flatten(l): + return [item for sublist in l for item in _flatten(sublist)] if isinstance(l, list) else [l] + +# for multiple predictions, modify from `learn_add.py` +def _reform_ids(flatten_pred_res, save_pred_res): + re = [] + i = 0 + for e in save_pred_res: + j = 0 + ids = [] + while j < len(e): + ids.append(flatten_pred_res[i + j]) + j += 1 + re.append(ids) + i = i + j + return re + +def _hamming_dist(A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) + +def _confidence_dist(A, B): + B = np.array(B) + A = np.clip(A, 1e-9, 1) + A = np.expand_dims(A, axis=0) + A = A.repeat(axis=0, repeats=(len(B))) + rows = np.array(range(len(B))) + rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) + cols = np.array(range(len(B[0]))) + cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) + return 1 - np.prod(A[rows, cols, B], axis = 1) From 499e582982dde08b69a546e82ed6652b40cb32a4 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 18:50:46 +0800 Subject: [PATCH 108/601] Delete utils.py --- utils/utils.py | 35 ----------------------------------- 1 file changed, 35 deletions(-) delete mode 100644 utils/utils.py diff --git a/utils/utils.py b/utils/utils.py deleted file mode 100644 index 742dc03..0000000 --- a/utils/utils.py +++ /dev/null @@ -1,35 +0,0 @@ -import numpy as np - -# for multiple predictions, modify from `learn_add.py` -def _flatten(l): - return [item for sublist in l for item in _flatten(sublist)] if isinstance(l, list) else [l] - -# for multiple predictions, modify from `learn_add.py` -def _reform_ids(flatten_pred_res, save_pred_res): - re = [] - i = 0 - for e in save_pred_res: - j = 0 - ids = [] - while j < len(e): - ids.append(flatten_pred_res[i + j]) - j += 1 - re.append(ids) - i = i + j - return re - -def _hamming_dist(A, B): - B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) - -def _confidence_dist(A, B): - B = np.array(B) - A = np.clip(A, 1e-9, 1) - A = np.expand_dims(A, axis=0) - A = A.repeat(axis=0, repeats=(len(B))) - rows = np.array(range(len(B))) - rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) - cols = np.array(range(len(B[0]))) - cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) - return 1 - np.prod(A[rows, cols, B], axis = 1) From 83a4258e8681c71ee9ecff2abf2757e1fafaa75c Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 18:51:21 +0800 Subject: [PATCH 109/601] Create utils.py --- utils/utils.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 utils/utils.py diff --git a/utils/utils.py b/utils/utils.py new file mode 100644 index 0000000..742dc03 --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,35 @@ +import numpy as np + +# for multiple predictions, modify from `learn_add.py` +def _flatten(l): + return [item for sublist in l for item in _flatten(sublist)] if isinstance(l, list) else [l] + +# for multiple predictions, modify from `learn_add.py` +def _reform_ids(flatten_pred_res, save_pred_res): + re = [] + i = 0 + for e in save_pred_res: + j = 0 + ids = [] + while j < len(e): + ids.append(flatten_pred_res[i + j]) + j += 1 + re.append(ids) + i = i + j + return re + +def _hamming_dist(A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) + +def _confidence_dist(A, B): + B = np.array(B) + A = np.clip(A, 1e-9, 1) + A = np.expand_dims(A, axis=0) + A = A.repeat(axis=0, repeats=(len(B))) + rows = np.array(range(len(B))) + rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) + cols = np.array(range(len(B[0]))) + cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) + return 1 - np.prod(A[rows, cols, B], axis = 1) From 2f2dd6d1fa2f463156ad7bceb6d75ae9175afbd2 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 7 Dec 2022 18:54:14 +0800 Subject: [PATCH 110/601] Update readme.md --- readme.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/readme.md b/readme.md index e3726a9..279257b 100644 --- a/readme.md +++ b/readme.md @@ -3,7 +3,17 @@ This is the code repository of abductive learning Package. ## Environment dependency -... + +#### Install Swipl +[http://www.swi-prolog.org/build/unix.html](http://www.swi-prolog.org/build/unix.html) + +#### Install required package + +```shell +pip install zoopt +pip install pyswip==0.2.9 +``` + ## Example share_example.py and nonshare_exaple.py are examples of grounded abductive learning. From 6a88da10344f49cdf6db7b7a7f6320d11fbedb16 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Thu, 8 Dec 2022 12:14:03 +0800 Subject: [PATCH 111/601] update parallel versio of get GKB --- abducer/kb.py | 53 ++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 42 insertions(+), 11 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index f44ffec..9e28283 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -22,6 +22,8 @@ from collections import defaultdict from itertools import product, combinations from utils.utils import _flatten, _reform_ids, _hamming_dist +from multiprocessing import Pool + import pyswip @@ -102,18 +104,44 @@ class ClsKB(KBBase): for address_num in range(max(self.len_list) + 1): self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat = address_num)) - def _get_GKB(self, pseudo_label_list, len_list): - all_X = [] - for len in len_list: - all_X += list(product(pseudo_label_list, repeat = len)) - - X = [] - Y = [] - for x in all_X: + # For parallel version of _get_GKB + def _get_XY_list(self, args): + pre_x, post_x_it = args[0], args[1] + XY_list = [] + for post_x in post_x_it: + x = (pre_x,) + post_x y = self.logic_forward(x) if y != np.inf: - X.append(x) - Y.append(y) + XY_list.append((x,y)) + return XY_list + + # Parallel get GKB + def _get_GKB(self, pseudo_label_list, len_list): + # all_X = [] + # for length in len_list: + # all_X += list(product(pseudo_label_list, repeat = length)) + + # X, Y = [], [] + # for x in all_X: + # y = self.logic_forward(x) + # if y != np.inf: + # X.append(x) + # Y.append(y) + + X, Y = [], [] + for length in len_list: + arg_list = [] + for pre_x in pseudo_label_list: + post_x_it = product(pseudo_label_list, repeat = length-1) + arg_list.append((pre_x, post_x_it)) + with Pool(processes=len(arg_list)) as pool: + ret_list = pool.map(self._get_XY_list, arg_list) + for XY_list in ret_list: + if len(XY_list) == 0: + continue + part_X, part_Y = zip(*XY_list) + X.extend(part_X) + Y.extend(part_Y) return X, Y def logic_forward(self): @@ -366,7 +394,10 @@ class RegKB(KBBase): import time if __name__ == "__main__": - pass + t1 = time.time() + kb = HWF_KB(True) + t2 = time.time() + print(t2-t1) # X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] From 903ec6801bfe389241819e2bca8c34b080528eb7 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 9 Dec 2022 10:52:21 +0800 Subject: [PATCH 112/601] add lint actions --- .github/workflows/lint.yml | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 .github/workflows/lint.yml diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 0000000..aac5232 --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,17 @@ +name: flake8 Lint + +on: [push, pull_request] + +jobs: + flake8-lint: + runs-on: ubuntu-latest + name: Lint + steps: + - name: Check out source repository + uses: actions/checkout@v3 + - name: Set up Python environment + uses: actions/setup-python@v4 + with: + python-version: "3.11" + - name: flake8 Lint + uses: py-actions/flake8@v2 \ No newline at end of file From 7457e083017d7aa7b87b509f536679ae094d8782 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 9 Dec 2022 11:05:29 +0800 Subject: [PATCH 113/601] update actions --- .github/workflows/lint.yml | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index aac5232..a15aea1 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -12,6 +12,10 @@ jobs: - name: Set up Python environment uses: actions/setup-python@v4 with: - python-version: "3.11" + python-version: "3.8" - name: flake8 Lint - uses: py-actions/flake8@v2 \ No newline at end of file + uses: py-actions/flake8@v2 + with: + max-line-length: "100" + path: "src" + plugins: "flake8-bugbear==22.1.11 flake8-black" \ No newline at end of file From 3904293bf91ac82825b592df1058309a8f4c6de7 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 9 Dec 2022 11:09:08 +0800 Subject: [PATCH 114/601] update actions --- .github/workflows/lint.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index a15aea1..7b4a1c4 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -17,5 +17,4 @@ jobs: uses: py-actions/flake8@v2 with: max-line-length: "100" - path: "src" - plugins: "flake8-bugbear==22.1.11 flake8-black" \ No newline at end of file + plugins: "flake8-bugbear" \ No newline at end of file From 8c9a40f359ea08183a589fe1627a6dbc899da64b Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 9 Dec 2022 11:19:33 +0800 Subject: [PATCH 115/601] update badge --- readme.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/readme.md b/readme.md index 279257b..3d343de 100644 --- a/readme.md +++ b/readme.md @@ -1,3 +1,5 @@ +[![flake8 Lint](https://github.com/AbductiveLearning/ABL-Package/actions/workflows/lint.yml/badge.svg?branch=Dev)](https://github.com/AbductiveLearning/ABL-Package/actions/workflows/lint.yml) + # ABL Package This is the code repository of abductive learning Package. From f59148a8ff9627643472e53163c031b893e9e093 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 9 Dec 2022 11:33:57 +0800 Subject: [PATCH 116/601] update badge --- .github/workflows/lint.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 7b4a1c4..7db534f 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -16,5 +16,5 @@ jobs: - name: flake8 Lint uses: py-actions/flake8@v2 with: - max-line-length: "100" + max-line-length: "110" plugins: "flake8-bugbear" \ No newline at end of file From fd6d7769717e31c4d6acb018257250ea445f2047 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 9 Dec 2022 16:13:34 +0800 Subject: [PATCH 117/601] update badge --- .github/workflows/lint.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 7db534f..e90dfad 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -17,4 +17,4 @@ jobs: uses: py-actions/flake8@v2 with: max-line-length: "110" - plugins: "flake8-bugbear" \ No newline at end of file + plugins: "flake8-bugbear flake8-black" \ No newline at end of file From 8c169e10aabf2d4b5fcffbc3cd66a554cea7710a Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 9 Dec 2022 16:18:58 +0800 Subject: [PATCH 118/601] update badge --- readme.md | 1 + 1 file changed, 1 insertion(+) diff --git a/readme.md b/readme.md index 3d343de..512a68f 100644 --- a/readme.md +++ b/readme.md @@ -1,4 +1,5 @@ [![flake8 Lint](https://github.com/AbductiveLearning/ABL-Package/actions/workflows/lint.yml/badge.svg?branch=Dev)](https://github.com/AbductiveLearning/ABL-Package/actions/workflows/lint.yml) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) # ABL Package From a583ab94e7b9f8cc424fec12d41a145d0c0f00ea Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sat, 10 Dec 2022 22:06:22 +0800 Subject: [PATCH 119/601] Change zoopt score for multiple predictions --- abducer/abducer_base.py | 182 +++++++++++++++++++++------------------- 1 file changed, 98 insertions(+), 84 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index e087377..1e8522d 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -1,16 +1,17 @@ # coding: utf-8 -#================================================================# +# ================================================================# # Copyright (C) 2021 Freecss All rights reserved. -# +# # File Name ：abducer_base.py # Author ：freecss # Email ：karlfreecss@gmail.com # Created Date ：2021/06/03 # Description ： # -#================================================================# +# ================================================================# import sys + sys.path.append(".") sys.path.append("..") @@ -18,30 +19,31 @@ import abc from abducer.kb import * import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from utils.utils import _confidence_dist, _flatten, _hamming_dist +from utils.utils import confidence_dist, flatten, hamming_dist +import math import time + class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func = 'confidence', zoopt = False, multiple_predictions = False, cache = True): + def __init__(self, kb, dist_func='confidence', zoopt=False, multiple_predictions=False, cache=True): self.kb = kb - assert(dist_func == 'hamming' or dist_func == 'confidence') - self.dist_func = dist_func + assert dist_func == 'hamming' or dist_func == 'confidence' + self.dist_func = dist_func self.zoopt = zoopt self.multiple_predictions = multiple_predictions self.cache = cache - + if self.cache: self.cache_min_address_num = {} self.cache_candidates = {} - def _get_cost_list(self, pred_res, pred_res_prob, candidates): if self.dist_func == 'hamming': - return _hamming_dist(pred_res, candidates) + return hamming_dist(pred_res, candidates) elif self.dist_func == 'confidence': mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) - return _confidence_dist(pred_res_prob, [list(map(lambda x : mapping[x], c)) for c in candidates]) + return confidence_dist(pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates]) def _get_one_candidate(self, pred_res, pred_res_prob, candidates): if len(candidates) == 0: @@ -54,37 +56,46 @@ class AbducerBase(abc.ABC): idxs = np.where(cost_list == min_address_num)[0] return [candidates[idx] for idx in idxs][0] - - # for zoopt - def _zoopt_address_score(self, pred_res, key, address_idx): - candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) - return 0 if len(candidates) > 0 else 1 - + # for zoopt + def _zoopt_score_multiple(self, pred_res, key, solution): + all_address_flag = reform_idx(solution, pred_res) + score = 0 + for idx in enumerate(len(pred_res)): + address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] + candidate = self.kb.address_by_idx([pred_res[idx]], key[idx], address_idx, True) + if len(candidate) > 0: + score += 1 + return score + + def _zoopt_address_score(self, pred_res, key, sol): + if not self.multiple_predictions: + address_idx = [idx for idx, i in enumerate(sol.get_x()) if i != 0] + candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + return 1 if len(candidates) > 0 else 0 + else: + return self._zoopt_score_multiple(pred_res, key, sol.get_x()) + def _constrain_address_num(self, solution, max_address_num): x = solution.get_x() return max_address_num - x.sum() - def _zoopt_get_address_idx(self, pred_res, key, max_address_num): - length = len(_flatten(pred_res)) - dimension = Dimension(size=length, - regs=[[0, 1]] * length, - tys=[False] * length) - objective = Objective(lambda sol: self._zoopt_address_score(pred_res, key, [idx for idx, i in enumerate(sol.get_x()) if i != 0]), - dim=dimension, - constraint=lambda sol: self._constrain_address_num(sol, max_address_num)) - parameter = Parameter(budget=100 * dimension.get_size(), autoset=True) + def zoopt_get_solution(self, pred_res, key, max_address_num): + length = len(flatten(pred_res)) + dimension = Dimension(size=length, regs=[[0, 1]] * length, tys=[False] * length) + objective = Objective( + lambda sol: -self._zoopt_address_score(pred_res, key, sol), + dim=dimension, + constraint=lambda sol: self._constrain_address_num(sol, max_address_num), + ) + parameter = Parameter(budget=100, autoset=True) solution = Opt.min(objective, parameter).get_x() - - address_idx = [idx for idx, i in enumerate(solution) if i != 0] - address_num = int(solution.sum()) - - return address_idx, address_num - + + return solution def _get_cache(self, data, max_address_num, require_more_address): pred_res, pred_res_prob, key = data if self.multiple_predictions: - pred_res = _flatten(pred_res) + pred_res = flatten(pred_res) key = tuple(key) if (tuple(pred_res), key) in self.cache_min_address_num: address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) @@ -95,132 +106,135 @@ class AbducerBase(abc.ABC): else: return self._get_one_candidate(pred_res, pred_res_prob, candidates) return None - + def _set_cache(self, pred_res, key, min_address_num, address_num, candidates): if self.multiple_predictions: - pred_res = _flatten(pred_res) + pred_res = flatten(pred_res) key = tuple(key) self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates - - def abduce(self, data, max_address_num = -1, require_more_address = 0): + def abduce(self, data, max_address_num=-1, require_more_address=0): pred_res, pred_res_prob, key = data if max_address_num == -1: - max_address_num = len(_flatten(pred_res)) - + max_address_num = len(flatten(pred_res)) + if self.cache: candidate = self._get_cache(data, max_address_num, require_more_address) if candidate is not None: return candidate if self.zoopt: - address_idx, address_num = self._zoopt_get_address_idx(pred_res, key, max_address_num) + solution = self.zoopt_get_solution(pred_res, key, max_address_num) + address_idx = [idx for idx, i in enumerate(solution) if i != 0] candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + address_num = int(solution.sum()) min_address_num = address_num else: - candidates, min_address_num, address_num = self.kb.abduce_candidates(pred_res, key, max_address_num, require_more_address, self.multiple_predictions) - + candidates, min_address_num, address_num = self.kb.abduce_candidates( + pred_res, key, max_address_num, require_more_address, self.multiple_predictions + ) + candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) - - + if self.cache: self._set_cache(pred_res, key, min_address_num, address_num, candidates) return candidate - + def abduce_rules(self, pred_res): return self.kb.abduce_rules(pred_res) - - - def batch_abduce(self, Z, Y, max_address_num = -1, require_more_address = 0): + + def batch_abduce(self, Z, Y, max_address_num=-1, require_more_address=0): if self.multiple_predictions: return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) else: - return [ - self.abduce((z, prob, y), max_address_num, require_more_address)\ - for z, prob, y in zip(Z['cls'], Z['prob'], Y) - ] + return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] - def __call__(self, Z, Y, max_address_num = -1, require_more_address = 0): + def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) - + + + + if __name__ == '__main__': prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] - + kb = add_KB() abd = AbducerBase(kb, 'confidence') - res = abd.abduce(([1, 1], prob1, 8), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob2, 8), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 17), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num = 1, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 17), max_address_num=1, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 20), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 20), max_address_num=2, require_more_address=0) print(res) print() - + kb = add_prolog_KB() abd = AbducerBase(kb, 'confidence') - res = abd.abduce(([1, 1], prob1, 8), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob2, 8), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 17), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num = 1, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 17), max_address_num=1, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 20), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 20), max_address_num=2, require_more_address=0) print(res) print() - + kb = add_prolog_KB() - abd = AbducerBase(kb, 'confidence', zoopt = True) - res = abd.abduce(([1, 1], prob1, 8), max_address_num = 2, require_more_address = 0) + abd = AbducerBase(kb, 'confidence', zoopt=True) + res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob2, 8), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 17), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num = 1, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 17), max_address_num=1, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 20), max_address_num = 2, require_more_address = 0) + res = abd.abduce(([1, 1], prob1, 20), max_address_num=2, require_more_address=0) print(res) print() - - kb = HWF_KB(len_list = [1, 3, 5]) + + kb = HWF_KB(len_list=[1, 3, 5]) abd = AbducerBase(kb, 'hamming') - res = abd.abduce((['5', '+', '2'], None, 3), max_address_num = 2, require_more_address = 0) + res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce((['5', '+', '2'], None, 64), max_address_num = 3, require_more_address = 0) + res = abd.abduce((['5', '+', '2'], None, 64), max_address_num=3, require_more_address=0) print(res) - res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num = 3, require_more_address = 0) + res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0) print(res) - res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num = 5, require_more_address = 3) + res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3) print(res) print() - + kb = HED_prolog_KB() abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) consist_exs = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] - consist_exs2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules + consist_exs2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] + # inconsist_exs = [[1, '+', 0, '=', 0], ['=', '=', '=', '=', 0], ['=', '=', 0, '=', '=', '=']] rules = ['my_op([0], [0], [1, 1])', 'my_op([1], [1], [0])', 'my_op([1], [0], [0])'] - + print(kb.logic_forward(consist_exs), kb.logic_forward(inconsist_exs)) print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) print() - + res = abd.abduce((consist_exs, None, [1] * len(consist_exs))) print(res) res = abd.abduce((inconsist_exs, None, [1] * len(consist_exs))) print(res) print() - + abduced_rules = abd.abduce_rules(consist_exs) print(abduced_rules) + From 3d14196107527bb55f5b91ddff064a036cb067ce Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sat, 10 Dec 2022 22:11:41 +0800 Subject: [PATCH 120/601] Update abducer_base.py --- abducer/abducer_base.py | 118 ++++++++++++++++++++++++++++------------ 1 file changed, 84 insertions(+), 34 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 1e8522d..7b89791 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -26,9 +26,16 @@ import time class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func='confidence', zoopt=False, multiple_predictions=False, cache=True): + def __init__( + self, + kb, + dist_func="confidence", + zoopt=False, + multiple_predictions=False, + cache=True, + ): self.kb = kb - assert dist_func == 'hamming' or dist_func == 'confidence' + assert dist_func == "hamming" or dist_func == "confidence" self.dist_func = dist_func self.zoopt = zoopt self.multiple_predictions = multiple_predictions @@ -39,11 +46,18 @@ class AbducerBase(abc.ABC): self.cache_candidates = {} def _get_cost_list(self, pred_res, pred_res_prob, candidates): - if self.dist_func == 'hamming': + if self.dist_func == "hamming": return hamming_dist(pred_res, candidates) - elif self.dist_func == 'confidence': - mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) - return confidence_dist(pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates]) + elif self.dist_func == "confidence": + mapping = dict( + zip( + self.kb.pseudo_label_list, + list(range(len(self.kb.pseudo_label_list))), + ) + ) + return confidence_dist( + pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates] + ) def _get_one_candidate(self, pred_res, pred_res_prob, candidates): if len(candidates) == 0: @@ -61,8 +75,12 @@ class AbducerBase(abc.ABC): all_address_flag = reform_idx(solution, pred_res) score = 0 for idx in enumerate(len(pred_res)): - address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] - candidate = self.kb.address_by_idx([pred_res[idx]], key[idx], address_idx, True) + address_idx = [ + i for i, flag in enumerate(all_address_flag[idx]) if flag != 0 + ] + candidate = self.kb.address_by_idx( + [pred_res[idx]], key[idx], address_idx, True + ) if len(candidate) > 0: score += 1 return score @@ -70,7 +88,9 @@ class AbducerBase(abc.ABC): def _zoopt_address_score(self, pred_res, key, sol): if not self.multiple_predictions: address_idx = [idx for idx, i in enumerate(sol.get_x()) if i != 0] - candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + candidates = self.kb.address_by_idx( + pred_res, key, address_idx, self.multiple_predictions + ) return 1 if len(candidates) > 0 else 0 else: return self._zoopt_score_multiple(pred_res, key, sol.get_x()) @@ -98,7 +118,11 @@ class AbducerBase(abc.ABC): pred_res = flatten(pred_res) key = tuple(key) if (tuple(pred_res), key) in self.cache_min_address_num: - address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) + address_num = min( + max_address_num, + self.cache_min_address_num[(tuple(pred_res), key)] + + require_more_address, + ) if (tuple(pred_res), key, address_num) in self.cache_candidates: candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] if self.zoopt: @@ -127,12 +151,18 @@ class AbducerBase(abc.ABC): if self.zoopt: solution = self.zoopt_get_solution(pred_res, key, max_address_num) address_idx = [idx for idx, i in enumerate(solution) if i != 0] - candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + candidates = self.kb.address_by_idx( + pred_res, key, address_idx, self.multiple_predictions + ) address_num = int(solution.sum()) min_address_num = address_num else: candidates, min_address_num, address_num = self.kb.abduce_candidates( - pred_res, key, max_address_num, require_more_address, self.multiple_predictions + pred_res, + key, + max_address_num, + require_more_address, + self.multiple_predictions, ) candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) @@ -147,23 +177,31 @@ class AbducerBase(abc.ABC): def batch_abduce(self, Z, Y, max_address_num=-1, require_more_address=0): if self.multiple_predictions: - return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) + return self.abduce( + (Z["cls"], Z["prob"], Y), max_address_num, require_more_address + ) else: - return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] + return [ + self.abduce((z, prob, y), max_address_num, require_more_address) + for z, prob, y in zip(Z["cls"], Z["prob"], Y) + ] def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) - - - -if __name__ == '__main__': - prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] - prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] +if __name__ == "__main__": + prob1 = [ + [0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], + [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], + ] + prob2 = [ + [0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], + [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], + ] kb = add_KB() - abd = AbducerBase(kb, 'confidence') + abd = AbducerBase(kb, "confidence") res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -177,7 +215,7 @@ if __name__ == '__main__': print() kb = add_prolog_KB() - abd = AbducerBase(kb, 'confidence') + abd = AbducerBase(kb, "confidence") res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -191,7 +229,7 @@ if __name__ == '__main__': print() kb = add_prolog_KB() - abd = AbducerBase(kb, 'confidence', zoopt=True) + abd = AbducerBase(kb, "confidence", zoopt=True) res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -205,24 +243,38 @@ if __name__ == '__main__': print() kb = HWF_KB(len_list=[1, 3, 5]) - abd = AbducerBase(kb, 'hamming') - res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) + abd = AbducerBase(kb, "hamming") + res = abd.abduce( + (["5", "+", "2"], None, 3), max_address_num=2, require_more_address=0 + ) print(res) - res = abd.abduce((['5', '+', '2'], None, 64), max_address_num=3, require_more_address=0) + res = abd.abduce( + (["5", "+", "2"], None, 64), max_address_num=3, require_more_address=0 + ) print(res) - res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0) + res = abd.abduce( + (["5", "+", "2"], None, 1.67), max_address_num=3, require_more_address=0 + ) print(res) - res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3) + res = abd.abduce( + (["5", "8", "8", "8", "8"], None, 3.17), + max_address_num=5, + require_more_address=3, + ) print(res) print() kb = HED_prolog_KB() abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) - consist_exs = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] - consist_exs2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules - inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] + consist_exs = [[1, "+", 0, "=", 0], [1, "+", 1, "=", 0], [0, "+", 0, "=", 1, 1]] + consist_exs2 = [ + [1, "+", 0, "=", 0], + [1, "+", 1, "=", 0], + [0, "+", 1, "=", 1, 1], + ] # not consistent with rules + inconsist_exs = [[1, "+", 0, "=", 0], [1, "=", 1, "=", 0], [0, "=", 0, "=", 1, 1]] # inconsist_exs = [[1, '+', 0, '=', 0], ['=', '=', '=', '=', 0], ['=', '=', 0, '=', '=', '=']] - rules = ['my_op([0], [0], [1, 1])', 'my_op([1], [1], [0])', 'my_op([1], [0], [0])'] + rules = ["my_op([0], [0], [1, 1])", "my_op([1], [1], [0])", "my_op([1], [0], [0])"] print(kb.logic_forward(consist_exs), kb.logic_forward(inconsist_exs)) print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) @@ -236,5 +288,3 @@ if __name__ == '__main__': abduced_rules = abd.abduce_rules(consist_exs) print(abduced_rules) - - From 3fdd013dbbfd8111bde48ac0e639c5bff4cfd32a Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Sat, 10 Dec 2022 22:15:48 +0800 Subject: [PATCH 121/601] Change zoopt score for multiple predictions --- abducer/abducer_base.py | 113 +++++++++++----------------------------- 1 file changed, 29 insertions(+), 84 deletions(-) diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 7b89791..72884f9 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -26,16 +26,9 @@ import time class AbducerBase(abc.ABC): - def __init__( - self, - kb, - dist_func="confidence", - zoopt=False, - multiple_predictions=False, - cache=True, - ): + def __init__(self, kb, dist_func='confidence', zoopt=False, multiple_predictions=False, cache=True): self.kb = kb - assert dist_func == "hamming" or dist_func == "confidence" + assert dist_func == 'hamming' or dist_func == 'confidence' self.dist_func = dist_func self.zoopt = zoopt self.multiple_predictions = multiple_predictions @@ -46,18 +39,11 @@ class AbducerBase(abc.ABC): self.cache_candidates = {} def _get_cost_list(self, pred_res, pred_res_prob, candidates): - if self.dist_func == "hamming": + if self.dist_func == 'hamming': return hamming_dist(pred_res, candidates) - elif self.dist_func == "confidence": - mapping = dict( - zip( - self.kb.pseudo_label_list, - list(range(len(self.kb.pseudo_label_list))), - ) - ) - return confidence_dist( - pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates] - ) + elif self.dist_func == 'confidence': + mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) + return confidence_dist(pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates]) def _get_one_candidate(self, pred_res, pred_res_prob, candidates): if len(candidates) == 0: @@ -75,12 +61,8 @@ class AbducerBase(abc.ABC): all_address_flag = reform_idx(solution, pred_res) score = 0 for idx in enumerate(len(pred_res)): - address_idx = [ - i for i, flag in enumerate(all_address_flag[idx]) if flag != 0 - ] - candidate = self.kb.address_by_idx( - [pred_res[idx]], key[idx], address_idx, True - ) + address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] + candidate = self.kb.address_by_idx([pred_res[idx]], key[idx], address_idx, True) if len(candidate) > 0: score += 1 return score @@ -88,9 +70,7 @@ class AbducerBase(abc.ABC): def _zoopt_address_score(self, pred_res, key, sol): if not self.multiple_predictions: address_idx = [idx for idx, i in enumerate(sol.get_x()) if i != 0] - candidates = self.kb.address_by_idx( - pred_res, key, address_idx, self.multiple_predictions - ) + candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) return 1 if len(candidates) > 0 else 0 else: return self._zoopt_score_multiple(pred_res, key, sol.get_x()) @@ -118,11 +98,7 @@ class AbducerBase(abc.ABC): pred_res = flatten(pred_res) key = tuple(key) if (tuple(pred_res), key) in self.cache_min_address_num: - address_num = min( - max_address_num, - self.cache_min_address_num[(tuple(pred_res), key)] - + require_more_address, - ) + address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) if (tuple(pred_res), key, address_num) in self.cache_candidates: candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] if self.zoopt: @@ -151,18 +127,12 @@ class AbducerBase(abc.ABC): if self.zoopt: solution = self.zoopt_get_solution(pred_res, key, max_address_num) address_idx = [idx for idx, i in enumerate(solution) if i != 0] - candidates = self.kb.address_by_idx( - pred_res, key, address_idx, self.multiple_predictions - ) + candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) address_num = int(solution.sum()) min_address_num = address_num else: candidates, min_address_num, address_num = self.kb.abduce_candidates( - pred_res, - key, - max_address_num, - require_more_address, - self.multiple_predictions, + pred_res, key, max_address_num, require_more_address, self.multiple_predictions ) candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) @@ -177,31 +147,20 @@ class AbducerBase(abc.ABC): def batch_abduce(self, Z, Y, max_address_num=-1, require_more_address=0): if self.multiple_predictions: - return self.abduce( - (Z["cls"], Z["prob"], Y), max_address_num, require_more_address - ) + return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) else: - return [ - self.abduce((z, prob, y), max_address_num, require_more_address) - for z, prob, y in zip(Z["cls"], Z["prob"], Y) - ] + return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) -if __name__ == "__main__": - prob1 = [ - [0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], - [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], - ] - prob2 = [ - [0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], - [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], - ] +if __name__ == '__main__': + prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] + prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] kb = add_KB() - abd = AbducerBase(kb, "confidence") + abd = AbducerBase(kb, 'confidence') res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -215,7 +174,7 @@ if __name__ == "__main__": print() kb = add_prolog_KB() - abd = AbducerBase(kb, "confidence") + abd = AbducerBase(kb, 'confidence') res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -229,7 +188,7 @@ if __name__ == "__main__": print() kb = add_prolog_KB() - abd = AbducerBase(kb, "confidence", zoopt=True) + abd = AbducerBase(kb, 'confidence', zoopt=True) res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -243,38 +202,24 @@ if __name__ == "__main__": print() kb = HWF_KB(len_list=[1, 3, 5]) - abd = AbducerBase(kb, "hamming") - res = abd.abduce( - (["5", "+", "2"], None, 3), max_address_num=2, require_more_address=0 - ) + abd = AbducerBase(kb, 'hamming') + res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) print(res) - res = abd.abduce( - (["5", "+", "2"], None, 64), max_address_num=3, require_more_address=0 - ) + res = abd.abduce((['5', '+', '2'], None, 64), max_address_num=3, require_more_address=0) print(res) - res = abd.abduce( - (["5", "+", "2"], None, 1.67), max_address_num=3, require_more_address=0 - ) + res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0) print(res) - res = abd.abduce( - (["5", "8", "8", "8", "8"], None, 3.17), - max_address_num=5, - require_more_address=3, - ) + res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3) print(res) print() kb = HED_prolog_KB() abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) - consist_exs = [[1, "+", 0, "=", 0], [1, "+", 1, "=", 0], [0, "+", 0, "=", 1, 1]] - consist_exs2 = [ - [1, "+", 0, "=", 0], - [1, "+", 1, "=", 0], - [0, "+", 1, "=", 1, 1], - ] # not consistent with rules - inconsist_exs = [[1, "+", 0, "=", 0], [1, "=", 1, "=", 0], [0, "=", 0, "=", 1, 1]] + consist_exs = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] + consist_exs2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules + inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] # inconsist_exs = [[1, '+', 0, '=', 0], ['=', '=', '=', '=', 0], ['=', '=', 0, '=', '=', '=']] - rules = ["my_op([0], [0], [1, 1])", "my_op([1], [1], [0])", "my_op([1], [0], [0])"] + rules = ['my_op([0], [0], [1, 1])', 'my_op([1], [1], [0])', 'my_op([1], [0], [0])'] print(kb.logic_forward(consist_exs), kb.logic_forward(inconsist_exs)) print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) From c3ed82dea85079d20287bc527f246982f1f0e827 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 12 Dec 2022 13:06:34 +0800 Subject: [PATCH 122/601] Update utils.py --- utils/utils.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/utils/utils.py b/utils/utils.py index 742dc03..d4cf947 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -1,11 +1,11 @@ import numpy as np # for multiple predictions, modify from `learn_add.py` -def _flatten(l): +def flatten(l): return [item for sublist in l for item in _flatten(sublist)] if isinstance(l, list) else [l] # for multiple predictions, modify from `learn_add.py` -def _reform_ids(flatten_pred_res, save_pred_res): +def reform_ids(flatten_pred_res, save_pred_res): re = [] i = 0 for e in save_pred_res: @@ -18,12 +18,12 @@ def _reform_ids(flatten_pred_res, save_pred_res): i = i + j return re -def _hamming_dist(A, B): +def hamming_dist(A, B): B = np.array(B) A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) return np.sum(A != B, axis = 1) -def _confidence_dist(A, B): +def confidence_dist(A, B): B = np.array(B) A = np.clip(A, 1e-9, 1) A = np.expand_dims(A, axis=0) From 15192a06c2c75fa4bd3dc1d8ee4518ec81ebd8fe Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 12 Dec 2022 13:08:42 +0800 Subject: [PATCH 123/601] Update utils.py --- utils/utils.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/utils/utils.py b/utils/utils.py index d4cf947..44ba6a1 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -2,19 +2,19 @@ import numpy as np # for multiple predictions, modify from `learn_add.py` def flatten(l): - return [item for sublist in l for item in _flatten(sublist)] if isinstance(l, list) else [l] + return [item for sublist in l for item in flatten(sublist)] if isinstance(l, list) else [l] # for multiple predictions, modify from `learn_add.py` -def reform_ids(flatten_pred_res, save_pred_res): +def reform_idx(flatten_pred_res, save_pred_res): re = [] i = 0 for e in save_pred_res: j = 0 - ids = [] + idx = [] while j < len(e): - ids.append(flatten_pred_res[i + j]) + idx.append(flatten_pred_res[i + j]) j += 1 - re.append(ids) + re.append(idx) i = i + j return re From 5bd5e02d492626b4935c4beb373a282e43815d22 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 12 Dec 2022 13:09:36 +0800 Subject: [PATCH 124/601] Update kb.py --- abducer/kb.py | 212 +++++++++++++++++++++++++------------------------- 1 file changed, 105 insertions(+), 107 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 9e28283..6e5f0d3 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -1,14 +1,14 @@ # coding: utf-8 -#================================================================# +# ================================================================# # Copyright (C) 2021 LAMDA All rights reserved. -# +# # File Name ：kb.py # Author ：freecss # Email ：karlfreecss@gmail.com # Created Date ：2021/06/03 # Description ： # -#================================================================# +# ================================================================# from abc import ABC, abstractmethod import bisect @@ -16,11 +16,12 @@ import copy import numpy as np import sys + sys.path.append("..") from collections import defaultdict from itertools import product, combinations -from utils.utils import _flatten, _reform_ids, _hamming_dist +from utils.utils import flatten, reform_idx, hamming_dist from multiprocessing import Pool @@ -28,35 +29,32 @@ import pyswip class KBBase(ABC): - def __init__(self, pseudo_label_list = None): + def __init__(self, pseudo_label_list=None): pass @abstractmethod def logic_forward(self): pass - + @abstractmethod def abduce_candidates(self): pass - - def address(self, address_num, pred_res, key, multiple_predictions = False): + def address(self, address_num, pred_res, key, multiple_predictions=False): new_candidates = [] if not multiple_predictions: address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) else: - address_idx_list = list(combinations(list(range(len(_flatten(pred_res)))), address_num)) - + address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num)) + for address_idx in address_idx_list: candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) new_candidates += candidates return new_candidates - - - - def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions = False): + + def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions=False): candidates = [] - + for address_num in range(len(pred_res) + 1): if address_num == 0: if abs(self.logic_forward(pred_res) - key) <= 1e-3: @@ -64,14 +62,14 @@ class KBBase(ABC): else: new_candidates = self.address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates - + if len(candidates) > 0: min_address_num = address_num break - + if address_num >= max_address_num: return [], 0, 0 - + for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if address_num > max_address_num: return candidates, min_address_num, address_num - 1 @@ -79,21 +77,19 @@ class KBBase(ABC): candidates += new_candidates return candidates, min_address_num, address_num - - - def __len__(self): pass + class ClsKB(KBBase): - def __init__(self, GKB_flag = False, pseudo_label_list = None, len_list = None): + def __init__(self, GKB_flag=False, pseudo_label_list=None, len_list=None): super().__init__() self.GKB_flag = GKB_flag self.pseudo_label_list = pseudo_label_list self.len_list = len_list self.prolog_flag = False - + if GKB_flag: self.base = {} X, Y = self._get_GKB(self.pseudo_label_list, self.len_list) @@ -102,8 +98,8 @@ class ClsKB(KBBase): else: self.all_address_candidate_dict = {} for address_num in range(max(self.len_list) + 1): - self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat = address_num)) - + self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat=address_num)) + # For parallel version of _get_GKB def _get_XY_list(self, args): pre_x, post_x_it = args[0], args[1] @@ -112,7 +108,7 @@ class ClsKB(KBBase): x = (pre_x,) + post_x y = self.logic_forward(x) if y != np.inf: - XY_list.append((x,y)) + XY_list.append((x, y)) return XY_list # Parallel get GKB @@ -120,7 +116,7 @@ class ClsKB(KBBase): # all_X = [] # for length in len_list: # all_X += list(product(pseudo_label_list, repeat = length)) - + # X, Y = [], [] # for x in all_X: # y = self.logic_forward(x) @@ -132,7 +128,7 @@ class ClsKB(KBBase): for length in len_list: arg_list = [] for pre_x in pseudo_label_list: - post_x_it = product(pseudo_label_list, repeat = length-1) + post_x_it = product(pseudo_label_list, repeat=length - 1) arg_list.append((pre_x, post_x_it)) with Pool(processes=len(arg_list)) as pool: ret_list = pool.map(self._get_XY_list, arg_list) @@ -143,57 +139,54 @@ class ClsKB(KBBase): X.extend(part_X) Y.extend(part_Y) return X, Y - + def logic_forward(self): pass - - def abduce_candidates(self, pred_res, key, max_address_num = -1, require_more_address = 0, multiple_predictions = False): + + def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: return self.abduce_from_GKB(pred_res, key, max_address_num, require_more_address) else: return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) - def abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): if self.base == {} or len(pred_res) not in self.len_list: return [] - + all_candidates = self.base[len(pred_res)][key] - + if len(all_candidates) == 0: candidates = [] min_address_num = 0 address_num = 0 else: - cost_list = _hamming_dist(pred_res, all_candidates) + cost_list = hamming_dist(pred_res, all_candidates) min_address_num = np.min(cost_list) address_num = min(max_address_num, min_address_num + require_more_address) idxs = np.where(cost_list <= address_num)[0] candidates = [all_candidates[idx] for idx in idxs] - + return candidates, min_address_num, address_num - - def address_by_idx(self, pred_res, key, address_idx, multiple_predictions = False): + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] abduce_c = self.all_address_candidate_dict[len(address_idx)] - + if multiple_predictions: save_pred_res = pred_res - pred_res = _flatten(pred_res) - + pred_res = flatten(pred_res) + for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - + if multiple_predictions: - candidate = _reform_ids(candidate, save_pred_res) - - if self.logic_forward(candidate) == key: + candidate = reform_idx(candidate, save_pred_res) + + if self.logic_forward(candidate) == key: candidates.append(candidate) return candidates - def _dict_len(self, dic): if not self.GKB_flag: @@ -209,20 +202,19 @@ class ClsKB(KBBase): class add_KB(ClsKB): - def __init__(self, GKB_flag = False, \ - pseudo_label_list = list(range(10)), \ - len_list = [2]): + def __init__(self, GKB_flag=False, pseudo_label_list=list(range(10)), len_list=[2]): super().__init__(GKB_flag, pseudo_label_list, len_list) - + def logic_forward(self, nums): return sum(nums) - + + class HWF_KB(ClsKB): - def __init__(self, GKB_flag = False, \ - pseudo_label_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], \ - len_list = [1, 3, 5, 7]): + def __init__( + self, GKB_flag=False, pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], len_list=[1, 3, 5, 7] + ): super().__init__(GKB_flag, pseudo_label_list, len_list) - + def valid_candidate(self, formula): if len(formula) % 2 == 0: return False @@ -232,11 +224,25 @@ class HWF_KB(ClsKB): if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: return False return True - + def logic_forward(self, formula): if not self.valid_candidate(formula): return np.inf - mapping = {'1':'1', '2':'2', '3':'3', '4':'4', '5':'5', '6':'6', '7':'7', '8':'8', '9':'9', '+':'+', '-':'-', 'times':'*', 'div':'/'} + mapping = { + '1': '1', + '2': '2', + '3': '3', + '4': '4', + '5': '5', + '6': '6', + '7': '7', + '8': '8', + '9': '9', + '+': '+', + '-': '-', + 'times': '*', + 'div': '/', + } formula = [mapping[f] for f in formula] return round(eval(''.join(formula)), 2) @@ -246,51 +252,48 @@ class prolog_KB(KBBase): super().__init__() self.pseudo_label_list = pseudo_label_list self.prolog = pyswip.Prolog() - - + def logic_forward(self): pass - + def abduce_candidates(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) - - - - def address_by_idx(self, pred_res, key, address_idx, multiple_predictions = False): + + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] # print(address_idx) if not multiple_predictions: query_string = self.get_query_string(pred_res, key, address_idx) else: - query_string = self.get_query_string_need__flatten(pred_res, key, address_idx) - + query_string = self.get_query_string_need_flatten(pred_res, key, address_idx) + if multiple_predictions: save_pred_res = pred_res - pred_res = _flatten(pred_res) + pred_res = flatten(pred_res) abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string))] for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - + if multiple_predictions: - candidate = _reform_ids(candidate, save_pred_res) - + candidate = reform_idx(candidate, save_pred_res) + candidates.append(candidate) - return candidates + return candidates + - class add_prolog_KB(prolog_KB): - def __init__(self, pseudo_label_list = list(range(10))): + def __init__(self, pseudo_label_list=list(range(10))): super().__init__(pseudo_label_list) for i in self.pseudo_label_list: self.prolog.assertz("pseudo_label(%s)" % i) self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") - + def logic_forward(self, nums): - return list(self.prolog.query("addition(%s, %s, Res)." %(nums[0], nums[1])))[0]['Res'] - + return list(self.prolog.query("addition(%s, %s, Res)." % (nums[0], nums[1])))[0]['Res'] + def get_query_string(self, pred_res, key, address_idx): query_string = "addition(" for idx, i in enumerate(pred_res): @@ -298,51 +301,47 @@ class add_prolog_KB(prolog_KB): query_string += tmp query_string += "%s)." % key return query_string - + class HED_prolog_KB(prolog_KB): - def __init__(self, pseudo_label_list = [0, 1, '+', '=']): + def __init__(self, pseudo_label_list=[0, 1, '+', '=']): super().__init__(pseudo_label_list) self.prolog.consult('./datasets/hed/learn_add.pl') - - # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` + + # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` def logic_forward(self, exs): return len(list(self.prolog.query("abduce_consistent_insts(%s)." % exs))) != 0 - - def get_query_string_need__flatten(self, pred_res, key, address_idx): - # _flatten - _flatten_pred_res = _flatten(pred_res) + def get_query_string_need_flatten(self, pred_res, key, address_idx): + # flatten + flatten_pred_res = flatten(pred_res) # add variables for prolog - for idx in range(len(_flatten_pred_res)): + for idx in range(len(flatten_pred_res)): if idx in address_idx: - _flatten_pred_res[idx] = 'X' + str(idx) - # un_flatten - new_pred_res = _reform_ids(_flatten_pred_res, pred_res) - + flatten_pred_res[idx] = 'X' + str(idx) + # unflatten + new_pred_res = reform_idx(flatten_pred_res, pred_res) + query_string = "abduce_consistent_insts(%s)." % new_pred_res return query_string.replace("'", "").replace("+", "'+'").replace("=", "'='") - - - + def consist_rule(self, exs, rules): - rule_str = "%s" % rules + rule_str = "%s" % rules rule_str = rule_str.replace("'", "") - return len(list(self.prolog.query("consistent_inst_feature(%s, %s)." %(exs, rule_str)))) != 0 - + return len(list(self.prolog.query("consistent_inst_feature(%s, %s)." % (exs, rule_str)))) != 0 + def abduce_rules(self, pred_res): prolog_rules = list(self.prolog.query("consistent_inst_feature(%s, X)." % pred_res))[0]['X'] rules = [] for rule in prolog_rules: rules.append(rule.value) return rules - + # def consist_rules(self, pred_res, rules): - class RegKB(KBBase): - def __init__(self, GKB_flag = False, X = None, Y = None): + def __init__(self, GKB_flag=False, X=None, Y=None): super().__init__() tmp_dict = {} for x, y in zip(X, Y): @@ -357,12 +356,11 @@ class RegKB(KBBase): def valid_candidate(self): pass - + def logic_forward(self): pass - - - def abduce_candidates(self, key, length = None): + + def abduce_candidates(self, key, length=None): if key is None: return self.get_all_candidates() @@ -392,14 +390,15 @@ class RegKB(KBBase): def __len__(self): return sum([sum(len(x) for x in D[0]) for D in self.base.values()]) + import time + if __name__ == "__main__": t1 = time.time() kb = HWF_KB(True) t2 = time.time() - print(t2-t1) - - + print(t2 - t1) + # X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] # Y = [2, 1, 1, 2, 2] # kb = ClsKB(X, Y) @@ -411,7 +410,7 @@ if __name__ == "__main__": # res = kb.get_candidates(None) # print(res) # print() - + # X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] # Y = [2, 1, 1, 2, 1.5, 1.5] # kb = RegKB(X, Y) @@ -422,4 +421,3 @@ if __name__ == "__main__": # print(res) # res = kb.get_candidates(None) # print(res) - From 723803b8f2b4a39d96f1bee7d17bdbb9626bec1a Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 12 Dec 2022 15:42:13 +0800 Subject: [PATCH 125/601] Framework for HED dataset (not complete) --- framework_hed.py | 263 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 263 insertions(+) create mode 100644 framework_hed.py diff --git a/framework_hed.py b/framework_hed.py new file mode 100644 index 0000000..1942e5c --- /dev/null +++ b/framework_hed.py @@ -0,0 +1,263 @@ +import numpy as np +from utils.utils import flatten, reform_idx + + +def get_rules_from_data(equations_true): + SAMPLES_PER_RULE = 3 + + select_index = np.random.randint(len(equations_true), size=SAMPLES_PER_RULE) + select_equations = np.array(equations_true)[select_index] + + +def get_consist_idx(exs, abducer): + consistent_ex_idx = [] + label = [] + for idx, e in enumerate(exs): + if abducer.kb.logic_forward([e]): + consistent_ex_idx.append(idx) + label.append(e) + return consistent_ex_idx, label + +def get_label(exs, solution, abducer): + all_address_flag = reform_idx(solution, exs) + consistent_ex_idx = [] + label = [] + for idx, ex in enumerate(exs): + address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] + candidate = abducer.kb.address_by_idx([ex], 1, address_idx, True) + if len(candidate) > 0: + consistent_ex_idx.append(idx) + label.append(candidate[0][0]) + return consistent_ex_idx, label + + +def get_percentage_precision(select_X, consistent_ex_idx, equation_label): + + images = [] + for idx in consistent_ex_idx: + images.append(select_X[idx]) + + ## TODO + model_labels = model.predict(images) + + assert(len(flatten(model_labels)) == len(flatten(equation_label))) + return (flatten(model_labels) == flatten(equation_label)).sum() / len(flatten(model_labels)) + + + + + +def abduce_and_train(model, abducer, train_X_true, select_num): + + select_index = np.random.randint(len(train_X_true), size=select_num) + select_X = train_X_true[select_index] + + + + exs = select_X.predict() + # e.g. when select_num == 10, exs = [[1, '+', 0, '=', 1, 0], [1, '+', 0, '=', 1, 0], [1, '+', 0, '=', 1, 0], [0, '+', 0, '=', 0], [1, '+', 0, '=', 1, 0],\ + # [1, '+', 0, '=', 1, 0], [1, '+', 0, '=', 1, 0], [1, '+', 0, '=', 1, 0], [0, '+', 0, '=', 0], [1, '+', 0, '=', 1, 0]] + + print("This is the model's current label:", exs) + + # 1. Check if it can abduce rules without changing any labels + consistent_ex_idx, equation_label = get_consist_idx(exs) + + + max_abduce_num = 10 + if len(consistent_ex_idx) == 0: + + # 2. Find the possible wrong position in symbols and Abduce the right symbol through logic module + solution = abducer.zoopt_get_solution(exs, [1] * len(exs), max_abduce_num) + consistent_ex_idx, equation_label = get_label(exs, solution, abducer) + + # Still cannot find + if len(consistent_ex_idx) == 0: + return 0, 0 + + + ## TODO: train + # train_pool_X = np.concatenate(select_X[consistent_ex_idx]).reshape( + # -1, h, w, d) + # train_pool_Y = np_utils.to_categorical( + # flatten(exs[consistent_ex_idx]), + # num_classes=len(labels)) # Convert the symbol to network output + # assert (len(train_pool_X) == len(train_pool_Y)) + # print("\nTrain pool size is :", len(train_pool_X)) + # print("Training...") + # base_model.fit(train_pool_X, + # train_pool_Y, + # batch_size=BATCHSIZE, + # epochs=NN_EPOCHS, + # verbose=0) + + # consistent_percentage, batch_label_model_precision = get_percentage_precision( + # base_model, select_equations, consist_re, shape) + + consistent_percentage = len(consistent_ex_idx) / select_num + batch_label_model_precision = get_percentage_precision(exs, consistent_ex_idx, equation_label) + + return consistent_percentage, batch_label_model_precision + +def get_rules(exs): + consistent_ex_idx, equation_label = get_consist_idx(exs) + consist_exs = [] + for idx in consistent_ex_idx: + consist_exs.append(exs[idx]) + if len(consist_exs) == 0: + return None + else: + return abducer.abduce_rule(consist_exs) + + + +def get_rules_from_data(train_X_true, samples_per_rule, logic_output_dim): + rules = [] + for _ in range(logic_output_dim): + while True: + select_index = np.random.randint(len(train_X_true), size=samples_per_rule) + select_X = train_X_true[select_index] + + ## TODO + exs = select_X.predict() + rule = get_rules(exs) + if rule != None: + break + rules.append(rule) + return rules + + +def get_mlp_vector(X, rules): + + ## TODO + exs = np.argmax(model.predict(X)) + + vector = [] + for rule in rules: + if abducer.kb.consist_rule(exs, rule): + vector.append(1) + else: + vector.append(0) + return vector + +def get_mlp_data(X_true, X_false, rules): + mlp_vectors = [] + mlp_labels = [] + for X in X_true: + mlp_vectors.append(get_mlp_vector(X, rules)) + mlp_labels.append(1) + for X in X_false: + mlp_vectors.append(get_mlp_vector(X, rules)) + mlp_labels.append(0) + + return np.array(mlp_vectors), np.array(mlp_labels) + + +def validation(train_X_true, train_X_false, val_X_true, val_X_false): + print("Now checking if we can go to next course") + samples_per_rule = 3 + logic_output_dim = 50 + print("Now checking if we can go to next course") + rules = get_rules_from_data(train_X_true, samples_per_rule, logic_output_dim) + mlp_train_vectors, mlp_train_labels = get_mlp_data(train_X_true, train_X_false, rules) + + index = np.array(list(range(len(mlp_train_labels)))) + np.random.shuffle(index) + mlp_train_vectors = mlp_train_vectors[index] + mlp_train_labels = mlp_train_labels[index] + + best_accuracy = 0 + + #Try three times to find the best mlp + for _ in range(3): + print("Training mlp...") + + ### TODO + # mlp_model = get_mlp_net(logic_output_dim) + # mlp_model.compile(loss='binary_crossentropy', + # optimizer='rmsprop', + # metrics=['accuracy']) + # mlp_model.fit(mlp_train_vectors, + # mlp_train_labels, + # epochs=MLP_EPOCHS, + # batch_size=MLP_BATCHSIZE, + # verbose=0) + #Prepare MLP validation data + + mlp_val_vectors, mlp_val_labels = get_mlp_data(val_X_true, val_X_false, rules) + + ## TODO + #Get MLP validation result + # result = mlp_model.evaluate(mlp_val_vectors, + # mlp_val_labels, + # batch_size=MLP_BATCHSIZE, + # verbose=0) + print("MLP validation result:", result) + accuracy = result[1] + + if accuracy > best_accuracy: + best_accuracy = accuracy + return best_accuracy + + + +def train_HED(model, abducer, train_data, test_data, epochs=50, select_num=10, verbose=-1): + train_X, train_Z, train_Y = train_data + test_X, test_Z, test_Y = test_data + + min_len = 5 + max_len = 8 + + cp_threshold = 0.9 + blmp_threshold = 0.9 + + cnt_threshold = 5 + acc_threshold = 0.86 + + # Start training / for each length of equations + for equation_len in range(min_len, max_len): + + ### TODO: get_data, e.g. + # train_X_true = train_X['True'][equation_len] + # train_X_true.append(train_X['True'][equation_len + 1]) + + + while True: + # Abduce and train NN + consistent_percentage, batch_label_model_precision = abduce_and_train(model, abducer, train_X_true, select_num) + if consistent_percentage == 0: + continue + + # Test if we can use mlp to evaluate + if consistent_percentage >= cp_threshold and batch_label_model_precision >= blmp_threshold: + condition_cnt += 1 + else: + condition_cnt = 0 + # The condition has been satisfied continuously five times + if condition_cnt >= cnt_threshold: + best_accuracy = validation(train_X_true, train_X_false, val_X_true, val_X_false) + + # decide next course or restart + if best_accuracy > acc_threshold: + # Save model and go to next course + ## TODO: model.save_weights() + break + + else: + # Restart current course: reload model + if equation_len == min_len: + ## TODO: model.set_weights(pretrain_model.get_weights()) + model.set_weights() + else: + ## TODO: model.load_weights() + model.load_weights() + print("Failed! Reload model.") + condition_cnt = 0 + + + + return model + + +if __name__ == "__main__": + pass From f33f80737b3aa82db08735c60f2d9056b00523c7 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Sun, 19 Feb 2023 18:11:58 +0800 Subject: [PATCH 126/601] complete the reproduce of ABL-HED --- .gitignore | 5 +- abducer/abducer_base.py | 115 +++++-- abducer/kb.py | 173 +++++++--- datasets/hed/BK.pl | 83 +++++ datasets/hed/equation_generator.py | 266 +++++++++++++++ datasets/hed/get_hed.py | 130 +++++++ datasets/hed/learn_add.pl | 81 +++++ example.py | 86 +++-- framework_hed.py | 532 ++++++++++++++++++----------- models/basic_model.py | 120 ++++--- models/nn.py | 152 +++++++++ models/wabl_models.py | 75 ++-- utils/plog.py | 47 +-- utils/utils.py | 46 ++- 14 files changed, 1513 insertions(+), 398 deletions(-) create mode 100644 datasets/hed/BK.pl create mode 100644 datasets/hed/equation_generator.py create mode 100644 datasets/hed/get_hed.py create mode 100644 datasets/hed/learn_add.pl create mode 100644 models/nn.py diff --git a/.gitignore b/.gitignore index 8ebaf8d..8ef59e4 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ *.pyc /results -raw/ \ No newline at end of file +raw/ +*.jpg +*.png +*.pk \ No newline at end of file diff --git a/abducer/abducer_base.py b/abducer/abducer_base.py index 72884f9..c0fd102 100644 --- a/abducer/abducer_base.py +++ b/abducer/abducer_base.py @@ -26,9 +26,16 @@ import time class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func='confidence', zoopt=False, multiple_predictions=False, cache=True): + def __init__( + self, + kb, + dist_func="confidence", + zoopt=False, + multiple_predictions=False, + cache=True, + ): self.kb = kb - assert dist_func == 'hamming' or dist_func == 'confidence' + assert dist_func == "hamming" or dist_func == "confidence" self.dist_func = dist_func self.zoopt = zoopt self.multiple_predictions = multiple_predictions @@ -39,11 +46,18 @@ class AbducerBase(abc.ABC): self.cache_candidates = {} def _get_cost_list(self, pred_res, pred_res_prob, candidates): - if self.dist_func == 'hamming': + if self.dist_func == "hamming": return hamming_dist(pred_res, candidates) - elif self.dist_func == 'confidence': - mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) - return confidence_dist(pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates]) + elif self.dist_func == "confidence": + mapping = dict( + zip( + self.kb.pseudo_label_list, + list(range(len(self.kb.pseudo_label_list))), + ) + ) + return confidence_dist( + pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates] + ) def _get_one_candidate(self, pred_res, pred_res_prob, candidates): if len(candidates) == 0: @@ -60,9 +74,13 @@ class AbducerBase(abc.ABC): def _zoopt_score_multiple(self, pred_res, key, solution): all_address_flag = reform_idx(solution, pred_res) score = 0 - for idx in enumerate(len(pred_res)): - address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] - candidate = self.kb.address_by_idx([pred_res[idx]], key[idx], address_idx, True) + for idx in range(len(pred_res)): + address_idx = [ + i for i, flag in enumerate(all_address_flag[idx]) if flag != 0 + ] + candidate = self.kb.address_by_idx( + [pred_res[idx]], key[idx], address_idx, True + ) if len(candidate) > 0: score += 1 return score @@ -70,7 +88,9 @@ class AbducerBase(abc.ABC): def _zoopt_address_score(self, pred_res, key, sol): if not self.multiple_predictions: address_idx = [idx for idx, i in enumerate(sol.get_x()) if i != 0] - candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + candidates = self.kb.address_by_idx( + pred_res, key, address_idx, self.multiple_predictions + ) return 1 if len(candidates) > 0 else 0 else: return self._zoopt_score_multiple(pred_res, key, sol.get_x()) @@ -98,7 +118,11 @@ class AbducerBase(abc.ABC): pred_res = flatten(pred_res) key = tuple(key) if (tuple(pred_res), key) in self.cache_min_address_num: - address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) + address_num = min( + max_address_num, + self.cache_min_address_num[(tuple(pred_res), key)] + + require_more_address, + ) if (tuple(pred_res), key, address_num) in self.cache_candidates: candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] if self.zoopt: @@ -127,12 +151,18 @@ class AbducerBase(abc.ABC): if self.zoopt: solution = self.zoopt_get_solution(pred_res, key, max_address_num) address_idx = [idx for idx, i in enumerate(solution) if i != 0] - candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + candidates = self.kb.address_by_idx( + pred_res, key, address_idx, self.multiple_predictions + ) address_num = int(solution.sum()) min_address_num = address_num else: candidates, min_address_num, address_num = self.kb.abduce_candidates( - pred_res, key, max_address_num, require_more_address, self.multiple_predictions + pred_res, + key, + max_address_num, + require_more_address, + self.multiple_predictions, ) candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) @@ -147,20 +177,31 @@ class AbducerBase(abc.ABC): def batch_abduce(self, Z, Y, max_address_num=-1, require_more_address=0): if self.multiple_predictions: - return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) + return self.abduce( + (Z["cls"], Z["prob"], Y), max_address_num, require_more_address + ) else: - return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] + return [ + self.abduce((z, prob, y), max_address_num, require_more_address) + for z, prob, y in zip(Z["cls"], Z["prob"], Y) + ] def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) -if __name__ == '__main__': - prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] - prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] +if __name__ == "__main__": + prob1 = [ + [0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], + [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], + ] + prob2 = [ + [0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], + [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], + ] kb = add_KB() - abd = AbducerBase(kb, 'confidence') + abd = AbducerBase(kb, "confidence") res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -174,7 +215,7 @@ if __name__ == '__main__': print() kb = add_prolog_KB() - abd = AbducerBase(kb, 'confidence') + abd = AbducerBase(kb, "confidence") res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -188,7 +229,7 @@ if __name__ == '__main__': print() kb = add_prolog_KB() - abd = AbducerBase(kb, 'confidence', zoopt=True) + abd = AbducerBase(kb, "confidence", zoopt=True) res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -202,24 +243,38 @@ if __name__ == '__main__': print() kb = HWF_KB(len_list=[1, 3, 5]) - abd = AbducerBase(kb, 'hamming') - res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) + abd = AbducerBase(kb, "hamming") + res = abd.abduce( + (["5", "+", "2"], None, 3), max_address_num=2, require_more_address=0 + ) print(res) - res = abd.abduce((['5', '+', '2'], None, 64), max_address_num=3, require_more_address=0) + res = abd.abduce( + (["5", "+", "2"], None, 64), max_address_num=3, require_more_address=0 + ) print(res) - res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0) + res = abd.abduce( + (["5", "+", "2"], None, 1.67), max_address_num=3, require_more_address=0 + ) print(res) - res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3) + res = abd.abduce( + (["5", "8", "8", "8", "8"], None, 3.17), + max_address_num=5, + require_more_address=3, + ) print(res) print() kb = HED_prolog_KB() abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) - consist_exs = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 0, '=', 1, 1]] - consist_exs2 = [[1, '+', 0, '=', 0], [1, '+', 1, '=', 0], [0, '+', 1, '=', 1, 1]] # not consistent with rules - inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] + consist_exs = [[1, "+", 0, "=", 0], [1, "+", 1, "=", 0], [0, "+", 0, "=", 1, 1]] + consist_exs2 = [ + [1, "+", 0, "=", 0], + [1, "+", 1, "=", 0], + [0, "+", 1, "=", 1, 1], + ] # not consistent with rules + inconsist_exs = [[1, "+", 0, "=", 0], [1, "=", 1, "=", 0], [0, "=", 0, "=", 1, 1]] # inconsist_exs = [[1, '+', 0, '=', 0], ['=', '=', '=', '=', 0], ['=', '=', 0, '=', '=', '=']] - rules = ['my_op([0], [0], [1, 1])', 'my_op([1], [1], [0])', 'my_op([1], [0], [0])'] + rules = ["my_op([0], [0], [1, 1])", "my_op([1], [1], [0])", "my_op([1], [0], [0])"] print(kb.logic_forward(consist_exs), kb.logic_forward(inconsist_exs)) print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) diff --git a/abducer/kb.py b/abducer/kb.py index 6e5f0d3..04a9f68 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -43,16 +43,29 @@ class KBBase(ABC): def address(self, address_num, pred_res, key, multiple_predictions=False): new_candidates = [] if not multiple_predictions: - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + address_idx_list = list( + combinations(list(range(len(pred_res))), address_num) + ) else: - address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num)) + address_idx_list = list( + combinations(list(range(len(flatten(pred_res)))), address_num) + ) for address_idx in address_idx_list: - candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) + candidates = self.address_by_idx( + pred_res, key, address_idx, multiple_predictions + ) new_candidates += candidates return new_candidates - def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions=False): + def abduction( + self, + pred_res, + key, + max_address_num, + require_more_address, + multiple_predictions=False, + ): candidates = [] for address_num in range(len(pred_res) + 1): @@ -60,7 +73,9 @@ class KBBase(ABC): if abs(self.logic_forward(pred_res) - key) <= 1e-3: candidates.append(pred_res) else: - new_candidates = self.address(address_num, pred_res, key, multiple_predictions) + new_candidates = self.address( + address_num, pred_res, key, multiple_predictions + ) candidates += new_candidates if len(candidates) > 0: @@ -70,10 +85,14 @@ class KBBase(ABC): if address_num >= max_address_num: return [], 0, 0 - for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): + for address_num in range( + min_address_num + 1, min_address_num + require_more_address + 1 + ): if address_num > max_address_num: return candidates, min_address_num, address_num - 1 - new_candidates = self.address(address_num, pred_res, key, multiple_predictions) + new_candidates = self.address( + address_num, pred_res, key, multiple_predictions + ) candidates += new_candidates return candidates, min_address_num, address_num @@ -98,7 +117,9 @@ class ClsKB(KBBase): else: self.all_address_candidate_dict = {} for address_num in range(max(self.len_list) + 1): - self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat=address_num)) + self.all_address_candidate_dict[address_num] = list( + product(self.pseudo_label_list, repeat=address_num) + ) # For parallel version of _get_GKB def _get_XY_list(self, args): @@ -143,11 +164,26 @@ class ClsKB(KBBase): def logic_forward(self): pass - def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): + def abduce_candidates( + self, + pred_res, + key, + max_address_num=-1, + require_more_address=0, + multiple_predictions=False, + ): if self.GKB_flag: - return self.abduce_from_GKB(pred_res, key, max_address_num, require_more_address) + return self.abduce_from_GKB( + pred_res, key, max_address_num, require_more_address + ) else: - return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) + return self.abduction( + pred_res, + key, + max_address_num, + require_more_address, + multiple_predictions, + ) def abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): if self.base == {} or len(pred_res) not in self.len_list: @@ -211,7 +247,24 @@ class add_KB(ClsKB): class HWF_KB(ClsKB): def __init__( - self, GKB_flag=False, pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], len_list=[1, 3, 5, 7] + self, + GKB_flag=False, + pseudo_label_list=[ + "1", + "2", + "3", + "4", + "5", + "6", + "7", + "8", + "9", + "+", + "-", + "times", + "div", + ], + len_list=[1, 3, 5, 7], ): super().__init__(GKB_flag, pseudo_label_list, len_list) @@ -219,9 +272,19 @@ class HWF_KB(ClsKB): if len(formula) % 2 == 0: return False for i in range(len(formula)): - if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']: + if i % 2 == 0 and formula[i] not in [ + "1", + "2", + "3", + "4", + "5", + "6", + "7", + "8", + "9", + ]: return False - if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: + if i % 2 != 0 and formula[i] not in ["+", "-", "times", "div"]: return False return True @@ -229,22 +292,22 @@ class HWF_KB(ClsKB): if not self.valid_candidate(formula): return np.inf mapping = { - '1': '1', - '2': '2', - '3': '3', - '4': '4', - '5': '5', - '6': '6', - '7': '7', - '8': '8', - '9': '9', - '+': '+', - '-': '-', - 'times': '*', - 'div': '/', + "1": "1", + "2": "2", + "3": "3", + "4": "4", + "5": "5", + "6": "6", + "7": "7", + "8": "8", + "9": "9", + "+": "+", + "-": "-", + "times": "*", + "div": "/", } formula = [mapping[f] for f in formula] - return round(eval(''.join(formula)), 2) + return round(eval("".join(formula)), 2) class prolog_KB(KBBase): @@ -256,8 +319,12 @@ class prolog_KB(KBBase): def logic_forward(self): pass - def abduce_candidates(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): - return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) + def abduce_candidates( + self, pred_res, key, max_address_num, require_more_address, multiple_predictions + ): + return self.abduction( + pred_res, key, max_address_num, require_more_address, multiple_predictions + ) def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] @@ -265,7 +332,9 @@ class prolog_KB(KBBase): if not multiple_predictions: query_string = self.get_query_string(pred_res, key, address_idx) else: - query_string = self.get_query_string_need_flatten(pred_res, key, address_idx) + query_string = self.get_query_string_need_flatten( + pred_res, key, address_idx + ) if multiple_predictions: save_pred_res = pred_res @@ -289,24 +358,28 @@ class add_prolog_KB(prolog_KB): super().__init__(pseudo_label_list) for i in self.pseudo_label_list: self.prolog.assertz("pseudo_label(%s)" % i) - self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") + self.prolog.assertz( + "addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2" + ) def logic_forward(self, nums): - return list(self.prolog.query("addition(%s, %s, Res)." % (nums[0], nums[1])))[0]['Res'] + return list(self.prolog.query("addition(%s, %s, Res)." % (nums[0], nums[1])))[ + 0 + ]["Res"] def get_query_string(self, pred_res, key, address_idx): query_string = "addition(" for idx, i in enumerate(pred_res): - tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' + tmp = "Z" + str(idx) + "," if idx in address_idx else str(i) + "," query_string += tmp query_string += "%s)." % key return query_string class HED_prolog_KB(prolog_KB): - def __init__(self, pseudo_label_list=[0, 1, '+', '=']): + def __init__(self, pseudo_label_list=[0, 1, "+", "="]): super().__init__(pseudo_label_list) - self.prolog.consult('./datasets/hed/learn_add.pl') + self.prolog.consult("./datasets/hed/learn_add.pl") # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` def logic_forward(self, exs): @@ -318,7 +391,7 @@ class HED_prolog_KB(prolog_KB): # add variables for prolog for idx in range(len(flatten_pred_res)): if idx in address_idx: - flatten_pred_res[idx] = 'X' + str(idx) + flatten_pred_res[idx] = "X" + str(idx) # unflatten new_pred_res = reform_idx(flatten_pred_res, pred_res) @@ -326,12 +399,30 @@ class HED_prolog_KB(prolog_KB): return query_string.replace("'", "").replace("+", "'+'").replace("=", "'='") def consist_rule(self, exs, rules): - rule_str = "%s" % rules - rule_str = rule_str.replace("'", "") - return len(list(self.prolog.query("consistent_inst_feature(%s, %s)." % (exs, rule_str)))) != 0 + consist = False + for rule in rules: + # print(rule) + if ( + len( + list( + self.prolog.query( + "consistent_inst_feature(%s, [%s])." % (exs, rule) + ) + ) + ) + != 0 + ): + consist = True + break + return consist def abduce_rules(self, pred_res): - prolog_rules = list(self.prolog.query("consistent_inst_feature(%s, X)." % pred_res))[0]['X'] + prolog_result = list( + self.prolog.query("consistent_inst_feature(%s, X)." % pred_res) + ) + if len(prolog_result) == 0: + return None + prolog_rules = prolog_result[0]["X"] rules = [] for rule in prolog_rules: rules.append(rule.value) diff --git a/datasets/hed/BK.pl b/datasets/hed/BK.pl new file mode 100644 index 0000000..441df4e --- /dev/null +++ b/datasets/hed/BK.pl @@ -0,0 +1,83 @@ +:- use_module(library(apply)). +:- use_module(library(lists)). +% :- use_module(library(tabling)). +% :- table valid_rules/2, op_rule/2. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% DCG parser for equations +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% symbols to be mapped +digit(1). +digit(0). + +% digits +digits([D]) --> [D], { digit(D) }. % empty list [] is not a digit +digits([D | T]) --> [D], !, digits(T), { digit(D) }. +digits(X):- + phrase(digits(X), X). +% More integrity constraints 1: +% This two clauses forbid the first digit to be 0. +% You may uncomment them to prune the search space +% length(X, L), +% (L > 1 -> X \= [0 | _]; true). + +% Equation definition +eq_arg([D]) --> [D], { \+ D == '+', \+ D == '=' }. +eq_arg([D | T]) --> [D], !, eq_arg(T), { \+ D == '+', \+ D == '=' }. +equation(eq(X, Y, Z)) --> + eq_arg(X), [+], eq_arg(Y), [=], eq_arg(Z). +% More integrity constraints 2: +% This clause restricts the length of arguments to be sane, +% You may uncomment them to prune the search space +% { length(X, LX), length(Y, LY), length(Z, LZ), +% LZ =< max(LX, LY) + 1, LZ >= max(LX, LY) }. +parse_eq(List_of_Terms, Eq) :- + phrase(equation(Eq), List_of_Terms). + +%%%%%%%%%%%%%%%%%%%%%% +%% Bit-wise operation +%%%%%%%%%%%%%%%%%%%%%% +% Abductive calculation with given pseudo-labels, abduces pseudo-labels as well as operation rules +calc(Rules, Pseudo) :- + calc([], Rules, Pseudo). +calc(Rules0, Rules1, Pseudo) :- + parse_eq(Pseudo, eq(X,Y,Z)), + bitwise_calc(Rules0, Rules1, X, Y, Z). + +% Bit-wise calculation that handles carrying +bitwise_calc(Rules, Rules1, X, Y, Z) :- + reverse(X, X1), reverse(Y, Y1), reverse(Z, Z1), + bitwise_calc_r(Rules, Rules1, X1, Y1, Z1), + maplist(digits, [X,Y,Z]). +bitwise_calc_r(Rs, Rs, [], Y, Y). +bitwise_calc_r(Rs, Rs, X, [], X). +bitwise_calc_r(Rules, Rules1, [D1 | X], [D2 | Y], [D3 | Z]) :- + abduce_op_rule(my_op([D1],[D2],Sum), Rules, Rules2), + ((Sum = [D3], Carry = []); (Sum = [C, D3], Carry = [C])), + bitwise_calc_r(Rules2, Rules3, X, Carry, X_carried), + bitwise_calc_r(Rules3, Rules1, X_carried, Y, Z). + +%%%%%%%%%%%%%%%%%%%%%%%%% +% Abduce operation rules +%%%%%%%%%%%%%%%%%%%%%%%%% +% Get an existed rule +abduce_op_rule(R, Rules, Rules) :- + member(R, Rules). +% Add a new rule +abduce_op_rule(R, Rules, [R|Rules]) :- + op_rule(R), + valid_rules(Rules, R). + +% Integrity Constraints +valid_rules([], _). +valid_rules([my_op([X1],[Y1],_)|Rs], my_op([X],[Y],Z)) :- + op_rule(my_op([X],[Y],Z)), + [X,Y] \= [X1,Y1], + [X,Y] \= [Y1,X1], + valid_rules(Rs, my_op([X],[Y],Z)). +valid_rules([my_op([Y],[X],Z)|Rs], my_op([X],[Y],Z)) :- + X \= Y, + valid_rules(Rs, my_op([X],[Y],Z)). + +op_rule(my_op([X],[Y],[Z])) :- digit(X), digit(Y), digit(Z). +op_rule(my_op([X],[Y],[Z1,Z2])) :- digit(X), digit(Y), digits([Z1,Z2]). diff --git a/datasets/hed/equation_generator.py b/datasets/hed/equation_generator.py new file mode 100644 index 0000000..3cf735b --- /dev/null +++ b/datasets/hed/equation_generator.py @@ -0,0 +1,266 @@ +import os +import itertools +import random +import numpy as np +from PIL import Image +import pickle + + +def get_sign_path_list(data_dir, sign_names): + sign_num = len(sign_names) + index_dict = dict(zip(sign_names, list(range(sign_num)))) + ret = [[] for _ in range(sign_num)] + for path in os.listdir(data_dir): + if path in sign_names: + index = index_dict[path] + sign_path = os.path.join(data_dir, path) + for p in os.listdir(sign_path): + ret[index].append(os.path.join(sign_path, p)) + return ret + + +def split_pool_by_rate(pools, rate, seed=None): + if seed is not None: + random.seed(seed) + ret1 = [] + ret2 = [] + for pool in pools: + random.shuffle(pool) + num = int(len(pool) * rate) + ret1.append(pool[:num]) + ret2.append(pool[num:]) + return ret1, ret2 + + +def int_to_system_form(num, system_num): + if num is 0: + return "0" + ret = "" + while num > 0: + ret += str(num % system_num) + num //= system_num + return ret[::-1] + + +def generator_equations( + left_opt_len, right_opt_len, res_opt_len, system_num, label, generate_type +): + expr_len = left_opt_len + right_opt_len + num_list = "".join([str(i) for i in range(system_num)]) + ret = [] + if generate_type == "all": + candidates = itertools.product(num_list, repeat=expr_len) + else: + candidates = ["".join(random.sample(["0", "1"] * expr_len, expr_len))] + random.shuffle(candidates) + for nums in candidates: + left_num = "".join(nums[:left_opt_len]) + right_num = "".join(nums[left_opt_len:]) + left_value = int(left_num, system_num) + right_value = int(right_num, system_num) + result_value = left_value + right_value + if label == "negative": + result_value += random.randint(-result_value, result_value) + if left_value + right_value == result_value: + continue + result_num = int_to_system_form(result_value, system_num) + # leading zeros + if res_opt_len != len(result_num): + continue + if (left_opt_len > 1 and left_num[0] == "0") or ( + right_opt_len > 1 and right_num[0] == "0" + ): + continue + + # add leading zeros + if res_opt_len < len(result_num): + continue + while len(result_num) < res_opt_len: + result_num = "0" + result_num + # continue + ret.append( + left_num + "+" + right_num + "=" + result_num + ) # current only consider '+' and '=' + # print(ret[-1]) + return ret + + +def generator_equation_by_len(equation_len, system_num=2, label=0, require_num=1): + generate_type = "one" + ret = [] + equation_sign_num = 2 # '+' and '=' + while len(ret) < require_num: + left_opt_len = random.randint(1, equation_len - 1 - equation_sign_num) + right_opt_len = random.randint( + 1, equation_len - left_opt_len - equation_sign_num + ) + res_opt_len = equation_len - left_opt_len - right_opt_len - equation_sign_num + ret.extend( + generator_equations( + left_opt_len, + right_opt_len, + res_opt_len, + system_num, + label, + generate_type, + ) + ) + return ret + + +def generator_equations_by_len( + equation_len, system_num=2, label=0, repeat_times=1, keep=1, generate_type="all" +): + ret = [] + equation_sign_num = 2 # '+' and '=' + for left_opt_len in range(1, equation_len - (2 + equation_sign_num) + 1): + for right_opt_len in range( + 1, equation_len - left_opt_len - (1 + equation_sign_num) + 1 + ): + res_opt_len = ( + equation_len - left_opt_len - right_opt_len - equation_sign_num + ) + for i in range(repeat_times): # generate more equations + if random.random() > keep ** (equation_len): + continue + ret.extend( + generator_equations( + left_opt_len, + right_opt_len, + res_opt_len, + system_num, + label, + generate_type, + ) + ) + return ret + + +def generator_equations_by_max_len( + max_equation_len, + system_num=2, + label=0, + repeat_times=1, + keep=1, + generate_type="all", + num_per_len=None, +): + ret = [] + equation_sign_num = 2 # '+' and '=' + for equation_len in range(3 + equation_sign_num, max_equation_len + 1): + if num_per_len is None: + ret.extend( + generator_equations_by_len( + equation_len, system_num, label, repeat_times, keep, generate_type + ) + ) + else: + ret.extend( + generator_equation_by_len( + equation_len, system_num, label, require_num=num_per_len + ) + ) + return ret + + +def generator_equation_images(image_pools, equations, signs, shape, seed, is_color): + if seed is not None: + random.seed(seed) + ret = [] + sign_num = len(signs) + sign_index_dict = dict(zip(signs, list(range(sign_num)))) + for equation in equations: + data = [] + for sign in equation: + index = sign_index_dict[sign] + pick = random.randint(0, len(image_pools[index]) - 1) + if is_color: + image = ( + Image.open(image_pools[index][pick]).convert("RGB").resize(shape) + ) + else: + image = Image.open(image_pools[index][pick]).convert("I").resize(shape) + image_array = np.array(image) + image_array = (image_array - 127) * (1.0 / 128) + data.append(image_array) + ret.append(np.array(data)) + return ret + + +def get_equation_std_data( + data_dir, + sign_dir_lists, + sign_output_lists, + shape=(28, 28), + train_max_equation_len=10, + test_max_equation_len=10, + system_num=2, + tmp_file_prev=None, + seed=None, + train_num_per_len=10, + test_num_per_len=10, + is_color=False, +): + tmp_file = "" + if tmp_file_prev is not None: + tmp_file = "%s_train_len_%d_test_len_%d_sys_%d_.pk" % ( + tmp_file_prev, + train_max_equation_len, + test_max_equation_len, + system_num, + ) + if os.path.exists(tmp_file): + return pickle.load(open(tmp_file, "rb")) + + image_pools = get_sign_path_list(data_dir, sign_dir_lists) + train_pool, test_pool = split_pool_by_rate(image_pools, 0.8, seed) + + ret = {} + for label in ["positive", "negative"]: + print("Generating equations.") + train_equations = generator_equations_by_max_len( + train_max_equation_len, system_num, label, num_per_len=train_num_per_len + ) + test_equations = generator_equations_by_max_len( + test_max_equation_len, system_num, label, num_per_len=test_num_per_len + ) + print(train_equations) + print(test_equations) + print("Generated equations.") + print("Generating equation image data.") + ret["train:%s" % (label)] = generator_equation_images( + train_pool, train_equations, sign_output_lists, shape, seed, is_color + ) + ret["test:%s" % (label)] = generator_equation_images( + test_pool, test_equations, sign_output_lists, shape, seed, is_color + ) + print("Generated equation image data.") + + if tmp_file_prev is not None: + pickle.dump(ret, open(tmp_file, "wb")) + return ret + + +if __name__ == "__main__": + data_dirs = [ + "./dataset/mnist_images", + "./dataset/random_images", + ] # , "../dataset/cifar10_images"] + tmp_file_prevs = [ + "mnist_equation_data", + "random_equation_data", + ] # , "cifar10_equation_data"] + for data_dir, tmp_file_prev in zip(data_dirs, tmp_file_prevs): + data = get_equation_std_data( + data_dir=data_dir, + sign_dir_lists=["0", "1", "10", "11"], + sign_output_lists=["0", "1", "+", "="], + shape=(28, 28), + train_max_equation_len=26, + test_max_equation_len=26, + system_num=2, + tmp_file_prev=tmp_file_prev, + train_num_per_len=300, + test_num_per_len=300, + is_color=False, + ) diff --git a/datasets/hed/get_hed.py b/datasets/hed/get_hed.py new file mode 100644 index 0000000..8b61b62 --- /dev/null +++ b/datasets/hed/get_hed.py @@ -0,0 +1,130 @@ +import os +import cv2 +import torch +import torchvision +import pickle +import numpy as np +import random +from collections import defaultdict +from torch.utils.data import Dataset +from torchvision.transforms import transforms + + +def get_data(img_dataset, train): + transform = transforms.Compose([transforms.ToTensor()]) + X = [] + Y = [] + if train: + positive = img_dataset["train:positive"] + negative = img_dataset["train:negative"] + else: + positive = img_dataset["test:positive"] + negative = img_dataset["test:negative"] + + for equation in positive: + equation = equation.astype(np.float32) + img_list = np.vsplit(equation, equation.shape[0]) + X.append(img_list) + Y.append(1) + + for equation in negative: + equation = equation.astype(np.float32) + img_list = np.vsplit(equation, equation.shape[0]) + X.append(img_list) + Y.append(0) + + return X, None, Y + + +def get_pretrain_data(labels, image_size=(28, 28, 1)): + transform = transforms.Compose([transforms.ToTensor()]) + X = [] + for label in labels: + label_path = os.path.join( + "./datasets/hed/dataset/mnist_images", label + ) + img_path_list = os.listdir(label_path) + for img_path in img_path_list: + img = cv2.imread( + os.path.join(label_path, img_path), cv2.IMREAD_GRAYSCALE + ) + img = cv2.resize(img, (image_size[1], image_size[0])) + X.append(np.array(img, dtype=np.float32)) + + X = [((img[:, :, np.newaxis] - 127) / 128.0) for img in X] + Y = [img.copy().reshape(image_size[0] * image_size[1] * image_size[2]) for img in X] + + X = [transform(img) for img in X] + return X, Y + + +# def get_pretrain_data(train_data, image_size=(28, 28, 1)): +# X = [] +# for label in [0, 1]: +# for _, equation_list in train_data[label].items(): +# for equation in equation_list: +# X = X + equation + +# X = np.array(X) +# index = np.array(list(range(len(X)))) +# np.random.shuffle(index) +# X = X[index] + +# X = [img for img in X] +# Y = [img.copy().reshape(image_size[0] * image_size[1] * image_size[2]) for img in X] + +# return X, Y + + +def divide_equations_by_len(equations, labels): + equations_by_len = {1: defaultdict(list), 0: defaultdict(list)} + for i, equation in enumerate(equations): + equations_by_len[labels[i]][len(equation)].append(equation) + return equations_by_len + + +def split_equation(equations_by_len, prop_train, prop_val): + """ + Split the equations in each length to training and validation data according to the proportion + """ + train_equations_by_len = {1: dict(), 0: dict()} + val_equations_by_len = {1: dict(), 0: dict()} + + for label in range(2): + for equation_len, equations in equations_by_len[label].items(): + random.shuffle(equations) + train_equations_by_len[label][equation_len] = equations[ + : len(equations) // (prop_train + prop_val) * prop_train + ] + val_equations_by_len[label][equation_len] = equations[ + len(equations) // (prop_train + prop_val) * prop_train : + ] + + return train_equations_by_len, val_equations_by_len + + +def get_hed(dataset="mnist", train=True): + + if dataset == "mnist": + with open( + "./datasets/hed/mnist_equation_data_train_len_26_test_len_26_sys_2_.pk", + "rb", + ) as f: + img_dataset = pickle.load(f) + elif dataset == "random": + with open( + "./datasets/hed/random_equation_data_train_len_26_test_len_26_sys_2_.pk", + "rb", + ) as f: + img_dataset = pickle.load(f) + else: + raise Exception("Undefined dataset") + + X, _, Y = get_data(img_dataset, train) + equations_by_len = divide_equations_by_len(X, Y) + + return equations_by_len + + +if __name__ == "__main__": + get_hed() diff --git a/datasets/hed/learn_add.pl b/datasets/hed/learn_add.pl new file mode 100644 index 0000000..af1d6bb --- /dev/null +++ b/datasets/hed/learn_add.pl @@ -0,0 +1,81 @@ +:- ensure_loaded(['BK.pl']). +:- thread_setconcurrency(_, 8). +:- use_module(library(thread)). + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% For propositionalisation +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +eval_inst_feature(Ex, Feature):- + eval_eq(Ex, Feature). + +%% Evaluate instance given feature +eval_eq(Ex, Feature):- + parse_eq(Ex, eq(X,Y,Z)), + bitwise_calc(Feature,_,X,Y,Z), !. + +%%%%%%%%%%%%%% +%% Abduction +%%%%%%%%%%%%%% +% Make abduction when given examples that have been interpreted as pseudo-labels +abduce(Exs, Delta_C) :- + abduce(Exs, [], Delta_C). +abduce([], Delta_C, Delta_C). +abduce([E|Exs], Delta_C0, Delta_C1) :- + calc(Delta_C0, Delta_C2, E), + abduce(Exs, Delta_C2, Delta_C1). + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Abduce pseudo-labels only +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +abduce_consistent_insts(Exs):- + abduce(Exs, _), !. +% (Experimental) Uncomment to use parallel abduction +% abduce_consistent_exs_concurrent(Exs), !. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Abduce Delta_C given pseudo-labels +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +consistent_inst_feature(Exs, Delta_C):- + abduce(Exs, Delta_C), !. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% (Experimental) Parallel abduction +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +abduce_consistent_exs_concurrent(Exs) :- + % Split the current data batch into grounding examples and variable examples (which need to be revised) + split_exs(Exs, Ground_Exs, Var_Exs), + % Find the simplest Delta_C for grounding examples. + abduce(Ground_Exs, Ground_Delta_C), !, + % Extend Ground Delta_C into all possible variations + extend_op_rule(Ground_Delta_C, Possible_Deltas), + % Concurrently abduce the variable examples + maplist(append([abduce2, Var_Exs, Ground_Exs]), [[Possible_Deltas]], Call_List), + maplist(=.., Goals, Call_List), + % writeln(Goals), + first_solution(Var_Exs, Goals, [local(inf)]). + +split_exs([],[],[]). +split_exs([E | Exs], [E | G_Exs], V_Exs):- + ground(E), !, + split_exs(Exs, G_Exs, V_Exs). +split_exs([E | Exs], G_Exs, [E | V_Exs]):- + split_exs(Exs, G_Exs, V_Exs). + +:- table extend_op_rule/2. + +extend_op_rule(Rules, Rules) :- + length(Rules, 4). +extend_op_rule(Rules, Ext) :- + op_rule(R), + valid_rules(Rules, R), + extend_op_rule([R|Rules], Ext). + +% abduction without learning new Delta_C (Because they have been extended!) +abduce2([], _, _). +abduce2([E|Exs], Ground_Exs, Delta_C) :- + % abduce by finding ground examples + member(E, Ground_Exs), + abduce2(Exs, Ground_Exs, Delta_C). +abduce2([E|Exs], Ground_Exs, Delta_C) :- + eval_inst_feature(E, Delta_C), + abduce2(Exs, Ground_Exs, Delta_C). diff --git a/example.py b/example.py index 7ceaeff..795d455 100644 --- a/example.py +++ b/example.py @@ -1,60 +1,102 @@ # coding: utf-8 -#================================================================# +# ================================================================# # Copyright (C) 2021 Freecss All rights reserved. -# +# # File Name ：share_example.py # Author ：freecss # Email ：karlfreecss@gmail.com # Created Date ：2021/06/07 # Description ： # -#================================================================# +# ================================================================# -from utils.plog import logger -import framework +from utils.plog import logger, INFO +import framework_hed import torch.nn as nn import torch -from models.lenet5 import LeNet5, SymbolNet -from models.basic_model import BasicModel + +from models.nn import LeNet5, SymbolNet, SymbolNetAutoencoder +from models.basic_model import BasicModel, BasicDataset from models.wabl_models import WABLBasicModel from multiprocessing import Pool import os from abducer.abducer_base import AbducerBase -from abducer.kb import add_KB, hwf_KB +from abducer.kb import add_KB, HWF_KB, HED_prolog_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf +from datasets.hed.get_hed import get_hed, get_pretrain_data, split_equation + def run_test(): # kb = add_KB(True) - kb = hwf_KB(True) - abducer = AbducerBase(kb) + + # kb = hwf_KB(True) + # abducer = AbducerBase(kb) + + kb = HED_prolog_KB() + abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True) recorder = logger() - # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) - # test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) - - train_data = get_hwf(train = True, get_pseudo_label = True) - test_data = get_hwf(train = False, get_pseudo_label = True) + # train_X, train_Z, train_Y = get_mnist_add(train=True, get_pseudo_label=True) + # test_X, test_Z, test_Y = get_mnist_add(train=False, get_pseudo_label=True) + + # train_data = get_hwf(train=True, get_pseudo_label=True) + # test_data = get_hwf(train=False, get_pseudo_label=True) + + total_train_data = get_hed(train=True) + train_data, val_data = split_equation(total_train_data, 3, 1) + test_data = get_hed(train=False) # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) + cls_autoencoder = SymbolNetAutoencoder(num_classes=len(kb.pseudo_label_list)) cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) - + + if not os.path.exists("./weights/pretrain_weights.pth"): + pretrain_data_X, pretrain_data_Y = get_pretrain_data(["0", "1", "10", "11"]) + pretrain_data = BasicDataset(pretrain_data_X, pretrain_data_Y) + pretrain_data_loader = torch.utils.data.DataLoader( + pretrain_data, + batch_size=64, + shuffle=True, + ) + framework_hed.pretrain(cls_autoencoder, pretrain_data_loader, recorder) + torch.save( + cls_autoencoder.base_model.state_dict(), "./weights/pretrain_weights.pth" + ) + cls.load_state_dict(torch.load("./weights/pretrain_weights.pth")) + criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) + optimizer = torch.optim.RMSprop( + cls.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6 + ) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, num_epochs=1, recorder=recorder) + + base_model = BasicModel( + cls, + criterion, + optimizer, + device, + save_interval=1, + save_dir=recorder.save_dir, + batch_size=32, + num_epochs=10, + recorder=recorder, + ) + model = WABLBasicModel(base_model, kb.pseudo_label_list) - res = framework.train(model, abducer, train_data, test_data, sample_num = 10000, verbose = 1) - recorder.print(res) - + res = framework_hed.train_with_rule( + model, abducer, train_data, val_data, recorder=recorder + ) + INFO(res) + recorder.dump() return True + if __name__ == "__main__": run_test() diff --git a/framework_hed.py b/framework_hed.py index 1942e5c..04b41b8 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -1,261 +1,395 @@ +# coding: utf-8 +# ================================================================# +# Copyright (C) 2021 Freecss All rights reserved. +# +# File Name ：framework.py +# Author ：freecss +# Email ：karlfreecss@gmail.com +# Created Date ：2021/06/07 +# Description ： +# +# ================================================================# + +import pickle as pk + import numpy as np -from utils.utils import flatten, reform_idx - - -def get_rules_from_data(equations_true): - SAMPLES_PER_RULE = 3 - - select_index = np.random.randint(len(equations_true), size=SAMPLES_PER_RULE) - select_equations = np.array(equations_true)[select_index] - - -def get_consist_idx(exs, abducer): - consistent_ex_idx = [] - label = [] - for idx, e in enumerate(exs): - if abducer.kb.logic_forward([e]): - consistent_ex_idx.append(idx) - label.append(e) - return consistent_ex_idx, label - -def get_label(exs, solution, abducer): - all_address_flag = reform_idx(solution, exs) - consistent_ex_idx = [] - label = [] - for idx, ex in enumerate(exs): - address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] - candidate = abducer.kb.address_by_idx([ex], 1, address_idx, True) - if len(candidate) > 0: - consistent_ex_idx.append(idx) - label.append(candidate[0][0]) - return consistent_ex_idx, label - - -def get_percentage_precision(select_X, consistent_ex_idx, equation_label): - - images = [] - for idx in consistent_ex_idx: - images.append(select_X[idx]) - - ## TODO - model_labels = model.predict(images) - - assert(len(flatten(model_labels)) == len(flatten(equation_label))) - return (flatten(model_labels) == flatten(equation_label)).sum() / len(flatten(model_labels)) - - - - -def abduce_and_train(model, abducer, train_X_true, select_num): +import random + +random_seed = random.randint(0, 10000) +print("Selected random seed is : ", random_seed) +np.random.seed(random_seed) +random.seed(random_seed) + +from models.nn import MLP +from models.basic_model import BasicModel, BasicDataset +import torch.nn as nn +import torch + +from utils.plog import INFO, DEBUG, clocker +from utils.utils import flatten, reform_idx, block_sample + +from sklearn.tree import DecisionTreeClassifier + + +def result_statistics(pred_Z, Z, Y, logic_forward, char_acc_flag): + result = {} + if char_acc_flag: + char_acc_num = 0 + char_num = 0 + for pred_z, z in zip(pred_Z, Z): + char_num += len(z) + for zidx in range(len(z)): + if pred_z[zidx] == z[zidx]: + char_acc_num += 1 + char_acc = char_acc_num / char_num + result["Character level accuracy"] = char_acc + + abl_acc_num = 0 + for pred_z, y in zip(pred_Z, Y): + if logic_forward(pred_z) == y: + abl_acc_num += 1 + abl_acc = abl_acc_num / len(Y) + result["ABL accuracy"] = abl_acc + + return result + + +def filter_data(X, abduced_Z): + finetune_Z = [] + finetune_X = [] + for abduced_x, abduced_z in zip(X, abduced_Z): + if abduced_z is not []: + finetune_X.append(abduced_x) + finetune_Z.append(abduced_z) + return finetune_X, finetune_Z + + +def pretrain(net, pretrain_data_loader, recorder): + INFO("Pretrain Start") + + criterion = nn.MSELoss() + optimizer = torch.optim.RMSprop( + net.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6 + ) + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + pretrain_model = BasicModel( + net, + criterion, + optimizer, + device, + save_interval=1, + save_dir=recorder.save_dir, + num_epochs=10, + recorder=recorder, + ) + + pretrain_model.fit(pretrain_data_loader) + + +def get_char_acc(model, X, consistent_pred_res): + pred_res = flatten(model.predict(X)["cls"]) + assert len(pred_res) == len(flatten(consistent_pred_res)) + return sum( + [ + pred_res[idx] == flatten(consistent_pred_res)[idx] + for idx in range(len(pred_res)) + ] + ) / len(pred_res) + + +def gen_mappings(chars, symbs): + n_char = len(chars) + n_symbs = len(symbs) + if n_char != n_symbs: + INFO("Characters and symbols size dosen't match.") + return + from itertools import permutations + + mappings = [] + perms = permutations(symbs) + for p in perms: + mappings.append(dict(zip(chars, list(p)))) + return mappings + + +def map_res(original_pred_res, m): + pred_res = [[m[symbol] for symbol in formula] for formula in original_pred_res] + return pred_res + - select_index = np.random.randint(len(train_X_true), size=select_num) - select_X = train_X_true[select_index] - - - - exs = select_X.predict() - # e.g. when select_num == 10, exs = [[1, '+', 0, '=', 1, 0], [1, '+', 0, '=', 1, 0], [1, '+', 0, '=', 1, 0], [0, '+', 0, '=', 0], [1, '+', 0, '=', 1, 0],\ - # [1, '+', 0, '=', 1, 0], [1, '+', 0, '=', 1, 0], [1, '+', 0, '=', 1, 0], [0, '+', 0, '=', 0], [1, '+', 0, '=', 1, 0]] - - print("This is the model's current label:", exs) - - # 1. Check if it can abduce rules without changing any labels - consistent_ex_idx, equation_label = get_consist_idx(exs) - - - max_abduce_num = 10 - if len(consistent_ex_idx) == 0: - - # 2. Find the possible wrong position in symbols and Abduce the right symbol through logic module - solution = abducer.zoopt_get_solution(exs, [1] * len(exs), max_abduce_num) - consistent_ex_idx, equation_label = get_label(exs, solution, abducer) - - # Still cannot find - if len(consistent_ex_idx) == 0: - return 0, 0 - - - ## TODO: train - # train_pool_X = np.concatenate(select_X[consistent_ex_idx]).reshape( - # -1, h, w, d) - # train_pool_Y = np_utils.to_categorical( - # flatten(exs[consistent_ex_idx]), - # num_classes=len(labels)) # Convert the symbol to network output - # assert (len(train_pool_X) == len(train_pool_Y)) - # print("\nTrain pool size is :", len(train_pool_X)) - # print("Training...") - # base_model.fit(train_pool_X, - # train_pool_Y, - # batch_size=BATCHSIZE, - # epochs=NN_EPOCHS, - # verbose=0) - - # consistent_percentage, batch_label_model_precision = get_percentage_precision( - # base_model, select_equations, consist_re, shape) - - consistent_percentage = len(consistent_ex_idx) / select_num - batch_label_model_precision = get_percentage_precision(exs, consistent_ex_idx, equation_label) - - return consistent_percentage, batch_label_model_precision - -def get_rules(exs): - consistent_ex_idx, equation_label = get_consist_idx(exs) - consist_exs = [] - for idx in consistent_ex_idx: - consist_exs.append(exs[idx]) - if len(consist_exs) == 0: - return None - else: - return abducer.abduce_rule(consist_exs) - - - -def get_rules_from_data(train_X_true, samples_per_rule, logic_output_dim): +def abduce_and_train(model, abducer, train_X_true, select_num): + select_idx = np.random.randint(len(train_X_true), size=select_num) + X = [] + for idx in select_idx: + X.append(train_X_true[idx]) + + pred_res = model.predict(X)["cls"] + + maps = gen_mappings(["+", "=", 0, 1], ["+", "=", 0, 1]) + + consistent_idx = [] + consistent_pred_res = [] + + import copy + + original_pred_res = copy.deepcopy(pred_res) + mapping = None + + for m in maps: + pred_res = map_res(original_pred_res, m) + remapping = {} + for key, value in m.items(): + remapping[value] = key + + max_abduce_num = 10 + solution = abducer.zoopt_get_solution( + pred_res, [1] * len(pred_res), max_abduce_num + ) + all_address_flag = reform_idx(solution, pred_res) + + consistent_idx_tmp = [] + consistent_pred_res_tmp = [] + + for idx in range(len(pred_res)): + address_idx = [ + i for i, flag in enumerate(all_address_flag[idx]) if flag != 0 + ] + candidate = abducer.kb.address_by_idx([pred_res[idx]], 1, address_idx, True) + if len(candidate) > 0: + consistent_idx_tmp.append(idx) + consistent_pred_res_tmp.append( + [remapping[symbol] for symbol in candidate[0][0]] + ) + + if len(consistent_idx_tmp) > len(consistent_idx): + consistent_idx = consistent_idx_tmp + consistent_pred_res = consistent_pred_res_tmp + mapping = m + + if len(consistent_idx) == 0: + return 0, 0, None + + INFO("Consistent predict results are:", map_res(consistent_pred_res, mapping)) + INFO("Train pool size is:", len(flatten(consistent_pred_res))) + INFO("Start to use abduced pseudo label to train model...") + model.train([X[idx] for idx in consistent_idx], consistent_pred_res) + + consistent_acc = len(consistent_idx) / select_num + char_acc = get_char_acc( + model, [X[idx] for idx in consistent_idx], consistent_pred_res + ) + INFO("consistent_acc is %s, char_acc is %s" % (consistent_acc, char_acc)) + return consistent_acc, char_acc, mapping + + +def get_rules_from_data( + model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim +): rules = [] for _ in range(logic_output_dim): while True: - select_index = np.random.randint(len(train_X_true), size=samples_per_rule) - select_X = train_X_true[select_index] - - ## TODO - exs = select_X.predict() - rule = get_rules(exs) - if rule != None: - break + select_idx = np.random.randint(len(train_X_true), size=samples_per_rule) + X = [] + for idx in select_idx: + X.append(train_X_true[idx]) + pred_res = model.predict(X)["cls"] + pred_res = [[mapping[symbol] for symbol in formula] for formula in pred_res] + + consistent_idx = [] + consistent_pred_res = [] + for idx in range(len(pred_res)): + if abducer.kb.logic_forward([pred_res[idx]]): + consistent_idx.append(idx) + consistent_pred_res.append(pred_res[idx]) + + if len(consistent_pred_res) != 0: + rule = abducer.abduce_rules(consistent_pred_res) + if rule != None: + break + rules.append(rule) + INFO('Learned rules from data:') + for rule in rules: + INFO(rule) return rules -def get_mlp_vector(X, rules): - - ## TODO - exs = np.argmax(model.predict(X)) - +def get_mlp_vector(model, abducer, mapping, X, rules): + pred_res = model.predict([X])["cls"] + pred_res = [[mapping[symbol] for symbol in formula] for formula in pred_res] vector = [] for rule in rules: - if abducer.kb.consist_rule(exs, rule): + if abducer.kb.consist_rule(pred_res, rule): vector.append(1) else: vector.append(0) return vector -def get_mlp_data(X_true, X_false, rules): + +def get_mlp_data(model, abducer, mapping, X_true, X_false, rules): mlp_vectors = [] mlp_labels = [] for X in X_true: - mlp_vectors.append(get_mlp_vector(X, rules)) + mlp_vectors.append(get_mlp_vector(model, abducer, mapping, X, rules)) mlp_labels.append(1) for X in X_false: - mlp_vectors.append(get_mlp_vector(X, rules)) + mlp_vectors.append(get_mlp_vector(model, abducer, mapping, X, rules)) mlp_labels.append(0) - - return np.array(mlp_vectors), np.array(mlp_labels) + + return np.array(mlp_vectors, dtype=np.float32), np.array(mlp_labels, dtype=np.int64) -def validation(train_X_true, train_X_false, val_X_true, val_X_false): - print("Now checking if we can go to next course") +def validation( + model, + abducer, + mapping, + train_X_true, + train_X_false, + val_X_true, + val_X_false, + recorder, +): + INFO("Now checking if we can go to next course") samples_per_rule = 3 logic_output_dim = 50 - print("Now checking if we can go to next course") - rules = get_rules_from_data(train_X_true, samples_per_rule, logic_output_dim) - mlp_train_vectors, mlp_train_labels = get_mlp_data(train_X_true, train_X_false, rules) - - index = np.array(list(range(len(mlp_train_labels)))) - np.random.shuffle(index) - mlp_train_vectors = mlp_train_vectors[index] - mlp_train_labels = mlp_train_labels[index] - + rules = get_rules_from_data( + model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim + ) + + mlp_train_vectors, mlp_train_labels = get_mlp_data( + model, abducer, mapping, train_X_true, train_X_false, rules + ) + + idx = np.array(list(range(len(mlp_train_labels)))) + np.random.shuffle(idx) + mlp_train_vectors = mlp_train_vectors[idx] + mlp_train_labels = mlp_train_labels[idx] + best_accuracy = 0 - - #Try three times to find the best mlp + + # Try three times to find the best mlp for _ in range(3): - print("Training mlp...") - - ### TODO - # mlp_model = get_mlp_net(logic_output_dim) - # mlp_model.compile(loss='binary_crossentropy', - # optimizer='rmsprop', - # metrics=['accuracy']) - # mlp_model.fit(mlp_train_vectors, - # mlp_train_labels, - # epochs=MLP_EPOCHS, - # batch_size=MLP_BATCHSIZE, - # verbose=0) - #Prepare MLP validation data - - mlp_val_vectors, mlp_val_labels = get_mlp_data(val_X_true, val_X_false, rules) - - ## TODO - #Get MLP validation result - # result = mlp_model.evaluate(mlp_val_vectors, - # mlp_val_labels, - # batch_size=MLP_BATCHSIZE, - # verbose=0) - print("MLP validation result:", result) - accuracy = result[1] + INFO("Training mlp...") + mlp = MLP(input_dim=logic_output_dim) + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.Adam(mlp.parameters(), lr=0.01, betas=(0.9, 0.999)) + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + mlp_model = BasicModel( + mlp, + criterion, + optimizer, + device, + batch_size=128, + num_epochs=60, + recorder=recorder, + ) + mlp_train_data = BasicDataset(mlp_train_vectors, mlp_train_labels) + mlp_train_data_loader = torch.utils.data.DataLoader( + mlp_train_data, + batch_size=128, + shuffle=True, + ) + loss = mlp_model.fit(mlp_train_data_loader) + INFO("mlp training loss is %f" % loss) + + mlp_val_vectors, mlp_val_labels = get_mlp_data( + model, abducer, mapping, val_X_true, val_X_false, rules + ) + + # Get MLP validation result + mlp_val_data = BasicDataset(mlp_val_vectors, mlp_val_labels) + mlp_val_data_loader = torch.utils.data.DataLoader( + mlp_val_data, + batch_size=64, + shuffle=True, + ) + + accuracy = mlp_model.val(mlp_val_data_loader) if accuracy > best_accuracy: best_accuracy = accuracy return best_accuracy - -def train_HED(model, abducer, train_data, test_data, epochs=50, select_num=10, verbose=-1): - train_X, train_Z, train_Y = train_data - test_X, test_Z, test_Y = test_data +def train_with_rule( + model, + abducer, + train_data, + val_data, + select_num=10, + recorder=None +): + train_X = train_data + val_X = val_data min_len = 5 max_len = 8 - cp_threshold = 0.9 - blmp_threshold = 0.9 - - cnt_threshold = 5 - acc_threshold = 0.86 - # Start training / for each length of equations for equation_len in range(min_len, max_len): - - ### TODO: get_data, e.g. - # train_X_true = train_X['True'][equation_len] - # train_X_true.append(train_X['True'][equation_len + 1]) - + INFO("============== equation_len:%d ================" % (equation_len)) + train_X_true = train_X[1][equation_len] + train_X_false = train_X[0][equation_len] + val_X_true = val_X[1][equation_len] + val_X_false = val_X[0][equation_len] + train_X_true.extend(train_X[1][equation_len + 1]) + train_X_false.extend(train_X[0][equation_len + 1]) + val_X_true.extend(val_X[1][equation_len + 1]) + val_X_false.extend(val_X[0][equation_len + 1]) + condition_cnt = 0 while True: # Abduce and train NN - consistent_percentage, batch_label_model_precision = abduce_and_train(model, abducer, train_X_true, select_num) - if consistent_percentage == 0: + consistent_acc, char_acc, mapping = abduce_and_train( + model, abducer, train_X_true, select_num + ) + if consistent_acc == 0: continue # Test if we can use mlp to evaluate - if consistent_percentage >= cp_threshold and batch_label_model_precision >= blmp_threshold: + if consistent_acc >= 0.9 and char_acc >= 0.9: condition_cnt += 1 else: condition_cnt = 0 + # The condition has been satisfied continuously five times - if condition_cnt >= cnt_threshold: - best_accuracy = validation(train_X_true, train_X_false, val_X_true, val_X_false) + if condition_cnt >= 5: + best_accuracy = validation( + model, + abducer, + mapping, + train_X_true, + train_X_false, + val_X_true, + val_X_false, + recorder, + ) + + INFO("best_accuracy is %f" % (best_accuracy)) # decide next course or restart - if best_accuracy > acc_threshold: - # Save model and go to next course - ## TODO: model.save_weights() + if best_accuracy > 0.86: + torch.save( + model.cls_list[0].model.state_dict(), + "./weights/train_weights_%d.pth" % equation_len, + ) break - else: - # Restart current course: reload model if equation_len == min_len: - ## TODO: model.set_weights(pretrain_model.get_weights()) - model.set_weights() + model.cls_list[0].model.load_state_dict( + torch.load("./weights/pretrain_weights.pth") + ) else: - ## TODO: model.load_weights() - model.load_weights() - print("Failed! Reload model.") + model.cls_list[0].model.load_state_dict( + torch.load( + "./weights/train_weights_%d.pth" % (equation_len - 1) + ) + ) condition_cnt = 0 - - return model diff --git a/models/basic_model.py b/models/basic_model.py index 11063a8..8c137e2 100644 --- a/models/basic_model.py +++ b/models/basic_model.py @@ -1,16 +1,17 @@ # coding: utf-8 -#================================================================# +# ================================================================# # Copyright (C) 2020 Freecss All rights reserved. -# +# # File Name ：basic_model.py # Author ：freecss # Email ：karlfreecss@gmail.com # Created Date ：2020/11/21 # Description ： # -#================================================================# +# ================================================================# import sys + sys.path.append("..") import torch @@ -19,6 +20,24 @@ from torch.utils.data import Dataset import os from multiprocessing import Pool + +class BasicDataset(Dataset): + def __init__(self, X, Y): + self.X = X + self.Y = Y + + def __len__(self): + return len(self.X) + + def __getitem__(self, index): + assert index < len(self), "index range error" + + img = self.X[index] + label = self.Y[index] + + return (img, label) + + class XYDataset(Dataset): def __init__(self, X, Y, transform=None): self.X = X @@ -31,8 +50,8 @@ class XYDataset(Dataset): return len(self.X) def __getitem__(self, index): - assert index < len(self), 'index range error' - + assert index < len(self), "index range error" + img = self.X[index] if self.transform is not None: img = self.transform(img) @@ -41,31 +60,35 @@ class XYDataset(Dataset): return (img, label) -class FakeRecorder(): + +class FakeRecorder: def __init__(self): pass def print(self, *x): pass -class BasicModel(): - def __init__(self, - model, - criterion, - optimizer, - device, - batch_size = 1, - num_epochs = 1, - stop_loss = 0.01, - num_workers = 0, - save_interval = None, - save_dir = None, - transform = None, - collate_fn = None, - recorder = None): + +class BasicModel: + def __init__( + self, + model, + criterion, + optimizer, + device, + batch_size=1, + num_epochs=1, + stop_loss=0.01, + num_workers=0, + save_interval=None, + save_dir=None, + transform=None, + collate_fn=None, + recorder=None, + ): self.model = model.to(device) - + self.batch_size = batch_size self.num_epochs = num_epochs self.stop_loss = stop_loss @@ -103,9 +126,7 @@ class BasicModel(): recorder.print("Model fitted, minimal loss is ", min_loss) return loss_value - def fit(self, data_loader = None, - X = None, - y = None): + def fit(self, data_loader=None, X=None, y=None): if data_loader is None: data_loader = self._data_loader(X, y) return self._fit(data_loader, self.num_epochs, self.stop_loss) @@ -115,7 +136,7 @@ class BasicModel(): criterion = self.criterion optimizer = self.optimizer device = self.device - + model.train() total_loss, total_num = 0.0, 0 @@ -136,7 +157,7 @@ class BasicModel(): def _predict(self, data_loader): model = self.model device = self.device - + model.eval() with torch.no_grad(): @@ -145,20 +166,20 @@ class BasicModel(): data = data.to(device) out = model(data) results.append(out) - + return torch.cat(results, axis=0) - def predict(self, data_loader = None, X = None, print_prefix = ""): + def predict(self, data_loader=None, X=None, print_prefix=""): recorder = self.recorder - recorder.print('Start Predict Class ', print_prefix) + recorder.print("Start Predict Class ", print_prefix) if data_loader is None: data_loader = self._data_loader(X) return self._predict(data_loader).argmax(axis=1).cpu().numpy() - def predict_proba(self, data_loader = None, X = None, print_prefix = ""): + def predict_proba(self, data_loader=None, X=None, print_prefix=""): recorder = self.recorder - recorder.print('Start Predict Probability ', print_prefix) + # recorder.print('Start Predict Probability ', print_prefix) if data_loader is None: data_loader = self._data_loader(X) @@ -168,7 +189,7 @@ class BasicModel(): model = self.model criterion = self.criterion device = self.device - + model.eval() total_correct_num, total_num, total_loss = 0, 0, 0.0 @@ -179,32 +200,37 @@ class BasicModel(): out = model(data) - correct_num = sum(target == out.argmax(axis=1)).item() + if len(out.shape) > 1: + correct_num = sum(target == out.argmax(axis=1)).item() + else: + correct_num = sum(target == (out > 0.5)).item() loss = criterion(out, target) total_loss += loss.item() * data.size(0) total_correct_num += correct_num total_num += data.size(0) - + mean_loss = total_loss / total_num accuracy = total_correct_num / total_num return mean_loss, accuracy - def val(self, data_loader = None, X = None, y = None, print_prefix = ""): + def val(self, data_loader=None, X=None, y=None, print_prefix=""): recorder = self.recorder - recorder.print('Start val ', print_prefix) + recorder.print("Start val ", print_prefix) if data_loader is None: data_loader = self._data_loader(X, y) mean_loss, accuracy = self._val(data_loader) - recorder.print('[%s] Val loss: %f, accuray: %f' % (print_prefix, mean_loss, accuracy)) + recorder.print( + "[%s] Val loss: %f, accuray: %f" % (print_prefix, mean_loss, accuracy) + ) return accuracy - def score(self, data_loader = None, X = None, y = None, print_prefix = ""): + def score(self, data_loader=None, X=None, y=None, print_prefix=""): return self.val(data_loader, X, y, print_prefix) - def _data_loader(self, X, y = None): + def _data_loader(self, X, y=None): collate_fn = self.collate_fn transform = self.transform @@ -212,9 +238,14 @@ class BasicModel(): y = [0] * len(X) dataset = XYDataset(X, y, transform=transform) sampler = None - data_loader = torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, \ - shuffle=False, sampler=sampler, num_workers=int(self.num_workers), \ - collate_fn=collate_fn) + data_loader = torch.utils.data.DataLoader( + dataset, + batch_size=self.batch_size, + shuffle=False, + sampler=sampler, + num_workers=int(self.num_workers), + collate_fn=collate_fn, + ) return data_loader def save(self, epoch_id, save_dir): @@ -237,7 +268,6 @@ class BasicModel(): load_path = os.path.join(load_dir, str(epoch_id) + "_opt.pth") self.optimizer.load_state_dict(torch.load(load_path)) + if __name__ == "__main__": pass - - diff --git a/models/nn.py b/models/nn.py new file mode 100644 index 0000000..cecbeea --- /dev/null +++ b/models/nn.py @@ -0,0 +1,152 @@ +# coding: utf-8 +# ================================================================# +# Copyright (C) 2021 Freecss All rights reserved. +# +# File Name ：lenet5.py +# Author ：freecss +# Email ：karlfreecss@gmail.com +# Created Date ：2021/03/03 +# Description ： +# +# ================================================================# + +import sys + +sys.path.append("..") + +import torchvision + +import torch +from torch import nn +from torch.nn import functional as F +from torch.autograd import Variable +import torchvision.transforms as transforms +import numpy as np + +from models.basic_model import BasicModel +import utils.plog as plog + + +class LeNet5(nn.Module): + def __init__(self, num_classes=10, image_size=(28, 28)): + super().__init__() + self.conv1 = nn.Conv2d(1, 6, 3, padding=1) + self.conv2 = nn.Conv2d(6, 16, 3) + self.conv3 = nn.Conv2d(16, 16, 3) + + feature_map_size = (np.array(image_size) // 2 - 2) // 2 - 2 + num_features = 16 * feature_map_size[0] * feature_map_size[1] + + self.fc1 = nn.Linear(num_features, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, num_classes) + + def forward(self, x): + """å‰å‘传播函数""" + x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) + x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2)) + x = F.relu(self.conv3(x)) + x = x.view(-1, self.num_flat_features(x)) + # print(x.size()) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + def num_flat_features(self, x): + size = x.size()[1:] + num_features = 1 + for s in size: + num_features *= s + return num_features + + +# class SymbolNet(nn.Module): +# def __init__(self, num_classes=4, image_size=(28, 28, 1)): +# super(SymbolNet, self).__init__() +# self.conv1 = nn.Sequential( +# nn.Conv2d(1, 32, 3, stride=1, padding=1), +# nn.ReLU(inplace=True), +# nn.BatchNorm2d(32), +# ) +# self.conv2 = nn.Sequential( +# nn.Conv2d(32, 64, 3, stride=1, padding=1), +# nn.ReLU(inplace=True), +# nn.MaxPool2d(kernel_size=2, stride=2), +# nn.BatchNorm2d(64), +# nn.Dropout(0.25), +# ) +# num_features = 64 * (image_size[0] // 2) * (image_size[1] // 2) +# self.fc1 = nn.Sequential( +# nn.Linear(num_features, 128), nn.ReLU(inplace=True), nn.Dropout(0.5) +# ) +# self.fc2 = nn.Sequential(nn.Linear(128, num_classes), nn.Softmax(dim=1)) + +# def forward(self, x): +# x = self.conv1(x) +# x = self.conv2(x) +# x = torch.flatten(x, 1) +# x = self.fc1(x) +# x = self.fc2(x) +# return x + + +class SymbolNet(nn.Module): + def __init__(self, num_classes=4, image_size=(28, 28, 1)): + super(SymbolNet, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(1, 32, 5, stride=1), + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.BatchNorm2d(32, momentum=0.99, eps=0.001), + ) + self.conv2 = nn.Sequential( + nn.Conv2d(32, 64, 5, padding=2, stride=1), + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.BatchNorm2d(64, momentum=0.99, eps=0.001), + ) + + num_features = 64 * (image_size[0] // 4 - 1) * (image_size[1] // 4 - 1) + self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU()) + self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU()) + self.fc3 = nn.Sequential(nn.Linear(84, num_classes), nn.Softmax(dim=1)) + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + x = torch.flatten(x, 1) + x = self.fc1(x) + x = self.fc2(x) + x = self.fc3(x) + return x + + +class SymbolNetAutoencoder(nn.Module): + def __init__(self, num_classes=4, image_size=(28, 28, 1)): + super(SymbolNetAutoencoder, self).__init__() + self.base_model = SymbolNet(num_classes, image_size) + self.fc1 = nn.Sequential(nn.Linear(num_classes, 100), nn.ReLU()) + self.fc2 = nn.Sequential( + nn.Linear(100, image_size[0] * image_size[1]), nn.ReLU() + ) + + def forward(self, x): + x = self.base_model(x) + x = self.fc1(x) + x = self.fc2(x) + return x + + +class MLP(nn.Module): + def __init__(self, input_dim=50, num_classes=2): + super(MLP, self).__init__() + assert input_dim > 0 + hidden_dim = int(np.sqrt(input_dim)) + self.fc1 = nn.Sequential(nn.Linear(input_dim, hidden_dim), nn.ReLU()) + self.fc2 = nn.Sequential(nn.Linear(hidden_dim, num_classes), nn.Softmax(dim=1)) + + def forward(self, x): + x = self.fc1(x) + x = self.fc2(x) + return x diff --git a/models/wabl_models.py b/models/wabl_models.py index b854039..9c97bbc 100644 --- a/models/wabl_models.py +++ b/models/wabl_models.py @@ -1,14 +1,14 @@ # coding: utf-8 -#================================================================# +# ================================================================# # Copyright (C) 2020 Freecss All rights reserved. -# +# # File Name ：models.py # Author ：freecss # Email ：karlfreecss@gmail.com # Created Date ：2020/04/02 # Description ： # -#================================================================# +# ================================================================# from itertools import chain from sklearn.tree import DecisionTreeClassifier @@ -29,14 +29,17 @@ import random from sklearn.neighbors import KNeighborsClassifier import numpy as np + def get_part_data(X, i): - return list(map(lambda x : x[i], X)) + return list(map(lambda x: x[i], X)) + def merge_data(X): - ret_mark = list(map(lambda x : len(x), X)) + ret_mark = list(map(lambda x: len(x), X)) ret_X = list(chain(*X)) return ret_X, ret_mark + def reshape_data(Y, marks): begin_mark = 0 ret_Y = [] @@ -48,42 +51,44 @@ def reshape_data(Y, marks): class WABLBasicModel: - def __init__(self, base_model, pseudo_label_list): self.cls_list = [] self.cls_list.append(base_model) self.pseudo_label_list = pseudo_label_list self.mapping = dict(zip(pseudo_label_list, list(range(len(pseudo_label_list))))) - self.remapping = dict(zip(list(range(len(pseudo_label_list))), pseudo_label_list)) + self.remapping = dict( + zip(list(range(len(pseudo_label_list))), pseudo_label_list) + ) def predict(self, X): data_X, marks = merge_data(X) - prob = self.cls_list[0].predict_proba(X = data_X) - _cls = prob.argmax(axis = 1) - cls = list(map(lambda x : self.remapping[x], _cls)) + prob = self.cls_list[0].predict_proba(X=data_X) + _cls = prob.argmax(axis=1) + cls = list(map(lambda x: self.remapping[x], _cls)) prob = reshape_data(prob, marks) cls = reshape_data(cls, marks) - return {"cls" : cls, "prob" : prob} + return {"cls": cls, "prob": prob} def valid(self, X, Y): data_X, _ = merge_data(X) _data_Y, _ = merge_data(Y) - data_Y = list(map(lambda y : self.mapping[y], _data_Y)) - score = self.cls_list[0].score(X = data_X, y = data_Y) + data_Y = list(map(lambda y: self.mapping[y], _data_Y)) + score = self.cls_list[0].score(X=data_X, y=data_Y) return score, [score] def train(self, X, Y): - #self.label_lists = [] + # self.label_lists = [] data_X, _ = merge_data(X) _data_Y, _ = merge_data(Y) - data_Y = list(map(lambda y : self.mapping[y], _data_Y)) - self.cls_list[0].fit(X = data_X, y = data_Y) + data_Y = list(map(lambda y: self.mapping[y], _data_Y)) + self.cls_list[0].fit(X=data_X, y=data_Y) + class DecisionTree(WABLBasicModel): - def __init__(self, code_len, label_lists, share = False): + def __init__(self, code_len, label_lists, share=False): self.code_len = code_len self._set_label_lists(label_lists) @@ -91,14 +96,19 @@ class DecisionTree(WABLBasicModel): self.share = share if share: # 本质上是åŒä¸€ä¸ªåˆ†ç±»å™¨ - self.cls_list.append(DecisionTreeClassifier(random_state = 0, min_samples_leaf = 3)) + self.cls_list.append( + DecisionTreeClassifier(random_state=0, min_samples_leaf=3) + ) self.cls_list = self.cls_list * self.code_len else: for _ in range(code_len): - self.cls_list.append(DecisionTreeClassifier(random_state = 0, min_samples_leaf = 3)) + self.cls_list.append( + DecisionTreeClassifier(random_state=0, min_samples_leaf=3) + ) + class KNN(WABLBasicModel): - def __init__(self, code_len, label_lists, share = False, k = 3): + def __init__(self, code_len, label_lists, share=False, k=3): self.code_len = code_len self._set_label_lists(label_lists) @@ -106,14 +116,15 @@ class KNN(WABLBasicModel): self.share = share if share: # 本质上是åŒä¸€ä¸ªåˆ†ç±»å™¨ - self.cls_list.append(KNeighborsClassifier(n_neighbors = k)) + self.cls_list.append(KNeighborsClassifier(n_neighbors=k)) self.cls_list = self.cls_list * self.code_len else: for _ in range(code_len): - self.cls_list.append(KNeighborsClassifier(n_neighbors = k)) + self.cls_list.append(KNeighborsClassifier(n_neighbors=k)) + class CNN(WABLBasicModel): - def __init__(self, base_model, code_len, label_lists, share = True): + def __init__(self, base_model, code_len, label_lists, share=True): assert share == True, "Not implemented" label_lists = [sorted(list(set(label_list))) for label_list in label_lists] @@ -126,27 +137,28 @@ class CNN(WABLBasicModel): if share: self.cls_list.append(base_model) - def train(self, X, Y, n_epoch = 100): - #self.label_lists = [] + def train(self, X, Y, n_epoch=100): + # self.label_lists = [] if self.share: # 因为是åŒä¸€ä¸ªåˆ†ç±»å™¨ï¼Œæ‰€ä»¥åªéœ€è¦æŠŠæ•°æ®æ”¾åœ¨ä¸€èµ·ï¼Œç„¶åŽè®­ç»ƒå…¶ä¸­ä»»æ„一个å³å¯ data_X, _ = merge_data(X) data_Y, _ = merge_data(Y) - self.cls_list[0].fit(X = data_X, y = data_Y, n_epoch = n_epoch) - #self.label_lists = [sorted(list(set(data_Y)))] * self.code_len + self.cls_list[0].fit(X=data_X, y=data_Y, n_epoch=n_epoch) + # self.label_lists = [sorted(list(set(data_Y)))] * self.code_len else: for i in range(self.code_len): data_X = get_part_data(X, i) data_Y = get_part_data(Y, i) self.cls_list[i].fit(data_X, data_Y) - #self.label_lists.append(sorted(list(set(data_Y)))) + # self.label_lists.append(sorted(list(set(data_Y)))) + if __name__ == "__main__": - #data_path = "utils/hamming_data/generated_data/hamming_7_3_0.20.pk" + # data_path = "utils/hamming_data/generated_data/hamming_7_3_0.20.pk" data_path = "datasets/generated_data/0_code_7_2_0.00.pk" codes, data, labels = pk.load(open(data_path, "rb")) - - cls = KNN(7, False, k = 3) + + cls = KNN(7, False, k=3) cls.train(data, labels) print(cls.valid(data, labels)) for res in cls.predict_proba(data): @@ -157,4 +169,3 @@ if __name__ == "__main__": print(res) break print("Trained") - diff --git a/utils/plog.py b/utils/plog.py index 0b8f5b5..3f091cb 100644 --- a/utils/plog.py +++ b/utils/plog.py @@ -1,14 +1,14 @@ # coding: utf-8 -#================================================================# +# ================================================================# # Copyright (C) 2020 Freecss All rights reserved. -# +# # File Name ：plog.py # Author ：freecss # Email ：karlfreecss@gmail.com # Created Date ：2020/10/23 # Description ： # -#================================================================# +# ================================================================# import time import logging @@ -19,13 +19,14 @@ import functools global recorder recorder = None + class ResultRecorder: def __init__(self): - logging.basicConfig(level=logging.DEBUG, filemode='a') + logging.basicConfig(level=logging.DEBUG, filemode="a") self.result = {} self.set_savefile() - + logging.info("===========================================================") logging.info("============= Result Recorder Version: 0.03 ===============") logging.info("===========================================================\n") @@ -33,25 +34,25 @@ class ResultRecorder: pass def set_savefile(self): - local_time = time.strftime("%Y%m%d_%H_%M_%S", time.localtime()) + local_time = time.strftime("%Y%m%d_%H_%M_%S", time.localtime()) save_dir = os.path.join("results", local_time) if not os.path.exists(save_dir): os.makedirs(save_dir) save_file_path = os.path.join(save_dir, "result.pk") save_file = open(save_file_path, "wb") - + self.save_dir = save_dir self.save_file = save_file filename = os.path.join(save_dir, "log.txt") file_handler = logging.FileHandler(filename) file_handler.setLevel(logging.DEBUG) - formatter = logging.Formatter('%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s') + formatter = logging.Formatter("%(asctime)s - %(levelname)s: %(message)s") file_handler.setFormatter(formatter) logging.getLogger().addHandler(file_handler) - def print(self, *argv, screen = False): + def print(self, *argv, screen=False): info = "" for data in argv: info += str(data) @@ -62,9 +63,8 @@ class ResultRecorder: def print_result(self, *argv): for data in argv: info = "#Result# %s" % str(data) - #print(info) logging.info(info) - + def store(self, *argv): for data in argv: if data.find(":") < 0: @@ -81,11 +81,11 @@ class ResultRecorder: self.result[label].append(data) def write_kv(self, label, data): - self.print_result({label : data}) - #self.print_result(label + ":" + str(data)) + self.print_result({label: data}) + # self.print_result(label + ":" + str(data)) self.store_kv(label, data) - def dump(self, save_file = None): + def dump(self, save_file=None): if save_file is None: save_file = self.save_file pk.dump(self.result, save_file) @@ -104,38 +104,43 @@ class ResultRecorder: self.write_kv("func:", context) return result + return clocked def __del__(self): self.dump() + def clocker(*argv): global recorder if recorder is None: recorder = ResultRecorder() return recorder.clock(*argv) -def INFO(*argv, screen = False): + +def INFO(*argv, screen=False): global recorder if recorder is None: recorder = ResultRecorder() - return recorder.print(*argv, screen = screen) + return recorder.print(*argv, screen=screen) + -def DEBUG(*argv, screen = False): +def DEBUG(*argv, screen=False): global recorder if recorder is None: recorder = ResultRecorder() - return recorder.print(*argv, screen = screen) + return recorder.print(*argv, screen=screen) + def logger(): global recorder if recorder is None: recorder = ResultRecorder() return recorder - + + if __name__ == "__main__": recorder = ResultRecorder() recorder.write_kv("test", 1) - recorder.set_savefile(pk_dir = "haha") + recorder.set_savefile(pk_dir="haha") recorder.write_kv("test", 1) - diff --git a/utils/utils.py b/utils/utils.py index 44ba6a1..57dea31 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -1,9 +1,17 @@ import numpy as np +from utils.plog import INFO +from collections import OrderedDict + # for multiple predictions, modify from `learn_add.py` def flatten(l): - return [item for sublist in l for item in flatten(sublist)] if isinstance(l, list) else [l] - + return ( + [item for sublist in l for item in flatten(sublist)] + if isinstance(l, list) + else [l] + ) + + # for multiple predictions, modify from `learn_add.py` def reform_idx(flatten_pred_res, save_pred_res): re = [] @@ -18,10 +26,25 @@ def reform_idx(flatten_pred_res, save_pred_res): i = i + j return re + +def block_sample(X, Z, Y, sample_num, epoch_idx): + part_num = len(X) // sample_num + if part_num == 0: + part_num = 1 + seg_idx = epoch_idx % part_num + INFO("seg_idx:", seg_idx, ", part num:", part_num, ", data num:", len(X)) + X = X[sample_num * seg_idx : sample_num * (seg_idx + 1)] + Z = Z[sample_num * seg_idx : sample_num * (seg_idx + 1)] + Y = Y[sample_num * seg_idx : sample_num * (seg_idx + 1)] + + return X, Z, Y + + def hamming_dist(A, B): B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) + A = np.expand_dims(A, axis=0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis=1) + def confidence_dist(A, B): B = np.array(B) @@ -29,7 +52,16 @@ def confidence_dist(A, B): A = np.expand_dims(A, axis=0) A = A.repeat(axis=0, repeats=(len(B))) rows = np.array(range(len(B))) - rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) + rows = np.expand_dims(rows, axis=1).repeat(axis=1, repeats=len(B[0])) cols = np.array(range(len(B[0]))) - cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) - return 1 - np.prod(A[rows, cols, B], axis = 1) + cols = np.expand_dims(cols, axis=0).repeat(axis=0, repeats=len(B)) + return 1 - np.prod(A[rows, cols, B], axis=1) + + +def copy_state_dict(state_dict): + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + if k.startswith('base_model'): + name = ".".join(k.split(".")[1:]) + new_state_dict[name] = v + return new_state_dict From 26a8ceeac6f94a7bcc62a54b0f821366a46da772 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 20 Feb 2023 14:36:40 +0800 Subject: [PATCH 127/601] Update example.py --- example.py | 79 +++++++++++++++++++++++------------------------------- 1 file changed, 34 insertions(+), 45 deletions(-) diff --git a/example.py b/example.py index 795d455..29c7629 100644 --- a/example.py +++ b/example.py @@ -11,14 +11,13 @@ # ================================================================# from utils.plog import logger, INFO -import framework_hed +from utils.utils import copy_state_dict import torch.nn as nn import torch - from models.nn import LeNet5, SymbolNet, SymbolNetAutoencoder from models.basic_model import BasicModel, BasicDataset -from models.wabl_models import WABLBasicModel +from models.wabl_models import DecisionTree, WABLBasicModel from multiprocessing import Pool import os @@ -26,14 +25,15 @@ from abducer.abducer_base import AbducerBase from abducer.kb import add_KB, HWF_KB, HED_prolog_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf -from datasets.hed.get_hed import get_hed, get_pretrain_data, split_equation +from datasets.hed.get_hed import get_hed, split_equation, get_pretrain_data +import framework def run_test(): # kb = add_KB(True) - # kb = hwf_KB(True) + # kb = HWF_KB(True) # abducer = AbducerBase(kb) kb = HED_prolog_KB() @@ -41,58 +41,47 @@ def run_test(): recorder = logger() - # train_X, train_Z, train_Y = get_mnist_add(train=True, get_pseudo_label=True) - # test_X, test_Z, test_Y = get_mnist_add(train=False, get_pseudo_label=True) - - # train_data = get_hwf(train=True, get_pseudo_label=True) - # test_data = get_hwf(train=False, get_pseudo_label=True) - total_train_data = get_hed(train=True) train_data, val_data = split_equation(total_train_data, 3, 1) test_data = get_hed(train=False) - + # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) cls_autoencoder = SymbolNetAutoencoder(num_classes=len(kb.pseudo_label_list)) cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) - + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + if not os.path.exists("./weights/pretrain_weights.pth"): - pretrain_data_X, pretrain_data_Y = get_pretrain_data(["0", "1", "10", "11"]) + INFO("Pretrain Start") + pretrain_data_X, pretrain_data_Y = get_pretrain_data(['0', '1', '10', '11']) pretrain_data = BasicDataset(pretrain_data_X, pretrain_data_Y) - pretrain_data_loader = torch.utils.data.DataLoader( - pretrain_data, - batch_size=64, - shuffle=True, - ) - framework_hed.pretrain(cls_autoencoder, pretrain_data_loader, recorder) - torch.save( - cls_autoencoder.base_model.state_dict(), "./weights/pretrain_weights.pth" - ) - cls.load_state_dict(torch.load("./weights/pretrain_weights.pth")) - + + criterion = nn.MSELoss() + optimizer = torch.optim.RMSprop(cls_autoencoder.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6) + + pretrain_model = BasicModel(cls_autoencoder, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, num_epochs=10, recorder=recorder) + framework.pretrain(pretrain_model, pretrain_data) + torch.save(cls_autoencoder.base_model.state_dict(), "./weights/pretrain_weights.pth") + cls.load_state_dict(cls_autoencoder.base_model.state_dict()) + + else: + cls.load_state_dict(torch.load("./weights/pretrain_weights.pth")) + criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.RMSprop( - cls.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6 - ) - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - base_model = BasicModel( - cls, - criterion, - optimizer, - device, - save_interval=1, - save_dir=recorder.save_dir, - batch_size=32, - num_epochs=10, - recorder=recorder, - ) + optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) + # optimizer = torch.optim.Adam(cls.parameters(), lr=0.00001, betas=(0.9, 0.99)) + + base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=10, recorder=recorder) model = WABLBasicModel(base_model, kb.pseudo_label_list) + + # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) + # test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) + + # train_data = get_hwf(train = True, get_pseudo_label = True) + # test_data = get_hwf(train = False, get_pseudo_label = True) - res = framework_hed.train_with_rule( - model, abducer, train_data, val_data, recorder=recorder - ) - INFO(res) + framework.train_with_rule(model, abducer, train_data, val_data, select_num=10, verbose=1) + # recorder.print(res) recorder.dump() return True From 85ba22bc27eca089cc308542612264ca5a4a47df Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 20 Feb 2023 14:37:20 +0800 Subject: [PATCH 128/601] Update example.py --- example.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/example.py b/example.py index 29c7629..a81b51d 100644 --- a/example.py +++ b/example.py @@ -26,7 +26,7 @@ from abducer.kb import add_KB, HWF_KB, HED_prolog_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf from datasets.hed.get_hed import get_hed, split_equation, get_pretrain_data -import framework +import framework_hed def run_test(): @@ -59,7 +59,7 @@ def run_test(): optimizer = torch.optim.RMSprop(cls_autoencoder.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6) pretrain_model = BasicModel(cls_autoencoder, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, num_epochs=10, recorder=recorder) - framework.pretrain(pretrain_model, pretrain_data) + framework_hed.pretrain(pretrain_model, pretrain_data) torch.save(cls_autoencoder.base_model.state_dict(), "./weights/pretrain_weights.pth") cls.load_state_dict(cls_autoencoder.base_model.state_dict()) @@ -80,7 +80,7 @@ def run_test(): # train_data = get_hwf(train = True, get_pseudo_label = True) # test_data = get_hwf(train = False, get_pseudo_label = True) - framework.train_with_rule(model, abducer, train_data, val_data, select_num=10, verbose=1) + framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, verbose=1) # recorder.print(res) recorder.dump() From 3678d2d0adac4aea352cc937456d3855c0bfba4b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 20 Feb 2023 14:38:12 +0800 Subject: [PATCH 129/601] Update framework_hed.py --- framework_hed.py | 295 ++++++++++++++++++++--------------------------- 1 file changed, 128 insertions(+), 167 deletions(-) diff --git a/framework_hed.py b/framework_hed.py index 04b41b8..36159da 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -11,26 +11,18 @@ # ================================================================# import pickle as pk - -import numpy as np - -import random - -random_seed = random.randint(0, 10000) -print("Selected random seed is : ", random_seed) -np.random.seed(random_seed) -random.seed(random_seed) - -from models.nn import MLP -from models.basic_model import BasicModel, BasicDataset -import torch.nn as nn +import math import torch +import torch.nn as nn +import numpy as np from utils.plog import INFO, DEBUG, clocker from utils.utils import flatten, reform_idx, block_sample +from utils.utils import copy_state_dict -from sklearn.tree import DecisionTreeClassifier - +from sklearn.linear_model import LogisticRegression +from models.nn import MLP +from models.basic_model import BasicModel, BasicDataset def result_statistics(pred_Z, Z, Y, logic_forward, char_acc_flag): result = {} @@ -65,59 +57,89 @@ def filter_data(X, abduced_Z): return finetune_X, finetune_Z -def pretrain(net, pretrain_data_loader, recorder): - INFO("Pretrain Start") + + +def train(model, abducer, train_data, test_data, epochs=50, sample_num=-1, verbose=-1): + train_X, train_Z, train_Y = train_data + test_X, test_Z, test_Y = test_data - criterion = nn.MSELoss() - optimizer = torch.optim.RMSprop( - net.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6 - ) - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - pretrain_model = BasicModel( - net, - criterion, - optimizer, - device, - save_interval=1, - save_dir=recorder.save_dir, - num_epochs=10, - recorder=recorder, - ) + # Set default parameters + if sample_num == -1: + sample_num = len(train_X) - pretrain_model.fit(pretrain_data_loader) + if verbose < 1: + verbose = epochs + char_acc_flag = 1 + if train_Z == None: + char_acc_flag = 0 + train_Z = [None] * len(train_X) -def get_char_acc(model, X, consistent_pred_res): - pred_res = flatten(model.predict(X)["cls"]) - assert len(pred_res) == len(flatten(consistent_pred_res)) - return sum( - [ - pred_res[idx] == flatten(consistent_pred_res)[idx] - for idx in range(len(pred_res)) - ] - ) / len(pred_res) + predict_func = clocker(model.predict) + train_func = clocker(model.train) + abduce_func = clocker(abducer.batch_abduce) + for epoch_idx in range(epochs): + X, Z, Y = block_sample(train_X, train_Z, train_Y, sample_num, epoch_idx) + preds_res = predict_func(X) + # input() + abduced_Z = abduce_func(preds_res, Y) -def gen_mappings(chars, symbs): - n_char = len(chars) - n_symbs = len(symbs) - if n_char != n_symbs: - INFO("Characters and symbols size dosen't match.") - return - from itertools import permutations + if ((epoch_idx + 1) % verbose == 0) or (epoch_idx == epochs - 1): + res = result_statistics(preds_res['cls'], Z, Y, abducer.kb.logic_forward, char_acc_flag) + INFO('epoch: ', epoch_idx + 1, ' ', res) + + finetune_X, finetune_Z = filter_data(X, abduced_Z) + if len(finetune_X) > 0: + # model.valid(finetune_X, finetune_Z) + train_func(finetune_X, finetune_Z) + else: + INFO("lack of data, all abduced failed", len(finetune_X)) - mappings = [] - perms = permutations(symbs) - for p in perms: - mappings.append(dict(zip(chars, list(p)))) - return mappings + return res -def map_res(original_pred_res, m): - pred_res = [[m[symbol] for symbol in formula] for formula in original_pred_res] + +def pretrain(pretrain_model, pretrain_data): + INFO("Pretrain Start") + pretrain_data_loader = torch.utils.data.DataLoader( + pretrain_data, + batch_size=64, + shuffle=True, + num_workers=2, + ) + pretrain_model.fit(pretrain_data_loader) + + +def get_char_acc(model, X, consistent_pred_res): + print('Abduced labels: ', flatten(consistent_pred_res)) + pred_res = flatten(model.predict(X)['cls']) + print('Current model\'s output:', pred_res) + assert len(pred_res) == len(flatten(consistent_pred_res)) + return sum([pred_res[idx] == flatten(consistent_pred_res)[idx] for idx in range(len(pred_res))]) / len(pred_res) + +def gen_mappings(chars, symbs): + n_char = len(chars) + n_symbs = len(symbs) + if n_char != n_symbs: + print('Characters and symbols size dosen\'t match.') + return + from itertools import permutations + mappings = [] + # returned mappings + perms = permutations(symbs) + for p in perms: + mappings.append(dict(zip(chars, list(p)))) + return mappings + +def map_res(pred_res, m): + for i in range(len(pred_res)): + for j in range(len(pred_res[i])): + pred_res[i][j] = m[pred_res[i][j]] return pred_res +def map_res(original_pred_res, m): + return [[m[symbol] for symbol in formula] for formula in original_pred_res] def abduce_and_train(model, abducer, train_X_true, select_num): select_idx = np.random.randint(len(train_X_true), size=select_num) @@ -125,68 +147,59 @@ def abduce_and_train(model, abducer, train_X_true, select_num): for idx in select_idx: X.append(train_X_true[idx]) - pred_res = model.predict(X)["cls"] - - maps = gen_mappings(["+", "=", 0, 1], ["+", "=", 0, 1]) - + pred_res = model.predict(X)['cls'] + + maps = gen_mappings(['+', '=', 0, 1],['+', '=', 0, 1]) + consistent_idx = [] consistent_pred_res = [] - + import copy original_pred_res = copy.deepcopy(pred_res) mapping = None - + for m in maps: pred_res = map_res(original_pred_res, m) remapping = {} for key, value in m.items(): remapping[value] = key - - max_abduce_num = 10 - solution = abducer.zoopt_get_solution( - pred_res, [1] * len(pred_res), max_abduce_num - ) + + max_abduce_num = 20 + solution = abducer.zoopt_get_solution(pred_res, [1] * len(pred_res), max_abduce_num) all_address_flag = reform_idx(solution, pred_res) consistent_idx_tmp = [] consistent_pred_res_tmp = [] - + for idx in range(len(pred_res)): - address_idx = [ - i for i, flag in enumerate(all_address_flag[idx]) if flag != 0 - ] + address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] candidate = abducer.kb.address_by_idx([pred_res[idx]], 1, address_idx, True) if len(candidate) > 0: consistent_idx_tmp.append(idx) - consistent_pred_res_tmp.append( - [remapping[symbol] for symbol in candidate[0][0]] - ) - + consistent_pred_res_tmp.append([remapping[symbol] for symbol in candidate[0][0]]) + if len(consistent_idx_tmp) > len(consistent_idx): consistent_idx = consistent_idx_tmp consistent_pred_res = consistent_pred_res_tmp mapping = m - + if len(consistent_idx) == 0: return 0, 0, None - - INFO("Consistent predict results are:", map_res(consistent_pred_res, mapping)) - INFO("Train pool size is:", len(flatten(consistent_pred_res))) + + INFO("Consistent predict results are: ", map_res(consistent_pred_res, mapping)) + INFO('Train pool size is:', len(flatten(consistent_pred_res))) + INFO("Start to use abduced pseudo label to train model...") model.train([X[idx] for idx in consistent_idx], consistent_pred_res) consistent_acc = len(consistent_idx) / select_num - char_acc = get_char_acc( - model, [X[idx] for idx in consistent_idx], consistent_pred_res - ) - INFO("consistent_acc is %s, char_acc is %s" % (consistent_acc, char_acc)) + char_acc = get_char_acc(model, [X[idx] for idx in consistent_idx], consistent_pred_res) + INFO('consistent_acc is %s, char_acc is %s' % (consistent_acc, char_acc)) return consistent_acc, char_acc, mapping -def get_rules_from_data( - model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim -): +def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim): rules = [] for _ in range(logic_output_dim): while True: @@ -194,8 +207,8 @@ def get_rules_from_data( X = [] for idx in select_idx: X.append(train_X_true[idx]) - pred_res = model.predict(X)["cls"] - pred_res = [[mapping[symbol] for symbol in formula] for formula in pred_res] + original_pred_res = model.predict(X)['cls'] + pred_res = map_res(original_pred_res, mapping) consistent_idx = [] consistent_pred_res = [] @@ -208,17 +221,16 @@ def get_rules_from_data( rule = abducer.abduce_rules(consistent_pred_res) if rule != None: break - rules.append(rule) + INFO('Learned rules from data:') - for rule in rules: - INFO(rule) + INFO(rules) return rules def get_mlp_vector(model, abducer, mapping, X, rules): - pred_res = model.predict([X])["cls"] - pred_res = [[mapping[symbol] for symbol in formula] for formula in pred_res] + original_pred_res = model.predict([X])['cls'] + pred_res = map_res(original_pred_res, mapping) vector = [] for rule in rules: if abducer.kb.consist_rule(pred_res, rule): @@ -241,26 +253,13 @@ def get_mlp_data(model, abducer, mapping, X_true, X_false, rules): return np.array(mlp_vectors, dtype=np.float32), np.array(mlp_labels, dtype=np.int64) -def validation( - model, - abducer, - mapping, - train_X_true, - train_X_false, - val_X_true, - val_X_false, - recorder, -): +def validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, val_X_false): INFO("Now checking if we can go to next course") samples_per_rule = 3 logic_output_dim = 50 - rules = get_rules_from_data( - model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim - ) + rules = get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim) - mlp_train_vectors, mlp_train_labels = get_mlp_data( - model, abducer, mapping, train_X_true, train_X_false, rules - ) + mlp_train_vectors, mlp_train_labels = get_mlp_data(model, abducer, mapping, train_X_true, train_X_false, rules) idx = np.array(list(range(len(mlp_train_labels)))) np.random.shuffle(idx) @@ -276,28 +275,18 @@ def validation( criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(mlp.parameters(), lr=0.01, betas=(0.9, 0.999)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - mlp_model = BasicModel( - mlp, - criterion, - optimizer, - device, - batch_size=128, - num_epochs=60, - recorder=recorder, - ) + + mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=60) mlp_train_data = BasicDataset(mlp_train_vectors, mlp_train_labels) mlp_train_data_loader = torch.utils.data.DataLoader( mlp_train_data, batch_size=128, - shuffle=True, + shuffle=True ) loss = mlp_model.fit(mlp_train_data_loader) INFO("mlp training loss is %f" % loss) - - mlp_val_vectors, mlp_val_labels = get_mlp_data( - model, abducer, mapping, val_X_true, val_X_false, rules - ) + + mlp_val_vectors, mlp_val_labels = get_mlp_data(model, abducer, mapping, val_X_true, val_X_false, rules) # Get MLP validation result mlp_val_data = BasicDataset(mlp_val_vectors, mlp_val_labels) @@ -306,7 +295,6 @@ def validation( batch_size=64, shuffle=True, ) - accuracy = mlp_model.val(mlp_val_data_loader) if accuracy > best_accuracy: @@ -314,41 +302,33 @@ def validation( return best_accuracy -def train_with_rule( - model, - abducer, - train_data, - val_data, - select_num=10, - recorder=None -): +def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num=10, verbose=-1): train_X = train_data val_X = val_data min_len = 5 - max_len = 8 + max_len = 18 # Start training / for each length of equations for equation_len in range(min_len, max_len): - INFO("============== equation_len:%d ================" % (equation_len)) + INFO("============== equation_len: %d-%d ================" % (equation_len, equation_len + 1)) train_X_true = train_X[1][equation_len] train_X_false = train_X[0][equation_len] val_X_true = val_X[1][equation_len] val_X_false = val_X[0][equation_len] + train_X_true.extend(train_X[1][equation_len + 1]) train_X_false.extend(train_X[0][equation_len + 1]) val_X_true.extend(val_X[1][equation_len + 1]) val_X_false.extend(val_X[0][equation_len + 1]) - condition_cnt = 0 + condition_cnt = 0 while True: # Abduce and train NN - consistent_acc, char_acc, mapping = abduce_and_train( - model, abducer, train_X_true, select_num - ) + consistent_acc, char_acc, mapping = abduce_and_train(model, abducer, train_X_true, select_num) if consistent_acc == 0: continue - + # Test if we can use mlp to evaluate if consistent_acc >= 0.9 and char_acc >= 0.9: condition_cnt += 1 @@ -357,37 +337,18 @@ def train_with_rule( # The condition has been satisfied continuously five times if condition_cnt >= 5: - best_accuracy = validation( - model, - abducer, - mapping, - train_X_true, - train_X_false, - val_X_true, - val_X_false, - recorder, - ) - - INFO("best_accuracy is %f" % (best_accuracy)) - + # Try to abduce rules in `validation` + best_accuracy = validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, val_X_false) + INFO('best_accuracy is %f' %(best_accuracy)) # decide next course or restart - if best_accuracy > 0.86: - torch.save( - model.cls_list[0].model.state_dict(), - "./weights/train_weights_%d.pth" % equation_len, - ) + if best_accuracy > 0.85: + torch.save(model.cls_list[0].model.state_dict(), "./weights/weights_%d.pth" % equation_len) break else: if equation_len == min_len: - model.cls_list[0].model.load_state_dict( - torch.load("./weights/pretrain_weights.pth") - ) + model.cls_list[0].model.load_state_dict(torch.load("./weights/pretrain_weights.pth")) else: - model.cls_list[0].model.load_state_dict( - torch.load( - "./weights/train_weights_%d.pth" % (equation_len - 1) - ) - ) + model.cls_list[0].model.load_state_dict(torch.load("./weights/weights_%d.pth" % (equation_len - 1))) condition_cnt = 0 return model From d7d854c966e749508c115657ae0af7ab27b771ee Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 20 Feb 2023 14:44:54 +0800 Subject: [PATCH 130/601] Add `get_final_rules` --- framework_hed.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/framework_hed.py b/framework_hed.py index 36159da..c1be4f8 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -299,7 +299,17 @@ def validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, if accuracy > best_accuracy: best_accuracy = accuracy - return best_accuracy + return best_accuracy, rules + +def get_final_rules(rules): + all_rule_dict = {} + rule_cnt = 0 + for rule in rules: + for r in rule: + all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 + rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items() if cnt >= 5} + rule_dict + return rule_dict def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num=10, verbose=-1): @@ -338,10 +348,11 @@ def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num= # The condition has been satisfied continuously five times if condition_cnt >= 5: # Try to abduce rules in `validation` - best_accuracy = validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, val_X_false) + best_accuracy, rules = validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, val_X_false) INFO('best_accuracy is %f' %(best_accuracy)) # decide next course or restart if best_accuracy > 0.85: + final_rules = get_final_rules(rules) torch.save(model.cls_list[0].model.state_dict(), "./weights/weights_%d.pth" % equation_len) break else: @@ -351,7 +362,7 @@ def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num= model.cls_list[0].model.load_state_dict(torch.load("./weights/weights_%d.pth" % (equation_len - 1))) condition_cnt = 0 - return model + return model, final_rules if __name__ == "__main__": From 410796c7405a52fcaed91b1eb8c17386d6d3e4ea Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 20 Feb 2023 14:45:40 +0800 Subject: [PATCH 131/601] Add `get_final_rules` --- framework_hed.py | 1 - 1 file changed, 1 deletion(-) diff --git a/framework_hed.py b/framework_hed.py index c1be4f8..ab08e58 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -308,7 +308,6 @@ def get_final_rules(rules): for r in rule: all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items() if cnt >= 5} - rule_dict return rule_dict From 1eafe55c713c72adfb9714b660ea040735c72283 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 20 Feb 2023 14:47:02 +0800 Subject: [PATCH 132/601] Update framework_hed.py --- framework_hed.py | 1 - 1 file changed, 1 deletion(-) diff --git a/framework_hed.py b/framework_hed.py index ab08e58..082ceea 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -303,7 +303,6 @@ def validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, def get_final_rules(rules): all_rule_dict = {} - rule_cnt = 0 for rule in rules: for r in rule: all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 From 680e9d42b684050d37d6a75a6e6b53080bafe811 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 20 Feb 2023 15:12:10 +0800 Subject: [PATCH 133/601] Update framework_hed.py --- framework_hed.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/framework_hed.py b/framework_hed.py index 082ceea..974561c 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -306,8 +306,9 @@ def get_final_rules(rules): for rule in rules: for r in rule: all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 - rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items() if cnt >= 5} - return rule_dict + rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items() if cnt > 5} + final_rules = [r for r in rule_dict] + return final_rules def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num=10, verbose=-1): @@ -315,7 +316,7 @@ def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num= val_X = val_data min_len = 5 - max_len = 18 + max_len = 8 # Start training / for each length of equations for equation_len in range(min_len, max_len): @@ -359,7 +360,9 @@ def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num= else: model.cls_list[0].model.load_state_dict(torch.load("./weights/weights_%d.pth" % (equation_len - 1))) condition_cnt = 0 - + + INFO('final_rules: ', final_rules) + return model, final_rules From 63da2da61d3df898f8e43723b6ab63fc91d00440 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 10:08:32 +0800 Subject: [PATCH 134/601] Update example.py --- example.py | 30 ++++++------------------------ 1 file changed, 6 insertions(+), 24 deletions(-) diff --git a/example.py b/example.py index a81b51d..72c0bca 100644 --- a/example.py +++ b/example.py @@ -11,28 +11,25 @@ # ================================================================# from utils.plog import logger, INFO -from utils.utils import copy_state_dict import torch.nn as nn import torch -from models.nn import LeNet5, SymbolNet, SymbolNetAutoencoder +from models.nn import LeNet5, SymbolNet from models.basic_model import BasicModel, BasicDataset from models.wabl_models import DecisionTree, WABLBasicModel from multiprocessing import Pool -import os from abducer.abducer_base import AbducerBase from abducer.kb import add_KB, HWF_KB, HED_prolog_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf -from datasets.hed.get_hed import get_hed, split_equation, get_pretrain_data -import framework_hed +from datasets.hed.get_hed import get_hed, split_equation +import framework def run_test(): # kb = add_KB(True) - # kb = HWF_KB(True) # abducer = AbducerBase(kb) @@ -46,25 +43,10 @@ def run_test(): test_data = get_hed(train=False) # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) - cls_autoencoder = SymbolNetAutoencoder(num_classes=len(kb.pseudo_label_list)) cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - if not os.path.exists("./weights/pretrain_weights.pth"): - INFO("Pretrain Start") - pretrain_data_X, pretrain_data_Y = get_pretrain_data(['0', '1', '10', '11']) - pretrain_data = BasicDataset(pretrain_data_X, pretrain_data_Y) - - criterion = nn.MSELoss() - optimizer = torch.optim.RMSprop(cls_autoencoder.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6) - - pretrain_model = BasicModel(cls_autoencoder, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, num_epochs=10, recorder=recorder) - framework_hed.pretrain(pretrain_model, pretrain_data) - torch.save(cls_autoencoder.base_model.state_dict(), "./weights/pretrain_weights.pth") - cls.load_state_dict(cls_autoencoder.base_model.state_dict()) - - else: - cls.load_state_dict(torch.load("./weights/pretrain_weights.pth")) + framework.hed_pretrain(kb, cls, recorder) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) @@ -80,8 +62,8 @@ def run_test(): # train_data = get_hwf(train = True, get_pseudo_label = True) # test_data = get_hwf(train = False, get_pseudo_label = True) - framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, verbose=1) - # recorder.print(res) + model, mapping = framework.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) + framework.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) recorder.dump() return True From 4170a1fe7d7ff97c572b00a190fd422c388bfd55 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 10:09:16 +0800 Subject: [PATCH 135/601] Update example.py --- example.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/example.py b/example.py index 72c0bca..30c50fc 100644 --- a/example.py +++ b/example.py @@ -24,7 +24,7 @@ from abducer.kb import add_KB, HWF_KB, HED_prolog_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf from datasets.hed.get_hed import get_hed, split_equation -import framework +import framework_hed def run_test(): @@ -46,7 +46,7 @@ def run_test(): cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - framework.hed_pretrain(kb, cls, recorder) + framework_hed.hed_pretrain(kb, cls, recorder) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) @@ -62,8 +62,8 @@ def run_test(): # train_data = get_hwf(train = True, get_pseudo_label = True) # test_data = get_hwf(train = False, get_pseudo_label = True) - model, mapping = framework.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) - framework.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) + model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) + framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) recorder.dump() return True From dd777ed4336494ab35352c1363565c4f94c23cb3 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 10:09:58 +0800 Subject: [PATCH 136/601] Add test_hed --- framework_hed.py | 260 ++++++++++++++++++++++++++--------------------- 1 file changed, 143 insertions(+), 117 deletions(-) diff --git a/framework_hed.py b/framework_hed.py index 974561c..e464ed0 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -11,18 +11,17 @@ # ================================================================# import pickle as pk -import math import torch import torch.nn as nn import numpy as np +import os from utils.plog import INFO, DEBUG, clocker -from utils.utils import flatten, reform_idx, block_sample -from utils.utils import copy_state_dict +from utils.utils import flatten, reform_idx, block_sample, gen_mappings, mapping_res, remapping_res -from sklearn.linear_model import LogisticRegression -from models.nn import MLP +from models.nn import MLP, SymbolNetAutoencoder from models.basic_model import BasicModel, BasicDataset +from datasets.hed.get_hed import get_pretrain_data def result_statistics(pred_Z, Z, Y, logic_forward, char_acc_flag): result = {} @@ -99,72 +98,54 @@ def train(model, abducer, train_data, test_data, epochs=50, sample_num=-1, verbo return res +def hed_pretrain(kb, cls, recorder): + cls_autoencoder = SymbolNetAutoencoder(num_classes=len(kb.pseudo_label_list)) + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + if not os.path.exists("./weights/pretrain_weights.pth"): + INFO("Pretrain Start") + pretrain_data_X, pretrain_data_Y = get_pretrain_data(['0', '1', '10', '11']) + pretrain_data = BasicDataset(pretrain_data_X, pretrain_data_Y) + pretrain_data_loader = torch.utils.data.DataLoader(pretrain_data, batch_size=64, shuffle=True) + + criterion = nn.MSELoss() + optimizer = torch.optim.RMSprop(cls_autoencoder.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6) -def pretrain(pretrain_model, pretrain_data): - INFO("Pretrain Start") - pretrain_data_loader = torch.utils.data.DataLoader( - pretrain_data, - batch_size=64, - shuffle=True, - num_workers=2, - ) - pretrain_model.fit(pretrain_data_loader) + pretrain_model = BasicModel(cls_autoencoder, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, num_epochs=10, recorder=recorder) + pretrain_model.fit(pretrain_data_loader) + torch.save(cls_autoencoder.base_model.state_dict(), "./weights/pretrain_weights.pth") + cls.load_state_dict(cls_autoencoder.base_model.state_dict()) + + else: + cls.load_state_dict(torch.load("./weights/pretrain_weights.pth")) -def get_char_acc(model, X, consistent_pred_res): - print('Abduced labels: ', flatten(consistent_pred_res)) - pred_res = flatten(model.predict(X)['cls']) - print('Current model\'s output:', pred_res) +def get_char_acc(model, X, consistent_pred_res, mapping): + original_pred_res = model.predict(X)['cls'] + pred_res = flatten(mapping_res(original_pred_res, mapping)) + INFO('Current model\'s output:', pred_res) + INFO('Abduced labels: ', flatten(consistent_pred_res)) assert len(pred_res) == len(flatten(consistent_pred_res)) return sum([pred_res[idx] == flatten(consistent_pred_res)[idx] for idx in range(len(pred_res))]) / len(pred_res) -def gen_mappings(chars, symbs): - n_char = len(chars) - n_symbs = len(symbs) - if n_char != n_symbs: - print('Characters and symbols size dosen\'t match.') - return - from itertools import permutations - mappings = [] - # returned mappings - perms = permutations(symbs) - for p in perms: - mappings.append(dict(zip(chars, list(p)))) - return mappings - -def map_res(pred_res, m): - for i in range(len(pred_res)): - for j in range(len(pred_res[i])): - pred_res[i][j] = m[pred_res[i][j]] - return pred_res - -def map_res(original_pred_res, m): - return [[m[symbol] for symbol in formula] for formula in original_pred_res] - -def abduce_and_train(model, abducer, train_X_true, select_num): + +def abduce_and_train(model, abducer, mapping, train_X_true, select_num): select_idx = np.random.randint(len(train_X_true), size=select_num) X = [] for idx in select_idx: X.append(train_X_true[idx]) - pred_res = model.predict(X)['cls'] + original_pred_res = model.predict(X)['cls'] - maps = gen_mappings(['+', '=', 0, 1],['+', '=', 0, 1]) + if mapping == None: + mappings = gen_mappings(['+', '=', 0, 1],['+', '=', 0, 1]) + else: + mappings = [mapping] consistent_idx = [] consistent_pred_res = [] - import copy - - original_pred_res = copy.deepcopy(pred_res) - mapping = None - - for m in maps: - pred_res = map_res(original_pred_res, m) - remapping = {} - for key, value in m.items(): - remapping[value] = key - + for m in mappings: + pred_res = mapping_res(original_pred_res, m) max_abduce_num = 20 solution = abducer.zoopt_get_solution(pred_res, [1] * len(pred_res), max_abduce_num) all_address_flag = reform_idx(solution, pred_res) @@ -177,28 +158,38 @@ def abduce_and_train(model, abducer, train_X_true, select_num): candidate = abducer.kb.address_by_idx([pred_res[idx]], 1, address_idx, True) if len(candidate) > 0: consistent_idx_tmp.append(idx) - consistent_pred_res_tmp.append([remapping[symbol] for symbol in candidate[0][0]]) + consistent_pred_res_tmp.append(candidate[0][0]) if len(consistent_idx_tmp) > len(consistent_idx): consistent_idx = consistent_idx_tmp consistent_pred_res = consistent_pred_res_tmp - mapping = m + if len(mappings) > 1: + mapping = m if len(consistent_idx) == 0: return 0, 0, None - INFO("Consistent predict results are: ", map_res(consistent_pred_res, mapping)) - INFO('Train pool size is:', len(flatten(consistent_pred_res))) + if len(mappings) > 1: + INFO('Final mapping is: ', mapping) + INFO('Train pool size is:', len(flatten(consistent_pred_res))) INFO("Start to use abduced pseudo label to train model...") - model.train([X[idx] for idx in consistent_idx], consistent_pred_res) + model.train([X[idx] for idx in consistent_idx], remapping_res(consistent_pred_res, mapping)) consistent_acc = len(consistent_idx) / select_num - char_acc = get_char_acc(model, [X[idx] for idx in consistent_idx], consistent_pred_res) + char_acc = get_char_acc(model, [X[idx] for idx in consistent_idx], consistent_pred_res, mapping) INFO('consistent_acc is %s, char_acc is %s' % (consistent_acc, char_acc)) return consistent_acc, char_acc, mapping +def output_rules(rules): + all_rule_dict = {} + for rule in rules: + for r in rule: + all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 + rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items()}# if cnt >= 5} + return rule_dict + def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim): rules = [] for _ in range(logic_output_dim): @@ -208,7 +199,7 @@ def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, for idx in select_idx: X.append(train_X_true[idx]) original_pred_res = model.predict(X)['cls'] - pred_res = map_res(original_pred_res, mapping) + pred_res = mapping_res(original_pred_res, mapping) consistent_idx = [] consistent_pred_res = [] @@ -222,15 +213,12 @@ def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, if rule != None: break rules.append(rule) - - INFO('Learned rules from data:') - INFO(rules) return rules def get_mlp_vector(model, abducer, mapping, X, rules): original_pred_res = model.predict([X])['cls'] - pred_res = map_res(original_pred_res, mapping) + pred_res = mapping_res(original_pred_res, mapping) vector = [] for rule in rules: if abducer.kb.consist_rule(pred_res, rule): @@ -249,25 +237,28 @@ def get_mlp_data(model, abducer, mapping, X_true, X_false, rules): for X in X_false: mlp_vectors.append(get_mlp_vector(model, abducer, mapping, X, rules)) mlp_labels.append(0) - return np.array(mlp_vectors, dtype=np.float32), np.array(mlp_labels, dtype=np.int64) +def get_all_mlp_data(model, abducer, mapping, X_true, X_false, rules, min_len, max_len): + for equation_len in range(min_len, max_len + 1): + mlp_vectors, mlp_labels = get_mlp_data(model, abducer, mapping, X_true[equation_len], X_false[equation_len], rules) + if equation_len == min_len: + all_mlp_vectors = mlp_vectors + all_mlp_labels = mlp_labels + else: + all_mlp_vectors = np.concatenate((all_mlp_vectors, mlp_vectors)) + all_mlp_labels = np.concatenate((all_mlp_labels, mlp_labels)) + return all_mlp_vectors, all_mlp_labels -def validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, val_X_false): - INFO("Now checking if we can go to next course") - samples_per_rule = 3 - logic_output_dim = 50 - rules = get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim) +def validation(model, abducer, mapping, logic_output_dim, rules, train_X_true, train_X_false, val_X_true, val_X_false): mlp_train_vectors, mlp_train_labels = get_mlp_data(model, abducer, mapping, train_X_true, train_X_false, rules) - - idx = np.array(list(range(len(mlp_train_labels)))) - np.random.shuffle(idx) - mlp_train_vectors = mlp_train_vectors[idx] - mlp_train_labels = mlp_train_labels[idx] - + mlp_train_data = BasicDataset(mlp_train_vectors, mlp_train_labels) + + mlp_val_vectors, mlp_val_labels = get_mlp_data(model, abducer, mapping, val_X_true, val_X_false, rules) + mlp_val_data = BasicDataset(mlp_val_vectors, mlp_val_labels) + best_accuracy = 0 - # Try three times to find the best mlp for _ in range(3): INFO("Training mlp...") @@ -277,46 +268,29 @@ def validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=60) - mlp_train_data = BasicDataset(mlp_train_vectors, mlp_train_labels) - mlp_train_data_loader = torch.utils.data.DataLoader( - mlp_train_data, - batch_size=128, - shuffle=True - ) + mlp_train_data_loader = torch.utils.data.DataLoader(mlp_train_data, batch_size=128, shuffle=True) + loss = mlp_model.fit(mlp_train_data_loader) - INFO("mlp training loss is %f" % loss) + INFO("mlp training final loss is %f" % loss) - mlp_val_vectors, mlp_val_labels = get_mlp_data(model, abducer, mapping, val_X_true, val_X_false, rules) - - # Get MLP validation result - mlp_val_data = BasicDataset(mlp_val_vectors, mlp_val_labels) - mlp_val_data_loader = torch.utils.data.DataLoader( - mlp_val_data, - batch_size=64, - shuffle=True, - ) + mlp_val_data_loader = torch.utils.data.DataLoader(mlp_val_data, batch_size=64, shuffle=True) accuracy = mlp_model.val(mlp_val_data_loader) if accuracy > best_accuracy: best_accuracy = accuracy - return best_accuracy, rules + return best_accuracy + + + -def get_final_rules(rules): - all_rule_dict = {} - for rule in rules: - for r in rule: - all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 - rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items() if cnt > 5} - final_rules = [r for r in rule_dict] - return final_rules -def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num=10, verbose=-1): +def train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8): train_X = train_data val_X = val_data - - min_len = 5 - max_len = 8 + + logic_output_dim = 50 + samples_per_rule = 3 # Start training / for each length of equations for equation_len in range(min_len, max_len): @@ -333,8 +307,11 @@ def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num= condition_cnt = 0 while True: + if equation_len == min_len: + mapping = None + # Abduce and train NN - consistent_acc, char_acc, mapping = abduce_and_train(model, abducer, train_X_true, select_num) + consistent_acc, char_acc, mapping = abduce_and_train(model, abducer, mapping, train_X_true, select_num) if consistent_acc == 0: continue @@ -346,12 +323,13 @@ def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num= # The condition has been satisfied continuously five times if condition_cnt >= 5: - # Try to abduce rules in `validation` - best_accuracy, rules = validation(model, abducer, mapping, train_X_true, train_X_false, val_X_true, val_X_false) - INFO('best_accuracy is %f' %(best_accuracy)) + INFO("Now checking if we can go to next course") + rules = get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim) + INFO('Learned rules from data:', output_rules(rules)) + best_accuracy = validation(model, abducer, mapping, logic_output_dim, rules, train_X_true, train_X_false, val_X_true, val_X_false) + INFO('best_accuracy is %f\n' %(best_accuracy)) # decide next course or restart if best_accuracy > 0.85: - final_rules = get_final_rules(rules) torch.save(model.cls_list[0].model.state_dict(), "./weights/weights_%d.pth" % equation_len) break else: @@ -360,11 +338,59 @@ def train_with_rule(model, abducer, train_data, val_data, epochs=50, select_num= else: model.cls_list[0].model.load_state_dict(torch.load("./weights/weights_%d.pth" % (equation_len - 1))) condition_cnt = 0 - - INFO('final_rules: ', final_rules) - - return model, final_rules + INFO('Reload Model and retrain') + + return model, mapping +def hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8): + train_X = train_data + test_X = test_data + + # Calcualte how many equations should be selected in each length + # for each length, there are select_equation_cnt[equation_len] rules + print("Now begin to train final mlp model") + select_equation_cnt = [] + len_cnt = max_len - min_len + 1 + logic_output_dim = 50 + select_equation_cnt += [0] * min_len + if logic_output_dim % len_cnt == 0: + select_equation_cnt += [logic_output_dim // len_cnt] * len_cnt + else: + select_equation_cnt += [logic_output_dim // len_cnt] * len_cnt + select_equation_cnt[-1] += logic_output_dim % len_cnt + assert sum(select_equation_cnt) == logic_output_dim + + # Abduce rules + rules = [] + samples_per_rule = 3 + for equation_len in range(min_len, max_len + 1): + equation_rules = get_rules_from_data(model, abducer, mapping, train_X[1][equation_len], samples_per_rule, select_equation_cnt[equation_len]) + rules.extend(equation_rules) + INFO('Learned rules from data:', output_rules(rules)) + + mlp_train_vectors, mlp_train_labels = get_all_mlp_data(model, abducer, mapping, train_X[1], train_X[0], rules, min_len, max_len) + mlp_train_data = BasicDataset(mlp_train_vectors, mlp_train_labels) + + # Try three times to find the best mlp + for _ in range(3): + mlp = MLP(input_dim=logic_output_dim) + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.Adam(mlp.parameters(), lr=0.01, betas=(0.9, 0.999)) + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=60) + mlp_train_data_loader = torch.utils.data.DataLoader(mlp_train_data, batch_size=128, shuffle=True) + + loss = mlp_model.fit(mlp_train_data_loader) + INFO("mlp training final loss is %f" % loss) + + for equation_len in range(5, 27): + mlp_test_vectors, mlp_test_labels = get_mlp_data(model, abducer, mapping, test_X[1][equation_len], test_X[0][equation_len], rules) + mlp_test_data = BasicDataset(mlp_test_vectors, mlp_test_labels) + mlp_test_data_loader = torch.utils.data.DataLoader(mlp_test_data, batch_size=64, shuffle=True) + accuracy = mlp_model.val(mlp_test_data_loader) + INFO("The accuracy of testing length %d equations is: %f" % (equation_len, accuracy)) + INFO("\n") if __name__ == "__main__": pass From ae472fccc9341c93ad3677239ea46222ab7d4b57 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 10:10:42 +0800 Subject: [PATCH 137/601] Add test_hed --- utils/utils.py | 69 +++++++++++++++++++++++++++----------------------- 1 file changed, 38 insertions(+), 31 deletions(-) diff --git a/utils/utils.py b/utils/utils.py index 57dea31..f4db6f2 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -2,16 +2,10 @@ import numpy as np from utils.plog import INFO from collections import OrderedDict - # for multiple predictions, modify from `learn_add.py` def flatten(l): - return ( - [item for sublist in l for item in flatten(sublist)] - if isinstance(l, list) - else [l] - ) - - + return [item for sublist in l for item in flatten(sublist)] if isinstance(l, list) else [l] + # for multiple predictions, modify from `learn_add.py` def reform_idx(flatten_pred_res, save_pred_res): re = [] @@ -26,6 +20,22 @@ def reform_idx(flatten_pred_res, save_pred_res): i = i + j return re +def hamming_dist(A, B): + B = np.array(B) + A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis = 1) + +def confidence_dist(A, B): + B = np.array(B) + A = np.clip(A, 1e-9, 1) + A = np.expand_dims(A, axis=0) + A = A.repeat(axis=0, repeats=(len(B))) + rows = np.array(range(len(B))) + rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) + cols = np.array(range(len(B[0]))) + cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) + return 1 - np.prod(A[rows, cols, B], axis = 1) + def block_sample(X, Z, Y, sample_num, epoch_idx): part_num = len(X) // sample_num @@ -40,28 +50,25 @@ def block_sample(X, Z, Y, sample_num, epoch_idx): return X, Z, Y -def hamming_dist(A, B): - B = np.array(B) - A = np.expand_dims(A, axis=0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis=1) - - -def confidence_dist(A, B): - B = np.array(B) - A = np.clip(A, 1e-9, 1) - A = np.expand_dims(A, axis=0) - A = A.repeat(axis=0, repeats=(len(B))) - rows = np.array(range(len(B))) - rows = np.expand_dims(rows, axis=1).repeat(axis=1, repeats=len(B[0])) - cols = np.array(range(len(B[0]))) - cols = np.expand_dims(cols, axis=0).repeat(axis=0, repeats=len(B)) - return 1 - np.prod(A[rows, cols, B], axis=1) +def gen_mappings(chars, symbs): + n_char = len(chars) + n_symbs = len(symbs) + if n_char != n_symbs: + print('Characters and symbols size dosen\'t match.') + return + from itertools import permutations + mappings = [] + # returned mappings + perms = permutations(symbs) + for p in perms: + mappings.append(dict(zip(chars, list(p)))) + return mappings +def mapping_res(original_pred_res, m): + return [[m[symbol] for symbol in formula] for formula in original_pred_res] -def copy_state_dict(state_dict): - new_state_dict = OrderedDict() - for k, v in state_dict.items(): - if k.startswith('base_model'): - name = ".".join(k.split(".")[1:]) - new_state_dict[name] = v - return new_state_dict +def remapping_res(pred_res, m): + remapping = {} + for key, value in m.items(): + remapping[value] = key + return [[remapping[symbol] for symbol in formula] for formula in pred_res] From 121827f8ba248603e8f63b5a1d6bbe5b86376208 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 14:58:05 +0800 Subject: [PATCH 138/601] Update kb.py --- abducer/kb.py | 172 ++++++++++++-------------------------------------- 1 file changed, 42 insertions(+), 130 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 04a9f68..959b4ac 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -43,29 +43,16 @@ class KBBase(ABC): def address(self, address_num, pred_res, key, multiple_predictions=False): new_candidates = [] if not multiple_predictions: - address_idx_list = list( - combinations(list(range(len(pred_res))), address_num) - ) + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) else: - address_idx_list = list( - combinations(list(range(len(flatten(pred_res)))), address_num) - ) + address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num)) for address_idx in address_idx_list: - candidates = self.address_by_idx( - pred_res, key, address_idx, multiple_predictions - ) + candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) new_candidates += candidates return new_candidates - def abduction( - self, - pred_res, - key, - max_address_num, - require_more_address, - multiple_predictions=False, - ): + def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions=False): candidates = [] for address_num in range(len(pred_res) + 1): @@ -73,9 +60,7 @@ class KBBase(ABC): if abs(self.logic_forward(pred_res) - key) <= 1e-3: candidates.append(pred_res) else: - new_candidates = self.address( - address_num, pred_res, key, multiple_predictions - ) + new_candidates = self.address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates if len(candidates) > 0: @@ -85,14 +70,10 @@ class KBBase(ABC): if address_num >= max_address_num: return [], 0, 0 - for address_num in range( - min_address_num + 1, min_address_num + require_more_address + 1 - ): + for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if address_num > max_address_num: return candidates, min_address_num, address_num - 1 - new_candidates = self.address( - address_num, pred_res, key, multiple_predictions - ) + new_candidates = self.address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates return candidates, min_address_num, address_num @@ -117,9 +98,7 @@ class ClsKB(KBBase): else: self.all_address_candidate_dict = {} for address_num in range(max(self.len_list) + 1): - self.all_address_candidate_dict[address_num] = list( - product(self.pseudo_label_list, repeat=address_num) - ) + self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat=address_num)) # For parallel version of _get_GKB def _get_XY_list(self, args): @@ -164,26 +143,11 @@ class ClsKB(KBBase): def logic_forward(self): pass - def abduce_candidates( - self, - pred_res, - key, - max_address_num=-1, - require_more_address=0, - multiple_predictions=False, - ): + def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: - return self.abduce_from_GKB( - pred_res, key, max_address_num, require_more_address - ) + return self.abduce_from_GKB(pred_res, key, max_address_num, require_more_address) else: - return self.abduction( - pred_res, - key, - max_address_num, - require_more_address, - multiple_predictions, - ) + return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) def abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): if self.base == {} or len(pred_res) not in self.len_list: @@ -247,24 +211,7 @@ class add_KB(ClsKB): class HWF_KB(ClsKB): def __init__( - self, - GKB_flag=False, - pseudo_label_list=[ - "1", - "2", - "3", - "4", - "5", - "6", - "7", - "8", - "9", - "+", - "-", - "times", - "div", - ], - len_list=[1, 3, 5, 7], + self, GKB_flag=False, pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], len_list=[1, 3, 5, 7] ): super().__init__(GKB_flag, pseudo_label_list, len_list) @@ -272,19 +219,9 @@ class HWF_KB(ClsKB): if len(formula) % 2 == 0: return False for i in range(len(formula)): - if i % 2 == 0 and formula[i] not in [ - "1", - "2", - "3", - "4", - "5", - "6", - "7", - "8", - "9", - ]: + if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']: return False - if i % 2 != 0 and formula[i] not in ["+", "-", "times", "div"]: + if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: return False return True @@ -292,22 +229,22 @@ class HWF_KB(ClsKB): if not self.valid_candidate(formula): return np.inf mapping = { - "1": "1", - "2": "2", - "3": "3", - "4": "4", - "5": "5", - "6": "6", - "7": "7", - "8": "8", - "9": "9", - "+": "+", - "-": "-", - "times": "*", - "div": "/", + '1': '1', + '2': '2', + '3': '3', + '4': '4', + '5': '5', + '6': '6', + '7': '7', + '8': '8', + '9': '9', + '+': '+', + '-': '-', + 'times': '*', + 'div': '/', } formula = [mapping[f] for f in formula] - return round(eval("".join(formula)), 2) + return round(eval(''.join(formula)), 2) class prolog_KB(KBBase): @@ -319,12 +256,8 @@ class prolog_KB(KBBase): def logic_forward(self): pass - def abduce_candidates( - self, pred_res, key, max_address_num, require_more_address, multiple_predictions - ): - return self.abduction( - pred_res, key, max_address_num, require_more_address, multiple_predictions - ) + def abduce_candidates(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] @@ -332,9 +265,7 @@ class prolog_KB(KBBase): if not multiple_predictions: query_string = self.get_query_string(pred_res, key, address_idx) else: - query_string = self.get_query_string_need_flatten( - pred_res, key, address_idx - ) + query_string = self.get_query_string_need_flatten(pred_res, key, address_idx) if multiple_predictions: save_pred_res = pred_res @@ -358,28 +289,24 @@ class add_prolog_KB(prolog_KB): super().__init__(pseudo_label_list) for i in self.pseudo_label_list: self.prolog.assertz("pseudo_label(%s)" % i) - self.prolog.assertz( - "addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2" - ) + self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") def logic_forward(self, nums): - return list(self.prolog.query("addition(%s, %s, Res)." % (nums[0], nums[1])))[ - 0 - ]["Res"] + return list(self.prolog.query("addition(%s, %s, Res)." % (nums[0], nums[1])))[0]['Res'] def get_query_string(self, pred_res, key, address_idx): query_string = "addition(" for idx, i in enumerate(pred_res): - tmp = "Z" + str(idx) + "," if idx in address_idx else str(i) + "," + tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' query_string += tmp query_string += "%s)." % key return query_string class HED_prolog_KB(prolog_KB): - def __init__(self, pseudo_label_list=[0, 1, "+", "="]): + def __init__(self, pseudo_label_list=[0, 1, '+', '=']): super().__init__(pseudo_label_list) - self.prolog.consult("./datasets/hed/learn_add.pl") + self.prolog.consult('./datasets/hed/learn_add.pl') # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` def logic_forward(self, exs): @@ -391,7 +318,7 @@ class HED_prolog_KB(prolog_KB): # add variables for prolog for idx in range(len(flatten_pred_res)): if idx in address_idx: - flatten_pred_res[idx] = "X" + str(idx) + flatten_pred_res[idx] = 'X' + str(idx) # unflatten new_pred_res = reform_idx(flatten_pred_res, pred_res) @@ -399,30 +326,15 @@ class HED_prolog_KB(prolog_KB): return query_string.replace("'", "").replace("+", "'+'").replace("=", "'='") def consist_rule(self, exs, rules): - consist = False - for rule in rules: - # print(rule) - if ( - len( - list( - self.prolog.query( - "consistent_inst_feature(%s, [%s])." % (exs, rule) - ) - ) - ) - != 0 - ): - consist = True - break - return consist + rules = str(rules).replace("\'","") + return len(list(self.prolog.query("eval_inst_feature(%s, %s)." % (exs, rules)))) != 0 def abduce_rules(self, pred_res): - prolog_result = list( - self.prolog.query("consistent_inst_feature(%s, X)." % pred_res) - ) + # print(pred_res) + prolog_result = list(self.prolog.query("consistent_inst_feature(%s, X)." % pred_res)) if len(prolog_result) == 0: return None - prolog_rules = prolog_result[0]["X"] + prolog_rules = prolog_result[0]['X'] rules = [] for rule in prolog_rules: rules.append(rule.value) From 54492a5a37effaa56554cd8ec95aab32b3093fb3 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 14:58:27 +0800 Subject: [PATCH 139/601] Update framework_hed.py --- framework_hed.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/framework_hed.py b/framework_hed.py index e464ed0..3476462 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -122,8 +122,8 @@ def hed_pretrain(kb, cls, recorder): def get_char_acc(model, X, consistent_pred_res, mapping): original_pred_res = model.predict(X)['cls'] pred_res = flatten(mapping_res(original_pred_res, mapping)) - INFO('Current model\'s output:', pred_res) - INFO('Abduced labels: ', flatten(consistent_pred_res)) + INFO('Current model\'s output: ', pred_res) + INFO('Abduced labels: ', flatten(consistent_pred_res)) assert len(pred_res) == len(flatten(consistent_pred_res)) return sum([pred_res[idx] == flatten(consistent_pred_res)[idx] for idx in range(len(pred_res))]) / len(pred_res) @@ -218,7 +218,7 @@ def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, def get_mlp_vector(model, abducer, mapping, X, rules): original_pred_res = model.predict([X])['cls'] - pred_res = mapping_res(original_pred_res, mapping) + pred_res = flatten(mapping_res(original_pred_res, mapping)) vector = [] for rule in rules: if abducer.kb.consist_rule(pred_res, rule): @@ -267,7 +267,7 @@ def validation(model, abducer, mapping, logic_output_dim, rules, train_X_true, t optimizer = torch.optim.Adam(mlp.parameters(), lr=0.01, betas=(0.9, 0.999)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=60) + mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=100) mlp_train_data_loader = torch.utils.data.DataLoader(mlp_train_data, batch_size=128, shuffle=True) loss = mlp_model.fit(mlp_train_data_loader) @@ -329,7 +329,7 @@ def train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len best_accuracy = validation(model, abducer, mapping, logic_output_dim, rules, train_X_true, train_X_false, val_X_true, val_X_false) INFO('best_accuracy is %f\n' %(best_accuracy)) # decide next course or restart - if best_accuracy > 0.85: + if best_accuracy > 0.88: torch.save(model.cls_list[0].model.state_dict(), "./weights/weights_%d.pth" % equation_len) break else: @@ -378,7 +378,7 @@ def hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len= optimizer = torch.optim.Adam(mlp.parameters(), lr=0.01, betas=(0.9, 0.999)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=60) + mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=100) mlp_train_data_loader = torch.utils.data.DataLoader(mlp_train_data, batch_size=128, shuffle=True) loss = mlp_model.fit(mlp_train_data_loader) From dc4d12e4b92fb30ee72168d9e8b2aaffe7fa488f Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 15:48:46 +0800 Subject: [PATCH 140/601] Delete equation_generator.py --- datasets/hed/equation_generator.py | 266 ----------------------------- 1 file changed, 266 deletions(-) delete mode 100644 datasets/hed/equation_generator.py diff --git a/datasets/hed/equation_generator.py b/datasets/hed/equation_generator.py deleted file mode 100644 index 3cf735b..0000000 --- a/datasets/hed/equation_generator.py +++ /dev/null @@ -1,266 +0,0 @@ -import os -import itertools -import random -import numpy as np -from PIL import Image -import pickle - - -def get_sign_path_list(data_dir, sign_names): - sign_num = len(sign_names) - index_dict = dict(zip(sign_names, list(range(sign_num)))) - ret = [[] for _ in range(sign_num)] - for path in os.listdir(data_dir): - if path in sign_names: - index = index_dict[path] - sign_path = os.path.join(data_dir, path) - for p in os.listdir(sign_path): - ret[index].append(os.path.join(sign_path, p)) - return ret - - -def split_pool_by_rate(pools, rate, seed=None): - if seed is not None: - random.seed(seed) - ret1 = [] - ret2 = [] - for pool in pools: - random.shuffle(pool) - num = int(len(pool) * rate) - ret1.append(pool[:num]) - ret2.append(pool[num:]) - return ret1, ret2 - - -def int_to_system_form(num, system_num): - if num is 0: - return "0" - ret = "" - while num > 0: - ret += str(num % system_num) - num //= system_num - return ret[::-1] - - -def generator_equations( - left_opt_len, right_opt_len, res_opt_len, system_num, label, generate_type -): - expr_len = left_opt_len + right_opt_len - num_list = "".join([str(i) for i in range(system_num)]) - ret = [] - if generate_type == "all": - candidates = itertools.product(num_list, repeat=expr_len) - else: - candidates = ["".join(random.sample(["0", "1"] * expr_len, expr_len))] - random.shuffle(candidates) - for nums in candidates: - left_num = "".join(nums[:left_opt_len]) - right_num = "".join(nums[left_opt_len:]) - left_value = int(left_num, system_num) - right_value = int(right_num, system_num) - result_value = left_value + right_value - if label == "negative": - result_value += random.randint(-result_value, result_value) - if left_value + right_value == result_value: - continue - result_num = int_to_system_form(result_value, system_num) - # leading zeros - if res_opt_len != len(result_num): - continue - if (left_opt_len > 1 and left_num[0] == "0") or ( - right_opt_len > 1 and right_num[0] == "0" - ): - continue - - # add leading zeros - if res_opt_len < len(result_num): - continue - while len(result_num) < res_opt_len: - result_num = "0" + result_num - # continue - ret.append( - left_num + "+" + right_num + "=" + result_num - ) # current only consider '+' and '=' - # print(ret[-1]) - return ret - - -def generator_equation_by_len(equation_len, system_num=2, label=0, require_num=1): - generate_type = "one" - ret = [] - equation_sign_num = 2 # '+' and '=' - while len(ret) < require_num: - left_opt_len = random.randint(1, equation_len - 1 - equation_sign_num) - right_opt_len = random.randint( - 1, equation_len - left_opt_len - equation_sign_num - ) - res_opt_len = equation_len - left_opt_len - right_opt_len - equation_sign_num - ret.extend( - generator_equations( - left_opt_len, - right_opt_len, - res_opt_len, - system_num, - label, - generate_type, - ) - ) - return ret - - -def generator_equations_by_len( - equation_len, system_num=2, label=0, repeat_times=1, keep=1, generate_type="all" -): - ret = [] - equation_sign_num = 2 # '+' and '=' - for left_opt_len in range(1, equation_len - (2 + equation_sign_num) + 1): - for right_opt_len in range( - 1, equation_len - left_opt_len - (1 + equation_sign_num) + 1 - ): - res_opt_len = ( - equation_len - left_opt_len - right_opt_len - equation_sign_num - ) - for i in range(repeat_times): # generate more equations - if random.random() > keep ** (equation_len): - continue - ret.extend( - generator_equations( - left_opt_len, - right_opt_len, - res_opt_len, - system_num, - label, - generate_type, - ) - ) - return ret - - -def generator_equations_by_max_len( - max_equation_len, - system_num=2, - label=0, - repeat_times=1, - keep=1, - generate_type="all", - num_per_len=None, -): - ret = [] - equation_sign_num = 2 # '+' and '=' - for equation_len in range(3 + equation_sign_num, max_equation_len + 1): - if num_per_len is None: - ret.extend( - generator_equations_by_len( - equation_len, system_num, label, repeat_times, keep, generate_type - ) - ) - else: - ret.extend( - generator_equation_by_len( - equation_len, system_num, label, require_num=num_per_len - ) - ) - return ret - - -def generator_equation_images(image_pools, equations, signs, shape, seed, is_color): - if seed is not None: - random.seed(seed) - ret = [] - sign_num = len(signs) - sign_index_dict = dict(zip(signs, list(range(sign_num)))) - for equation in equations: - data = [] - for sign in equation: - index = sign_index_dict[sign] - pick = random.randint(0, len(image_pools[index]) - 1) - if is_color: - image = ( - Image.open(image_pools[index][pick]).convert("RGB").resize(shape) - ) - else: - image = Image.open(image_pools[index][pick]).convert("I").resize(shape) - image_array = np.array(image) - image_array = (image_array - 127) * (1.0 / 128) - data.append(image_array) - ret.append(np.array(data)) - return ret - - -def get_equation_std_data( - data_dir, - sign_dir_lists, - sign_output_lists, - shape=(28, 28), - train_max_equation_len=10, - test_max_equation_len=10, - system_num=2, - tmp_file_prev=None, - seed=None, - train_num_per_len=10, - test_num_per_len=10, - is_color=False, -): - tmp_file = "" - if tmp_file_prev is not None: - tmp_file = "%s_train_len_%d_test_len_%d_sys_%d_.pk" % ( - tmp_file_prev, - train_max_equation_len, - test_max_equation_len, - system_num, - ) - if os.path.exists(tmp_file): - return pickle.load(open(tmp_file, "rb")) - - image_pools = get_sign_path_list(data_dir, sign_dir_lists) - train_pool, test_pool = split_pool_by_rate(image_pools, 0.8, seed) - - ret = {} - for label in ["positive", "negative"]: - print("Generating equations.") - train_equations = generator_equations_by_max_len( - train_max_equation_len, system_num, label, num_per_len=train_num_per_len - ) - test_equations = generator_equations_by_max_len( - test_max_equation_len, system_num, label, num_per_len=test_num_per_len - ) - print(train_equations) - print(test_equations) - print("Generated equations.") - print("Generating equation image data.") - ret["train:%s" % (label)] = generator_equation_images( - train_pool, train_equations, sign_output_lists, shape, seed, is_color - ) - ret["test:%s" % (label)] = generator_equation_images( - test_pool, test_equations, sign_output_lists, shape, seed, is_color - ) - print("Generated equation image data.") - - if tmp_file_prev is not None: - pickle.dump(ret, open(tmp_file, "wb")) - return ret - - -if __name__ == "__main__": - data_dirs = [ - "./dataset/mnist_images", - "./dataset/random_images", - ] # , "../dataset/cifar10_images"] - tmp_file_prevs = [ - "mnist_equation_data", - "random_equation_data", - ] # , "cifar10_equation_data"] - for data_dir, tmp_file_prev in zip(data_dirs, tmp_file_prevs): - data = get_equation_std_data( - data_dir=data_dir, - sign_dir_lists=["0", "1", "10", "11"], - sign_output_lists=["0", "1", "+", "="], - shape=(28, 28), - train_max_equation_len=26, - test_max_equation_len=26, - system_num=2, - tmp_file_prev=tmp_file_prev, - train_num_per_len=300, - test_num_per_len=300, - is_color=False, - ) From 4d7b6570cbb3e804da69f4d5045d6aca11e9c56b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 15:49:13 +0800 Subject: [PATCH 141/601] Create README.md --- datasets/hed/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 datasets/hed/README.md diff --git a/datasets/hed/README.md b/datasets/hed/README.md new file mode 100644 index 0000000..1044f38 --- /dev/null +++ b/datasets/hed/README.md @@ -0,0 +1 @@ +Download the Handwritten Equation Decipherment dataset from [NJU Box](https://box.nju.edu.cn/d/23cd88c9325b4d96a571/) to this folder From b87793bf3adae9c1edf473759d5a893b13be18c3 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 16:50:24 +0800 Subject: [PATCH 142/601] Update README.md --- datasets/hed/README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/datasets/hed/README.md b/datasets/hed/README.md index 1044f38..f48c97d 100644 --- a/datasets/hed/README.md +++ b/datasets/hed/README.md @@ -1 +1,4 @@ -Download the Handwritten Equation Decipherment dataset from [NJU Box](https://box.nju.edu.cn/d/23cd88c9325b4d96a571/) to this folder +Download the Handwritten Equation Decipherment dataset from [NJU Box](https://box.nju.edu.cn/f/391c2d48c32b436cb833/) to this folder and unzip it: +``` +unzip HED.zip +``` From 7fbe96fa79c1aef602edb5b167fdaa19c659e5d3 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 22 Feb 2023 17:39:53 +0800 Subject: [PATCH 143/601] Update get_hed.py --- datasets/hed/get_hed.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datasets/hed/get_hed.py b/datasets/hed/get_hed.py index 8b61b62..b317d94 100644 --- a/datasets/hed/get_hed.py +++ b/datasets/hed/get_hed.py @@ -41,7 +41,7 @@ def get_pretrain_data(labels, image_size=(28, 28, 1)): X = [] for label in labels: label_path = os.path.join( - "./datasets/hed/dataset/mnist_images", label + "./datasets/hed/mnist_images", label ) img_path_list = os.listdir(label_path) for img_path in img_path_list: From 28149e36c5b1ca4415d0a82c0439bf2efe97c304 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Wed, 22 Feb 2023 18:53:40 +0800 Subject: [PATCH 144/601] add 'weights' folder --- weights/all_weights_here.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 weights/all_weights_here.txt diff --git a/weights/all_weights_here.txt b/weights/all_weights_here.txt new file mode 100644 index 0000000..e69de29 From 7a424c8eac0c0c2d9d63cc2b9ec713d1c280231d Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 24 Feb 2023 17:03:51 +0800 Subject: [PATCH 145/601] Update framework_hed.py --- framework_hed.py | 176 ++++++++++++++++------------------------------- 1 file changed, 59 insertions(+), 117 deletions(-) diff --git a/framework_hed.py b/framework_hed.py index 3476462..b7439c3 100644 --- a/framework_hed.py +++ b/framework_hed.py @@ -119,7 +119,7 @@ def hed_pretrain(kb, cls, recorder): cls.load_state_dict(torch.load("./weights/pretrain_weights.pth")) -def get_char_acc(model, X, consistent_pred_res, mapping): +def _get_char_acc(model, X, consistent_pred_res, mapping): original_pred_res = model.predict(X)['cls'] pred_res = flatten(mapping_res(original_pred_res, mapping)) INFO('Current model\'s output: ', pred_res) @@ -177,22 +177,29 @@ def abduce_and_train(model, abducer, mapping, train_X_true, select_num): model.train([X[idx] for idx in consistent_idx], remapping_res(consistent_pred_res, mapping)) consistent_acc = len(consistent_idx) / select_num - char_acc = get_char_acc(model, [X[idx] for idx in consistent_idx], consistent_pred_res, mapping) + char_acc = _get_char_acc(model, [X[idx] for idx in consistent_idx], consistent_pred_res, mapping) INFO('consistent_acc is %s, char_acc is %s' % (consistent_acc, char_acc)) return consistent_acc, char_acc, mapping +def _remove_duplicate_rule(rule_dict): + add_nums_dict = {} + for r in list(rule_dict): + add_nums = str(r.split(']')[0].split('[')[1]) + str(r.split(']')[1].split('[')[1]) # r = 'my_op([1], [0], [1, 0])' then add_nums = '10' + if add_nums in add_nums_dict: + old_r = add_nums_dict[add_nums] + if rule_dict[r] >= rule_dict[old_r]: + rule_dict.pop(old_r) + add_nums_dict[add_nums] = r + else: + rule_dict.pop(r) + else: + add_nums_dict[add_nums] = r + return list(rule_dict) -def output_rules(rules): - all_rule_dict = {} - for rule in rules: - for r in rule: - all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 - rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items()}# if cnt >= 5} - return rule_dict -def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim): +def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, samples_num): rules = [] - for _ in range(logic_output_dim): + for _ in range(samples_num): while True: select_idx = np.random.randint(len(train_X_true), size=samples_per_rule) X = [] @@ -213,83 +220,32 @@ def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, if rule != None: break rules.append(rule) - return rules - - -def get_mlp_vector(model, abducer, mapping, X, rules): - original_pred_res = model.predict([X])['cls'] - pred_res = flatten(mapping_res(original_pred_res, mapping)) - vector = [] - for rule in rules: - if abducer.kb.consist_rule(pred_res, rule): - vector.append(1) - else: - vector.append(0) - return vector - - -def get_mlp_data(model, abducer, mapping, X_true, X_false, rules): - mlp_vectors = [] - mlp_labels = [] - for X in X_true: - mlp_vectors.append(get_mlp_vector(model, abducer, mapping, X, rules)) - mlp_labels.append(1) - for X in X_false: - mlp_vectors.append(get_mlp_vector(model, abducer, mapping, X, rules)) - mlp_labels.append(0) - return np.array(mlp_vectors, dtype=np.float32), np.array(mlp_labels, dtype=np.int64) - -def get_all_mlp_data(model, abducer, mapping, X_true, X_false, rules, min_len, max_len): - for equation_len in range(min_len, max_len + 1): - mlp_vectors, mlp_labels = get_mlp_data(model, abducer, mapping, X_true[equation_len], X_false[equation_len], rules) - if equation_len == min_len: - all_mlp_vectors = mlp_vectors - all_mlp_labels = mlp_labels - else: - all_mlp_vectors = np.concatenate((all_mlp_vectors, mlp_vectors)) - all_mlp_labels = np.concatenate((all_mlp_labels, mlp_labels)) - return all_mlp_vectors, all_mlp_labels - - -def validation(model, abducer, mapping, logic_output_dim, rules, train_X_true, train_X_false, val_X_true, val_X_false): - mlp_train_vectors, mlp_train_labels = get_mlp_data(model, abducer, mapping, train_X_true, train_X_false, rules) - mlp_train_data = BasicDataset(mlp_train_vectors, mlp_train_labels) - mlp_val_vectors, mlp_val_labels = get_mlp_data(model, abducer, mapping, val_X_true, val_X_false, rules) - mlp_val_data = BasicDataset(mlp_val_vectors, mlp_val_labels) + all_rule_dict = {} + for rule in rules: + for r in rule: + all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 + rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items() if cnt >= 5} + rules = _remove_duplicate_rule(rule_dict) - best_accuracy = 0 - # Try three times to find the best mlp - for _ in range(3): - INFO("Training mlp...") - mlp = MLP(input_dim=logic_output_dim) - criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.Adam(mlp.parameters(), lr=0.01, betas=(0.9, 0.999)) - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=100) - mlp_train_data_loader = torch.utils.data.DataLoader(mlp_train_data, batch_size=128, shuffle=True) - - loss = mlp_model.fit(mlp_train_data_loader) - INFO("mlp training final loss is %f" % loss) - - mlp_val_data_loader = torch.utils.data.DataLoader(mlp_val_data, batch_size=64, shuffle=True) - accuracy = mlp_model.val(mlp_val_data_loader) - - if accuracy > best_accuracy: - best_accuracy = accuracy - return best_accuracy - - + return rules +def _get_consist_rule_acc(model, abducer, mapping, rules, X): + cnt = 0 + for x in X: + original_pred_res = model.predict([x])['cls'] + pred_res = flatten(mapping_res(original_pred_res, mapping)) + if abducer.kb.consist_rule(pred_res, rules): + cnt += 1 + return cnt / len(X) def train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8): train_X = train_data val_X = val_data - logic_output_dim = 50 + samples_num = 50 samples_per_rule = 3 # Start training / for each length of equations @@ -324,12 +280,15 @@ def train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len # The condition has been satisfied continuously five times if condition_cnt >= 5: INFO("Now checking if we can go to next course") - rules = get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, logic_output_dim) - INFO('Learned rules from data:', output_rules(rules)) - best_accuracy = validation(model, abducer, mapping, logic_output_dim, rules, train_X_true, train_X_false, val_X_true, val_X_false) - INFO('best_accuracy is %f\n' %(best_accuracy)) + rules = get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, samples_num) + INFO('Learned rules from data:', rules) + + true_consist_rule_acc = _get_consist_rule_acc(model, abducer, mapping, rules, val_X_true) + false_consist_rule_acc = _get_consist_rule_acc(model, abducer, mapping, rules, val_X_false) + + INFO('consist_rule_acc is %f, %f\n' %(true_consist_rule_acc, false_consist_rule_acc)) # decide next course or restart - if best_accuracy > 0.88: + if true_consist_rule_acc > 0.9 and false_consist_rule_acc < 0.1: torch.save(model.cls_list[0].model.state_dict(), "./weights/weights_%d.pth" % equation_len) break else: @@ -347,50 +306,33 @@ def hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len= test_X = test_data # Calcualte how many equations should be selected in each length - # for each length, there are select_equation_cnt[equation_len] rules + # for each length, there are equation_samples_num[equation_len] rules print("Now begin to train final mlp model") - select_equation_cnt = [] + equation_samples_num = [] len_cnt = max_len - min_len + 1 - logic_output_dim = 50 - select_equation_cnt += [0] * min_len - if logic_output_dim % len_cnt == 0: - select_equation_cnt += [logic_output_dim // len_cnt] * len_cnt + samples_num = 50 + equation_samples_num += [0] * min_len + if samples_num % len_cnt == 0: + equation_samples_num += [samples_num // len_cnt] * len_cnt else: - select_equation_cnt += [logic_output_dim // len_cnt] * len_cnt - select_equation_cnt[-1] += logic_output_dim % len_cnt - assert sum(select_equation_cnt) == logic_output_dim + equation_samples_num += [samples_num // len_cnt] * len_cnt + equation_samples_num[-1] += samples_num % len_cnt + assert sum(equation_samples_num) == samples_num # Abduce rules rules = [] samples_per_rule = 3 for equation_len in range(min_len, max_len + 1): - equation_rules = get_rules_from_data(model, abducer, mapping, train_X[1][equation_len], samples_per_rule, select_equation_cnt[equation_len]) + equation_rules = get_rules_from_data(model, abducer, mapping, train_X[1][equation_len], samples_per_rule, equation_samples_num[equation_len]) rules.extend(equation_rules) - INFO('Learned rules from data:', output_rules(rules)) - - mlp_train_vectors, mlp_train_labels = get_all_mlp_data(model, abducer, mapping, train_X[1], train_X[0], rules, min_len, max_len) - mlp_train_data = BasicDataset(mlp_train_vectors, mlp_train_labels) - - # Try three times to find the best mlp - for _ in range(3): - mlp = MLP(input_dim=logic_output_dim) - criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.Adam(mlp.parameters(), lr=0.01, betas=(0.9, 0.999)) - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - mlp_model = BasicModel(mlp, criterion, optimizer, device, batch_size=128, num_epochs=100) - mlp_train_data_loader = torch.utils.data.DataLoader(mlp_train_data, batch_size=128, shuffle=True) - - loss = mlp_model.fit(mlp_train_data_loader) - INFO("mlp training final loss is %f" % loss) + rules = list(set(rules)) + INFO('Learned rules from data:', rules) + - for equation_len in range(5, 27): - mlp_test_vectors, mlp_test_labels = get_mlp_data(model, abducer, mapping, test_X[1][equation_len], test_X[0][equation_len], rules) - mlp_test_data = BasicDataset(mlp_test_vectors, mlp_test_labels) - mlp_test_data_loader = torch.utils.data.DataLoader(mlp_test_data, batch_size=64, shuffle=True) - accuracy = mlp_model.val(mlp_test_data_loader) - INFO("The accuracy of testing length %d equations is: %f" % (equation_len, accuracy)) - INFO("\n") + for equation_len in range(5, 27): + true_consist_rule_acc = _get_consist_rule_acc(model, abducer, mapping, rules, test_X[1][equation_len]) + false_consist_rule_acc = _get_consist_rule_acc(model, abducer, mapping, rules, test_X[0][equation_len]) + INFO('consist_rule_acc of testing length %d equations are %f, %f' %(equation_len, true_consist_rule_acc, false_consist_rule_acc)) if __name__ == "__main__": pass From 7dc2ce032aa1006967ee62b2aaf445d0edc39cdb Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Wed, 1 Mar 2023 16:48:46 +0800 Subject: [PATCH 146/601] update TODO in kb.py --- abducer/kb.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 959b4ac..6d467ea 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -48,15 +48,19 @@ class KBBase(ABC): address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num)) for address_idx in address_idx_list: + # TODO: è¦ä¹ˆaddress_by_idx放这个类，è¦ä¹ˆå®šä¹‰abstractmethod candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) new_candidates += candidates return new_candidates - def abduction(self, pred_res, key, max_address_num, require_more_address, multiple_predictions=False): + def abduction(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False): candidates = [] + # TODO: 这里的len(pred_res)考虑了multiple_predictionså—？ for address_num in range(len(pred_res) + 1): if address_num == 0: + # TODO: ä¸æ˜¯æ‰€æœ‰çš„key都是数字，也å¯ä»¥æ˜¯å­—符串，甚至列表？ + # TODO: check type (str int float multiple_pred ...) if abs(self.logic_forward(pred_res) - key) <= 1e-3: candidates.append(pred_res) else: @@ -88,7 +92,8 @@ class ClsKB(KBBase): self.GKB_flag = GKB_flag self.pseudo_label_list = pseudo_label_list self.len_list = len_list - self.prolog_flag = False + self.prolog_flag = False # TODO：没用？ + # TODO: 既然pseudo_label_list len_list prolog_flag都存为self了，那为啥åˆä¼ åˆ°åŽé¢çš„方法里，或者说self就没用上 if GKB_flag: self.base = {} @@ -143,6 +148,7 @@ class ClsKB(KBBase): def logic_forward(self): pass + # TODO: abductionå’Œabduce_candidates命å上太相近了，å³ä½¿è¿™æ ·ï¼Œä¹Ÿæœ‰å…¶ä¸­ä¸€ä¸ªæ˜¯ç§æœ‰æ–¹æ³•åŠ "_"å§ def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: return self.abduce_from_GKB(pred_res, key, max_address_num, require_more_address) From ed60061451bf14c3ffabd4b4d9a5e38208677929 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 1 Mar 2023 18:34:26 +0800 Subject: [PATCH 147/601] Update kb.py --- abducer/kb.py | 45 +++++++++++++++++++++------------------------ 1 file changed, 21 insertions(+), 24 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index 6d467ea..b78ab64 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -21,7 +21,7 @@ sys.path.append("..") from collections import defaultdict from itertools import product, combinations -from utils.utils import flatten, reform_idx, hamming_dist +from utils.utils import flatten, reform_idx, hamming_dist, check_is_equal from multiprocessing import Pool @@ -39,8 +39,12 @@ class KBBase(ABC): @abstractmethod def abduce_candidates(self): pass + + @abstractmethod + def address_by_idx(self): + pass - def address(self, address_num, pred_res, key, multiple_predictions=False): + def _address(self, address_num, pred_res, key, multiple_predictions=False): new_candidates = [] if not multiple_predictions: address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) @@ -48,23 +52,19 @@ class KBBase(ABC): address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num)) for address_idx in address_idx_list: - # TODO: è¦ä¹ˆaddress_by_idx放这个类，è¦ä¹ˆå®šä¹‰abstractmethod candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) new_candidates += candidates return new_candidates - def abduction(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False): + def _abduce_by_abduction(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False): candidates = [] - # TODO: 这里的len(pred_res)考虑了multiple_predictionså—？ - for address_num in range(len(pred_res) + 1): + for address_num in range(len(flatten(pred_res)) + 1): if address_num == 0: - # TODO: ä¸æ˜¯æ‰€æœ‰çš„key都是数字，也å¯ä»¥æ˜¯å­—符串，甚至列表？ - # TODO: check type (str int float multiple_pred ...) - if abs(self.logic_forward(pred_res) - key) <= 1e-3: + if check_is_equal(pred_res, key): candidates.append(pred_res) else: - new_candidates = self.address(address_num, pred_res, key, multiple_predictions) + new_candidates = self._address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates if len(candidates) > 0: @@ -77,7 +77,7 @@ class KBBase(ABC): for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if address_num > max_address_num: return candidates, min_address_num, address_num - 1 - new_candidates = self.address(address_num, pred_res, key, multiple_predictions) + new_candidates = self._address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates return candidates, min_address_num, address_num @@ -92,12 +92,10 @@ class ClsKB(KBBase): self.GKB_flag = GKB_flag self.pseudo_label_list = pseudo_label_list self.len_list = len_list - self.prolog_flag = False # TODO：没用？ - # TODO: 既然pseudo_label_list len_list prolog_flag都存为self了，那为啥åˆä¼ åˆ°åŽé¢çš„方法里，或者说self就没用上 if GKB_flag: self.base = {} - X, Y = self._get_GKB(self.pseudo_label_list, self.len_list) + X, Y = self._get_GKB() for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(x) else: @@ -117,10 +115,10 @@ class ClsKB(KBBase): return XY_list # Parallel get GKB - def _get_GKB(self, pseudo_label_list, len_list): + def _get_GKB(self): # all_X = [] # for length in len_list: - # all_X += list(product(pseudo_label_list, repeat = length)) + # all_X += list(product(self.pseudo_label_list, repeat = length)) # X, Y = [], [] # for x in all_X: @@ -130,10 +128,10 @@ class ClsKB(KBBase): # Y.append(y) X, Y = [], [] - for length in len_list: + for length in self.len_list: arg_list = [] - for pre_x in pseudo_label_list: - post_x_it = product(pseudo_label_list, repeat=length - 1) + for pre_x in self.pseudo_label_list: + post_x_it = product(self.pseudo_label_list, repeat=length - 1) arg_list.append((pre_x, post_x_it)) with Pool(processes=len(arg_list)) as pool: ret_list = pool.map(self._get_XY_list, arg_list) @@ -148,14 +146,13 @@ class ClsKB(KBBase): def logic_forward(self): pass - # TODO: abductionå’Œabduce_candidates命å上太相近了，å³ä½¿è¿™æ ·ï¼Œä¹Ÿæœ‰å…¶ä¸­ä¸€ä¸ªæ˜¯ç§æœ‰æ–¹æ³•åŠ "_"å§ def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: - return self.abduce_from_GKB(pred_res, key, max_address_num, require_more_address) + return self._abduce_from_GKB(pred_res, key, max_address_num, require_more_address) else: - return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) + return self._abduce_by_abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) - def abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): + def _abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): if self.base == {} or len(pred_res) not in self.len_list: return [] @@ -263,7 +260,7 @@ class prolog_KB(KBBase): pass def abduce_candidates(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): - return self.abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) + return self._abduce_by_abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] From b08a58aab7c0600acf2f175e004ae9d3b250d90d Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 2 Mar 2023 09:24:06 +0800 Subject: [PATCH 148/601] Add check_equal --- utils/utils.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/utils/utils.py b/utils/utils.py index f4db6f2..1138361 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -72,3 +72,18 @@ def remapping_res(pred_res, m): for key, value in m.items(): remapping[value] = key return [[remapping[symbol] for symbol in formula] for formula in pred_res] + +def check_equal(a, b): + if isinstance(a, (int, float)) and isinstance(b, (int, float)): + return abs(a - b) <= 1e-3 + + if isinstance(a, list) and isinstance(b, list): + if len(a) != len(b): + return False + for i in range(len(a)): + if not check_equal(a[i], b[i]): + return False + return True + + else: + return a == b From 650c172d61e4b9ef6c468ae07e2826086c6f3dec Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Thu, 2 Mar 2023 09:24:41 +0800 Subject: [PATCH 149/601] Update kb.py --- abducer/kb.py | 27 ++++++++------------------- 1 file changed, 8 insertions(+), 19 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index b78ab64..b9beb3f 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -21,7 +21,7 @@ sys.path.append("..") from collections import defaultdict from itertools import product, combinations -from utils.utils import flatten, reform_idx, hamming_dist, check_is_equal +from utils.utils import flatten, reform_idx, hamming_dist, check_equal from multiprocessing import Pool @@ -56,12 +56,12 @@ class KBBase(ABC): new_candidates += candidates return new_candidates - def _abduce_by_abduction(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False): + def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False): candidates = [] for address_num in range(len(flatten(pred_res)) + 1): if address_num == 0: - if check_is_equal(pred_res, key): + if check_equal(self.logic_forward(pred_res), key): candidates.append(pred_res) else: new_candidates = self._address(address_num, pred_res, key, multiple_predictions) @@ -114,19 +114,8 @@ class ClsKB(KBBase): XY_list.append((x, y)) return XY_list - # Parallel get GKB + # Parallel _get_GKB def _get_GKB(self): - # all_X = [] - # for length in len_list: - # all_X += list(product(self.pseudo_label_list, repeat = length)) - - # X, Y = [], [] - # for x in all_X: - # y = self.logic_forward(x) - # if y != np.inf: - # X.append(x) - # Y.append(y) - X, Y = [], [] for length in self.len_list: arg_list = [] @@ -148,11 +137,11 @@ class ClsKB(KBBase): def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: - return self._abduce_from_GKB(pred_res, key, max_address_num, require_more_address) + return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address) else: - return self._abduce_by_abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) + return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) - def _abduce_from_GKB(self, pred_res, key, max_address_num, require_more_address): + def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address): if self.base == {} or len(pred_res) not in self.len_list: return [] @@ -260,7 +249,7 @@ class prolog_KB(KBBase): pass def abduce_candidates(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): - return self._abduce_by_abduction(pred_res, key, max_address_num, require_more_address, multiple_predictions) + return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] From fcda456cb60328b97f45afe00d928cb80cc13090 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Thu, 2 Mar 2023 15:05:37 +0800 Subject: [PATCH 150/601] update TODO in kb.py --- abducer/kb.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/abducer/kb.py b/abducer/kb.py index b9beb3f..bbd6c93 100644 --- a/abducer/kb.py +++ b/abducer/kb.py @@ -137,6 +137,7 @@ class ClsKB(KBBase): def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: + # TODO： 这里有å¯èƒ½æ˜¯multiple_predictionså— return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address) else: return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) @@ -200,7 +201,7 @@ class add_KB(ClsKB): def logic_forward(self, nums): return sum(nums) - +# TODO：这是个回归任务（对于y而言），在logic_forward加roundå˜æˆç¦»æ•£çš„分类任务固然å¯è¡Œï¼Œä½†æœ€å¥½è¿˜æ˜¯ç”¨RegKBå§ï¼Œä½œä¸ºä¾‹å­ç¤ºèŒƒã€‚还需è¦å¯¹ä¸‹é¢çš„ClsKB进行修改（è§TODO） class HWF_KB(ClsKB): def __init__( self, GKB_flag=False, pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], len_list=[1, 3, 5, 7] @@ -334,7 +335,13 @@ class HED_prolog_KB(prolog_KB): # def consist_rules(self, pred_res, rules): - +# TODO：这里需è¦ä¿®æ”¹ä¸€ä¸‹è¿™ä¸ªç±»ï¼ŒåŽŸæœ¬çš„RegKB是对GKB而言的，现在需è¦å’ŒClsKB一样åŒæ—¶æ”¯æŒGKBå’ŒéžGKB。需è¦è¡¥å……éžGKB部分（å¯èƒ½ç»§æ‰¿_abduce_by_search就行），以åŠä¿®æ”¹GKB部分_abduce_by_GKB的逻辑（原本逻辑是找与key最近的yçš„abduce结果，现在改æˆä¸Žkey在一定误差范围内的yçš„abduce结果） +# TODO：我ç†è§£çš„RegKB是这样的： +# TODO：1. 对GKB而言，å³_abduce_by_GKB，给定keyå’Œlength，还需è¦ä¸€ä¸ªself.max_err，返回所有与keyç»å¯¹å€¼å°äºŽmax_errçš„abduction结果 +# TODO：比如GKB里的y有[1.3, 1.49, 1.50, 1.52, 1.6]，若key=1.5，max_err=1e-5，则返回[y=1.50]çš„abduction结果；若key=1.5，max_err=0.05，则返回所有[y=1.49, 1.50, 1.52]çš„abduction结果 +# TODO：因此在二分查找bisect_leftåŽï¼Œéœ€è¦åˆ†åˆ«å¾€å‰å’Œå¾€åŽé历，从GKB里找符åˆè¯¯å·®çš„y +# TODO：self.max_err默认值å–很å°å°±è¡Œï¼Œæ¯”如HWF这类任务；但有些任务（比如法院刑期预测）的max_err需è¦å¤§äº›ï¼Œå› æ­¤å¯ä»¥ç”±ç”¨æˆ·è‡ªå®šä¹‰ +# TODO：2. 对éžGKB而言，估计直接用_abduce_by_search就行，check_equalé‚£é™å®šä¸ºæ•°å­—且控制回归误差max_err class RegKB(KBBase): def __init__(self, GKB_flag=False, X=None, Y=None): super().__init__() @@ -355,7 +362,7 @@ class RegKB(KBBase): def logic_forward(self): pass - def abduce_candidates(self, key, length=None): + def _abduce_by_GKB(self, key, length=None): if key is None: return self.get_all_candidates() From 37c1c446d9e3c25146c8d08db5db64b0b22ff213 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 3 Mar 2023 14:00:51 +0800 Subject: [PATCH 151/601] update package structure --- weights/all_weights_here.txt => abl/__init__.py | 0 abl/abducer/__init__.py | 0 {abducer => abl/abducer}/abducer_base.py | 11 ++++++----- {abducer => abl/abducer}/kb.py | 4 ++-- framework.py => abl/framework.py | 2 +- framework_hed.py => abl/framework_hed.py | 13 ++++++++----- abl/models/__init__.py | 0 {models => abl/models}/basic_model.py | 0 {models => abl/models}/lenet5.py | 0 {models => abl/models}/nn.py | 5 ----- {models => abl/models}/wabl_models.py | 1 - {utils => abl/utils}/plog.py | 0 {utils => abl/utils}/utils.py | 2 +- {datasets => examples/datasets}/data_generator.py | 0 {datasets => examples/datasets}/hed/BK.pl | 0 {datasets => examples/datasets}/hed/README.md | 0 {datasets => examples/datasets}/hed/get_hed.py | 0 {datasets => examples/datasets}/hed/learn_add.pl | 0 {datasets => examples/datasets}/hwf/README.md | 0 {datasets => examples/datasets}/hwf/get_hwf.py | 0 .../datasets}/mnist_add/get_mnist_add.py | 0 .../datasets}/mnist_add/test_data.txt | 0 .../datasets}/mnist_add/train_data.txt | 0 example.py => examples/example.py | 0 nonshare_example.py => examples/nonshare_example.py | 0 share_example.py => examples/share_example.py | 0 examples/weights/all_weights_here.txt | 0 27 files changed, 18 insertions(+), 20 deletions(-) rename weights/all_weights_here.txt => abl/__init__.py (100%) create mode 100644 abl/abducer/__init__.py rename {abducer => abl/abducer}/abducer_base.py (98%) rename {abducer => abl/abducer}/kb.py (99%) rename framework.py => abl/framework.py (98%) rename framework_hed.py => abl/framework_hed.py (97%) create mode 100644 abl/models/__init__.py rename {models => abl/models}/basic_model.py (100%) rename {models => abl/models}/lenet5.py (100%) rename {models => abl/models}/nn.py (97%) rename {models => abl/models}/wabl_models.py (99%) rename {utils => abl/utils}/plog.py (100%) rename {utils => abl/utils}/utils.py (98%) rename {datasets => examples/datasets}/data_generator.py (100%) rename {datasets => examples/datasets}/hed/BK.pl (100%) rename {datasets => examples/datasets}/hed/README.md (100%) rename {datasets => examples/datasets}/hed/get_hed.py (100%) rename {datasets => examples/datasets}/hed/learn_add.pl (100%) rename {datasets => examples/datasets}/hwf/README.md (100%) rename {datasets => examples/datasets}/hwf/get_hwf.py (100%) rename {datasets => examples/datasets}/mnist_add/get_mnist_add.py (100%) rename {datasets => examples/datasets}/mnist_add/test_data.txt (100%) rename {datasets => examples/datasets}/mnist_add/train_data.txt (100%) rename example.py => examples/example.py (100%) rename nonshare_example.py => examples/nonshare_example.py (100%) rename share_example.py => examples/share_example.py (100%) create mode 100644 examples/weights/all_weights_here.txt diff --git a/weights/all_weights_here.txt b/abl/__init__.py similarity index 100% rename from weights/all_weights_here.txt rename to abl/__init__.py diff --git a/abl/abducer/__init__.py b/abl/abducer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/abducer/abducer_base.py b/abl/abducer/abducer_base.py similarity index 98% rename from abducer/abducer_base.py rename to abl/abducer/abducer_base.py index c0fd102..9a483c9 100644 --- a/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -10,16 +10,17 @@ # # ================================================================# -import sys +# import sys -sys.path.append(".") -sys.path.append("..") +# sys.path.append(".") +# sys.path.append("..") import abc -from abducer.kb import * +# TODO å°½é‡åˆ«ç”¨import * +from .kb import * import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from utils.utils import confidence_dist, flatten, hamming_dist +from ..utils.utils import confidence_dist, flatten, hamming_dist import math import time diff --git a/abducer/kb.py b/abl/abducer/kb.py similarity index 99% rename from abducer/kb.py rename to abl/abducer/kb.py index bbd6c93..8fb727c 100644 --- a/abducer/kb.py +++ b/abl/abducer/kb.py @@ -21,7 +21,7 @@ sys.path.append("..") from collections import defaultdict from itertools import product, combinations -from utils.utils import flatten, reform_idx, hamming_dist, check_equal +from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal from multiprocessing import Pool @@ -299,7 +299,7 @@ class add_prolog_KB(prolog_KB): class HED_prolog_KB(prolog_KB): def __init__(self, pseudo_label_list=[0, 1, '+', '=']): super().__init__(pseudo_label_list) - self.prolog.consult('./datasets/hed/learn_add.pl') + self.prolog.consult('../examples/datasets/hed/learn_add.pl') # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` def logic_forward(self, exs): diff --git a/framework.py b/abl/framework.py similarity index 98% rename from framework.py rename to abl/framework.py index 2a1fdad..c3a5c6e 100644 --- a/framework.py +++ b/abl/framework.py @@ -14,7 +14,7 @@ import pickle as pk import numpy as np -from utils.plog import INFO, DEBUG, clocker +from .utils.plog import INFO, DEBUG, clocker def block_sample(X, Z, Y, sample_num, epoch_idx): part_num = (len(X) // sample_num) diff --git a/framework_hed.py b/abl/framework_hed.py similarity index 97% rename from framework_hed.py rename to abl/framework_hed.py index b7439c3..d942be6 100644 --- a/framework_hed.py +++ b/abl/framework_hed.py @@ -16,12 +16,15 @@ import torch.nn as nn import numpy as np import os -from utils.plog import INFO, DEBUG, clocker -from utils.utils import flatten, reform_idx, block_sample, gen_mappings, mapping_res, remapping_res +from .utils.plog import INFO, DEBUG, clocker +from .utils.utils import flatten, reform_idx, block_sample, gen_mappings, mapping_res, remapping_res -from models.nn import MLP, SymbolNetAutoencoder -from models.basic_model import BasicModel, BasicDataset -from datasets.hed.get_hed import get_pretrain_data +from .models.nn import MLP, SymbolNetAutoencoder +from .models.basic_model import BasicModel, BasicDataset + +import sys +sys.path.append("..") +from examples.datasets.hed.get_hed import get_pretrain_data def result_statistics(pred_Z, Z, Y, logic_forward, char_acc_flag): result = {} diff --git a/abl/models/__init__.py b/abl/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/basic_model.py b/abl/models/basic_model.py similarity index 100% rename from models/basic_model.py rename to abl/models/basic_model.py diff --git a/models/lenet5.py b/abl/models/lenet5.py similarity index 100% rename from models/lenet5.py rename to abl/models/lenet5.py diff --git a/models/nn.py b/abl/models/nn.py similarity index 97% rename from models/nn.py rename to abl/models/nn.py index cecbeea..7a0f560 100644 --- a/models/nn.py +++ b/abl/models/nn.py @@ -10,9 +10,6 @@ # # ================================================================# -import sys - -sys.path.append("..") import torchvision @@ -23,8 +20,6 @@ from torch.autograd import Variable import torchvision.transforms as transforms import numpy as np -from models.basic_model import BasicModel -import utils.plog as plog class LeNet5(nn.Module): diff --git a/models/wabl_models.py b/abl/models/wabl_models.py similarity index 99% rename from models/wabl_models.py rename to abl/models/wabl_models.py index 9c97bbc..3b682ee 100644 --- a/models/wabl_models.py +++ b/abl/models/wabl_models.py @@ -21,7 +21,6 @@ from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF -from models.basic_model import BasicModel import pickle as pk import random diff --git a/utils/plog.py b/abl/utils/plog.py similarity index 100% rename from utils/plog.py rename to abl/utils/plog.py diff --git a/utils/utils.py b/abl/utils/utils.py similarity index 98% rename from utils/utils.py rename to abl/utils/utils.py index 1138361..65c85fc 100644 --- a/utils/utils.py +++ b/abl/utils/utils.py @@ -1,5 +1,5 @@ import numpy as np -from utils.plog import INFO +from .plog import INFO from collections import OrderedDict # for multiple predictions, modify from `learn_add.py` diff --git a/datasets/data_generator.py b/examples/datasets/data_generator.py similarity index 100% rename from datasets/data_generator.py rename to examples/datasets/data_generator.py diff --git a/datasets/hed/BK.pl b/examples/datasets/hed/BK.pl similarity index 100% rename from datasets/hed/BK.pl rename to examples/datasets/hed/BK.pl diff --git a/datasets/hed/README.md b/examples/datasets/hed/README.md similarity index 100% rename from datasets/hed/README.md rename to examples/datasets/hed/README.md diff --git a/datasets/hed/get_hed.py b/examples/datasets/hed/get_hed.py similarity index 100% rename from datasets/hed/get_hed.py rename to examples/datasets/hed/get_hed.py diff --git a/datasets/hed/learn_add.pl b/examples/datasets/hed/learn_add.pl similarity index 100% rename from datasets/hed/learn_add.pl rename to examples/datasets/hed/learn_add.pl diff --git a/datasets/hwf/README.md b/examples/datasets/hwf/README.md similarity index 100% rename from datasets/hwf/README.md rename to examples/datasets/hwf/README.md diff --git a/datasets/hwf/get_hwf.py b/examples/datasets/hwf/get_hwf.py similarity index 100% rename from datasets/hwf/get_hwf.py rename to examples/datasets/hwf/get_hwf.py diff --git a/datasets/mnist_add/get_mnist_add.py b/examples/datasets/mnist_add/get_mnist_add.py similarity index 100% rename from datasets/mnist_add/get_mnist_add.py rename to examples/datasets/mnist_add/get_mnist_add.py diff --git a/datasets/mnist_add/test_data.txt b/examples/datasets/mnist_add/test_data.txt similarity index 100% rename from datasets/mnist_add/test_data.txt rename to examples/datasets/mnist_add/test_data.txt diff --git a/datasets/mnist_add/train_data.txt b/examples/datasets/mnist_add/train_data.txt similarity index 100% rename from datasets/mnist_add/train_data.txt rename to examples/datasets/mnist_add/train_data.txt diff --git a/example.py b/examples/example.py similarity index 100% rename from example.py rename to examples/example.py diff --git a/nonshare_example.py b/examples/nonshare_example.py similarity index 100% rename from nonshare_example.py rename to examples/nonshare_example.py diff --git a/share_example.py b/examples/share_example.py similarity index 100% rename from share_example.py rename to examples/share_example.py diff --git a/examples/weights/all_weights_here.txt b/examples/weights/all_weights_here.txt new file mode 100644 index 0000000..e69de29 From fe33d3b4eae6c7156c8984d20de8d10ad61cc8c7 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 3 Mar 2023 15:31:08 +0800 Subject: [PATCH 152/601] remove unnecessary lines --- abl/abducer/kb.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 8fb727c..9a66922 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -15,10 +15,6 @@ import bisect import copy import numpy as np -import sys - -sys.path.append("..") - from collections import defaultdict from itertools import product, combinations from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal From 7e4102c7091489e97648ff1f6b005f8d89f960c8 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 16:22:36 +0800 Subject: [PATCH 153/601] Update kb.py --- abl/abducer/kb.py | 373 +++++++++++++++++++++++++++++----------------- 1 file changed, 239 insertions(+), 134 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 9a66922..66a134f 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -31,6 +31,15 @@ class KBBase(ABC): @abstractmethod def logic_forward(self): pass + + def _logic_forward(self, xs, multiple_predictions=False): + if not multiple_predictions: + return self.logic_forward(xs) + else: + res = [] + for x in xs: + res.append(self.logic_forward(x)) + return res @abstractmethod def abduce_candidates(self): @@ -40,7 +49,7 @@ class KBBase(ABC): def address_by_idx(self): pass - def _address(self, address_num, pred_res, key, multiple_predictions=False): + def _address(self, address_num, pred_res, key, multiple_predictions): new_candidates = [] if not multiple_predictions: address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) @@ -52,12 +61,12 @@ class KBBase(ABC): new_candidates += candidates return new_candidates - def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False): + def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): candidates = [] for address_num in range(len(flatten(pred_res)) + 1): if address_num == 0: - if check_equal(self.logic_forward(pred_res), key): + if check_equal(self._logic_forward(pred_res, multiple_predictions), key, self.max_err): candidates.append(pred_res) else: new_candidates = self._address(address_num, pred_res, key, multiple_predictions) @@ -88,16 +97,14 @@ class ClsKB(KBBase): self.GKB_flag = GKB_flag self.pseudo_label_list = pseudo_label_list self.len_list = len_list + self.max_err = 0 if GKB_flag: self.base = {} X, Y = self._get_GKB() for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(x) - else: - self.all_address_candidate_dict = {} - for address_num in range(max(self.len_list) + 1): - self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat=address_num)) + # For parallel version of _get_GKB def _get_XY_list(self, args): @@ -133,33 +140,57 @@ class ClsKB(KBBase): def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: - # TODO： 这里有å¯èƒ½æ˜¯multiple_predictionså— - return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address) + return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions) else: return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) - def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address): - if self.base == {} or len(pred_res) not in self.len_list: - return [] - - all_candidates = self.base[len(pred_res)][key] + def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + if self.base == {}: + return [], 0, 0 - if len(all_candidates) == 0: - candidates = [] - min_address_num = 0 - address_num = 0 + if not multiple_predictions: + if len(pred_res) not in self.len_list: + return [], 0, 0 + all_candidates = self.base[len(pred_res)][key] + if len(all_candidates) == 0: + return [], 0, 0 + else: + cost_list = hamming_dist(pred_res, all_candidates) + min_address_num = np.min(cost_list) + address_num = min(max_address_num, min_address_num + require_more_address) + idxs = np.where(cost_list <= address_num)[0] + candidates = [all_candidates[idx] for idx in idxs] + return candidates, min_address_num, address_num + else: - cost_list = hamming_dist(pred_res, all_candidates) - min_address_num = np.min(cost_list) + min_address_num = 0 + all_candidates_save = [] + cost_list_save = [] + + for p_res, k in zip(pred_res, key): + if len(p_res) not in self.len_list: + return [], 0, 0 + all_candidates = self.base[len(p_res)][k] + if len(all_candidates) == 0: + return [], 0, 0 + else: + all_candidates_save.append(all_candidates) + cost_list = hamming_dist(p_res, all_candidates) + min_address_num += np.min(cost_list) + cost_list_save.append(cost_list) + + multiple_all_candidates = [flatten(c) for c in product(*all_candidates_save)] + assert len(multiple_all_candidates[0]) == len(flatten(pred_res)) + multiple_cost_list = np.array([sum(cost) for cost in product(*cost_list_save)]) + assert len(multiple_all_candidates) == len(multiple_cost_list) address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(cost_list <= address_num)[0] - candidates = [all_candidates[idx] for idx in idxs] - - return candidates, min_address_num, address_num + idxs = np.where(multiple_cost_list <= address_num)[0] + candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] + return candidates, min_address_num, address_num def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] - abduce_c = self.all_address_candidate_dict[len(address_idx)] + abduce_c = list(product(self.pseudo_label_list, repeat=len(address_idx))) if multiple_predictions: save_pred_res = pred_res @@ -173,7 +204,7 @@ class ClsKB(KBBase): if multiple_predictions: candidate = reform_idx(candidate, save_pred_res) - if self.logic_forward(candidate) == key: + if check_equal(self._logic_forward(candidate, multiple_predictions), key): candidates.append(candidate) return candidates @@ -197,50 +228,13 @@ class add_KB(ClsKB): def logic_forward(self, nums): return sum(nums) -# TODO：这是个回归任务（对于y而言），在logic_forward加roundå˜æˆç¦»æ•£çš„分类任务固然å¯è¡Œï¼Œä½†æœ€å¥½è¿˜æ˜¯ç”¨RegKBå§ï¼Œä½œä¸ºä¾‹å­ç¤ºèŒƒã€‚还需è¦å¯¹ä¸‹é¢çš„ClsKB进行修改（è§TODO） -class HWF_KB(ClsKB): - def __init__( - self, GKB_flag=False, pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], len_list=[1, 3, 5, 7] - ): - super().__init__(GKB_flag, pseudo_label_list, len_list) - - def valid_candidate(self, formula): - if len(formula) % 2 == 0: - return False - for i in range(len(formula)): - if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']: - return False - if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: - return False - return True - - def logic_forward(self, formula): - if not self.valid_candidate(formula): - return np.inf - mapping = { - '1': '1', - '2': '2', - '3': '3', - '4': '4', - '5': '5', - '6': '6', - '7': '7', - '8': '8', - '9': '9', - '+': '+', - '-': '-', - 'times': '*', - 'div': '/', - } - formula = [mapping[f] for f in formula] - return round(eval(''.join(formula)), 2) - class prolog_KB(KBBase): def __init__(self, pseudo_label_list): super().__init__() self.pseudo_label_list = pseudo_label_list self.prolog = pyswip.Prolog() + self.max_err = 0 def logic_forward(self): pass @@ -295,11 +289,11 @@ class add_prolog_KB(prolog_KB): class HED_prolog_KB(prolog_KB): def __init__(self, pseudo_label_list=[0, 1, '+', '=']): super().__init__(pseudo_label_list) - self.prolog.consult('../examples/datasets/hed/learn_add.pl') + self.prolog.consult('./datasets/hed/learn_add.pl') # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` def logic_forward(self, exs): - return len(list(self.prolog.query("abduce_consistent_insts(%s)." % exs))) != 0 + return len(list(self.prolog.query("abduce_consistent_insts([%s])." % exs))) != 0 def get_query_string_need_flatten(self, pred_res, key, address_idx): # flatten @@ -329,93 +323,204 @@ class HED_prolog_KB(prolog_KB): rules.append(rule.value) return rules - # def consist_rules(self, pred_res, rules): -# TODO：这里需è¦ä¿®æ”¹ä¸€ä¸‹è¿™ä¸ªç±»ï¼ŒåŽŸæœ¬çš„RegKB是对GKB而言的，现在需è¦å’ŒClsKB一样åŒæ—¶æ”¯æŒGKBå’ŒéžGKB。需è¦è¡¥å……éžGKB部分（å¯èƒ½ç»§æ‰¿_abduce_by_search就行），以åŠä¿®æ”¹GKB部分_abduce_by_GKB的逻辑（原本逻辑是找与key最近的yçš„abduce结果，现在改æˆä¸Žkey在一定误差范围内的yçš„abduce结果） -# TODO：我ç†è§£çš„RegKB是这样的： -# TODO：1. 对GKB而言，å³_abduce_by_GKB，给定keyå’Œlength，还需è¦ä¸€ä¸ªself.max_err，返回所有与keyç»å¯¹å€¼å°äºŽmax_errçš„abduction结果 -# TODO：比如GKB里的y有[1.3, 1.49, 1.50, 1.52, 1.6]，若key=1.5，max_err=1e-5，则返回[y=1.50]çš„abduction结果；若key=1.5，max_err=0.05，则返回所有[y=1.49, 1.50, 1.52]çš„abduction结果 -# TODO：因此在二分查找bisect_leftåŽï¼Œéœ€è¦åˆ†åˆ«å¾€å‰å’Œå¾€åŽé历，从GKB里找符åˆè¯¯å·®çš„y -# TODO：self.max_err默认值å–很å°å°±è¡Œï¼Œæ¯”如HWF这类任务；但有些任务（比如法院刑期预测）的max_err需è¦å¤§äº›ï¼Œå› æ­¤å¯ä»¥ç”±ç”¨æˆ·è‡ªå®šä¹‰ -# TODO：2. 对éžGKB而言，估计直接用_abduce_by_search就行，check_equalé‚£é™å®šä¸ºæ•°å­—且控制回归误差max_err class RegKB(KBBase): - def __init__(self, GKB_flag=False, X=None, Y=None): + def __init__(self, GKB_flag=False, pseudo_label_list=None, len_list=None, max_err=1e-3): super().__init__() - tmp_dict = {} - for x, y in zip(X, Y): - tmp_dict.setdefault(len(x), defaultdict(list))[y].append(np.array(x)) - - self.base = {} - for l in tmp_dict.keys(): - data = sorted(list(zip(tmp_dict[l].keys(), tmp_dict[l].values()))) - X = [x for y, x in data] - Y = [y for y, x in data] - self.base[l] = (X, Y) - - def valid_candidate(self): - pass + self.GKB_flag = GKB_flag + self.pseudo_label_list = pseudo_label_list + self.len_list = len_list + self.max_err = max_err + + if GKB_flag: + self.base = {} + X, Y = self._get_GKB() + for x, y in zip(X, Y): + self.base.setdefault(len(x), defaultdict(list))[y].append(x) + + # For parallel version of _get_GKB + def _get_XY_list(self, args): + pre_x, post_x_it = args[0], args[1] + XY_list = [] + for post_x in post_x_it: + x = (pre_x,) + post_x + y = self.logic_forward(x) + if y != np.inf: + XY_list.append((x, y)) + return XY_list + + # Parallel _get_GKB + def _get_GKB(self): + X, Y = [], [] + for length in self.len_list: + arg_list = [] + for pre_x in self.pseudo_label_list: + post_x_it = product(self.pseudo_label_list, repeat=length - 1) + arg_list.append((pre_x, post_x_it)) + with Pool(processes=len(arg_list)) as pool: + ret_list = pool.map(self._get_XY_list, arg_list) + for XY_list in ret_list: + if len(XY_list) == 0: + continue + part_X, part_Y = zip(*XY_list) + X.extend(part_X) + Y.extend(part_Y) + return X, Y def logic_forward(self): pass - def _abduce_by_GKB(self, key, length=None): - if key is None: - return self.get_all_candidates() + def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): + if self.GKB_flag: + return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions) + else: + return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) - length = self._length(length) + def _regression_find_candidate_GKB(self, pred_res, key): + potential_candidates = self.base[len(pred_res)] + key_list = sorted(potential_candidates) + key_idx = bisect.bisect_left(key_list, key) + + all_candidates = [] + for idx in range(key_idx - 1, 0, -1): + k = key_list[idx] + if abs(k - key) <= self.max_err: + all_candidates += potential_candidates[k] + else: + break + + for idx in range(key_idx, len(key_list)): + k = key_list[idx] + if abs(k - key) <= self.max_err: + all_candidates += potential_candidates[k] + else: + break + return all_candidates + + def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + if self.base == {}: + return [], 0, 0 - min_err = 999999 + if not multiple_predictions: + if len(pred_res) not in self.len_list: + return [], 0, 0 + all_candidates = self._regression_find_candidate_GKB(pred_res, key) + if len(all_candidates) == 0: + return [], 0, 0 + else: + cost_list = hamming_dist(pred_res, all_candidates) + min_address_num = np.min(cost_list) + address_num = min(max_address_num, min_address_num + require_more_address) + idxs = np.where(cost_list <= address_num)[0] + candidates = [all_candidates[idx] for idx in idxs] + return candidates, min_address_num, address_num + + else: + min_address_num = 0 + all_candidates_save = [] + cost_list_save = [] + + for p_res, k in zip(pred_res, key): + if len(p_res) not in self.len_list: + return [], 0, 0 + all_candidates = self._regression_find_candidate_GKB(p_res, k) + if len(all_candidates) == 0: + return [], 0, 0 + else: + all_candidates_save.append(all_candidates) + cost_list = hamming_dist(p_res, all_candidates) + min_address_num += np.min(cost_list) + cost_list_save.append(cost_list) + + multiple_all_candidates = [flatten(c) for c in product(*all_candidates_save)] + assert len(multiple_all_candidates[0]) == len(flatten(pred_res)) + multiple_cost_list = np.array([sum(cost) for cost in product(*cost_list_save)]) + assert len(multiple_all_candidates) == len(multiple_cost_list) + address_num = min(max_address_num, min_address_num + require_more_address) + idxs = np.where(multiple_cost_list <= address_num)[0] + candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] + return candidates, min_address_num, address_num + + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] - for l in length: - X, Y = self.base[l] - - idx = bisect.bisect_left(Y, key) - begin = max(0, idx - 1) - end = min(idx + 2, len(X)) - - for idx in range(begin, end): - err = abs(Y[idx] - key) - if abs(err - min_err) < 1e-9: - candidates.extend(X[idx]) - elif err < min_err: - candidates = copy.deepcopy(X[idx]) - min_err = err - return candidates + abduce_c = list(product(self.pseudo_label_list, repeat=len(address_idx))) - def get_all_candidates(self): - return sum([sum(D[0], []) for D in self.base.values()], []) + if multiple_predictions: + save_pred_res = pred_res + pred_res = flatten(pred_res) + + for c in abduce_c: + candidate = pred_res.copy() + for i, idx in enumerate(address_idx): + candidate[idx] = c[i] + + if multiple_predictions: + candidate = reform_idx(candidate, save_pred_res) + + if check_equal(self._logic_forward(candidate, multiple_predictions), key, self.max_err): + candidates.append(candidate) + return candidates + + def _dict_len(self, dic): + if not self.GKB_flag: + return 0 + else: + return sum(len(c) for c in dic.values()) def __len__(self): - return sum([sum(len(x) for x in D[0]) for D in self.base.values()]) + if not self.GKB_flag: + return 0 + else: + return sum(self._dict_len(v) for v in self.base.values()) + + +class HWF_KB(RegKB): + def __init__( + self, GKB_flag=False, + pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], + len_list=[1, 3, 5, 7], + max_err=1e-3 + ): + super().__init__(GKB_flag, pseudo_label_list, len_list, max_err) + + def valid_candidate(self, formula): + if len(formula) % 2 == 0: + return False + for i in range(len(formula)): + if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']: + return False + if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']: + return False + return True + + def logic_forward(self, formula): + if not self.valid_candidate(formula): + return np.inf + mapping = { + '1': '1', + '2': '2', + '3': '3', + '4': '4', + '5': '5', + '6': '6', + '7': '7', + '8': '8', + '9': '9', + '+': '+', + '-': '-', + 'times': '*', + 'div': '/', + } + formula = [mapping[f] for f in formula] + return round(eval(''.join(formula)), 2) import time if __name__ == "__main__": t1 = time.time() - kb = HWF_KB(True) + kb = add_KB(True) t2 = time.time() print(t2 - t1) - # X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"] - # Y = [2, 1, 1, 2, 2] - # kb = ClsKB(X, Y) - # print('len(kb):', len(kb)) - # res = kb.get_candidates(2, 5) - # print(res) - # res = kb.get_candidates(2, 3) - # print(res) - # res = kb.get_candidates(None) - # print(res) - # print() - - # X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"] - # Y = [2, 1, 1, 2, 1.5, 1.5] - # kb = RegKB(X, Y) - # print('len(kb):', len(kb)) - # res = kb.get_candidates(1.6) - # print(res) - # res = kb.get_candidates(1.6, length = 9) - # print(res) - # res = kb.get_candidates(None) - # print(res) + From 09083f9c2e847ddc290522db02db1212e8da4be8 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 16:24:12 +0800 Subject: [PATCH 154/601] Update abducer_base.py --- abl/abducer/abducer_base.py | 192 +++++++++++++++++------------------- 1 file changed, 90 insertions(+), 102 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 9a483c9..811f0ea 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -10,33 +10,16 @@ # # ================================================================# -# import sys - -# sys.path.append(".") -# sys.path.append("..") - import abc -# TODO å°½é‡åˆ«ç”¨import * from .kb import * import numpy as np from zoopt import Dimension, Objective, Parameter, Opt from ..utils.utils import confidence_dist, flatten, hamming_dist -import math -import time - - class AbducerBase(abc.ABC): - def __init__( - self, - kb, - dist_func="confidence", - zoopt=False, - multiple_predictions=False, - cache=True, - ): + def __init__(self, kb, dist_func='confidence', zoopt=False, multiple_predictions=False, cache=True): self.kb = kb - assert dist_func == "hamming" or dist_func == "confidence" + assert dist_func == 'hamming' or dist_func == 'confidence' self.dist_func = dist_func self.zoopt = zoopt self.multiple_predictions = multiple_predictions @@ -47,41 +30,42 @@ class AbducerBase(abc.ABC): self.cache_candidates = {} def _get_cost_list(self, pred_res, pred_res_prob, candidates): - if self.dist_func == "hamming": + if self.dist_func == 'hamming': + if self.multiple_predictions: + pred_res = flatten(pred_res) + candidates = [flatten(c) for c in candidates] + return hamming_dist(pred_res, candidates) - elif self.dist_func == "confidence": - mapping = dict( - zip( - self.kb.pseudo_label_list, - list(range(len(self.kb.pseudo_label_list))), - ) - ) - return confidence_dist( - pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates] - ) + + elif self.dist_func == 'confidence': + if self.multiple_predictions: + pred_res_prob = flatten(pred_res_prob) + candidates = [flatten(c) for c in candidates] + + mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) + candidates = [list(map(lambda x: mapping[x], c)) for c in candidates] + return confidence_dist(pred_res_prob, candidates) def _get_one_candidate(self, pred_res, pred_res_prob, candidates): if len(candidates) == 0: return [] elif len(candidates) == 1 or self.zoopt: return candidates[0] + else: cost_list = self._get_cost_list(pred_res, pred_res_prob, candidates) min_address_num = np.min(cost_list) idxs = np.where(cost_list == min_address_num)[0] - return [candidates[idx] for idx in idxs][0] + candidate = [candidates[idx] for idx in idxs][0] + return candidate # for zoopt def _zoopt_score_multiple(self, pred_res, key, solution): all_address_flag = reform_idx(solution, pred_res) score = 0 for idx in range(len(pred_res)): - address_idx = [ - i for i, flag in enumerate(all_address_flag[idx]) if flag != 0 - ] - candidate = self.kb.address_by_idx( - [pred_res[idx]], key[idx], address_idx, True - ) + address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] + candidate = self.kb.address_by_idx([pred_res[idx]], key[idx], address_idx, True) if len(candidate) > 0: score += 1 return score @@ -89,9 +73,7 @@ class AbducerBase(abc.ABC): def _zoopt_address_score(self, pred_res, key, sol): if not self.multiple_predictions: address_idx = [idx for idx, i in enumerate(sol.get_x()) if i != 0] - candidates = self.kb.address_by_idx( - pred_res, key, address_idx, self.multiple_predictions - ) + candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) return 1 if len(candidates) > 0 else 0 else: return self._zoopt_score_multiple(pred_res, key, sol.get_x()) @@ -108,7 +90,7 @@ class AbducerBase(abc.ABC): dim=dimension, constraint=lambda sol: self._constrain_address_num(sol, max_address_num), ) - parameter = Parameter(budget=100, autoset=True) + parameter = Parameter(budget=100, intermediate_result=False, autoset=True) solution = Opt.min(objective, parameter).get_x() return solution @@ -119,11 +101,7 @@ class AbducerBase(abc.ABC): pred_res = flatten(pred_res) key = tuple(key) if (tuple(pred_res), key) in self.cache_min_address_num: - address_num = min( - max_address_num, - self.cache_min_address_num[(tuple(pred_res), key)] - + require_more_address, - ) + address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) if (tuple(pred_res), key, address_num) in self.cache_candidates: candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] if self.zoopt: @@ -152,18 +130,12 @@ class AbducerBase(abc.ABC): if self.zoopt: solution = self.zoopt_get_solution(pred_res, key, max_address_num) address_idx = [idx for idx, i in enumerate(solution) if i != 0] - candidates = self.kb.address_by_idx( - pred_res, key, address_idx, self.multiple_predictions - ) + candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) address_num = int(solution.sum()) min_address_num = address_num else: candidates, min_address_num, address_num = self.kb.abduce_candidates( - pred_res, - key, - max_address_num, - require_more_address, - self.multiple_predictions, + pred_res, key, max_address_num, require_more_address, self.multiple_predictions ) candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) @@ -177,32 +149,21 @@ class AbducerBase(abc.ABC): return self.kb.abduce_rules(pred_res) def batch_abduce(self, Z, Y, max_address_num=-1, require_more_address=0): - if self.multiple_predictions: - return self.abduce( - (Z["cls"], Z["prob"], Y), max_address_num, require_more_address - ) - else: - return [ - self.abduce((z, prob, y), max_address_num, require_more_address) - for z, prob, y in zip(Z["cls"], Z["prob"], Y) - ] + # if self.multiple_predictions: + return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) + # else: + # return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) -if __name__ == "__main__": - prob1 = [ - [0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], - [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], - ] - prob2 = [ - [0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], - [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], - ] +if __name__ == '__main__': + prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] + prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] - kb = add_KB() - abd = AbducerBase(kb, "confidence") + kb = add_KB(True) + abd = AbducerBase(kb, 'confidence') res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -214,9 +175,23 @@ if __name__ == "__main__": res = abd.abduce(([1, 1], prob1, 20), max_address_num=2, require_more_address=0) print(res) print() + + + multiple_prob = [[[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]], + [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]] + + + kb = add_KB() + abd = AbducerBase(kb, 'confidence', multiple_predictions=True) + res = abd.abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address_num=4, require_more_address=0) + print(res) + res = abd.abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address_num=4, require_more_address=1) + print(res) + print() + kb = add_prolog_KB() - abd = AbducerBase(kb, "confidence") + abd = AbducerBase(kb, 'confidence') res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -230,7 +205,7 @@ if __name__ == "__main__": print() kb = add_prolog_KB() - abd = AbducerBase(kb, "confidence", zoopt=True) + abd = AbducerBase(kb, 'confidence', zoopt=True) res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) @@ -243,42 +218,55 @@ if __name__ == "__main__": print(res) print() - kb = HWF_KB(len_list=[1, 3, 5]) - abd = AbducerBase(kb, "hamming") - res = abd.abduce( - (["5", "+", "2"], None, 3), max_address_num=2, require_more_address=0 - ) + kb = HWF_KB(True, len_list=[1, 3, 5], max_err = 0.1) + abd = AbducerBase(kb, 'hamming') + res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) + print(res) + res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) + print(res) + + kb = HWF_KB(True, len_list=[1, 3, 5], max_err = 1) + abd = AbducerBase(kb, 'hamming') + res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) + print(res) + res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0) + print(res) + res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3) + print(res) + print() + + kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1) + abd = AbducerBase(kb, 'hamming', multiple_predictions=True) + res = abd.abduce(([['5', '+', '2'], ['5', '+', '9']], None, [3, 64]), max_address_num=6, require_more_address=0) + print(res) + print() + + kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1) + abd = AbducerBase(kb, 'hamming') + res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) + print(res) + res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) print(res) - res = abd.abduce( - (["5", "+", "2"], None, 64), max_address_num=3, require_more_address=0 - ) + + kb = HWF_KB(len_list=[1, 3, 5], max_err = 1) + abd = AbducerBase(kb, 'hamming') + res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) print(res) - res = abd.abduce( - (["5", "+", "2"], None, 1.67), max_address_num=3, require_more_address=0 - ) + res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0) print(res) - res = abd.abduce( - (["5", "8", "8", "8", "8"], None, 3.17), - max_address_num=5, - require_more_address=3, - ) + res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3) print(res) print() kb = HED_prolog_KB() abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) - consist_exs = [[1, "+", 0, "=", 0], [1, "+", 1, "=", 0], [0, "+", 0, "=", 1, 1]] - consist_exs2 = [ - [1, "+", 0, "=", 0], - [1, "+", 1, "=", 0], - [0, "+", 1, "=", 1, 1], - ] # not consistent with rules - inconsist_exs = [[1, "+", 0, "=", 0], [1, "=", 1, "=", 0], [0, "=", 0, "=", 1, 1]] + consist_exs = [[1, 1, '+', 0, '=', 1, 1], [1, '+', 1, '=', 1, 0], [0, '+', 0, '=', 0]] + inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] # inconsist_exs = [[1, '+', 0, '=', 0], ['=', '=', '=', '=', 0], ['=', '=', 0, '=', '=', '=']] - rules = ["my_op([0], [0], [1, 1])", "my_op([1], [1], [0])", "my_op([1], [0], [0])"] + rules = ['my_op([0], [0], [0])', 'my_op([1], [1], [1, 0])'] - print(kb.logic_forward(consist_exs), kb.logic_forward(inconsist_exs)) - print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules)) + print(kb._logic_forward(consist_exs, True), kb._logic_forward(inconsist_exs, True)) + print(kb.consist_rule([1, '+', 1, '=', 1, 0], rules), kb.consist_rule([1, '+', 1, '=', 1, 1], rules)) print() res = abd.abduce((consist_exs, None, [1] * len(consist_exs))) From 6ead422a1c8f0a12117370b6d83e79ca3dd8a229 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 16:24:43 +0800 Subject: [PATCH 155/601] Update utils.py --- abl/utils/utils.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/abl/utils/utils.py b/abl/utils/utils.py index 65c85fc..05b97a4 100644 --- a/abl/utils/utils.py +++ b/abl/utils/utils.py @@ -4,7 +4,10 @@ from collections import OrderedDict # for multiple predictions, modify from `learn_add.py` def flatten(l): - return [item for sublist in l for item in flatten(sublist)] if isinstance(l, list) else [l] + # return [item for sublist in l for item in flatten(sublist)] if isinstance(l, (list, tuple)) else [l] + if not isinstance(l[0], (list, tuple)): + return l + return [item for sublist in l for item in sublist] if isinstance(l, (list, tuple)) else [l] # for multiple predictions, modify from `learn_add.py` def reform_idx(flatten_pred_res, save_pred_res): From af8753bdcacec34af50a39840252c6aad9c84d2b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 16:36:26 +0800 Subject: [PATCH 156/601] Update abducer_base.py --- abl/abducer/abducer_base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 811f0ea..36a0e76 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -11,7 +11,6 @@ # ================================================================# import abc -from .kb import * import numpy as np from zoopt import Dimension, Objective, Parameter, Opt from ..utils.utils import confidence_dist, flatten, hamming_dist @@ -159,6 +158,8 @@ class AbducerBase(abc.ABC): if __name__ == '__main__': + from kb import add_KB, add_prolog_KB, HWF_KB, HED_prolog_KB + prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] From 956d64ba1a6da8ea3ab784199df50110acc04c48 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 16:38:25 +0800 Subject: [PATCH 157/601] Update abducer_base.py --- abl/abducer/abducer_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 36a0e76..9fcfa0a 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -13,7 +13,7 @@ import abc import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from ..utils.utils import confidence_dist, flatten, hamming_dist +from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist class AbducerBase(abc.ABC): def __init__(self, kb, dist_func='confidence', zoopt=False, multiple_predictions=False, cache=True): From cf15052331af5b89678cb397ffa09b98924ffa2f Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 16:48:46 +0800 Subject: [PATCH 158/601] Update utils.py --- abl/utils/utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/abl/utils/utils.py b/abl/utils/utils.py index 05b97a4..fbb5bfb 100644 --- a/abl/utils/utils.py +++ b/abl/utils/utils.py @@ -76,9 +76,9 @@ def remapping_res(pred_res, m): remapping[value] = key return [[remapping[symbol] for symbol in formula] for formula in pred_res] -def check_equal(a, b): +def check_equal(a, b, max_err=0): if isinstance(a, (int, float)) and isinstance(b, (int, float)): - return abs(a - b) <= 1e-3 + return abs(a - b) <= max_err if isinstance(a, list) and isinstance(b, list): if len(a) != len(b): @@ -89,4 +89,4 @@ def check_equal(a, b): return True else: - return a == b + return a == b From 930b549d4eab631fd700a90c1cfce867d13feb49 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 17:01:31 +0800 Subject: [PATCH 159/601] Update kb.py --- abl/abducer/kb.py | 170 +++++++++++++++------------------------------- 1 file changed, 56 insertions(+), 114 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 66a134f..cd15dd3 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -25,8 +25,46 @@ import pyswip class KBBase(ABC): - def __init__(self, pseudo_label_list=None): - pass + def __init__(self, pseudo_label_list=None, len_list=None, GKB_flag=False, max_err=0): + self.pseudo_label_list = pseudo_label_list + self.len_list = len_list + self.GKB_flag = GKB_flag + self.max_err = max_err + + if GKB_flag: + self.base = {} + X, Y = self._get_GKB() + for x, y in zip(X, Y): + self.base.setdefault(len(x), defaultdict(list))[y].append(x) + + # For parallel version of _get_GKB + def _get_XY_list(self, args): + pre_x, post_x_it = args[0], args[1] + XY_list = [] + for post_x in post_x_it: + x = (pre_x,) + post_x + y = self.logic_forward(x) + if y != np.inf: + XY_list.append((x, y)) + return XY_list + + # Parallel _get_GKB + def _get_GKB(self): + X, Y = [], [] + for length in self.len_list: + arg_list = [] + for pre_x in self.pseudo_label_list: + post_x_it = product(self.pseudo_label_list, repeat=length - 1) + arg_list.append((pre_x, post_x_it)) + with Pool(processes=len(arg_list)) as pool: + ret_list = pool.map(self._get_XY_list, arg_list) + for XY_list in ret_list: + if len(XY_list) == 0: + continue + part_X, part_Y = zip(*XY_list) + X.extend(part_X) + Y.extend(part_Y) + return X, Y @abstractmethod def logic_forward(self): @@ -87,53 +125,22 @@ class KBBase(ABC): return candidates, min_address_num, address_num + def _dict_len(self, dic): + if not self.GKB_flag: + return 0 + else: + return sum(len(c) for c in dic.values()) + def __len__(self): - pass + if not self.GKB_flag: + return 0 + else: + return sum(self._dict_len(v) for v in self.base.values()) class ClsKB(KBBase): - def __init__(self, GKB_flag=False, pseudo_label_list=None, len_list=None): - super().__init__() - self.GKB_flag = GKB_flag - self.pseudo_label_list = pseudo_label_list - self.len_list = len_list - self.max_err = 0 - - if GKB_flag: - self.base = {} - X, Y = self._get_GKB() - for x, y in zip(X, Y): - self.base.setdefault(len(x), defaultdict(list))[y].append(x) - - - # For parallel version of _get_GKB - def _get_XY_list(self, args): - pre_x, post_x_it = args[0], args[1] - XY_list = [] - for post_x in post_x_it: - x = (pre_x,) + post_x - y = self.logic_forward(x) - if y != np.inf: - XY_list.append((x, y)) - return XY_list - - # Parallel _get_GKB - def _get_GKB(self): - X, Y = [], [] - for length in self.len_list: - arg_list = [] - for pre_x in self.pseudo_label_list: - post_x_it = product(self.pseudo_label_list, repeat=length - 1) - arg_list.append((pre_x, post_x_it)) - with Pool(processes=len(arg_list)) as pool: - ret_list = pool.map(self._get_XY_list, arg_list) - for XY_list in ret_list: - if len(XY_list) == 0: - continue - part_X, part_Y = zip(*XY_list) - X.extend(part_X) - Y.extend(part_Y) - return X, Y + def __init__(self, pseudo_label_list, len_list, GKB_flag): + super().__init__(pseudo_label_list, len_list, GKB_flag) def logic_forward(self): pass @@ -208,22 +215,10 @@ class ClsKB(KBBase): candidates.append(candidate) return candidates - def _dict_len(self, dic): - if not self.GKB_flag: - return 0 - else: - return sum(len(c) for c in dic.values()) - - def __len__(self): - if not self.GKB_flag: - return 0 - else: - return sum(self._dict_len(v) for v in self.base.values()) - class add_KB(ClsKB): - def __init__(self, GKB_flag=False, pseudo_label_list=list(range(10)), len_list=[2]): - super().__init__(GKB_flag, pseudo_label_list, len_list) + def __init__(self, pseudo_label_list=list(range(10)), len_list=[2], GKB_flag=False): + super().__init__(pseudo_label_list, len_list, GKB_flag) def logic_forward(self, nums): return sum(nums) @@ -231,10 +226,8 @@ class add_KB(ClsKB): class prolog_KB(KBBase): def __init__(self, pseudo_label_list): - super().__init__() - self.pseudo_label_list = pseudo_label_list + super().__init__(pseudo_label_list) self.prolog = pyswip.Prolog() - self.max_err = 0 def logic_forward(self): pass @@ -326,46 +319,7 @@ class HED_prolog_KB(prolog_KB): class RegKB(KBBase): def __init__(self, GKB_flag=False, pseudo_label_list=None, len_list=None, max_err=1e-3): - super().__init__() - self.GKB_flag = GKB_flag - self.pseudo_label_list = pseudo_label_list - self.len_list = len_list - self.max_err = max_err - - if GKB_flag: - self.base = {} - X, Y = self._get_GKB() - for x, y in zip(X, Y): - self.base.setdefault(len(x), defaultdict(list))[y].append(x) - - # For parallel version of _get_GKB - def _get_XY_list(self, args): - pre_x, post_x_it = args[0], args[1] - XY_list = [] - for post_x in post_x_it: - x = (pre_x,) + post_x - y = self.logic_forward(x) - if y != np.inf: - XY_list.append((x, y)) - return XY_list - - # Parallel _get_GKB - def _get_GKB(self): - X, Y = [], [] - for length in self.len_list: - arg_list = [] - for pre_x in self.pseudo_label_list: - post_x_it = product(self.pseudo_label_list, repeat=length - 1) - arg_list.append((pre_x, post_x_it)) - with Pool(processes=len(arg_list)) as pool: - ret_list = pool.map(self._get_XY_list, arg_list) - for XY_list in ret_list: - if len(XY_list) == 0: - continue - part_X, part_Y = zip(*XY_list) - X.extend(part_X) - Y.extend(part_Y) - return X, Y + super().__init__(pseudo_label_list, len_list, GKB_flag, max_err) def logic_forward(self): pass @@ -461,18 +415,6 @@ class RegKB(KBBase): candidates.append(candidate) return candidates - def _dict_len(self, dic): - if not self.GKB_flag: - return 0 - else: - return sum(len(c) for c in dic.values()) - - def __len__(self): - if not self.GKB_flag: - return 0 - else: - return sum(self._dict_len(v) for v in self.base.values()) - class HWF_KB(RegKB): def __init__( From 173138f64802a0ea02fdd2a5a51137bb68e65638 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 17:02:04 +0800 Subject: [PATCH 160/601] Update kb.py --- abl/abducer/kb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index cd15dd3..7ac26c8 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -461,7 +461,7 @@ import time if __name__ == "__main__": t1 = time.time() - kb = add_KB(True) + kb = add_KB(GKB_flag=True) t2 = time.time() print(t2 - t1) From 4478e4e3de5ea0ea0dec93562750dc334eec5e8a Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 18:40:15 +0800 Subject: [PATCH 161/601] Rearrange abduce_by_GKB and address_by_idx to Base --- abl/abducer/kb.py | 258 ++++++++++++++-------------------------------- 1 file changed, 80 insertions(+), 178 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 7ac26c8..7c494c5 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -10,20 +10,6 @@ # # ================================================================# -from abc import ABC, abstractmethod -import bisect -import copy -import numpy as np - -from collections import defaultdict -from itertools import product, combinations -from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal - -from multiprocessing import Pool - -import pyswip - - class KBBase(ABC): def __init__(self, pseudo_label_list=None, len_list=None, GKB_flag=False, max_err=0): self.pseudo_label_list = pseudo_label_list @@ -79,78 +65,16 @@ class KBBase(ABC): res.append(self.logic_forward(x)) return res - @abstractmethod - def abduce_candidates(self): - pass - - @abstractmethod - def address_by_idx(self): - pass - - def _address(self, address_num, pred_res, key, multiple_predictions): - new_candidates = [] - if not multiple_predictions: - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) - else: - address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num)) - - for address_idx in address_idx_list: - candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) - new_candidates += candidates - return new_candidates - - def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): - candidates = [] - - for address_num in range(len(flatten(pred_res)) + 1): - if address_num == 0: - if check_equal(self._logic_forward(pred_res, multiple_predictions), key, self.max_err): - candidates.append(pred_res) - else: - new_candidates = self._address(address_num, pred_res, key, multiple_predictions) - candidates += new_candidates - - if len(candidates) > 0: - min_address_num = address_num - break - - if address_num >= max_address_num: - return [], 0, 0 - - for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): - if address_num > max_address_num: - return candidates, min_address_num, address_num - 1 - new_candidates = self._address(address_num, pred_res, key, multiple_predictions) - candidates += new_candidates - - return candidates, min_address_num, address_num - - def _dict_len(self, dic): - if not self.GKB_flag: - return 0 - else: - return sum(len(c) for c in dic.values()) - - def __len__(self): - if not self.GKB_flag: - return 0 - else: - return sum(self._dict_len(v) for v in self.base.values()) - - -class ClsKB(KBBase): - def __init__(self, pseudo_label_list, len_list, GKB_flag): - super().__init__(pseudo_label_list, len_list, GKB_flag) - - def logic_forward(self): - pass - def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions) else: return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) - + + @abstractmethod + def _find_candidate_GKB(self): + pass + def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): if self.base == {}: return [], 0, 0 @@ -158,7 +82,7 @@ class ClsKB(KBBase): if not multiple_predictions: if len(pred_res) not in self.len_list: return [], 0, 0 - all_candidates = self.base[len(pred_res)][key] + all_candidates = self._find_candidate_GKB(pred_res, key) if len(all_candidates) == 0: return [], 0, 0 else: @@ -168,7 +92,7 @@ class ClsKB(KBBase): idxs = np.where(cost_list <= address_num)[0] candidates = [all_candidates[idx] for idx in idxs] return candidates, min_address_num, address_num - + else: min_address_num = 0 all_candidates_save = [] @@ -177,7 +101,7 @@ class ClsKB(KBBase): for p_res, k in zip(pred_res, key): if len(p_res) not in self.len_list: return [], 0, 0 - all_candidates = self.base[len(p_res)][k] + all_candidates = self._regression_find_candidate_GKB(p_res, k) if len(all_candidates) == 0: return [], 0, 0 else: @@ -194,7 +118,7 @@ class ClsKB(KBBase): idxs = np.where(multiple_cost_list <= address_num)[0] candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] return candidates, min_address_num, address_num - + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] abduce_c = list(product(self.pseudo_label_list, repeat=len(address_idx))) @@ -211,10 +135,71 @@ class ClsKB(KBBase): if multiple_predictions: candidate = reform_idx(candidate, save_pred_res) - if check_equal(self._logic_forward(candidate, multiple_predictions), key): + if check_equal(self._logic_forward(candidate, multiple_predictions), key, self.max_err): candidates.append(candidate) return candidates + def _address(self, address_num, pred_res, key, multiple_predictions): + new_candidates = [] + if not multiple_predictions: + address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + else: + address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num)) + + for address_idx in address_idx_list: + candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) + new_candidates += candidates + return new_candidates + + def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + candidates = [] + + for address_num in range(len(flatten(pred_res)) + 1): + if address_num == 0: + if check_equal(self._logic_forward(pred_res, multiple_predictions), key, self.max_err): + candidates.append(pred_res) + else: + new_candidates = self._address(address_num, pred_res, key, multiple_predictions) + candidates += new_candidates + + if len(candidates) > 0: + min_address_num = address_num + break + + if address_num >= max_address_num: + return [], 0, 0 + + for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): + if address_num > max_address_num: + return candidates, min_address_num, address_num - 1 + new_candidates = self._address(address_num, pred_res, key, multiple_predictions) + candidates += new_candidates + + return candidates, min_address_num, address_num + + def _dict_len(self, dic): + if not self.GKB_flag: + return 0 + else: + return sum(len(c) for c in dic.values()) + + def __len__(self): + if not self.GKB_flag: + return 0 + else: + return sum(self._dict_len(v) for v in self.base.values()) + + +class ClsKB(KBBase): + def __init__(self, pseudo_label_list, len_list, GKB_flag): + super().__init__(pseudo_label_list, len_list, GKB_flag) + + def logic_forward(self): + pass + + def _find_candidate_GKB(self, pred_res, key): + return self.base[len(pred_res)][key] + class add_KB(ClsKB): def __init__(self, pseudo_label_list=list(range(10)), len_list=[2], GKB_flag=False): @@ -232,16 +217,13 @@ class prolog_KB(KBBase): def logic_forward(self): pass - def abduce_candidates(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): - return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) - + def _find_candidate_GKB(self): + pass + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] # print(address_idx) - if not multiple_predictions: - query_string = self.get_query_string(pred_res, key, address_idx) - else: - query_string = self.get_query_string_need_flatten(pred_res, key, address_idx) + query_string = self.get_query_string(pred_res, key, address_idx) if multiple_predictions: save_pred_res = pred_res @@ -284,21 +266,18 @@ class HED_prolog_KB(prolog_KB): super().__init__(pseudo_label_list) self.prolog.consult('./datasets/hed/learn_add.pl') - # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py` def logic_forward(self, exs): return len(list(self.prolog.query("abduce_consistent_insts([%s])." % exs))) != 0 - def get_query_string_need_flatten(self, pred_res, key, address_idx): - # flatten + def get_query_string(self, pred_res, key, address_idx): flatten_pred_res = flatten(pred_res) # add variables for prolog for idx in range(len(flatten_pred_res)): if idx in address_idx: flatten_pred_res[idx] = 'X' + str(idx) - # unflatten - new_pred_res = reform_idx(flatten_pred_res, pred_res) + pred_res = reform_idx(flatten_pred_res, pred_res) - query_string = "abduce_consistent_insts(%s)." % new_pred_res + query_string = "abduce_consistent_insts(%s)." % pred_res return query_string.replace("'", "").replace("+", "'+'").replace("=", "'='") def consist_rule(self, exs, rules): @@ -324,13 +303,7 @@ class RegKB(KBBase): def logic_forward(self): pass - def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): - if self.GKB_flag: - return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions) - else: - return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) - - def _regression_find_candidate_GKB(self, pred_res, key): + def _find_candidate_GKB(self, pred_res, key): potential_candidates = self.base[len(pred_res)] key_list = sorted(potential_candidates) key_idx = bisect.bisect_left(key_list, key) @@ -351,70 +324,6 @@ class RegKB(KBBase): break return all_candidates - def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): - if self.base == {}: - return [], 0, 0 - - if not multiple_predictions: - if len(pred_res) not in self.len_list: - return [], 0, 0 - all_candidates = self._regression_find_candidate_GKB(pred_res, key) - if len(all_candidates) == 0: - return [], 0, 0 - else: - cost_list = hamming_dist(pred_res, all_candidates) - min_address_num = np.min(cost_list) - address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(cost_list <= address_num)[0] - candidates = [all_candidates[idx] for idx in idxs] - return candidates, min_address_num, address_num - - else: - min_address_num = 0 - all_candidates_save = [] - cost_list_save = [] - - for p_res, k in zip(pred_res, key): - if len(p_res) not in self.len_list: - return [], 0, 0 - all_candidates = self._regression_find_candidate_GKB(p_res, k) - if len(all_candidates) == 0: - return [], 0, 0 - else: - all_candidates_save.append(all_candidates) - cost_list = hamming_dist(p_res, all_candidates) - min_address_num += np.min(cost_list) - cost_list_save.append(cost_list) - - multiple_all_candidates = [flatten(c) for c in product(*all_candidates_save)] - assert len(multiple_all_candidates[0]) == len(flatten(pred_res)) - multiple_cost_list = np.array([sum(cost) for cost in product(*cost_list_save)]) - assert len(multiple_all_candidates) == len(multiple_cost_list) - address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(multiple_cost_list <= address_num)[0] - candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] - return candidates, min_address_num, address_num - - def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): - candidates = [] - abduce_c = list(product(self.pseudo_label_list, repeat=len(address_idx))) - - if multiple_predictions: - save_pred_res = pred_res - pred_res = flatten(pred_res) - - for c in abduce_c: - candidate = pred_res.copy() - for i, idx in enumerate(address_idx): - candidate[idx] = c[i] - - if multiple_predictions: - candidate = reform_idx(candidate, save_pred_res) - - if check_equal(self._logic_forward(candidate, multiple_predictions), key, self.max_err): - candidates.append(candidate) - return candidates - class HWF_KB(RegKB): def __init__( @@ -456,13 +365,6 @@ class HWF_KB(RegKB): formula = [mapping[f] for f in formula] return round(eval(''.join(formula)), 2) - -import time - if __name__ == "__main__": - t1 = time.time() - kb = add_KB(GKB_flag=True) - t2 = time.time() - print(t2 - t1) - + pass From 9d9684723846c3e63af3913e78ee45d39e474665 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 18:42:19 +0800 Subject: [PATCH 162/601] Rearrange abduce_by_GKB and address_by_idx to Base --- abl/abducer/kb.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 7c494c5..8db7bba 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -10,6 +10,19 @@ # # ================================================================# +from abc import ABC, abstractmethod +import bisect +import copy +import numpy as np + +from collections import defaultdict +from itertools import product, combinations +from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal + +from multiprocessing import Pool + +import pyswip + class KBBase(ABC): def __init__(self, pseudo_label_list=None, len_list=None, GKB_flag=False, max_err=0): self.pseudo_label_list = pseudo_label_list From 1d1873b6eba87726043838ed69cad12f997a64a6 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 18:56:24 +0800 Subject: [PATCH 163/601] Rearrange sorted in RegKB --- abl/abducer/kb.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 8db7bba..137647b 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -63,6 +63,9 @@ class KBBase(ABC): part_X, part_Y = zip(*XY_list) X.extend(part_X) Y.extend(part_Y) + sorted_XY = sorted(list(zip(Y, X))) + X = [x for y, x in sorted_XY] + Y = [y for y, x in sorted_XY] return X, Y @abstractmethod @@ -318,7 +321,7 @@ class RegKB(KBBase): def _find_candidate_GKB(self, pred_res, key): potential_candidates = self.base[len(pred_res)] - key_list = sorted(potential_candidates) + key_list = list(potential_candidates.keys()) key_idx = bisect.bisect_left(key_list, key) all_candidates = [] @@ -378,6 +381,9 @@ class HWF_KB(RegKB): formula = [mapping[f] for f in formula] return round(eval(''.join(formula)), 2) + +import time + if __name__ == "__main__": pass From ce406011bf538f0ec5567f360a9079113c192fe1 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 18:59:18 +0800 Subject: [PATCH 164/601] Rearrange sorted in RegKB --- abl/abducer/kb.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 137647b..3be0875 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -63,9 +63,10 @@ class KBBase(ABC): part_X, part_Y = zip(*XY_list) X.extend(part_X) Y.extend(part_Y) - sorted_XY = sorted(list(zip(Y, X))) - X = [x for y, x in sorted_XY] - Y = [y for y, x in sorted_XY] + if self.max_err != 0: + sorted_XY = sorted(list(zip(Y, X))) + X = [x for y, x in sorted_XY] + Y = [y for y, x in sorted_XY] return X, Y @abstractmethod From 253c9e20ba5b1fa75815405439513d5976bd66a3 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 19:08:21 +0800 Subject: [PATCH 165/601] Update kb.py --- abl/abducer/kb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 3be0875..7eee9f5 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -118,7 +118,7 @@ class KBBase(ABC): for p_res, k in zip(pred_res, key): if len(p_res) not in self.len_list: return [], 0, 0 - all_candidates = self._regression_find_candidate_GKB(p_res, k) + all_candidates = self._find_candidate_GKB(p_res, k) if len(all_candidates) == 0: return [], 0, 0 else: From 1cd3570693f18225f80566af925e14dd5be40b27 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 3 Mar 2023 20:02:15 +0800 Subject: [PATCH 166/601] make code short --- abl/abducer/kb.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 3be0875..627d6c8 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -77,9 +77,7 @@ class KBBase(ABC): if not multiple_predictions: return self.logic_forward(xs) else: - res = [] - for x in xs: - res.append(self.logic_forward(x)) + res = [self.logic_forward(x) for x in xs] return res def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): From ea66df54fbf5a1987c1c76c8edbc7e5ab336e40d Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Fri, 3 Mar 2023 20:04:19 +0800 Subject: [PATCH 167/601] remove round in reg --- abl/abducer/kb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 4178c4e..0cff3fa 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -378,7 +378,7 @@ class HWF_KB(RegKB): 'div': '/', } formula = [mapping[f] for f in formula] - return round(eval(''.join(formula)), 2) + return eval(''.join(formula)) import time From 53586d94f862c86ed0c9bec494801f410fc472fd Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Fri, 3 Mar 2023 21:53:06 +0800 Subject: [PATCH 168/601] Create add.pl --- examples/datasets/mnist_add/add.pl | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 examples/datasets/mnist_add/add.pl diff --git a/examples/datasets/mnist_add/add.pl b/examples/datasets/mnist_add/add.pl new file mode 100644 index 0000000..96f0869 --- /dev/null +++ b/examples/datasets/mnist_add/add.pl @@ -0,0 +1,2 @@ +pseudo_label(N) :- between(0, 9, N). +logic_forward([Z1, Z2], Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2. From 8ba7c509e80c2cc7c01914a53d832ae8d13360dc Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 6 Mar 2023 05:27:41 +0000 Subject: [PATCH 169/601] Update prologKB --- abl/abducer/abducer_base.py | 13 +++--- abl/abducer/kb.py | 63 ++++++++++++++---------------- abl/framework_hed.py | 4 +- examples/datasets/hed/learn_add.pl | 3 ++ 4 files changed, 42 insertions(+), 41 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 9fcfa0a..777533a 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -163,7 +163,7 @@ if __name__ == '__main__': prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] - kb = add_KB(True) + kb = add_KB(GKB_flag=True) abd = AbducerBase(kb, 'confidence') res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) @@ -219,14 +219,15 @@ if __name__ == '__main__': print(res) print() - kb = HWF_KB(True, len_list=[1, 3, 5], max_err = 0.1) + kb = HWF_KB(GKB_flag=True, len_list=[1, 3, 5], max_err = 0.1) abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) print(res) res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) print(res) + print() - kb = HWF_KB(True, len_list=[1, 3, 5], max_err = 1) + kb = HWF_KB(GKB_flag=True, len_list=[1, 3, 5], max_err = 1) abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) print(res) @@ -270,11 +271,11 @@ if __name__ == '__main__': print(kb.consist_rule([1, '+', 1, '=', 1, 0], rules), kb.consist_rule([1, '+', 1, '=', 1, 1], rules)) print() - res = abd.abduce((consist_exs, None, [1] * len(consist_exs))) + res = abd.abduce((consist_exs, None, [None] * len(consist_exs))) print(res) - res = abd.abduce((inconsist_exs, None, [1] * len(consist_exs))) + res = abd.abduce((inconsist_exs, None, [None] * len(inconsist_exs))) print(res) print() abduced_rules = abd.abduce_rules(consist_exs) - print(abduced_rules) + print(abduced_rules) \ No newline at end of file diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 0cff3fa..e23e637 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -229,30 +229,52 @@ class prolog_KB(KBBase): super().__init__(pseudo_label_list) self.prolog = pyswip.Prolog() - def logic_forward(self): - pass + def logic_forward(self, pseudo_labels): + result = list(self.prolog.query("logic_forward(%s, Res)." % pseudo_labels))[0]['Res'] + if result == 'true': + return True + elif result == 'false': + return False + return result + + def _address_pred_res(self, pred_res, address_idx, multiple_predictions): + import re + address_pred_res = pred_res.copy() + if multiple_predictions: + address_pred_res = flatten(address_pred_res) + + for idx in range(len(address_pred_res)): + if idx in address_idx: + address_pred_res[idx] = 'P' + str(idx) + if multiple_predictions: + address_pred_res = reform_idx(address_pred_res, pred_res) + + regex = r"'P\d+'" + return re.sub(regex, lambda x: x.group().replace("'", ""), str(address_pred_res)) + + def get_query_string(self, pred_res, key, address_idx, multiple_predictions): + query_string = "logic_forward(" + query_string += self._address_pred_res(pred_res, address_idx, multiple_predictions) + key_is_none_flag = key is None or (type(key) == list and key[0] is None) + query_string += ",%s)." % key if not key_is_none_flag else ")." + return query_string def _find_candidate_GKB(self): pass def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] - # print(address_idx) - query_string = self.get_query_string(pred_res, key, address_idx) - + query_string = self.get_query_string(pred_res, key, address_idx, multiple_predictions) if multiple_predictions: save_pred_res = pred_res pred_res = flatten(pred_res) - abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string))] for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - if multiple_predictions: candidate = reform_idx(candidate, save_pred_res) - candidates.append(candidate) return candidates @@ -264,37 +286,12 @@ class add_prolog_KB(prolog_KB): self.prolog.assertz("pseudo_label(%s)" % i) self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") - def logic_forward(self, nums): - return list(self.prolog.query("addition(%s, %s, Res)." % (nums[0], nums[1])))[0]['Res'] - - def get_query_string(self, pred_res, key, address_idx): - query_string = "addition(" - for idx, i in enumerate(pred_res): - tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ',' - query_string += tmp - query_string += "%s)." % key - return query_string - class HED_prolog_KB(prolog_KB): def __init__(self, pseudo_label_list=[0, 1, '+', '=']): super().__init__(pseudo_label_list) self.prolog.consult('./datasets/hed/learn_add.pl') - def logic_forward(self, exs): - return len(list(self.prolog.query("abduce_consistent_insts([%s])." % exs))) != 0 - - def get_query_string(self, pred_res, key, address_idx): - flatten_pred_res = flatten(pred_res) - # add variables for prolog - for idx in range(len(flatten_pred_res)): - if idx in address_idx: - flatten_pred_res[idx] = 'X' + str(idx) - pred_res = reform_idx(flatten_pred_res, pred_res) - - query_string = "abduce_consistent_insts(%s)." % pred_res - return query_string.replace("'", "").replace("+", "'+'").replace("=", "'='") - def consist_rule(self, exs, rules): rules = str(rules).replace("\'","") return len(list(self.prolog.query("eval_inst_feature(%s, %s)." % (exs, rules)))) != 0 diff --git a/abl/framework_hed.py b/abl/framework_hed.py index d942be6..171c247 100644 --- a/abl/framework_hed.py +++ b/abl/framework_hed.py @@ -150,7 +150,7 @@ def abduce_and_train(model, abducer, mapping, train_X_true, select_num): for m in mappings: pred_res = mapping_res(original_pred_res, m) max_abduce_num = 20 - solution = abducer.zoopt_get_solution(pred_res, [1] * len(pred_res), max_abduce_num) + solution = abducer.zoopt_get_solution(pred_res, [None] * len(pred_res), max_abduce_num) all_address_flag = reform_idx(solution, pred_res) consistent_idx_tmp = [] @@ -158,7 +158,7 @@ def abduce_and_train(model, abducer, mapping, train_X_true, select_num): for idx in range(len(pred_res)): address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] - candidate = abducer.kb.address_by_idx([pred_res[idx]], 1, address_idx, True) + candidate = abducer.kb.address_by_idx([pred_res[idx]], None, address_idx, True) if len(candidate) > 0: consistent_idx_tmp.append(idx) consistent_pred_res_tmp.append(candidate[0][0]) diff --git a/examples/datasets/hed/learn_add.pl b/examples/datasets/hed/learn_add.pl index af1d6bb..fbf698f 100644 --- a/examples/datasets/hed/learn_add.pl +++ b/examples/datasets/hed/learn_add.pl @@ -32,6 +32,9 @@ abduce_consistent_insts(Exs):- % (Experimental) Uncomment to use parallel abduction % abduce_consistent_exs_concurrent(Exs), !. +logic_forward(Exs, X) :- abduce_consistent_insts([Exs]) -> X = true ; X = false. +logic_forward(Exs) :- abduce_consistent_insts(Exs). + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Abduce Delta_C given pseudo-labels %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% From 3c736686d1a65d7a0d339c907547e6ebf224255b Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 6 Mar 2023 13:31:04 +0800 Subject: [PATCH 170/601] Change the sorting condition in _get_GKB --- abl/abducer/kb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index e23e637..7aff683 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -63,7 +63,7 @@ class KBBase(ABC): part_X, part_Y = zip(*XY_list) X.extend(part_X) Y.extend(part_Y) - if self.max_err != 0: + if type(Y[0]) in (int, float): sorted_XY = sorted(list(zip(Y, X))) X = [x for y, x in sorted_XY] Y = [y for y, x in sorted_XY] From 2160df0bb79ccc20f2f484bc1a29af07105fc3d3 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 6 Mar 2023 13:57:17 +0800 Subject: [PATCH 171/601] Update kb.py --- abl/abducer/kb.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 7aff683..71bdcbe 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -282,9 +282,7 @@ class prolog_KB(KBBase): class add_prolog_KB(prolog_KB): def __init__(self, pseudo_label_list=list(range(10))): super().__init__(pseudo_label_list) - for i in self.pseudo_label_list: - self.prolog.assertz("pseudo_label(%s)" % i) - self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2") + self.prolog.consult('../datasets/mnist_add/add.pl') class HED_prolog_KB(prolog_KB): From 52f8451d56475b5ac0a30786b4d56c788679a561 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 6 Mar 2023 08:02:17 +0000 Subject: [PATCH 172/601] Update prolog_KB --- abl/abducer/abducer_base.py | 8 ++++---- abl/abducer/kb.py | 15 ++------------- examples/example.py | 4 ++-- 3 files changed, 8 insertions(+), 19 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 777533a..cb50ee3 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -158,7 +158,7 @@ class AbducerBase(abc.ABC): if __name__ == '__main__': - from kb import add_KB, add_prolog_KB, HWF_KB, HED_prolog_KB + from kb import add_KB, prolog_KB, HWF_KB prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] @@ -191,7 +191,7 @@ if __name__ == '__main__': print() - kb = add_prolog_KB() + kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='../examples/datasets/mnist_add/add.pl') abd = AbducerBase(kb, 'confidence') res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) @@ -205,7 +205,7 @@ if __name__ == '__main__': print(res) print() - kb = add_prolog_KB() + kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='../examples/datasets/mnist_add/add.pl') abd = AbducerBase(kb, 'confidence', zoopt=True) res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) print(res) @@ -260,7 +260,7 @@ if __name__ == '__main__': print(res) print() - kb = HED_prolog_KB() + kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/hed.pl') abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) consist_exs = [[1, 1, '+', 0, '=', 1, 1], [1, '+', 1, '=', 1, 0], [0, '+', 0, '=', 0]] inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 71bdcbe..c5fc675 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -225,9 +225,10 @@ class add_KB(ClsKB): class prolog_KB(KBBase): - def __init__(self, pseudo_label_list): + def __init__(self, pseudo_label_list, pl_file): super().__init__(pseudo_label_list) self.prolog = pyswip.Prolog() + self.prolog.consult(pl_file) def logic_forward(self, pseudo_labels): result = list(self.prolog.query("logic_forward(%s, Res)." % pseudo_labels))[0]['Res'] @@ -278,18 +279,6 @@ class prolog_KB(KBBase): candidates.append(candidate) return candidates - -class add_prolog_KB(prolog_KB): - def __init__(self, pseudo_label_list=list(range(10))): - super().__init__(pseudo_label_list) - self.prolog.consult('../datasets/mnist_add/add.pl') - - -class HED_prolog_KB(prolog_KB): - def __init__(self, pseudo_label_list=[0, 1, '+', '=']): - super().__init__(pseudo_label_list) - self.prolog.consult('./datasets/hed/learn_add.pl') - def consist_rule(self, exs, rules): rules = str(rules).replace("\'","") return len(list(self.prolog.query("eval_inst_feature(%s, %s)." % (exs, rules)))) != 0 diff --git a/examples/example.py b/examples/example.py index 30c50fc..ee0b898 100644 --- a/examples/example.py +++ b/examples/example.py @@ -20,7 +20,7 @@ from models.wabl_models import DecisionTree, WABLBasicModel from multiprocessing import Pool from abducer.abducer_base import AbducerBase -from abducer.kb import add_KB, HWF_KB, HED_prolog_KB +from abducer.kb import add_KB, HWF_KB, prolog_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf from datasets.hed.get_hed import get_hed, split_equation @@ -33,7 +33,7 @@ def run_test(): # kb = HWF_KB(True) # abducer = AbducerBase(kb) - kb = HED_prolog_KB() + kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/hed.pl') abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True) recorder = logger() From 42a43b3cca4d5ebf85efcdb02990de136a58ce86 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Mon, 6 Mar 2023 16:24:28 +0800 Subject: [PATCH 173/601] update para of method --- abl/abducer/kb.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index c5fc675..632bd42 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -24,6 +24,7 @@ from multiprocessing import Pool import pyswip class KBBase(ABC): + # TODO：有些ä¸èƒ½æ˜¯é»˜è®¤å‚数，必须给定 def __init__(self, pseudo_label_list=None, len_list=None, GKB_flag=False, max_err=0): self.pseudo_label_list = pseudo_label_list self.len_list = len_list @@ -70,7 +71,7 @@ class KBBase(ABC): return X, Y @abstractmethod - def logic_forward(self): + def logic_forward(self, pseudo_labels): pass def _logic_forward(self, xs, multiple_predictions=False): @@ -87,7 +88,7 @@ class KBBase(ABC): return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) @abstractmethod - def _find_candidate_GKB(self): + def _find_candidate_GKB(self, pred_res, key): pass def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): @@ -260,7 +261,7 @@ class prolog_KB(KBBase): query_string += ",%s)." % key if not key_is_none_flag else ")." return query_string - def _find_candidate_GKB(self): + def _find_candidate_GKB(self, pred_res, key): pass def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): From 4e909320037df506320b74b54f11605eecadb9ac Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Mon, 6 Mar 2023 16:39:12 +0800 Subject: [PATCH 174/601] update package for example.py --- examples/example.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/examples/example.py b/examples/example.py index ee0b898..a6a5194 100644 --- a/examples/example.py +++ b/examples/example.py @@ -10,21 +10,24 @@ # # ================================================================# -from utils.plog import logger, INFO +import sys +sys.path.append("../") + +from abl.utils.plog import logger, INFO import torch.nn as nn import torch -from models.nn import LeNet5, SymbolNet -from models.basic_model import BasicModel, BasicDataset -from models.wabl_models import DecisionTree, WABLBasicModel +from abl.models.nn import LeNet5, SymbolNet +from abl.models.basic_model import BasicModel, BasicDataset +from abl.models.wabl_models import DecisionTree, WABLBasicModel from multiprocessing import Pool -from abducer.abducer_base import AbducerBase -from abducer.kb import add_KB, HWF_KB, prolog_KB +from abl.abducer.abducer_base import AbducerBase +from abl.abducer.kb import add_KB, HWF_KB, prolog_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf from datasets.hed.get_hed import get_hed, split_equation -import framework_hed +from abl import framework_hed def run_test(): From c98e516e43898b0c431c339f62009aa8a63bb701 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 6 Mar 2023 08:47:45 +0000 Subject: [PATCH 175/601] change pl_file name for HED --- abl/abducer/abducer_base.py | 2 +- abl/abducer/kb.py | 3 +-- examples/example.py | 2 +- 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index cb50ee3..712eb46 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -260,7 +260,7 @@ if __name__ == '__main__': print(res) print() - kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/hed.pl') + kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) consist_exs = [[1, 1, '+', 0, '=', 1, 1], [1, '+', 1, '=', 1, 0], [0, '+', 0, '=', 0]] inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 632bd42..d937dcf 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -24,8 +24,7 @@ from multiprocessing import Pool import pyswip class KBBase(ABC): - # TODO：有些ä¸èƒ½æ˜¯é»˜è®¤å‚数，必须给定 - def __init__(self, pseudo_label_list=None, len_list=None, GKB_flag=False, max_err=0): + def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0): self.pseudo_label_list = pseudo_label_list self.len_list = len_list self.GKB_flag = GKB_flag diff --git a/examples/example.py b/examples/example.py index a6a5194..c74f1b3 100644 --- a/examples/example.py +++ b/examples/example.py @@ -36,7 +36,7 @@ def run_test(): # kb = HWF_KB(True) # abducer = AbducerBase(kb) - kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/hed.pl') + kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True) recorder = logger() From fe52f91385c17ba639f3a9f7635d6f78825f7e4c Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Mon, 6 Mar 2023 19:39:59 +0800 Subject: [PATCH 176/601] speed up --- abl/utils/utils.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/abl/utils/utils.py b/abl/utils/utils.py index fbb5bfb..acc7f94 100644 --- a/abl/utils/utils.py +++ b/abl/utils/utils.py @@ -7,6 +7,7 @@ def flatten(l): # return [item for sublist in l for item in flatten(sublist)] if isinstance(l, (list, tuple)) else [l] if not isinstance(l[0], (list, tuple)): return l + # TODO ç¨å¾®å¯¹æ¯”一下和itertools.chain.from_iterable(nested_list)的速度区别，看看哪个好 return [item for sublist in l for item in sublist] if isinstance(l, (list, tuple)) else [l] # for multiple predictions, modify from `learn_add.py` @@ -14,13 +15,8 @@ def reform_idx(flatten_pred_res, save_pred_res): re = [] i = 0 for e in save_pred_res: - j = 0 - idx = [] - while j < len(e): - idx.append(flatten_pred_res[i + j]) - j += 1 - re.append(idx) - i = i + j + re.append(flatten_pred_res[i:i + len(e)]) + i += len(e) return re def hamming_dist(A, B): From e436421fb52e3d6502f630aff25c8e704397030f Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Mon, 6 Mar 2023 20:09:11 +0800 Subject: [PATCH 177/601] Update framework_hed.py --- abl/framework_hed.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abl/framework_hed.py b/abl/framework_hed.py index 171c247..a823b13 100644 --- a/abl/framework_hed.py +++ b/abl/framework_hed.py @@ -214,7 +214,7 @@ def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, consistent_idx = [] consistent_pred_res = [] for idx in range(len(pred_res)): - if abducer.kb.logic_forward([pred_res[idx]]): + if abducer.kb.logic_forward(pred_res[idx]): consistent_idx.append(idx) consistent_pred_res.append(pred_res[idx]) From bfd6dc8a5c37c680c683a3c3d9c4efe756853248 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Tue, 7 Mar 2023 00:01:08 +0800 Subject: [PATCH 178/601] update TODO in kb.py --- abl/abducer/kb.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index d937dcf..9ae919a 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -80,6 +80,7 @@ class KBBase(ABC): res = [self.logic_forward(x) for x in xs] return res + # TODO：这里max_address_num默认值-1，åŽé¢è¿è¡Œä¼šæœ‰é—®é¢˜å— def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): if self.GKB_flag: return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions) @@ -134,8 +135,10 @@ class KBBase(ABC): candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] return candidates, min_address_num, address_num + # TODO：应该也是内部使用的方法？ def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] + # TODO：product combinations本身就是迭代器，如果没有其他用途，ä¸ç”¨è½¬list，直接放到循环那å³å¯ï¼ŒçœåŽ»ä¸€äº›æ—¶é—´ï¼Œä¸‹é¢çš„åŒç† abduce_c = list(product(self.pseudo_label_list, repeat=len(address_idx))) if multiple_predictions: @@ -209,7 +212,8 @@ class ClsKB(KBBase): def __init__(self, pseudo_label_list, len_list, GKB_flag): super().__init__(pseudo_label_list, len_list, GKB_flag) - def logic_forward(self): + # TODO:这里以åŠRegKBå¯ä»¥ä¸å®žçŽ°logic_forwardå—，这样用户继承åŽä¸å®žçŽ°logic_forward就会报错 + def logic_forward(self, pseudo_labels): pass def _find_candidate_GKB(self, pred_res, key): @@ -243,13 +247,15 @@ class prolog_KB(KBBase): address_pred_res = pred_res.copy() if multiple_predictions: address_pred_res = flatten(address_pred_res) - + + # TODO：å¯ä»¥ç›´æŽ¥å¯¹address_idx循环？ for idx in range(len(address_pred_res)): if idx in address_idx: address_pred_res[idx] = 'P' + str(idx) if multiple_predictions: address_pred_res = reform_idx(address_pred_res, pred_res) + # TODO：ä¸çŸ¥é“有没有更简æ´çš„方法 regex = r"'P\d+'" return re.sub(regex, lambda x: x.group().replace("'", ""), str(address_pred_res)) @@ -269,6 +275,7 @@ class prolog_KB(KBBase): if multiple_predictions: save_pred_res = pred_res pred_res = flatten(pred_res) + # TODO:这里åŽé¢çš„那个list应该也ä¸éœ€è¦ abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string))] for c in abduce_c: candidate = pred_res.copy() @@ -289,17 +296,15 @@ class prolog_KB(KBBase): if len(prolog_result) == 0: return None prolog_rules = prolog_result[0]['X'] - rules = [] - for rule in prolog_rules: - rules.append(rule.value) + rules = [rule.value for rule in prolog_rules] return rules - +# TODO：和ClsKBçš„å‚数顺åºä¸ç»Ÿä¸€ class RegKB(KBBase): def __init__(self, GKB_flag=False, pseudo_label_list=None, len_list=None, max_err=1e-3): super().__init__(pseudo_label_list, len_list, GKB_flag, max_err) - def logic_forward(self): + def logic_forward(self, pseudo_labels): pass def _find_candidate_GKB(self, pred_res, key): @@ -333,6 +338,7 @@ class HWF_KB(RegKB): ): super().__init__(GKB_flag, pseudo_label_list, len_list, max_err) + # TODO：应该是é™æ€æ–¹æ³• def valid_candidate(self, formula): if len(formula) % 2 == 0: return False From e911ef1103d74cc05b2bd9e3c973764b3c707111 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 7 Mar 2023 00:17:55 +0000 Subject: [PATCH 179/601] Update several files --- abl/abducer/abducer_base.py | 4 ++-- abl/abducer/kb.py | 44 +++++++++++++------------------------ abl/utils/utils.py | 9 ++++---- 3 files changed, 21 insertions(+), 36 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 712eb46..bd76831 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -219,7 +219,7 @@ if __name__ == '__main__': print(res) print() - kb = HWF_KB(GKB_flag=True, len_list=[1, 3, 5], max_err = 0.1) + kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 0.1) abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) print(res) @@ -227,7 +227,7 @@ if __name__ == '__main__': print(res) print() - kb = HWF_KB(GKB_flag=True, len_list=[1, 3, 5], max_err = 1) + kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 1) abd = AbducerBase(kb, 'hamming') res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) print(res) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 9ae919a..5b82f03 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -80,8 +80,7 @@ class KBBase(ABC): res = [self.logic_forward(x) for x in xs] return res - # TODO：这里max_address_num默认值-1，åŽé¢è¿è¡Œä¼šæœ‰é—®é¢˜å— - def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False): + def abduce_candidates(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False): if self.GKB_flag: return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions) else: @@ -135,11 +134,9 @@ class KBBase(ABC): candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] return candidates, min_address_num, address_num - # TODO：应该也是内部使用的方法？ def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] - # TODO：product combinations本身就是迭代器，如果没有其他用途，ä¸ç”¨è½¬list，直接放到循环那å³å¯ï¼ŒçœåŽ»ä¸€äº›æ—¶é—´ï¼Œä¸‹é¢çš„åŒç† - abduce_c = list(product(self.pseudo_label_list, repeat=len(address_idx))) + abduce_c = product(self.pseudo_label_list, repeat=len(address_idx)) if multiple_predictions: save_pred_res = pred_res @@ -160,9 +157,9 @@ class KBBase(ABC): def _address(self, address_num, pred_res, key, multiple_predictions): new_candidates = [] if not multiple_predictions: - address_idx_list = list(combinations(list(range(len(pred_res))), address_num)) + address_idx_list = combinations(list(range(len(pred_res))), address_num) else: - address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num)) + address_idx_list = combinations(list(range(len(flatten(pred_res)))), address_num) for address_idx in address_idx_list: candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) @@ -212,10 +209,6 @@ class ClsKB(KBBase): def __init__(self, pseudo_label_list, len_list, GKB_flag): super().__init__(pseudo_label_list, len_list, GKB_flag) - # TODO:这里以åŠRegKBå¯ä»¥ä¸å®žçŽ°logic_forwardå—，这样用户继承åŽä¸å®žçŽ°logic_forward就会报错 - def logic_forward(self, pseudo_labels): - pass - def _find_candidate_GKB(self, pred_res, key): return self.base[len(pred_res)][key] @@ -248,10 +241,8 @@ class prolog_KB(KBBase): if multiple_predictions: address_pred_res = flatten(address_pred_res) - # TODO：å¯ä»¥ç›´æŽ¥å¯¹address_idx循环？ - for idx in range(len(address_pred_res)): - if idx in address_idx: - address_pred_res[idx] = 'P' + str(idx) + for idx in address_idx: + address_pred_res[idx] = 'P' + str(idx) if multiple_predictions: address_pred_res = reform_idx(address_pred_res, pred_res) @@ -275,8 +266,7 @@ class prolog_KB(KBBase): if multiple_predictions: save_pred_res = pred_res pred_res = flatten(pred_res) - # TODO:这里åŽé¢çš„那个list应该也ä¸éœ€è¦ - abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string))] + abduce_c = [list(z.values()) for z in self.prolog.query(query_string)] for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): @@ -299,14 +289,11 @@ class prolog_KB(KBBase): rules = [rule.value for rule in prolog_rules] return rules -# TODO：和ClsKBçš„å‚数顺åºä¸ç»Ÿä¸€ + class RegKB(KBBase): - def __init__(self, GKB_flag=False, pseudo_label_list=None, len_list=None, max_err=1e-3): + def __init__(self, pseudo_label_list=None, len_list=None, GKB_flag=False, max_err=1e-3): super().__init__(pseudo_label_list, len_list, GKB_flag, max_err) - def logic_forward(self, pseudo_labels): - pass - def _find_candidate_GKB(self, pred_res, key): potential_candidates = self.base[len(pred_res)] key_list = list(potential_candidates.keys()) @@ -331,15 +318,15 @@ class RegKB(KBBase): class HWF_KB(RegKB): def __init__( - self, GKB_flag=False, + self, pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], len_list=[1, 3, 5, 7], + GKB_flag=False, max_err=1e-3 ): - super().__init__(GKB_flag, pseudo_label_list, len_list, max_err) + super().__init__(pseudo_label_list, len_list, GKB_flag, max_err) - # TODO：应该是é™æ€æ–¹æ³• - def valid_candidate(self, formula): + def _valid_candidate(self, formula): if len(formula) % 2 == 0: return False for i in range(len(formula)): @@ -350,7 +337,7 @@ class HWF_KB(RegKB): return True def logic_forward(self, formula): - if not self.valid_candidate(formula): + if not self._valid_candidate(formula): return np.inf mapping = { '1': '1', @@ -374,5 +361,4 @@ class HWF_KB(RegKB): import time if __name__ == "__main__": - pass - + pass \ No newline at end of file diff --git a/abl/utils/utils.py b/abl/utils/utils.py index acc7f94..d986065 100644 --- a/abl/utils/utils.py +++ b/abl/utils/utils.py @@ -1,16 +1,15 @@ import numpy as np from .plog import INFO from collections import OrderedDict +from itertools import chain -# for multiple predictions, modify from `learn_add.py` +# for multiple predictions def flatten(l): - # return [item for sublist in l for item in flatten(sublist)] if isinstance(l, (list, tuple)) else [l] if not isinstance(l[0], (list, tuple)): return l - # TODO ç¨å¾®å¯¹æ¯”一下和itertools.chain.from_iterable(nested_list)的速度区别，看看哪个好 - return [item for sublist in l for item in sublist] if isinstance(l, (list, tuple)) else [l] + return list(chain.from_iterable(l)) -# for multiple predictions, modify from `learn_add.py` +# for multiple predictions def reform_idx(flatten_pred_res, save_pred_res): re = [] i = 0 From b75b2897b953353ddade8afae51499367a21b0a8 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 7 Mar 2023 00:27:49 +0000 Subject: [PATCH 180/601] Rearrange address_by_idx to abducer_base --- abl/abducer/abducer_base.py | 10 ++++++---- abl/framework_hed.py | 2 +- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index bd76831..cf14bb7 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -64,7 +64,7 @@ class AbducerBase(abc.ABC): score = 0 for idx in range(len(pred_res)): address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] - candidate = self.kb.address_by_idx([pred_res[idx]], key[idx], address_idx, True) + candidate = self.address_by_idx([pred_res[idx]], key[idx], address_idx) if len(candidate) > 0: score += 1 return score @@ -72,7 +72,7 @@ class AbducerBase(abc.ABC): def _zoopt_address_score(self, pred_res, key, sol): if not self.multiple_predictions: address_idx = [idx for idx, i in enumerate(sol.get_x()) if i != 0] - candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + candidates = self.address_by_idx(pred_res, key, address_idx) return 1 if len(candidates) > 0 else 0 else: return self._zoopt_score_multiple(pred_res, key, sol.get_x()) @@ -115,6 +115,9 @@ class AbducerBase(abc.ABC): key = tuple(key) self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates + + def address_by_idx(self, pred_res, key, address_idx): + return self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) def abduce(self, data, max_address_num=-1, require_more_address=0): pred_res, pred_res_prob, key = data @@ -129,7 +132,7 @@ class AbducerBase(abc.ABC): if self.zoopt: solution = self.zoopt_get_solution(pred_res, key, max_address_num) address_idx = [idx for idx, i in enumerate(solution) if i != 0] - candidates = self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + candidates = self.address_by_idx(pred_res, key, address_idx) address_num = int(solution.sum()) min_address_num = address_num else: @@ -156,7 +159,6 @@ class AbducerBase(abc.ABC): def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) - if __name__ == '__main__': from kb import add_KB, prolog_KB, HWF_KB diff --git a/abl/framework_hed.py b/abl/framework_hed.py index a823b13..3f09ff6 100644 --- a/abl/framework_hed.py +++ b/abl/framework_hed.py @@ -158,7 +158,7 @@ def abduce_and_train(model, abducer, mapping, train_X_true, select_num): for idx in range(len(pred_res)): address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] - candidate = abducer.kb.address_by_idx([pred_res[idx]], None, address_idx, True) + candidate = abducer.address_by_idx([pred_res[idx]], None, address_idx) if len(candidate) > 0: consistent_idx_tmp.append(idx) consistent_pred_res_tmp.append(candidate[0][0]) From f65a277e4d2f3faef35b5d805898e8a1bb579dd0 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Tue, 7 Mar 2023 09:31:46 +0800 Subject: [PATCH 181/601] complete knn model for HED --- example.py | 59 ++++--- framework_hed_knn.py | 407 +++++++++++++++++++++++++++++++++++++++++++ utils/utils.py | 79 ++++++--- 3 files changed, 502 insertions(+), 43 deletions(-) create mode 100644 framework_hed_knn.py diff --git a/example.py b/example.py index 30c50fc..3751db3 100644 --- a/example.py +++ b/example.py @@ -11,12 +11,14 @@ # ================================================================# from utils.plog import logger, INFO +from utils.utils import reduce_dimension import torch.nn as nn import torch from models.nn import LeNet5, SymbolNet from models.basic_model import BasicModel, BasicDataset from models.wabl_models import DecisionTree, WABLBasicModel +from sklearn.neighbors import KNeighborsClassifier from multiprocessing import Pool from abducer.abducer_base import AbducerBase @@ -25,6 +27,7 @@ from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf from datasets.hed.get_hed import get_hed, split_equation import framework_hed +import framework_hed_knn def run_test(): @@ -41,29 +44,45 @@ def run_test(): total_train_data = get_hed(train=True) train_data, val_data = split_equation(total_train_data, 3, 1) test_data = get_hed(train=False) - - # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) - cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - framework_hed.hed_pretrain(kb, cls, recorder) - - criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) - # optimizer = torch.optim.Adam(cls.parameters(), lr=0.00001, betas=(0.9, 0.99)) - - - base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=10, recorder=recorder) + + # ======================== non-NN model ========================== # + reduce_dimension(train_data) + reduce_dimension(val_data) + reduce_dimension(test_data) + base_model = KNeighborsClassifier(n_neighbors=3) + pretrain_data_X, pretrain_data_Y = framework_hed_knn.hed_pretrain(base_model) model = WABLBasicModel(base_model, kb.pseudo_label_list) - - # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) - # test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) + model, mapping = framework_hed_knn.train_with_rule( + model, abducer, train_data, val_data, (pretrain_data_X, pretrain_data_Y), select_num=10, min_len=5, max_len=8 + ) + framework_hed_knn.hed_test( + model, abducer, mapping, train_data, test_data, min_len=5, max_len=8 + ) + # ============================ End =============================== # + + # ========================== NN model ============================ # + # # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) + # cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) + # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + # framework_hed.hed_pretrain(kb, cls, recorder) + + # criterion = nn.CrossEntropyLoss() + # optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) + # # optimizer = torch.optim.Adam(cls.parameters(), lr=0.00001, betas=(0.9, 0.99)) + + # base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=10, recorder=recorder) + # model = WABLBasicModel(base_model, kb.pseudo_label_list) + + # # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) + # # test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) - # train_data = get_hwf(train = True, get_pseudo_label = True) - # test_data = get_hwf(train = False, get_pseudo_label = True) + # # train_data = get_hwf(train = True, get_pseudo_label = True) + # # test_data = get_hwf(train = False, get_pseudo_label = True) - model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) - framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) + # model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) + # framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) + # ============================ End =============================== # recorder.dump() return True diff --git a/framework_hed_knn.py b/framework_hed_knn.py new file mode 100644 index 0000000..a5e5506 --- /dev/null +++ b/framework_hed_knn.py @@ -0,0 +1,407 @@ +# coding: utf-8 +# ================================================================# +# Copyright (C) 2021 Freecss All rights reserved. +# +# File Name ：framework.py +# Author ：freecss +# Email ：karlfreecss@gmail.com +# Created Date ：2021/06/07 +# Description ： +# +# ================================================================# + +import pickle as pk +import torch +import torch.nn as nn +import numpy as np +import os + +from utils.plog import INFO, DEBUG, clocker +from utils.utils import ( + flatten, + reform_idx, + block_sample, + gen_mappings, + mapping_res, + remapping_res, + extract_feature, +) + +from models.nn import MLP, SymbolNetAutoencoder +from models.basic_model import BasicModel, BasicDataset +from datasets.hed.get_hed import get_pretrain_data + + +def result_statistics(pred_Z, Z, Y, logic_forward, char_acc_flag): + result = {} + if char_acc_flag: + char_acc_num = 0 + char_num = 0 + for pred_z, z in zip(pred_Z, Z): + char_num += len(z) + for zidx in range(len(z)): + if pred_z[zidx] == z[zidx]: + char_acc_num += 1 + char_acc = char_acc_num / char_num + result["Character level accuracy"] = char_acc + + abl_acc_num = 0 + for pred_z, y in zip(pred_Z, Y): + if logic_forward(pred_z) == y: + abl_acc_num += 1 + abl_acc = abl_acc_num / len(Y) + result["ABL accuracy"] = abl_acc + + return result + + +def filter_data(X, abduced_Z): + finetune_Z = [] + finetune_X = [] + for abduced_x, abduced_z in zip(X, abduced_Z): + if abduced_z is not []: + finetune_X.append(abduced_x) + finetune_Z.append(abduced_z) + return finetune_X, finetune_Z + + +def hed_pretrain(cls, image_size=(28, 28, 1)): + import cv2 + + INFO("Pretrain Start") + pretrain_data_X, pretrain_data_Y = [], [] + for i, label in enumerate(["0", "1", "10", "11"]): + label_path = os.path.join("./datasets/hed/dataset/mnist_images", label) + img_path_list = os.listdir(label_path) + for j in range(10): + img = cv2.imread( + os.path.join(label_path, img_path_list[j]), cv2.IMREAD_GRAYSCALE + ) + img = np.array(cv2.resize(img, (image_size[1], image_size[0])), np.float32) + img = (img - 127) / 128.0 + pretrain_data_X.append( + extract_feature(img.reshape((1, image_size[0], image_size[1]))) + ) + pretrain_data_Y.append(i) + cls.fit(pretrain_data_X, pretrain_data_Y) + import random + + for i, label in enumerate(["0", "1", "10", "11"]): + label_path = os.path.join("./datasets/hed/dataset/mnist_images", label) + img_path_list = os.listdir(label_path) + cnt = 0 + for j in range(50): + img = cv2.imread( + os.path.join(label_path, random.choice(img_path_list)), + cv2.IMREAD_GRAYSCALE, + ) + img = np.array(cv2.resize(img, (image_size[1], image_size[0])), np.float32) + img = (img - 127) / 128.0 + predict_label = cls.predict( + [extract_feature(img.reshape((1, image_size[0], image_size[1])))] + ) + # predict_label = cls.predict_proba( + # [ + # extract_feature( + # np.array(img, dtype=np.float32).reshape( + # (1, image_size[0], image_size[1]) + # ) + # ) + # ] + # ).argmax(axis=1) + + if predict_label == i: + cnt += 1 + INFO( + "%d predict accuracy is " % i, + cnt / 50, + ) + + return pretrain_data_X, pretrain_data_Y + + +def _get_char_acc(model, X, consistent_pred_res, mapping): + original_pred_res = model.predict(X)["cls"] + pred_res = flatten(mapping_res(original_pred_res, mapping)) + INFO("Current model's output: ", pred_res) + INFO("Abduced labels: ", flatten(consistent_pred_res)) + assert len(pred_res) == len(flatten(consistent_pred_res)) + return sum( + [ + pred_res[idx] == flatten(consistent_pred_res)[idx] + for idx in range(len(pred_res)) + ] + ) / len(pred_res) + + +def abduce_and_train(model, abducer, mapping, train_X_true, pretrain_data, select_num): + select_idx = np.random.randint(len(train_X_true), size=select_num) + X = [] + for idx in select_idx: + X.append(train_X_true[idx]) + + original_pred_res = model.predict(X)["cls"] + + if mapping == None: + mappings = gen_mappings(["+", "=", 0, 1], ["+", "=", 0, 1]) + else: + mappings = [mapping] + + consistent_idx = [] + consistent_pred_res = [] + + for m in mappings: + pred_res = mapping_res(original_pred_res, m) + max_abduce_num = 20 + solution = abducer.zoopt_get_solution( + pred_res, [1] * len(pred_res), max_abduce_num + ) + all_address_flag = reform_idx(solution, pred_res) + + consistent_idx_tmp = [] + consistent_pred_res_tmp = [] + + for idx in range(len(pred_res)): + address_idx = [ + i for i, flag in enumerate(all_address_flag[idx]) if flag != 0 + ] + candidate = abducer.kb.address_by_idx([pred_res[idx]], 1, address_idx, True) + if len(candidate) > 0: + consistent_idx_tmp.append(idx) + consistent_pred_res_tmp.append(candidate[0][0]) + + if len(consistent_idx_tmp) > len(consistent_idx): + consistent_idx = consistent_idx_tmp + consistent_pred_res = consistent_pred_res_tmp + if len(mappings) > 1: + mapping = m + + if len(consistent_idx) == 0: + return 0, 0, None + + if len(mappings) > 1: + INFO("Final mapping is: ", mapping) + + INFO("Train pool size is:", len(flatten(consistent_pred_res))) + INFO("Start to use abduced pseudo label to train model...") + pretrain_data_X, pretrain_data_Y = pretrain_data + pretrain_mappping = {0: 0, 1: 1, 2: "+", 3: "="} + pretrain_data_X = [[X] for X in pretrain_data_X] + pretrain_data_Y = [[pretrain_mappping[Y]] for Y in pretrain_data_Y] + model.train( + [X[idx] for idx in consistent_idx] + pretrain_data_X, + remapping_res(consistent_pred_res + pretrain_data_Y, mapping), + ) + + consistent_acc = len(consistent_idx) / select_num + char_acc = _get_char_acc( + model, [X[idx] for idx in consistent_idx], consistent_pred_res, mapping + ) + INFO("consistent_acc is %s, char_acc is %s" % (consistent_acc, char_acc)) + return consistent_acc, char_acc, mapping + + +def _remove_duplicate_rule(rule_dict): + add_nums_dict = {} + for r in list(rule_dict): + add_nums = str(r.split("]")[0].split("[")[1]) + str( + r.split("]")[1].split("[")[1] + ) # r = 'my_op([1], [0], [1, 0])' then add_nums = '10' + if add_nums in add_nums_dict: + old_r = add_nums_dict[add_nums] + if rule_dict[r] >= rule_dict[old_r]: + rule_dict.pop(old_r) + add_nums_dict[add_nums] = r + else: + rule_dict.pop(r) + else: + add_nums_dict[add_nums] = r + return list(rule_dict) + + +def get_rules_from_data( + model, abducer, mapping, train_X_true, samples_per_rule, samples_num +): + rules = [] + for _ in range(samples_num): + while True: + select_idx = np.random.randint(len(train_X_true), size=samples_per_rule) + X = [] + for idx in select_idx: + X.append(train_X_true[idx]) + original_pred_res = model.predict(X)["cls"] + pred_res = mapping_res(original_pred_res, mapping) + + consistent_idx = [] + consistent_pred_res = [] + for idx in range(len(pred_res)): + if abducer.kb.logic_forward([pred_res[idx]]): + consistent_idx.append(idx) + consistent_pred_res.append(pred_res[idx]) + + if len(consistent_pred_res) != 0: + rule = abducer.abduce_rules(consistent_pred_res) + if rule != None: + break + rules.append(rule) + + all_rule_dict = {} + for rule in rules: + for r in rule: + all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1 + rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items() if cnt >= 5} + rules = _remove_duplicate_rule(rule_dict) + + return rules + + +def _get_consist_rule_acc(model, abducer, mapping, rules, X): + cnt = 0 + for x in X: + original_pred_res = model.predict([x])["cls"] + pred_res = flatten(mapping_res(original_pred_res, mapping)) + if abducer.kb.consist_rule(pred_res, rules): + cnt += 1 + return cnt / len(X) + + +def train_with_rule( + model, + abducer, + train_data, + val_data, + pretrain_data, + select_num=10, + min_len=5, + max_len=8, +): + train_X = train_data + val_X = val_data + + samples_num = 50 + samples_per_rule = 3 + + # Start training / for each length of equations + for equation_len in range(min_len, max_len): + INFO( + "============== equation_len: %d-%d ================" + % (equation_len, equation_len + 1) + ) + train_X_true = train_X[1][equation_len] + train_X_false = train_X[0][equation_len] + val_X_true = val_X[1][equation_len] + val_X_false = val_X[0][equation_len] + + train_X_true.extend(train_X[1][equation_len + 1]) + train_X_false.extend(train_X[0][equation_len + 1]) + val_X_true.extend(val_X[1][equation_len + 1]) + val_X_false.extend(val_X[0][equation_len + 1]) + + condition_cnt = 0 + while True: + if equation_len == min_len: + mapping = None + + # Abduce and train NN + consistent_acc, char_acc, mapping = abduce_and_train( + model, abducer, mapping, train_X_true, pretrain_data, select_num + ) + if consistent_acc == 0: + continue + + # Test if we can use mlp to evaluate + if consistent_acc >= 0.9 and char_acc >= 0.9: + condition_cnt += 1 + else: + condition_cnt = 0 + + # The condition has been satisfied continuously five times + if condition_cnt >= 5: + INFO("Now checking if we can go to next course") + rules = get_rules_from_data( + model, abducer, mapping, train_X_true, samples_per_rule, samples_num + ) + INFO("Learned rules from data:", rules) + + true_consist_rule_acc = _get_consist_rule_acc( + model, abducer, mapping, rules, val_X_true + ) + false_consist_rule_acc = _get_consist_rule_acc( + model, abducer, mapping, rules, val_X_false + ) + + INFO( + "consist_rule_acc is %f, %f\n" + % (true_consist_rule_acc, false_consist_rule_acc) + ) + # decide next course or restart + if true_consist_rule_acc > 0.9 and false_consist_rule_acc < 0.1: + break + else: + if equation_len == min_len: + # model.cls_list[0].model.load_state_dict( + # torch.load("./weights/pretrain_weights.pth") + # ) + pretrain_data_X, pretrain_data_Y = pretrain_data + model.cls_list[0].fit(pretrain_data_X, pretrain_data_Y) + else: + pretrain_data_X, pretrain_data_Y = pretrain_data + model.cls_list[0].fit(pretrain_data_X, pretrain_data_Y) + # model.cls_list[0].model.load_state_dict( + # torch.load("./weights/weights_%d.pth" % (equation_len - 1)) + # ) + condition_cnt = 0 + INFO("Reload Model and retrain") + + return model, mapping + + +def hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8): + train_X = train_data + test_X = test_data + + # Calcualte how many equations should be selected in each length + # for each length, there are equation_samples_num[equation_len] rules + print("Now begin to train final mlp model") + equation_samples_num = [] + len_cnt = max_len - min_len + 1 + samples_num = 50 + equation_samples_num += [0] * min_len + if samples_num % len_cnt == 0: + equation_samples_num += [samples_num // len_cnt] * len_cnt + else: + equation_samples_num += [samples_num // len_cnt] * len_cnt + equation_samples_num[-1] += samples_num % len_cnt + assert sum(equation_samples_num) == samples_num + + # Abduce rules + rules = [] + samples_per_rule = 3 + for equation_len in range(min_len, max_len + 1): + equation_rules = get_rules_from_data( + model, + abducer, + mapping, + train_X[1][equation_len], + samples_per_rule, + equation_samples_num[equation_len], + ) + rules.extend(equation_rules) + rules = list(set(rules)) + INFO("Learned rules from data:", rules) + + for equation_len in range(5, 27): + true_consist_rule_acc = _get_consist_rule_acc( + model, abducer, mapping, rules, test_X[1][equation_len] + ) + false_consist_rule_acc = _get_consist_rule_acc( + model, abducer, mapping, rules, test_X[0][equation_len] + ) + INFO( + "consist_rule_acc of testing length %d equations are %f, %f" + % (equation_len, true_consist_rule_acc, false_consist_rule_acc) + ) + + +if __name__ == "__main__": + pass diff --git a/utils/utils.py b/utils/utils.py index 1138361..5cd433d 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -1,11 +1,18 @@ +import torch +import torch.nn as nn import numpy as np from utils.plog import INFO from collections import OrderedDict # for multiple predictions, modify from `learn_add.py` def flatten(l): - return [item for sublist in l for item in flatten(sublist)] if isinstance(l, list) else [l] - + return ( + [item for sublist in l for item in flatten(sublist)] + if isinstance(l, list) + else [l] + ) + + # for multiple predictions, modify from `learn_add.py` def reform_idx(flatten_pred_res, save_pred_res): re = [] @@ -20,10 +27,12 @@ def reform_idx(flatten_pred_res, save_pred_res): i = i + j return re + def hamming_dist(A, B): B = np.array(B) - A = np.expand_dims(A, axis = 0).repeat(axis=0, repeats=(len(B))) - return np.sum(A != B, axis = 1) + A = np.expand_dims(A, axis=0).repeat(axis=0, repeats=(len(B))) + return np.sum(A != B, axis=1) + def confidence_dist(A, B): B = np.array(B) @@ -31,10 +40,10 @@ def confidence_dist(A, B): A = np.expand_dims(A, axis=0) A = A.repeat(axis=0, repeats=(len(B))) rows = np.array(range(len(B))) - rows = np.expand_dims(rows, axis = 1).repeat(axis = 1, repeats = len(B[0])) + rows = np.expand_dims(rows, axis=1).repeat(axis=1, repeats=len(B[0])) cols = np.array(range(len(B[0]))) - cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) - return 1 - np.prod(A[rows, cols, B], axis = 1) + cols = np.expand_dims(cols, axis=0).repeat(axis=0, repeats=len(B)) + return 1 - np.prod(A[rows, cols, B], axis=1) def block_sample(X, Z, Y, sample_num, epoch_idx): @@ -51,32 +60,36 @@ def block_sample(X, Z, Y, sample_num, epoch_idx): def gen_mappings(chars, symbs): - n_char = len(chars) - n_symbs = len(symbs) - if n_char != n_symbs: - print('Characters and symbols size dosen\'t match.') - return - from itertools import permutations - mappings = [] - # returned mappings - perms = permutations(symbs) - for p in perms: - mappings.append(dict(zip(chars, list(p)))) - return mappings + n_char = len(chars) + n_symbs = len(symbs) + if n_char != n_symbs: + print("Characters and symbols size dosen't match.") + return + from itertools import permutations + + mappings = [] + # returned mappings + perms = permutations(symbs) + for p in perms: + mappings.append(dict(zip(chars, list(p)))) + return mappings + def mapping_res(original_pred_res, m): return [[m[symbol] for symbol in formula] for formula in original_pred_res] + def remapping_res(pred_res, m): remapping = {} for key, value in m.items(): remapping[value] = key return [[remapping[symbol] for symbol in formula] for formula in pred_res] + def check_equal(a, b): if isinstance(a, (int, float)) and isinstance(b, (int, float)): return abs(a - b) <= 1e-3 - + if isinstance(a, list) and isinstance(b, list): if len(a) != len(b): return False @@ -84,6 +97,26 @@ def check_equal(a, b): if not check_equal(a[i], b[i]): return False return True - - else: - return a == b + + else: + return a == b + + +def extract_feature(img): + extractor = nn.AvgPool2d(2, stride=2) + feature_map = np.array(extractor(torch.Tensor(img))) + return feature_map.reshape((-1,)) + return np.concatenate( + (np.squeeze(np.sum(img, axis=1)), np.squeeze(np.sum(img, axis=2))), axis=0 + ) + + +def reduce_dimension(data): + for truth_value in [0, 1]: + for equation_len in range(5, 27): + equations = data[truth_value][equation_len] + reduced_equations = [ + [extract_feature(symbol_img) for symbol_img in equation] + for equation in equations + ] + data[truth_value][equation_len] = reduced_equations From 935a06f499427c2aa3a2da12c311d92adadfafdd Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 7 Mar 2023 14:39:36 +0800 Subject: [PATCH 182/601] Remove default parameters in RegKB --- abl/abducer/kb.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 5b82f03..9009940 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -291,7 +291,7 @@ class prolog_KB(KBBase): class RegKB(KBBase): - def __init__(self, pseudo_label_list=None, len_list=None, GKB_flag=False, max_err=1e-3): + def __init__(self, pseudo_label_list, len_list, GKB_flag, max_err): super().__init__(pseudo_label_list, len_list, GKB_flag, max_err) def _find_candidate_GKB(self, pred_res, key): @@ -361,4 +361,4 @@ class HWF_KB(RegKB): import time if __name__ == "__main__": - pass \ No newline at end of file + pass From 5290ebd804984c87481c2693d44552ef2eceaa62 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 7 Mar 2023 15:58:56 +0800 Subject: [PATCH 183/601] Modify mapping in HWF_KB --- abl/abducer/kb.py | 19 ++----------------- 1 file changed, 2 insertions(+), 17 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 9009940..d656e41 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -339,26 +339,11 @@ class HWF_KB(RegKB): def logic_forward(self, formula): if not self._valid_candidate(formula): return np.inf - mapping = { - '1': '1', - '2': '2', - '3': '3', - '4': '4', - '5': '5', - '6': '6', - '7': '7', - '8': '8', - '9': '9', - '+': '+', - '-': '-', - 'times': '*', - 'div': '/', - } + mapping = {str(i): str(i) for i in range(1, 10)} + mapping.update({'+': '+', '-': '-', 'times': '*', 'div': '/'}) formula = [mapping[f] for f in formula] return eval(''.join(formula)) -import time - if __name__ == "__main__": pass From 34eabfb61367a5bf64990de360634c151f74913f Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Tue, 7 Mar 2023 23:04:01 +0800 Subject: [PATCH 184/601] update todo in abducer --- abl/abducer/abducer_base.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index cf14bb7..c5c21fd 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -40,7 +40,8 @@ class AbducerBase(abc.ABC): if self.multiple_predictions: pred_res_prob = flatten(pred_res_prob) candidates = [flatten(c) for c in candidates] - + + # TODO：这里应该在类创建时就æå‰å­˜å¥½ï¼Œæ¯æ¬¡éƒ½é‡æ–°è®¡ç®—也太费时了 mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) candidates = [list(map(lambda x: mapping[x], c)) for c in candidates] return confidence_dist(pred_res_prob, candidates) @@ -53,15 +54,22 @@ class AbducerBase(abc.ABC): else: cost_list = self._get_cost_list(pred_res, pred_res_prob, candidates) + # TODO：这里很怪，按ç†argmin就行了 min_address_num = np.min(cost_list) idxs = np.where(cost_list == min_address_num)[0] + # TODO：这里也很怪，å–ç¬¬ä¸€ä¸ªå°±è¡Œäº†å§ candidate = [candidates[idx] for idx in idxs][0] return candidate + # TODO：这里对zoopt的使用ä¸å¤ªå¯¹ã€‚zoopt想è¦æ±‚解的是，修改哪几个符å·çš„ä½ç½®ï¼ˆè¡¨ç¤ºä¸º01串），能得到“最好â€çš„å绎结果，ç†è®ºä¸Šèƒ½æ¯”kb._address中的枚举法æœç´¢æ¬¡æ•°æ›´å°‘。 + # TODO：而这里“最好â€çš„定义，和ä¸ç”¨zooptæœç´¢æ—¶çš„定义一致，目å‰è¦ä¹ˆ'hamming'ã€è¦ä¹ˆ'confidence' + # TODO: 因此，zoopt的作用，有点类似èžåˆkb（得到若干å绎结果）和abducer（选一个å绎结果）的功能。 + # TODO：下é¢ä¸¤ä¸ªå‡½æ•°çš„score，应该是得到candidatesåŽï¼Œè°ƒç”¨_get_one_candidate计算？（没细看） # for zoopt def _zoopt_score_multiple(self, pred_res, key, solution): all_address_flag = reform_idx(solution, pred_res) score = 0 + # TODO：原版ablçš„score是这样计算的å—，我记得没有把若干预测结果拆开然åŽåˆ†åˆ«addressçš„æ“作 for idx in range(len(pred_res)): address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] candidate = self.address_by_idx([pred_res[idx]], key[idx], address_idx) @@ -94,6 +102,8 @@ class AbducerBase(abc.ABC): return solution + # TODO：cache移到kb里å§ï¼Œæ¯”如_abduce_by_search里，它存的是若干å绎结果，ä¸æ¶‰åŠä»Žè‹¥å¹²å绎结果中选一个 + # TODO：python也有自带的用装饰器实现的缓存方法，比如functools.lru_cacheã€cachetools等，åŽé¢ç¨å¾®è°ƒç ”一下和手动缓存的优劣，看看用哪个好 def _get_cache(self, data, max_address_num, require_more_address): pred_res, pred_res_prob, key = data if self.multiple_predictions: From 19f29e40880ff76af8c445a443663f260cfee397 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Wed, 8 Mar 2023 17:38:41 +0800 Subject: [PATCH 185/601] Update abducer_base.py and related files --- abl/abducer/abducer_base.py | 108 ++++++++++-------------------------- abl/abducer/kb.py | 61 +++++++++++++++----- abl/framework_hed.py | 2 +- 3 files changed, 78 insertions(+), 93 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index c5c21fd..a70db14 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -16,17 +16,14 @@ from zoopt import Dimension, Objective, Parameter, Opt from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func='confidence', zoopt=False, multiple_predictions=False, cache=True): + def __init__(self, kb, dist_func='hamming', zoopt=False, multiple_predictions=False): self.kb = kb assert dist_func == 'hamming' or dist_func == 'confidence' self.dist_func = dist_func self.zoopt = zoopt self.multiple_predictions = multiple_predictions - self.cache = cache - - if self.cache: - self.cache_min_address_num = {} - self.cache_candidates = {} + if dist_func == 'confidence': + self.mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) def _get_cost_list(self, pred_res, pred_res_prob, candidates): if self.dist_func == 'hamming': @@ -40,10 +37,7 @@ class AbducerBase(abc.ABC): if self.multiple_predictions: pred_res_prob = flatten(pred_res_prob) candidates = [flatten(c) for c in candidates] - - # TODO：这里应该在类创建时就æå‰å­˜å¥½ï¼Œæ¯æ¬¡éƒ½é‡æ–°è®¡ç®—也太费时了 - mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) - candidates = [list(map(lambda x: mapping[x], c)) for c in candidates] + candidates = [list(map(lambda x: self.mapping[x], c)) for c in candidates] return confidence_dist(pred_res_prob, candidates) def _get_one_candidate(self, pred_res, pred_res_prob, candidates): @@ -54,46 +48,38 @@ class AbducerBase(abc.ABC): else: cost_list = self._get_cost_list(pred_res, pred_res_prob, candidates) - # TODO：这里很怪，按ç†argmin就行了 - min_address_num = np.min(cost_list) - idxs = np.where(cost_list == min_address_num)[0] - # TODO：这里也很怪，å–ç¬¬ä¸€ä¸ªå°±è¡Œäº†å§ - candidate = [candidates[idx] for idx in idxs][0] + candidate = candidates[np.argmin(cost_list)] return candidate - # TODO：这里对zoopt的使用ä¸å¤ªå¯¹ã€‚zoopt想è¦æ±‚解的是，修改哪几个符å·çš„ä½ç½®ï¼ˆè¡¨ç¤ºä¸º01串），能得到“最好â€çš„å绎结果，ç†è®ºä¸Šèƒ½æ¯”kb._address中的枚举法æœç´¢æ¬¡æ•°æ›´å°‘。 - # TODO：而这里“最好â€çš„定义，和ä¸ç”¨zooptæœç´¢æ—¶çš„定义一致，目å‰è¦ä¹ˆ'hamming'ã€è¦ä¹ˆ'confidence' - # TODO: 因此，zoopt的作用，有点类似èžåˆkb（得到若干å绎结果）和abducer（选一个å绎结果）的功能。 - # TODO：下é¢ä¸¤ä¸ªå‡½æ•°çš„score，应该是得到candidatesåŽï¼Œè°ƒç”¨_get_one_candidate计算？（没细看） - # for zoopt - def _zoopt_score_multiple(self, pred_res, key, solution): - all_address_flag = reform_idx(solution, pred_res) - score = 0 - # TODO：原版ablçš„score是这样计算的å—，我记得没有把若干预测结果拆开然åŽåˆ†åˆ«addressçš„æ“作 - for idx in range(len(pred_res)): - address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0] - candidate = self.address_by_idx([pred_res[idx]], key[idx], address_idx) - if len(candidate) > 0: - score += 1 - return score - - def _zoopt_address_score(self, pred_res, key, sol): + def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): if not self.multiple_predictions: - address_idx = [idx for idx, i in enumerate(sol.get_x()) if i != 0] + address_idx = np.where(sol.get_x() != 0)[0] candidates = self.address_by_idx(pred_res, key, address_idx) - return 1 if len(candidates) > 0 else 0 + if len(candidates) > 0: + return np.min(self._get_cost_list(pred_res, pred_res_prob, candidates)) + else: + return len(pred_res) else: - return self._zoopt_score_multiple(pred_res, key, sol.get_x()) - + all_address_flag = reform_idx(sol.get_x(), pred_res) + score = 0 + for idx in range(len(pred_res)): + address_idx = np.where(all_address_flag[idx] != 0)[0] + candidates = self.address_by_idx([pred_res[idx]], key[idx], address_idx) + if len(candidates) > 0: + score += np.min(self._get_cost_list(pred_res[idx], pred_res_prob[idx], candidates)) + else: + score += len(pred_res) + return -self._zoopt_score_multiple(pred_res, key, sol.get_x()) + def _constrain_address_num(self, solution, max_address_num): x = solution.get_x() return max_address_num - x.sum() - def zoopt_get_solution(self, pred_res, key, max_address_num): + def zoopt_get_solution(self, pred_res, pred_res_prob, key, max_address_num): length = len(flatten(pred_res)) dimension = Dimension(size=length, regs=[[0, 1]] * length, tys=[False] * length) objective = Objective( - lambda sol: -self._zoopt_address_score(pred_res, key, sol), + lambda sol: self._zoopt_address_score(pred_res, pred_res_prob, key, sol), dim=dimension, constraint=lambda sol: self._constrain_address_num(sol, max_address_num), ) @@ -101,31 +87,7 @@ class AbducerBase(abc.ABC): solution = Opt.min(objective, parameter).get_x() return solution - - # TODO：cache移到kb里å§ï¼Œæ¯”如_abduce_by_search里，它存的是若干å绎结果，ä¸æ¶‰åŠä»Žè‹¥å¹²å绎结果中选一个 - # TODO：python也有自带的用装饰器实现的缓存方法，比如functools.lru_cacheã€cachetools等，åŽé¢ç¨å¾®è°ƒç ”一下和手动缓存的优劣，看看用哪个好 - def _get_cache(self, data, max_address_num, require_more_address): - pred_res, pred_res_prob, key = data - if self.multiple_predictions: - pred_res = flatten(pred_res) - key = tuple(key) - if (tuple(pred_res), key) in self.cache_min_address_num: - address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) - if (tuple(pred_res), key, address_num) in self.cache_candidates: - candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] - if self.zoopt: - return candidates[0] - else: - return self._get_one_candidate(pred_res, pred_res_prob, candidates) - return None - - def _set_cache(self, pred_res, key, min_address_num, address_num, candidates): - if self.multiple_predictions: - pred_res = flatten(pred_res) - key = tuple(key) - self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num - self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates - + def address_by_idx(self, pred_res, key, address_idx): return self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) @@ -134,27 +96,17 @@ class AbducerBase(abc.ABC): if max_address_num == -1: max_address_num = len(flatten(pred_res)) - if self.cache: - candidate = self._get_cache(data, max_address_num, require_more_address) - if candidate is not None: - return candidate - if self.zoopt: - solution = self.zoopt_get_solution(pred_res, key, max_address_num) - address_idx = [idx for idx, i in enumerate(solution) if i != 0] + solution = self.zoopt_get_solution(pred_res, pred_res_prob, key, max_address_num) + address_idx = np.where(solution != 0)[0] candidates = self.address_by_idx(pred_res, key, address_idx) - address_num = int(solution.sum()) - min_address_num = address_num else: - candidates, min_address_num, address_num = self.kb.abduce_candidates( + candidates = self.kb.abduce_candidates( pred_res, key, max_address_num, require_more_address, self.multiple_predictions ) candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) - if self.cache: - self._set_cache(pred_res, key, min_address_num, address_num, candidates) - return candidate def abduce_rules(self, pred_res): @@ -283,9 +235,9 @@ if __name__ == '__main__': print(kb.consist_rule([1, '+', 1, '=', 1, 0], rules), kb.consist_rule([1, '+', 1, '=', 1, 1], rules)) print() - res = abd.abduce((consist_exs, None, [None] * len(consist_exs))) + res = abd.abduce((consist_exs, [None] * len(consist_exs), [None] * len(consist_exs))) print(res) - res = abd.abduce((inconsist_exs, None, [None] * len(inconsist_exs))) + res = abd.abduce((inconsist_exs, [None] * len(consist_exs), [None] * len(inconsist_exs))) print(res) print() diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index d656e41..2a29d12 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -17,24 +17,30 @@ import numpy as np from collections import defaultdict from itertools import product, combinations -from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal +from utils.utils import flatten, reform_idx, hamming_dist, check_equal from multiprocessing import Pool +from functools import lru_cache import pyswip class KBBase(ABC): - def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0): + def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0):#, abduce_cache=True): self.pseudo_label_list = pseudo_label_list self.len_list = len_list self.GKB_flag = GKB_flag self.max_err = max_err + # self.abduce_cache = abduce_cache if GKB_flag: self.base = {} X, Y = self._get_GKB() for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(x) + + # if abduce_cache: + # self.cache_min_address_num = {} + # self.cache_candidates = {} # For parallel version of _get_GKB def _get_XY_list(self, args): @@ -92,21 +98,21 @@ class KBBase(ABC): def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): if self.base == {}: - return [], 0, 0 + return [] if not multiple_predictions: if len(pred_res) not in self.len_list: - return [], 0, 0 + return [] all_candidates = self._find_candidate_GKB(pred_res, key) if len(all_candidates) == 0: - return [], 0, 0 + return [] else: cost_list = hamming_dist(pred_res, all_candidates) min_address_num = np.min(cost_list) address_num = min(max_address_num, min_address_num + require_more_address) idxs = np.where(cost_list <= address_num)[0] candidates = [all_candidates[idx] for idx in idxs] - return candidates, min_address_num, address_num + return candidates else: min_address_num = 0 @@ -115,10 +121,10 @@ class KBBase(ABC): for p_res, k in zip(pred_res, key): if len(p_res) not in self.len_list: - return [], 0, 0 + return [] all_candidates = self._find_candidate_GKB(p_res, k) if len(all_candidates) == 0: - return [], 0, 0 + return [] else: all_candidates_save.append(all_candidates) cost_list = hamming_dist(p_res, all_candidates) @@ -126,13 +132,31 @@ class KBBase(ABC): cost_list_save.append(cost_list) multiple_all_candidates = [flatten(c) for c in product(*all_candidates_save)] - assert len(multiple_all_candidates[0]) == len(flatten(pred_res)) multiple_cost_list = np.array([sum(cost) for cost in product(*cost_list_save)]) - assert len(multiple_all_candidates) == len(multiple_cost_list) address_num = min(max_address_num, min_address_num + require_more_address) idxs = np.where(multiple_cost_list <= address_num)[0] candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] - return candidates, min_address_num, address_num + return candidates + + # TODO：python也有自带的用装饰器实现的缓存方法，比如functools.lru_cacheã€cachetools等，åŽé¢ç¨å¾®è°ƒç ”一下和手动缓存的优劣，看看用哪个好 + # def _get_abduce_cache(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + # if multiple_predictions: + # pred_res = flatten(pred_res) + # key = tuple(key) + # if (tuple(pred_res), key) in self.cache_min_address_num: + # address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) + # if (tuple(pred_res), key, address_num) in self.cache_candidates: + # candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] + # return candidates + # return None + + # def _set_abduce_cache(self, pred_res, key, min_address_num, address_num, candidates, multiple_predictions): + # if multiple_predictions: + # pred_res = flatten(pred_res) + # key = tuple(key) + # self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num + # self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] @@ -166,7 +190,13 @@ class KBBase(ABC): new_candidates += candidates return new_candidates + # @lru_cache(maxsize=100) def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + # if self.abduce_cache: + # candidates = self._get_abduce_cache(pred_res, key, max_address_num, require_more_address, multiple_predictions) + # if candidates is not None: + # return candidates + candidates = [] for address_num in range(len(flatten(pred_res)) + 1): @@ -182,15 +212,18 @@ class KBBase(ABC): break if address_num >= max_address_num: - return [], 0, 0 + return [] for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if address_num > max_address_num: return candidates, min_address_num, address_num - 1 new_candidates = self._address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates + + # if self.abduce_cache: + # self._set_abduce_cache(pred_res, key, min_address_num, address_num, candidates, multiple_predictions) - return candidates, min_address_num, address_num + return candidates def _dict_len(self, dic): if not self.GKB_flag: @@ -346,4 +379,4 @@ class HWF_KB(RegKB): if __name__ == "__main__": - pass + pass \ No newline at end of file diff --git a/abl/framework_hed.py b/abl/framework_hed.py index 3f09ff6..d339909 100644 --- a/abl/framework_hed.py +++ b/abl/framework_hed.py @@ -150,7 +150,7 @@ def abduce_and_train(model, abducer, mapping, train_X_true, select_num): for m in mappings: pred_res = mapping_res(original_pred_res, m) max_abduce_num = 20 - solution = abducer.zoopt_get_solution(pred_res, [None] * len(pred_res), max_abduce_num) + solution = abducer.zoopt_get_solution(pred_res, [None] * len(pred_res), [None] * len(pred_res), max_abduce_num) all_address_flag = reform_idx(solution, pred_res) consistent_idx_tmp = [] From 24ce8ff87ef318d57338cbcdb8da5da58b29a11a Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 8 Mar 2023 10:18:08 +0000 Subject: [PATCH 186/601] Update several files --- abl/abducer/abducer_base.py | 4 ++-- abl/framework_hed.py | 2 +- abl/utils/utils.py | 3 ++- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index a70db14..61cab25 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -68,8 +68,8 @@ class AbducerBase(abc.ABC): if len(candidates) > 0: score += np.min(self._get_cost_list(pred_res[idx], pred_res_prob[idx], candidates)) else: - score += len(pred_res) - return -self._zoopt_score_multiple(pred_res, key, sol.get_x()) + score += len(pred_res[idx]) + return score def _constrain_address_num(self, solution, max_address_num): x = solution.get_x() diff --git a/abl/framework_hed.py b/abl/framework_hed.py index d339909..ab88318 100644 --- a/abl/framework_hed.py +++ b/abl/framework_hed.py @@ -291,7 +291,7 @@ def train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len INFO('consist_rule_acc is %f, %f\n' %(true_consist_rule_acc, false_consist_rule_acc)) # decide next course or restart - if true_consist_rule_acc > 0.9 and false_consist_rule_acc < 0.1: + if true_consist_rule_acc > 0.95 and false_consist_rule_acc < 0.1: torch.save(model.cls_list[0].model.state_dict(), "./weights/weights_%d.pth" % equation_len) break else: diff --git a/abl/utils/utils.py b/abl/utils/utils.py index d986065..d5209a6 100644 --- a/abl/utils/utils.py +++ b/abl/utils/utils.py @@ -19,7 +19,8 @@ def reform_idx(flatten_pred_res, save_pred_res): return re def hamming_dist(A, B): - B = np.array(B) + A = np.array(A, dtype=' Date: Wed, 8 Mar 2023 10:19:00 +0000 Subject: [PATCH 187/601] Update several files --- abl/abducer/abducer_base.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 61cab25..54dbec1 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -85,7 +85,6 @@ class AbducerBase(abc.ABC): ) parameter = Parameter(budget=100, intermediate_result=False, autoset=True) solution = Opt.min(objective, parameter).get_x() - return solution def address_by_idx(self, pred_res, key, address_idx): @@ -106,7 +105,6 @@ class AbducerBase(abc.ABC): ) candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) - return candidate def abduce_rules(self, pred_res): From acfc25e2085009aa2f08bfc57e987ae6eb582283 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 9 Mar 2023 09:06:50 +0800 Subject: [PATCH 188/601] Update kb.py --- abl/abducer/kb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 2a29d12..370b790 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -17,7 +17,7 @@ import numpy as np from collections import defaultdict from itertools import product, combinations -from utils.utils import flatten, reform_idx, hamming_dist, check_equal +from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal from multiprocessing import Pool From 8e8aa76735eeee73cfc2a026ccddf008f99ea531 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 9 Mar 2023 10:54:18 +0800 Subject: [PATCH 189/601] Add cache in abduce_by_search --- abl/abducer/kb.py | 39 ++++++++------------------------------- abl/utils/utils.py | 16 ++++++++++++++-- 2 files changed, 22 insertions(+), 33 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 370b790..6cecba4 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -17,7 +17,7 @@ import numpy as np from collections import defaultdict from itertools import product, combinations -from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal +from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal, to_hashable, hashable_to_list from multiprocessing import Pool @@ -25,22 +25,17 @@ from functools import lru_cache import pyswip class KBBase(ABC): - def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0):#, abduce_cache=True): + def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0): self.pseudo_label_list = pseudo_label_list self.len_list = len_list self.GKB_flag = GKB_flag self.max_err = max_err - # self.abduce_cache = abduce_cache if GKB_flag: self.base = {} X, Y = self._get_GKB() for x, y in zip(X, Y): self.base.setdefault(len(x), defaultdict(list))[y].append(x) - - # if abduce_cache: - # self.cache_min_address_num = {} - # self.cache_candidates = {} # For parallel version of _get_GKB def _get_XY_list(self, args): @@ -90,7 +85,7 @@ class KBBase(ABC): if self.GKB_flag: return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions) else: - return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions) + return self._abduce_by_search(to_hashable(pred_res), to_hashable(key), max_address_num, require_more_address, multiple_predictions) @abstractmethod def _find_candidate_GKB(self, pred_res, key): @@ -137,27 +132,7 @@ class KBBase(ABC): idxs = np.where(multiple_cost_list <= address_num)[0] candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] return candidates - - # TODO：python也有自带的用装饰器实现的缓存方法，比如functools.lru_cacheã€cachetools等，åŽé¢ç¨å¾®è°ƒç ”一下和手动缓存的优劣，看看用哪个好 - # def _get_abduce_cache(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): - # if multiple_predictions: - # pred_res = flatten(pred_res) - # key = tuple(key) - # if (tuple(pred_res), key) in self.cache_min_address_num: - # address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address) - # if (tuple(pred_res), key, address_num) in self.cache_candidates: - # candidates = self.cache_candidates[(tuple(pred_res), key, address_num)] - # return candidates - # return None - - # def _set_abduce_cache(self, pred_res, key, min_address_num, address_num, candidates, multiple_predictions): - # if multiple_predictions: - # pred_res = flatten(pred_res) - # key = tuple(key) - # self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num - # self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates - - + def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] abduce_c = product(self.pseudo_label_list, repeat=len(address_idx)) @@ -190,13 +165,15 @@ class KBBase(ABC): new_candidates += candidates return new_candidates - # @lru_cache(maxsize=100) + @lru_cache(maxsize=100) def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): # if self.abduce_cache: # candidates = self._get_abduce_cache(pred_res, key, max_address_num, require_more_address, multiple_predictions) # if candidates is not None: # return candidates - + pred_res = hashable_to_list(pred_res) + key = hashable_to_list(key) + candidates = [] for address_num in range(len(flatten(pred_res)) + 1): diff --git a/abl/utils/utils.py b/abl/utils/utils.py index d5209a6..90376aa 100644 --- a/abl/utils/utils.py +++ b/abl/utils/utils.py @@ -35,7 +35,6 @@ def confidence_dist(A, B): cols = np.expand_dims(cols, axis = 0).repeat(axis = 0, repeats = len(B)) return 1 - np.prod(A[rows, cols, B], axis = 1) - def block_sample(X, Z, Y, sample_num, epoch_idx): part_num = len(X) // sample_num if part_num == 0: @@ -48,7 +47,6 @@ def block_sample(X, Z, Y, sample_num, epoch_idx): return X, Z, Y - def gen_mappings(chars, symbs): n_char = len(chars) n_symbs = len(symbs) @@ -86,3 +84,17 @@ def check_equal(a, b, max_err=0): else: return a == b + +def to_hashable(l): + if type(l) is not list: + return l + if type(l[0]) is not list: + return tuple(l) + return tuple(tuple(sublist) for sublist in l) + +def hashable_to_list(t): + if type(t) is not tuple: + return t + if type(t[0]) is not tuple: + return list(t) + return [list(subtuple) for subtuple in t] \ No newline at end of file From e735693610fb9cd9568cf8dc64daa96b8ac46772 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 9 Mar 2023 13:14:47 +0800 Subject: [PATCH 190/601] Fix a minor bug --- abl/abducer/kb.py | 17 +---------------- 1 file changed, 1 insertion(+), 16 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 6cecba4..333901e 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -113,7 +113,6 @@ class KBBase(ABC): min_address_num = 0 all_candidates_save = [] cost_list_save = [] - for p_res, k in zip(pred_res, key): if len(p_res) not in self.len_list: return [] @@ -136,7 +135,6 @@ class KBBase(ABC): def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): candidates = [] abduce_c = product(self.pseudo_label_list, repeat=len(address_idx)) - if multiple_predictions: save_pred_res = pred_res pred_res = flatten(pred_res) @@ -145,10 +143,8 @@ class KBBase(ABC): candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - if multiple_predictions: candidate = reform_idx(candidate, save_pred_res) - if check_equal(self._logic_forward(candidate, multiple_predictions), key, self.max_err): candidates.append(candidate) return candidates @@ -167,15 +163,10 @@ class KBBase(ABC): @lru_cache(maxsize=100) def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): - # if self.abduce_cache: - # candidates = self._get_abduce_cache(pred_res, key, max_address_num, require_more_address, multiple_predictions) - # if candidates is not None: - # return candidates pred_res = hashable_to_list(pred_res) key = hashable_to_list(key) candidates = [] - for address_num in range(len(flatten(pred_res)) + 1): if address_num == 0: if check_equal(self._logic_forward(pred_res, multiple_predictions), key, self.max_err): @@ -183,23 +174,17 @@ class KBBase(ABC): else: new_candidates = self._address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates - if len(candidates) > 0: min_address_num = address_num break - if address_num >= max_address_num: return [] for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if address_num > max_address_num: - return candidates, min_address_num, address_num - 1 + return candidates new_candidates = self._address(address_num, pred_res, key, multiple_predictions) candidates += new_candidates - - # if self.abduce_cache: - # self._set_abduce_cache(pred_res, key, min_address_num, address_num, candidates, multiple_predictions) - return candidates def _dict_len(self, dic): From 7b209e73a501cf8c761c5a65091988b515651f99 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Tue, 14 Mar 2023 14:53:30 +0800 Subject: [PATCH 191/601] update TODO in abducer_base --- abl/abducer/abducer_base.py | 1 + 1 file changed, 1 insertion(+) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 54dbec1..e2a16bf 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -62,6 +62,7 @@ class AbducerBase(abc.ABC): else: all_address_flag = reform_idx(sol.get_x(), pred_res) score = 0 + # TODO：这个循环里，和上é¢if not self.multiple_predictions部分逻辑完全一样å§ï¼Œåº”该把上é¢å°è£…一下，然åŽä¸‹é¢å¾ªçŽ¯é‡Œè°ƒç”¨å°è£…方法å³å¯ for idx in range(len(pred_res)): address_idx = np.where(all_address_flag[idx] != 0)[0] candidates = self.address_by_idx([pred_res[idx]], key[idx], address_idx) From c5fadfcbb69f1c52cc5926312def088e396cfd5f Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Tue, 14 Mar 2023 15:05:48 +0800 Subject: [PATCH 192/601] Rearrange rules to --- abl/abducer/kb.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 333901e..feba8c4 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -271,12 +271,16 @@ class prolog_KB(KBBase): candidates.append(candidate) return candidates + +class HED_prolog_KB(prolog_KB): + def __init__(self, pseudo_label_list, pl_file): + super().__init__(pseudo_label_list, pl_file) + def consist_rule(self, exs, rules): rules = str(rules).replace("\'","") return len(list(self.prolog.query("eval_inst_feature(%s, %s)." % (exs, rules)))) != 0 def abduce_rules(self, pred_res): - # print(pred_res) prolog_result = list(self.prolog.query("consistent_inst_feature(%s, X)." % pred_res)) if len(prolog_result) == 0: return None From 88a8f33f169fbd6306367609efbf98522252d0b9 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 14 Mar 2023 15:08:13 +0800 Subject: [PATCH 193/601] Add `_get_zoopt_score` --- abl/abducer/abducer_base.py | 35 ++++++++++++++++------------------- 1 file changed, 16 insertions(+), 19 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index e2a16bf..d52f74b 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -50,26 +50,23 @@ class AbducerBase(abc.ABC): cost_list = self._get_cost_list(pred_res, pred_res_prob, candidates) candidate = candidates[np.argmin(cost_list)] return candidate - + + def _get_zoopt_score(self, sol_x, pred_res, pred_res_prob, key): + address_idx = np.where(sol_x != 0)[0] + candidates = self.address_by_idx(pred_res, key, address_idx) + if len(candidates) > 0: + return np.min(self._get_cost_list(pred_res, pred_res_prob, candidates)) + else: + return len(pred_res) + def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): if not self.multiple_predictions: - address_idx = np.where(sol.get_x() != 0)[0] - candidates = self.address_by_idx(pred_res, key, address_idx) - if len(candidates) > 0: - return np.min(self._get_cost_list(pred_res, pred_res_prob, candidates)) - else: - return len(pred_res) + return self._get_address_score(sol.get_x(), pred_res, pred_res_prob, key) else: all_address_flag = reform_idx(sol.get_x(), pred_res) score = 0 - # TODO：这个循环里，和上é¢if not self.multiple_predictions部分逻辑完全一样å§ï¼Œåº”该把上é¢å°è£…一下，然åŽä¸‹é¢å¾ªçŽ¯é‡Œè°ƒç”¨å°è£…方法å³å¯ for idx in range(len(pred_res)): - address_idx = np.where(all_address_flag[idx] != 0)[0] - candidates = self.address_by_idx([pred_res[idx]], key[idx], address_idx) - if len(candidates) > 0: - score += np.min(self._get_cost_list(pred_res[idx], pred_res_prob[idx], candidates)) - else: - score += len(pred_res[idx]) + score += self._get_address_score(all_address_flag[idx], pred_res[idx], pred_res_prob[idx], key) return score def _constrain_address_num(self, solution, max_address_num): @@ -112,10 +109,10 @@ class AbducerBase(abc.ABC): return self.kb.abduce_rules(pred_res) def batch_abduce(self, Z, Y, max_address_num=-1, require_more_address=0): - # if self.multiple_predictions: - return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) - # else: - # return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] + if self.multiple_predictions: + return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) + else: + return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) @@ -241,4 +238,4 @@ if __name__ == '__main__': print() abduced_rules = abd.abduce_rules(consist_exs) - print(abduced_rules) \ No newline at end of file + print(abduced_rules) From a71af2e5ab4bb953931d15d86f0ab23107f8c0a8 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Tue, 14 Mar 2023 15:08:37 +0800 Subject: [PATCH 194/601] Rearrange rules to `HED_prolog_KB` --- abl/abducer/kb.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 333901e..fb606f3 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -271,12 +271,16 @@ class prolog_KB(KBBase): candidates.append(candidate) return candidates + +class HED_prolog_KB(prolog_KB): + def __init__(self, pseudo_label_list, pl_file): + super().__init__(pseudo_label_list, pl_file) + def consist_rule(self, exs, rules): rules = str(rules).replace("\'","") return len(list(self.prolog.query("eval_inst_feature(%s, %s)." % (exs, rules)))) != 0 def abduce_rules(self, pred_res): - # print(pred_res) prolog_result = list(self.prolog.query("consistent_inst_feature(%s, X)." % pred_res)) if len(prolog_result) == 0: return None @@ -341,4 +345,4 @@ class HWF_KB(RegKB): if __name__ == "__main__": - pass \ No newline at end of file + pass From 176f4bdd20e4dc08479cbf160874d11aeca7cac3 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Tue, 14 Mar 2023 15:26:35 +0800 Subject: [PATCH 195/601] fix method name bug in abducer_base --- abl/abducer/abducer_base.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index d52f74b..06845a3 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -51,7 +51,7 @@ class AbducerBase(abc.ABC): candidate = candidates[np.argmin(cost_list)] return candidate - def _get_zoopt_score(self, sol_x, pred_res, pred_res_prob, key): + def _zoopt_address_score_single(self, sol_x, pred_res, pred_res_prob, key): address_idx = np.where(sol_x != 0)[0] candidates = self.address_by_idx(pred_res, key, address_idx) if len(candidates) > 0: @@ -61,12 +61,12 @@ class AbducerBase(abc.ABC): def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): if not self.multiple_predictions: - return self._get_address_score(sol.get_x(), pred_res, pred_res_prob, key) + return self._zoopt_address_score_single(sol.get_x(), pred_res, pred_res_prob, key) else: all_address_flag = reform_idx(sol.get_x(), pred_res) score = 0 for idx in range(len(pred_res)): - score += self._get_address_score(all_address_flag[idx], pred_res[idx], pred_res_prob[idx], key) + score += self._zoopt_address_score_single(all_address_flag[idx], pred_res[idx], pred_res_prob[idx], key) return score def _constrain_address_num(self, solution, max_address_num): From d80e9228d3ac70f08fb3633e16efad71d9a1ccf7 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Tue, 14 Mar 2023 17:35:40 +0800 Subject: [PATCH 196/601] update TODO in kb --- abl/abducer/kb.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index fb606f3..8bb7854 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -26,6 +26,10 @@ import pyswip class KBBase(ABC): def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0): + # TODO：添加一下类型检查，比如 + # if not isinstance(X, (np.ndarray, spmatrix)): + # raise TypeError("X should be numpy array or sparse matrix") + self.pseudo_label_list = pseudo_label_list self.len_list = len_list self.GKB_flag = GKB_flag @@ -161,6 +165,7 @@ class KBBase(ABC): new_candidates += candidates return new_candidates + # TODO：在类åˆå§‹åŒ–时应该有一个cache（默认True）的å‚数，用户å¯ä»¥æŒ‡å®šæ˜¯å¦ç”¨cache（若KB会å˜ï¼Œé‚£ä¸èƒ½ç”¨cache） @lru_cache(maxsize=100) def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): pred_res = hashable_to_list(pred_res) From 2aa88634abacf1a1e419fb50320f45ab40791315 Mon Sep 17 00:00:00 2001 From: Tony-HYX <605698554@qq.com> Date: Tue, 14 Mar 2023 18:06:42 +0800 Subject: [PATCH 197/601] update TODO in kb --- abl/abducer/kb.py | 1 + 1 file changed, 1 insertion(+) diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 8bb7854..fafc1a8 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -166,6 +166,7 @@ class KBBase(ABC): return new_candidates # TODO：在类åˆå§‹åŒ–时应该有一个cache（默认True）的å‚数，用户å¯ä»¥æŒ‡å®šæ˜¯å¦ç”¨cache（若KB会å˜ï¼Œé‚£ä¸èƒ½ç”¨cache） + # TODO：如果是multiple_predictions=True，那这个cache几乎ä¸ä¼šå‘½ä¸­å§ï¼Ÿ @lru_cache(maxsize=100) def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): pred_res = hashable_to_list(pred_res) From 87c3ba1b71dbc372bf826cb66ada556f8fda8c38 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Wed, 15 Mar 2023 14:13:31 +0800 Subject: [PATCH 198/601] Remove --- abl/abducer/abducer_base.py | 226 +++++++++++++++++------------ abl/abducer/kb.py | 102 +++++-------- examples/datasets/hed/learn_add.pl | 2 +- 3 files changed, 168 insertions(+), 162 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 54dbec1..b7e6526 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -16,27 +16,19 @@ from zoopt import Dimension, Objective, Parameter, Opt from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist class AbducerBase(abc.ABC): - def __init__(self, kb, dist_func='hamming', zoopt=False, multiple_predictions=False): + def __init__(self, kb, dist_func='hamming', zoopt=False): self.kb = kb assert dist_func == 'hamming' or dist_func == 'confidence' self.dist_func = dist_func self.zoopt = zoopt - self.multiple_predictions = multiple_predictions if dist_func == 'confidence': self.mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list))))) def _get_cost_list(self, pred_res, pred_res_prob, candidates): if self.dist_func == 'hamming': - if self.multiple_predictions: - pred_res = flatten(pred_res) - candidates = [flatten(c) for c in candidates] - return hamming_dist(pred_res, candidates) elif self.dist_func == 'confidence': - if self.multiple_predictions: - pred_res_prob = flatten(pred_res_prob) - candidates = [flatten(c) for c in candidates] candidates = [list(map(lambda x: self.mapping[x], c)) for c in candidates] return confidence_dist(pred_res_prob, candidates) @@ -50,26 +42,24 @@ class AbducerBase(abc.ABC): cost_list = self._get_cost_list(pred_res, pred_res_prob, candidates) candidate = candidates[np.argmin(cost_list)] return candidate - - def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): - if not self.multiple_predictions: - address_idx = np.where(sol.get_x() != 0)[0] - candidates = self.address_by_idx(pred_res, key, address_idx) - if len(candidates) > 0: - return np.min(self._get_cost_list(pred_res, pred_res_prob, candidates)) - else: - return len(pred_res) + + def _zoopt_address_score_single(self, sol_x, pred_res, pred_res_prob, key): + address_idx = np.where(sol_x != 0)[0] + candidates = self.address_by_idx(pred_res, key, address_idx) + if len(candidates) > 0: + return np.min(self._get_cost_list(pred_res, pred_res_prob, candidates)) else: - all_address_flag = reform_idx(sol.get_x(), pred_res) - score = 0 - for idx in range(len(pred_res)): - address_idx = np.where(all_address_flag[idx] != 0)[0] - candidates = self.address_by_idx([pred_res[idx]], key[idx], address_idx) - if len(candidates) > 0: - score += np.min(self._get_cost_list(pred_res[idx], pred_res_prob[idx], candidates)) - else: - score += len(pred_res[idx]) - return score + return len(pred_res) + + def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): + # if not self.multiple_predictions: + return self._zoopt_address_score_single(sol.get_x(), pred_res, pred_res_prob, key) + # else: + # all_address_flag = reform_idx(sol.get_x(), pred_res) + # score = 0 + # for idx in range(len(pred_res)): + # score += self._zoopt_address_score_single(all_address_flag[idx], pred_res[idx], pred_res_prob[idx], key) + # return score def _constrain_address_num(self, solution, max_address_num): x = solution.get_x() @@ -88,21 +78,29 @@ class AbducerBase(abc.ABC): return solution def address_by_idx(self, pred_res, key, address_idx): - return self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions) + return self.kb.address_by_idx(pred_res, key, address_idx) - def abduce(self, data, max_address_num=-1, require_more_address=0): + def abduce(self, data, max_address=-1, require_more_address=0): pred_res, pred_res_prob, key = data - if max_address_num == -1: - max_address_num = len(flatten(pred_res)) + # if max_address_num == -1: + # max_address_num = len(flatten(pred_res)) + + assert(type(max_address) in (int, float)) + if max_address == -1: + max_address_num = len(pred_res) + elif type(max_address) == float: + assert(max_address >= 0 and max_address <= 1) + max_address_num = round(len(pred_res) * max_address) + else: + assert(max_address >= 0) + max_address_num = max_address if self.zoopt: solution = self.zoopt_get_solution(pred_res, pred_res_prob, key, max_address_num) address_idx = np.where(solution != 0)[0] candidates = self.address_by_idx(pred_res, key, address_idx) else: - candidates = self.kb.abduce_candidates( - pred_res, key, max_address_num, require_more_address, self.multiple_predictions - ) + candidates = self.kb.abduce_candidates(pred_res, key, max_address_num, require_more_address) candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) return candidate @@ -110,134 +108,170 @@ class AbducerBase(abc.ABC): def abduce_rules(self, pred_res): return self.kb.abduce_rules(pred_res) - def batch_abduce(self, Z, Y, max_address_num=-1, require_more_address=0): - # if self.multiple_predictions: - return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address) - # else: - # return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] + def batch_abduce(self, data, max_address=-1, require_more_address=0): + Z1, Z2, Y = data + return [self.abduce((z, prob, y), max_address, require_more_address) for z, prob, y in zip(Z1, Z2, Y)] def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address_num, require_more_address) if __name__ == '__main__': - from kb import add_KB, prolog_KB, HWF_KB + from kb import add_KB, prolog_KB, HWF_KB, HED_prolog_KB - prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] - prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]] + prob1 = [[[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]] + prob2 = [[[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]] + print('add_KB with GKB:') kb = add_KB(GKB_flag=True) abd = AbducerBase(kb, 'confidence') - res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [8]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob2, [8]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num=1, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=1, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 20), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [20]), max_address=2, require_more_address=0) print(res) print() - - multiple_prob = [[[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]], - [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]] - - + print('add_KB without GKB:') kb = add_KB() - abd = AbducerBase(kb, 'confidence', multiple_predictions=True) - res = abd.abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address_num=4, require_more_address=0) + abd = AbducerBase(kb, 'confidence') + res = abd.batch_abduce(([[1, 1]], prob1, [8]), max_address=2, require_more_address=0) + print(res) + res = abd.batch_abduce(([[1, 1]], prob2, [8]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address_num=4, require_more_address=1) + res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=2, require_more_address=0) + print(res) + res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=1, require_more_address=0) + print(res) + res = abd.batch_abduce(([[1, 1]], prob1, [20]), max_address=2, require_more_address=0) print(res) print() - + print('prolog_KB with add.pl:') kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='../examples/datasets/mnist_add/add.pl') abd = AbducerBase(kb, 'confidence') - res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [8]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob2, [8]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num=1, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=1, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 20), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [20]), max_address=2, require_more_address=0) print(res) print() + print('prolog_KB with add.pl using zoopt:') kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='../examples/datasets/mnist_add/add.pl') abd = AbducerBase(kb, 'confidence', zoopt=True) - res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [8]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob2, [8]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=2, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 17), max_address_num=1, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=1, require_more_address=0) print(res) - res = abd.abduce(([1, 1], prob1, 20), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([[1, 1]], prob1, [20]), max_address=2, require_more_address=0) print(res) print() - - kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 0.1) - abd = AbducerBase(kb, 'hamming') - res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) + + print('add_KB with multiple inputs at once:') + multiple_prob = [[[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]], + [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]] + + kb = add_KB() + abd = AbducerBase(kb, 'confidence') + res = abd.batch_abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address=4, require_more_address=0) print(res) - res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) + res = abd.batch_abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address=4, require_more_address=1) print(res) print() - kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 1) + print('HWF_KB with GKB, max_err=0.1') + kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 0.1) abd = AbducerBase(kb, 'hamming') - res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) + res = abd.batch_abduce(([['5', '+', '2']], [None], [3]), max_address=2, require_more_address=0) print(res) - res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0) + res = abd.batch_abduce(([['5', '+', '9']], [None], [65]), max_address=3, require_more_address=0) print(res) - res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3) + res = abd.batch_abduce(([['5', '8', '8', '8', '8']], [None], [3.17]), max_address=5, require_more_address=3) print(res) print() + print('HWF_KB without GKB, max_err=0.1') kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1) - abd = AbducerBase(kb, 'hamming', multiple_predictions=True) - res = abd.abduce(([['5', '+', '2'], ['5', '+', '9']], None, [3, 64]), max_address_num=6, require_more_address=0) + abd = AbducerBase(kb, 'hamming') + res = abd.batch_abduce(([['5', '+', '2']], [None], [3]), max_address=2, require_more_address=0) + print(res) + res = abd.batch_abduce(([['5', '+', '9']], [None], [65]), max_address=3, require_more_address=0) + print(res) + res = abd.batch_abduce(([['5', '8', '8', '8', '8']], [None], [3.17]), max_address=5, require_more_address=3) print(res) print() - kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1) + print('HWF_KB with GKB, max_err=1') + kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 1) abd = AbducerBase(kb, 'hamming') - res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0) + res = abd.batch_abduce(([['5', '+', '9']], [None], [65]), max_address=3, require_more_address=0) print(res) - res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) + res = abd.batch_abduce(([['5', '+', '2']], [None], [1.67]), max_address=3, require_more_address=0) print(res) + res = abd.batch_abduce(([['5', '8', '8', '8', '8']], [None], [3.17]), max_address=5, require_more_address=3) + print(res) + print() + print('HWF_KB without GKB, max_err=1') kb = HWF_KB(len_list=[1, 3, 5], max_err = 1) abd = AbducerBase(kb, 'hamming') - res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0) + res = abd.batch_abduce(([['5', '+', '9']], [None], [65]), max_address=3, require_more_address=0) + print(res) + res = abd.batch_abduce(([['5', '+', '2']], [None], [1.67]), max_address=3, require_more_address=0) + print(res) + res = abd.batch_abduce(([['5', '8', '8', '8', '8']], [None], [3.17]), max_address=5, require_more_address=3) + print(res) + print() + + print('HWF_KB with multiple inputs at once:') + kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1) + abd = AbducerBase(kb, 'hamming') + res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 64]), max_address=1, require_more_address=0) print(res) - res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0) + res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 64]), max_address=3, require_more_address=0) print(res) - res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3) + res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 65]), max_address=3, require_more_address=0) + print(res) + print() + print('max_address is float') + res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 64]), max_address=0.5, require_more_address=0) + print(res) + res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 64]), max_address=0.9, require_more_address=0) print(res) print() - kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') - abd = AbducerBase(kb, zoopt=True, multiple_predictions=True) + kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') + abd = AbducerBase(kb, zoopt=True) consist_exs = [[1, 1, '+', 0, '=', 1, 1], [1, '+', 1, '=', 1, 0], [0, '+', 0, '=', 0]] inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] - # inconsist_exs = [[1, '+', 0, '=', 0], ['=', '=', '=', '=', 0], ['=', '=', 0, '=', '=', '=']] rules = ['my_op([0], [0], [0])', 'my_op([1], [1], [1, 0])'] - print(kb._logic_forward(consist_exs, True), kb._logic_forward(inconsist_exs, True)) - print(kb.consist_rule([1, '+', 1, '=', 1, 0], rules), kb.consist_rule([1, '+', 1, '=', 1, 1], rules)) + print(kb.logic_forward(consist_exs)) + print(kb.logic_forward(inconsist_exs)) print() - - res = abd.abduce((consist_exs, [None] * len(consist_exs), [None] * len(consist_exs))) - print(res) - res = abd.abduce((inconsist_exs, [None] * len(consist_exs), [None] * len(inconsist_exs))) - print(res) + print(kb.consist_rule([1, '+', 1, '=', 1, 0], rules)) + print(kb.consist_rule([1, '+', 1, '=', 1, 1], rules)) print() - abduced_rules = abd.abduce_rules(consist_exs) - print(abduced_rules) \ No newline at end of file + # res = abd.abduce((consist_exs, [None] * len(consist_exs), [None] * len(consist_exs))) + # print(res) + # res = abd.batch_abduce((inconsist_exs, [None] * len(consist_exs), [None] * len(inconsist_exs))) + # print(res) + # print() + + # abduced_rules = abd.batch_abduce_rules(consist_exs) + # print(abduced_rules) \ No newline at end of file diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index feba8c4..42a5f6c 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -17,7 +17,7 @@ import numpy as np from collections import defaultdict from itertools import product, combinations -from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal, to_hashable, hashable_to_list +from utils.utils import flatten, reform_idx, hamming_dist, check_equal, to_hashable, hashable_to_list from multiprocessing import Pool @@ -25,11 +25,16 @@ from functools import lru_cache import pyswip class KBBase(ABC): - def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0): + def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0, cache_size=128): + # TODO：添加一下类型检查，比如 + # if not isinstance(X, (np.ndarray, spmatrix)): + # raise TypeError("X should be numpy array or sparse matrix") + self.pseudo_label_list = pseudo_label_list self.len_list = len_list self.GKB_flag = GKB_flag self.max_err = max_err + self.cache_size = cache_size if GKB_flag: self.base = {} @@ -73,106 +78,73 @@ class KBBase(ABC): @abstractmethod def logic_forward(self, pseudo_labels): pass - - def _logic_forward(self, xs, multiple_predictions=False): - if not multiple_predictions: - return self.logic_forward(xs) - else: - res = [self.logic_forward(x) for x in xs] - return res - def abduce_candidates(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False): + def abduce_candidates(self, pred_res, key, max_address_num, require_more_address=0): if self.GKB_flag: - return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions) + return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address) else: - return self._abduce_by_search(to_hashable(pred_res), to_hashable(key), max_address_num, require_more_address, multiple_predictions) + return self._abduce_by_search(to_hashable(pred_res), to_hashable(key), max_address_num, require_more_address) @abstractmethod def _find_candidate_GKB(self, pred_res, key): pass - def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address): if self.base == {}: return [] - - if not multiple_predictions: - if len(pred_res) not in self.len_list: - return [] - all_candidates = self._find_candidate_GKB(pred_res, key) - if len(all_candidates) == 0: - return [] - else: - cost_list = hamming_dist(pred_res, all_candidates) - min_address_num = np.min(cost_list) - address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(cost_list <= address_num)[0] - candidates = [all_candidates[idx] for idx in idxs] - return candidates - + + if len(pred_res) not in self.len_list: + return [] + all_candidates = self._find_candidate_GKB(pred_res, key) + if len(all_candidates) == 0: + return [] else: - min_address_num = 0 - all_candidates_save = [] - cost_list_save = [] - for p_res, k in zip(pred_res, key): - if len(p_res) not in self.len_list: - return [] - all_candidates = self._find_candidate_GKB(p_res, k) - if len(all_candidates) == 0: - return [] - else: - all_candidates_save.append(all_candidates) - cost_list = hamming_dist(p_res, all_candidates) - min_address_num += np.min(cost_list) - cost_list_save.append(cost_list) - - multiple_all_candidates = [flatten(c) for c in product(*all_candidates_save)] - multiple_cost_list = np.array([sum(cost) for cost in product(*cost_list_save)]) + cost_list = hamming_dist(pred_res, all_candidates) + min_address_num = np.min(cost_list) address_num = min(max_address_num, min_address_num + require_more_address) - idxs = np.where(multiple_cost_list <= address_num)[0] - candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs] + idxs = np.where(cost_list <= address_num)[0] + candidates = [all_candidates[idx] for idx in idxs] return candidates - def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): + def address_by_idx(self, pred_res, key, address_idx): candidates = [] abduce_c = product(self.pseudo_label_list, repeat=len(address_idx)) - if multiple_predictions: - save_pred_res = pred_res - pred_res = flatten(pred_res) + # if multiple_predictions: + # save_pred_res = pred_res + # pred_res = flatten(pred_res) for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - if multiple_predictions: - candidate = reform_idx(candidate, save_pred_res) - if check_equal(self._logic_forward(candidate, multiple_predictions), key, self.max_err): + # if multiple_predictions: + # candidate = reform_idx(candidate, save_pred_res) + if check_equal(self.logic_forward(candidate), key, self.max_err): candidates.append(candidate) return candidates - def _address(self, address_num, pred_res, key, multiple_predictions): + def _address(self, address_num, pred_res, key): new_candidates = [] - if not multiple_predictions: - address_idx_list = combinations(list(range(len(pred_res))), address_num) - else: - address_idx_list = combinations(list(range(len(flatten(pred_res)))), address_num) + address_idx_list = combinations(list(range(len(pred_res))), address_num) for address_idx in address_idx_list: - candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions) + candidates = self.address_by_idx(pred_res, key, address_idx) new_candidates += candidates return new_candidates - @lru_cache(maxsize=100) - def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions): + # TODO：在类åˆå§‹åŒ–时应该有一个cache（默认True）的å‚数，用户å¯ä»¥æŒ‡å®šæ˜¯å¦ç”¨cache（若KB会å˜ï¼Œé‚£ä¸èƒ½ç”¨cache） + @lru_cache(maxsize=None) + def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address): pred_res = hashable_to_list(pred_res) key = hashable_to_list(key) candidates = [] for address_num in range(len(flatten(pred_res)) + 1): if address_num == 0: - if check_equal(self._logic_forward(pred_res, multiple_predictions), key, self.max_err): + if check_equal(self.logic_forward(pred_res), key, self.max_err): candidates.append(pred_res) else: - new_candidates = self._address(address_num, pred_res, key, multiple_predictions) + new_candidates = self._address(address_num, pred_res, key) candidates += new_candidates if len(candidates) > 0: min_address_num = address_num @@ -183,7 +155,7 @@ class KBBase(ABC): for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1): if address_num > max_address_num: return candidates - new_candidates = self._address(address_num, pred_res, key, multiple_predictions) + new_candidates = self._address(address_num, pred_res, key) candidates += new_candidates return candidates diff --git a/examples/datasets/hed/learn_add.pl b/examples/datasets/hed/learn_add.pl index fbf698f..35a71c6 100644 --- a/examples/datasets/hed/learn_add.pl +++ b/examples/datasets/hed/learn_add.pl @@ -32,7 +32,7 @@ abduce_consistent_insts(Exs):- % (Experimental) Uncomment to use parallel abduction % abduce_consistent_exs_concurrent(Exs), !. -logic_forward(Exs, X) :- abduce_consistent_insts([Exs]) -> X = true ; X = false. +logic_forward(Exs, X) :- abduce_consistent_insts(Exs) -> X = true ; X = false. logic_forward(Exs) :- abduce_consistent_insts(Exs). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% From b49fb76f0e004e79c968b4767fa15d790c10aee0 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Wed, 15 Mar 2023 14:33:21 +0800 Subject: [PATCH 199/601] Remove --- abl/abducer/abducer_base.py | 5 +---- abl/abducer/kb.py | 2 +- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index c5527f9..b7e6526 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -10,13 +10,10 @@ # # ================================================================# -import sys -sys.path.append('/home/huwc/ABL-Package/abl/') - import abc import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from utils.utils import confidence_dist, flatten, reform_idx, hamming_dist +from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist class AbducerBase(abc.ABC): def __init__(self, kb, dist_func='hamming', zoopt=False): diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index 42a5f6c..f7d8091 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -17,7 +17,7 @@ import numpy as np from collections import defaultdict from itertools import product, combinations -from utils.utils import flatten, reform_idx, hamming_dist, check_equal, to_hashable, hashable_to_list +from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal, to_hashable, hashable_to_list from multiprocessing import Pool From f9dee56b548d67aaae53de38e667fdf50d0185e7 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Wed, 15 Mar 2023 16:18:19 +0800 Subject: [PATCH 200/601] Update abduce for HED --- abl/abducer/abducer_base.py | 39 ++++++++++++++++++------------------- abl/abducer/kb.py | 32 +++++++++++------------------- abl/utils/utils.py | 10 ++++++++-- 3 files changed, 38 insertions(+), 43 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index b7e6526..620a4ce 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -13,7 +13,7 @@ import abc import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist +from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist, nested_length class AbducerBase(abc.ABC): def __init__(self, kb, dist_func='hamming', zoopt=False): @@ -52,14 +52,14 @@ class AbducerBase(abc.ABC): return len(pred_res) def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): - # if not self.multiple_predictions: - return self._zoopt_address_score_single(sol.get_x(), pred_res, pred_res_prob, key) - # else: - # all_address_flag = reform_idx(sol.get_x(), pred_res) - # score = 0 - # for idx in range(len(pred_res)): - # score += self._zoopt_address_score_single(all_address_flag[idx], pred_res[idx], pred_res_prob[idx], key) - # return score + all_address_flag = reform_idx(sol.get_x(), pred_res) + if nested_length(pred_res) == 1: + return self._zoopt_address_score_single(all_address_flag[idx], pred_res, pred_res_prob, key) + else: + score = 0 + for idx in range(nested_length(pred_res)): + score += self._zoopt_address_score_single(all_address_flag[idx], [pred_res[idx]], [pred_res_prob[idx]], [key[idx]]) + return score def _constrain_address_num(self, solution, max_address_num): x = solution.get_x() @@ -78,19 +78,18 @@ class AbducerBase(abc.ABC): return solution def address_by_idx(self, pred_res, key, address_idx): + # print(pred_res, address_idx) return self.kb.address_by_idx(pred_res, key, address_idx) def abduce(self, data, max_address=-1, require_more_address=0): pred_res, pred_res_prob, key = data - # if max_address_num == -1: - # max_address_num = len(flatten(pred_res)) assert(type(max_address) in (int, float)) if max_address == -1: - max_address_num = len(pred_res) + max_address_num = len(flatten(pred_res)) elif type(max_address) == float: assert(max_address >= 0 and max_address <= 1) - max_address_num = round(len(pred_res) * max_address) + max_address_num = round(len(flatten(pred_res)) * max_address) else: assert(max_address >= 0) max_address_num = max_address @@ -267,11 +266,11 @@ if __name__ == '__main__': print(kb.consist_rule([1, '+', 1, '=', 1, 1], rules)) print() - # res = abd.abduce((consist_exs, [None] * len(consist_exs), [None] * len(consist_exs))) - # print(res) - # res = abd.batch_abduce((inconsist_exs, [None] * len(consist_exs), [None] * len(inconsist_exs))) - # print(res) - # print() + res = abd.abduce((consist_exs, [[[None]]] * len(consist_exs), [None] * len(consist_exs))) + print(res) + res = abd.abduce((inconsist_exs, [[[None]]] * len(consist_exs), [None] * len(inconsist_exs))) + print(res) + print() - # abduced_rules = abd.batch_abduce_rules(consist_exs) - # print(abduced_rules) \ No newline at end of file + abduced_rules = abd.abduce_rules(consist_exs) + print(abduced_rules) \ No newline at end of file diff --git a/abl/abducer/kb.py b/abl/abducer/kb.py index f7d8091..1421a97 100644 --- a/abl/abducer/kb.py +++ b/abl/abducer/kb.py @@ -109,16 +109,10 @@ class KBBase(ABC): def address_by_idx(self, pred_res, key, address_idx): candidates = [] abduce_c = product(self.pseudo_label_list, repeat=len(address_idx)) - # if multiple_predictions: - # save_pred_res = pred_res - # pred_res = flatten(pred_res) - for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - # if multiple_predictions: - # candidate = reform_idx(candidate, save_pred_res) if check_equal(self.logic_forward(candidate), key, self.max_err): candidates.append(candidate) return candidates @@ -139,7 +133,7 @@ class KBBase(ABC): key = hashable_to_list(key) candidates = [] - for address_num in range(len(flatten(pred_res)) + 1): + for address_num in range(len(pred_res) + 1): if address_num == 0: if check_equal(self.logic_forward(pred_res), key, self.max_err): candidates.append(pred_res) @@ -202,24 +196,22 @@ class prolog_KB(KBBase): return False return result - def _address_pred_res(self, pred_res, address_idx, multiple_predictions): + def _address_pred_res(self, pred_res, address_idx): import re address_pred_res = pred_res.copy() - if multiple_predictions: - address_pred_res = flatten(address_pred_res) + address_pred_res = flatten(address_pred_res) for idx in address_idx: address_pred_res[idx] = 'P' + str(idx) - if multiple_predictions: - address_pred_res = reform_idx(address_pred_res, pred_res) + address_pred_res = reform_idx(address_pred_res, pred_res) # TODO：ä¸çŸ¥é“有没有更简æ´çš„方法 regex = r"'P\d+'" return re.sub(regex, lambda x: x.group().replace("'", ""), str(address_pred_res)) - def get_query_string(self, pred_res, key, address_idx, multiple_predictions): + def get_query_string(self, pred_res, key, address_idx): query_string = "logic_forward(" - query_string += self._address_pred_res(pred_res, address_idx, multiple_predictions) + query_string += self._address_pred_res(pred_res, address_idx) key_is_none_flag = key is None or (type(key) == list and key[0] is None) query_string += ",%s)." % key if not key_is_none_flag else ")." return query_string @@ -227,19 +219,17 @@ class prolog_KB(KBBase): def _find_candidate_GKB(self, pred_res, key): pass - def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False): + def address_by_idx(self, pred_res, key, address_idx): candidates = [] - query_string = self.get_query_string(pred_res, key, address_idx, multiple_predictions) - if multiple_predictions: - save_pred_res = pred_res - pred_res = flatten(pred_res) + query_string = self.get_query_string(pred_res, key, address_idx) + save_pred_res = pred_res + pred_res = flatten(pred_res) abduce_c = [list(z.values()) for z in self.prolog.query(query_string)] for c in abduce_c: candidate = pred_res.copy() for i, idx in enumerate(address_idx): candidate[idx] = c[i] - if multiple_predictions: - candidate = reform_idx(candidate, save_pred_res) + candidate = reform_idx(candidate, save_pred_res) candidates.append(candidate) return candidates diff --git a/abl/utils/utils.py b/abl/utils/utils.py index 90376aa..a0d5cd1 100644 --- a/abl/utils/utils.py +++ b/abl/utils/utils.py @@ -3,14 +3,20 @@ from .plog import INFO from collections import OrderedDict from itertools import chain -# for multiple predictions +def nested_length(l): + if not isinstance(l[0], (list, tuple)): + return 1 + return len(l) + def flatten(l): if not isinstance(l[0], (list, tuple)): return l return list(chain.from_iterable(l)) -# for multiple predictions def reform_idx(flatten_pred_res, save_pred_res): + if not isinstance(save_pred_res[0], (list, tuple)): + return flatten_pred_res + re = [] i = 0 for e in save_pred_res: From 5d633ff86bb0283076b46d2b13ee274764c6457f Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 15 Mar 2023 16:21:06 +0800 Subject: [PATCH 201/601] Update abducer_base.py --- abl/abducer/abducer_base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 620a4ce..15f989b 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -54,7 +54,7 @@ class AbducerBase(abc.ABC): def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): all_address_flag = reform_idx(sol.get_x(), pred_res) if nested_length(pred_res) == 1: - return self._zoopt_address_score_single(all_address_flag[idx], pred_res, pred_res_prob, key) + return self._zoopt_address_score_single(all_address_flag, pred_res, pred_res_prob, key) else: score = 0 for idx in range(nested_length(pred_res)): @@ -273,4 +273,4 @@ if __name__ == '__main__': print() abduced_rules = abd.abduce_rules(consist_exs) - print(abduced_rules) \ No newline at end of file + print(abduced_rules) From 7bccd9ca6eb304597d5cc30bcc1855b65849be56 Mon Sep 17 00:00:00 2001 From: troyyyyy <49091847+troyyyyy@users.noreply.github.com> Date: Wed, 15 Mar 2023 18:36:27 +0800 Subject: [PATCH 202/601] zoopt_score in ABL-HED --- abl/abducer/abducer_base.py | 47 +++++++++++++++++++++++++++++++++---- 1 file changed, 43 insertions(+), 4 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 15f989b..d33a7e6 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -52,13 +52,52 @@ class AbducerBase(abc.ABC): return len(pred_res) def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): - all_address_flag = reform_idx(sol.get_x(), pred_res) if nested_length(pred_res) == 1: - return self._zoopt_address_score_single(all_address_flag, pred_res, pred_res_prob, key) + return self._zoopt_address_score_single(sol.get_x(), pred_res, pred_res_prob, key) else: + all_address_flag = reform_idx(sol.get_x(), pred_res) + lefted_idx = [i for i in range(len(pred_res))] + candidate_size = [] + while lefted_idx: + temp_idx = [] + temp_idx.append(lefted_idx.pop(0)) + max_candidate_idx = [] + found = False + for idx in range(-1, len(pred_res)): + if (not idx in temp_idx) and (idx >= 0): + temp_idx.append(idx) + + pred = [] + k = [] + address_flag = [] + for idx in temp_idx: + pred.append(pred_res[idx]) + k.append(key[idx]) + address_flag += list(all_address_flag[idx]) + address_idx = np.where(np.array(address_flag) != 0)[0] + candidate = self.address_by_idx(pred, k, address_idx) + if len(candidate) == 0: + if len(temp_idx) > 1: + temp_idx.pop() + else: + if len(temp_idx) > len(max_candidate_idx): + found = True + max_candidate_idx = temp_idx.copy() + removed = [i for i in lefted_idx if i in max_candidate_idx] + + if found: + candidate_size.append(len(removed) + 1) + lefted_idx = [i for i in lefted_idx if i not in max_candidate_idx] + + candidate_size.sort() score = 0 - for idx in range(nested_length(pred_res)): - score += self._zoopt_address_score_single(all_address_flag[idx], [pred_res[idx]], [pred_res_prob[idx]], [key[idx]]) + import math + for i in range(0, len(candidate_size)): + score -= math.exp(-i) * candidate_size[i] + + # score = 0 + # for idx in range(nested_length(pred_res)): + # score += self._zoopt_address_score_single(all_address_flag[idx], [pred_res[idx]], [pred_res_prob[idx]], [key[idx]]) return score def _constrain_address_num(self, solution, max_address_num): From 895b9e4290354309c0f5f015e388506f361e1c31 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 16 Mar 2023 09:00:41 +0800 Subject: [PATCH 203/601] add HED_Abducer --- abl/abducer/abducer_base.py | 118 +++++++++++++++++++----------------- 1 file changed, 64 insertions(+), 54 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index d33a7e6..b7edd4c 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -10,10 +10,13 @@ # # ================================================================# +import sys +sys.path.append('/home/huwc/ABL-Package/abl') + import abc import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist, nested_length +from utils.utils import confidence_dist, flatten, reform_idx, hamming_dist class AbducerBase(abc.ABC): def __init__(self, kb, dist_func='hamming', zoopt=False): @@ -52,53 +55,12 @@ class AbducerBase(abc.ABC): return len(pred_res) def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): - if nested_length(pred_res) == 1: - return self._zoopt_address_score_single(sol.get_x(), pred_res, pred_res_prob, key) + address_idx = np.where(sol.get_x() != 0)[0] + candidates = self.address_by_idx(pred_res, key, address_idx) + if len(candidates) > 0: + return np.min(self._get_cost_list(pred_res, pred_res_prob, candidates)) else: - all_address_flag = reform_idx(sol.get_x(), pred_res) - lefted_idx = [i for i in range(len(pred_res))] - candidate_size = [] - while lefted_idx: - temp_idx = [] - temp_idx.append(lefted_idx.pop(0)) - max_candidate_idx = [] - found = False - for idx in range(-1, len(pred_res)): - if (not idx in temp_idx) and (idx >= 0): - temp_idx.append(idx) - - pred = [] - k = [] - address_flag = [] - for idx in temp_idx: - pred.append(pred_res[idx]) - k.append(key[idx]) - address_flag += list(all_address_flag[idx]) - address_idx = np.where(np.array(address_flag) != 0)[0] - candidate = self.address_by_idx(pred, k, address_idx) - if len(candidate) == 0: - if len(temp_idx) > 1: - temp_idx.pop() - else: - if len(temp_idx) > len(max_candidate_idx): - found = True - max_candidate_idx = temp_idx.copy() - removed = [i for i in lefted_idx if i in max_candidate_idx] - - if found: - candidate_size.append(len(removed) + 1) - lefted_idx = [i for i in lefted_idx if i not in max_candidate_idx] - - candidate_size.sort() - score = 0 - import math - for i in range(0, len(candidate_size)): - score -= math.exp(-i) * candidate_size[i] - - # score = 0 - # for idx in range(nested_length(pred_res)): - # score += self._zoopt_address_score_single(all_address_flag[idx], [pred_res[idx]], [pred_res_prob[idx]], [key[idx]]) - return score + return len(pred_res) def _constrain_address_num(self, solution, max_address_num): x = solution.get_x() @@ -143,15 +105,63 @@ class AbducerBase(abc.ABC): candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) return candidate - def abduce_rules(self, pred_res): - return self.kb.abduce_rules(pred_res) - def batch_abduce(self, data, max_address=-1, require_more_address=0): Z1, Z2, Y = data return [self.abduce((z, prob, y), max_address, require_more_address) for z, prob, y in zip(Z1, Z2, Y)] - def __call__(self, Z, Y, max_address_num=-1, require_more_address=0): - return self.batch_abduce(Z, Y, max_address_num, require_more_address) + def __call__(self, Z, Y, max_address=-1, require_more_address=0): + return self.batch_abduce(Z, Y, max_address, require_more_address) + +class HED_Abducer(AbducerBase): + def __init__(self, kb, dist_func='hamming'): + super().__init__(kb, dist_func, zoopt=True) + + def _address_by_idxs(self, pred_res, key, all_address_flag, idxs): + pred = [] + k = [] + address_flag = [] + for idx in idxs: + pred.append(pred_res[idx]) + k.append(key[idx]) + address_flag += list(all_address_flag[idx]) + address_idx = np.where(np.array(address_flag) != 0)[0] + candidate = self.address_by_idx(pred, k, address_idx) + return candidate + + def _zoopt_address_score(self, pred_res, pred_res_prob, key, sol): + all_address_flag = reform_idx(sol.get_x(), pred_res) + lefted_idxs = [i for i in range(len(pred_res))] + candidate_size = [] + while lefted_idxs: + idxs = [] + idxs.append(lefted_idxs.pop(0)) + max_candidate_idxs = [] + found = False + for idx in range(-1, len(pred_res)): + if (not idx in idxs) and (idx >= 0): + idxs.append(idx) + candidate = self._address_by_idxs(pred_res, key, all_address_flag, idxs) + if len(candidate) == 0: + if len(idxs) > 1: + idxs.pop() + else: + if len(idxs) > len(max_candidate_idxs): + found = True + max_candidate_idxs = idxs.copy() + removed = [i for i in lefted_idxs if i in max_candidate_idxs] + if found: + candidate_size.append(len(removed) + 1) + lefted_idxs = [i for i in lefted_idxs if i not in max_candidate_idxs] + candidate_size.sort() + score = 0 + import math + for i in range(0, len(candidate_size)): + score -= math.exp(-i) * candidate_size[i] + return score + + def abduce_rules(self, pred_res): + return self.kb.abduce_rules(pred_res) + if __name__ == '__main__': from kb import add_KB, prolog_KB, HWF_KB, HED_prolog_KB @@ -293,7 +303,7 @@ if __name__ == '__main__': print() kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') - abd = AbducerBase(kb, zoopt=True) + abd = HED_Abducer(kb) consist_exs = [[1, 1, '+', 0, '=', 1, 1], [1, '+', 1, '=', 1, 0], [0, '+', 0, '=', 0]] inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] rules = ['my_op([0], [0], [0])', 'my_op([1], [1], [1, 0])'] @@ -312,4 +322,4 @@ if __name__ == '__main__': print() abduced_rules = abd.abduce_rules(consist_exs) - print(abduced_rules) + print(abduced_rules) \ No newline at end of file From 84d2443163705ccaa584df228d03b42e533e5dc4 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 16 Mar 2023 09:01:19 +0800 Subject: [PATCH 204/601] add HED_Abducer --- abl/abducer/abducer_base.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index b7edd4c..d5bff1e 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -10,13 +10,10 @@ # # ================================================================# -import sys -sys.path.append('/home/huwc/ABL-Package/abl') - import abc import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from utils.utils import confidence_dist, flatten, reform_idx, hamming_dist +from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist class AbducerBase(abc.ABC): def __init__(self, kb, dist_func='hamming', zoopt=False): From 7b4c1b6390a2cc946e1dc67e5bd759d03eacc458 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 16 Mar 2023 09:03:36 +0800 Subject: [PATCH 205/601] Remove unnecessary functions --- abl/utils/utils.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/abl/utils/utils.py b/abl/utils/utils.py index a0d5cd1..05e6f1c 100644 --- a/abl/utils/utils.py +++ b/abl/utils/utils.py @@ -3,11 +3,6 @@ from .plog import INFO from collections import OrderedDict from itertools import chain -def nested_length(l): - if not isinstance(l[0], (list, tuple)): - return 1 - return len(l) - def flatten(l): if not isinstance(l[0], (list, tuple)): return l From 8511d618baf59563d3d55ade88c4d34a7795d367 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 16 Mar 2023 09:12:35 +0800 Subject: [PATCH 206/601] Update abducer examples --- abl/abducer/abducer_base.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index d5bff1e..f24c60d 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -10,10 +10,13 @@ # # ================================================================# +import sys +sys.path.append('/home/huwc/ABL-Package/abl') + import abc import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist +from utils.utils import confidence_dist, flatten, reform_idx, hamming_dist class AbducerBase(abc.ABC): def __init__(self, kb, dist_func='hamming', zoopt=False): @@ -302,21 +305,28 @@ if __name__ == '__main__': kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') abd = HED_Abducer(kb) consist_exs = [[1, 1, '+', 0, '=', 1, 1], [1, '+', 1, '=', 1, 0], [0, '+', 0, '=', 0]] - inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] + inconsist_exs1 = [[1, 1, '+', 0, '=', 1, 1], [1, '+', 1, '=', 1, 0], [0, '+', 0, '=', 0], [0, '+', 0, '=', 1]] + inconsist_exs2 = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]] rules = ['my_op([0], [0], [0])', 'my_op([1], [1], [1, 0])'] + print('HED_kb logic forward') print(kb.logic_forward(consist_exs)) - print(kb.logic_forward(inconsist_exs)) + print(kb.logic_forward(inconsist_exs1), kb.logic_forward(inconsist_exs2)) print() + print('HED_kb consist rule') print(kb.consist_rule([1, '+', 1, '=', 1, 0], rules)) print(kb.consist_rule([1, '+', 1, '=', 1, 1], rules)) print() + print('HED_Abducer abduce') res = abd.abduce((consist_exs, [[[None]]] * len(consist_exs), [None] * len(consist_exs))) print(res) - res = abd.abduce((inconsist_exs, [[[None]]] * len(consist_exs), [None] * len(inconsist_exs))) + res = abd.abduce((inconsist_exs1, [[[None]]] * len(inconsist_exs1), [None] * len(inconsist_exs1))) + print(res) + res = abd.abduce((inconsist_exs2, [[[None]]] * len(inconsist_exs2), [None] * len(inconsist_exs2))) print(res) print() + print('HED_Abducer abduce rules') abduced_rules = abd.abduce_rules(consist_exs) print(abduced_rules) \ No newline at end of file From 2d605871673c27ae8e3c2470464cd88f8be08148 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 16 Mar 2023 09:13:23 +0800 Subject: [PATCH 207/601] Update abducer examples --- abl/abducer/abducer_base.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index f24c60d..b0c0d8f 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -10,13 +10,10 @@ # # ================================================================# -import sys -sys.path.append('/home/huwc/ABL-Package/abl') - import abc import numpy as np from zoopt import Dimension, Objective, Parameter, Opt -from utils.utils import confidence_dist, flatten, reform_idx, hamming_dist +from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist class AbducerBase(abc.ABC): def __init__(self, kb, dist_func='hamming', zoopt=False): From 5884584dfe77de85c1795601adb28a37c33c6f29 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 16 Mar 2023 09:33:30 +0800 Subject: [PATCH 208/601] Update batch_abduce --- abl/abducer/abducer_base.py | 84 ++++++++++++++++++------------------- 1 file changed, 41 insertions(+), 43 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index b0c0d8f..4597292 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -76,7 +76,6 @@ class AbducerBase(abc.ABC): return solution def address_by_idx(self, pred_res, key, address_idx): - # print(pred_res, address_idx) return self.kb.address_by_idx(pred_res, key, address_idx) def abduce(self, data, max_address=-1, require_more_address=0): @@ -102,9 +101,8 @@ class AbducerBase(abc.ABC): candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) return candidate - def batch_abduce(self, data, max_address=-1, require_more_address=0): - Z1, Z2, Y = data - return [self.abduce((z, prob, y), max_address, require_more_address) for z, prob, y in zip(Z1, Z2, Y)] + def batch_abduce(self, Z, Y, max_address=-1, require_more_address=0): + return [self.abduce((z, prob, y), max_address, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] def __call__(self, Z, Y, max_address=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address, require_more_address) @@ -169,60 +167,60 @@ if __name__ == '__main__': print('add_KB with GKB:') kb = add_KB(GKB_flag=True) abd = AbducerBase(kb, 'confidence') - res = abd.batch_abduce(([[1, 1]], prob1, [8]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [8], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob2, [8]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob2}, [8], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [17], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=1, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [17], max_address=1, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [20]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [20], max_address=2, require_more_address=0) print(res) print() print('add_KB without GKB:') kb = add_KB() abd = AbducerBase(kb, 'confidence') - res = abd.batch_abduce(([[1, 1]], prob1, [8]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [8], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob2, [8]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob2}, [8], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [17], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=1, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [17], max_address=1, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [20]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [20], max_address=2, require_more_address=0) print(res) print() print('prolog_KB with add.pl:') kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='../examples/datasets/mnist_add/add.pl') abd = AbducerBase(kb, 'confidence') - res = abd.batch_abduce(([[1, 1]], prob1, [8]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [8], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob2, [8]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob2}, [8], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [17], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=1, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [17], max_address=1, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [20]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [20], max_address=2, require_more_address=0) print(res) print() print('prolog_KB with add.pl using zoopt:') kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='../examples/datasets/mnist_add/add.pl') abd = AbducerBase(kb, 'confidence', zoopt=True) - res = abd.batch_abduce(([[1, 1]], prob1, [8]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [8], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob2, [8]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob2}, [8], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [17], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [17]), max_address=1, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [17], max_address=1, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1]], prob1, [20]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1]], 'prob':prob1}, [20], max_address=2, require_more_address=0) print(res) print() @@ -232,70 +230,70 @@ if __name__ == '__main__': kb = add_KB() abd = AbducerBase(kb, 'confidence') - res = abd.batch_abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address=4, require_more_address=0) + res = abd.batch_abduce({'cls':[[1, 1], [1, 2]], 'prob':multiple_prob}, [4, 8], max_address=4, require_more_address=0) print(res) - res = abd.batch_abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address=4, require_more_address=1) + res = abd.batch_abduce({'cls':[[1, 1], [1, 2]], 'prob':multiple_prob}, [4, 8], max_address=4, require_more_address=1) print(res) print() print('HWF_KB with GKB, max_err=0.1') kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 0.1) abd = AbducerBase(kb, 'hamming') - res = abd.batch_abduce(([['5', '+', '2']], [None], [3]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2']], 'prob':[None]}, [3], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '+', '9']], [None], [65]), max_address=3, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '9']], 'prob':[None]}, [65], max_address=3, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '8', '8', '8', '8']], [None], [3.17]), max_address=5, require_more_address=3) + res = abd.batch_abduce({'cls':[['5', '8', '8', '8', '8']], 'prob':[None]}, [3.17], max_address=5, require_more_address=3) print(res) print() print('HWF_KB without GKB, max_err=0.1') kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1) abd = AbducerBase(kb, 'hamming') - res = abd.batch_abduce(([['5', '+', '2']], [None], [3]), max_address=2, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2']], 'prob':[None]}, [3], max_address=2, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '+', '9']], [None], [65]), max_address=3, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '9']], 'prob':[None]}, [65], max_address=3, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '8', '8', '8', '8']], [None], [3.17]), max_address=5, require_more_address=3) + res = abd.batch_abduce({'cls':[['5', '8', '8', '8', '8']], 'prob':[None]}, [3.17], max_address=5, require_more_address=3) print(res) print() print('HWF_KB with GKB, max_err=1') kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 1) abd = AbducerBase(kb, 'hamming') - res = abd.batch_abduce(([['5', '+', '9']], [None], [65]), max_address=3, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '9']], 'prob':[None]}, [65], max_address=3, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '+', '2']], [None], [1.67]), max_address=3, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2']], 'prob':[None]}, [1.67], max_address=3, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '8', '8', '8', '8']], [None], [3.17]), max_address=5, require_more_address=3) + res = abd.batch_abduce({'cls':[['5', '8', '8', '8', '8']], 'prob':[None]}, [3.17], max_address=5, require_more_address=3) print(res) print() print('HWF_KB without GKB, max_err=1') kb = HWF_KB(len_list=[1, 3, 5], max_err = 1) abd = AbducerBase(kb, 'hamming') - res = abd.batch_abduce(([['5', '+', '9']], [None], [65]), max_address=3, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '9']], 'prob':[None]}, [65], max_address=3, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '+', '2']], [None], [1.67]), max_address=3, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2']], 'prob':[None]}, [1.67], max_address=3, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '8', '8', '8', '8']], [None], [3.17]), max_address=5, require_more_address=3) + res = abd.batch_abduce({'cls':[['5', '8', '8', '8', '8']], 'prob':[None]}, [3.17], max_address=5, require_more_address=3) print(res) print() print('HWF_KB with multiple inputs at once:') kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1) abd = AbducerBase(kb, 'hamming') - res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 64]), max_address=1, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2'], ['5', '+', '9']], 'prob':[None, None]}, [3, 64], max_address=1, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 64]), max_address=3, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2'], ['5', '+', '9']], 'prob':[None, None]}, [3, 64], max_address=3, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 65]), max_address=3, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2'], ['5', '+', '9']], 'prob':[None, None]}, [3, 65], max_address=3, require_more_address=0) print(res) print() print('max_address is float') - res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 64]), max_address=0.5, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2'], ['5', '+', '9']], 'prob':[None, None]}, [3, 64], max_address=0.5, require_more_address=0) print(res) - res = abd.batch_abduce(([['5', '+', '2'], ['5', '+', '9']], [None, None], [3, 64]), max_address=0.9, require_more_address=0) + res = abd.batch_abduce({'cls':[['5', '+', '2'], ['5', '+', '9']], 'prob':[None, None]}, [3, 64], max_address=0.9, require_more_address=0) print(res) print() From e0d4abbd3bbb09207f9e76a7f08e417148f7460a Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 16 Mar 2023 09:37:41 +0800 Subject: [PATCH 209/601] Save option for mnist_add --- examples/example.py | 38 +++++++++++++++++++++++--------------- 1 file changed, 23 insertions(+), 15 deletions(-) diff --git a/examples/example.py b/examples/example.py index c74f1b3..39e8c3f 100644 --- a/examples/example.py +++ b/examples/example.py @@ -32,31 +32,37 @@ from abl import framework_hed def run_test(): - # kb = add_KB(True) + kb = add_KB() # kb = HWF_KB(True) - # abducer = AbducerBase(kb) + abducer = AbducerBase(kb, 'confidence') - kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') - abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True) + # kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') + # abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True) recorder = logger() - total_train_data = get_hed(train=True) - train_data, val_data = split_equation(total_train_data, 3, 1) - test_data = get_hed(train=False) + # total_train_data = get_hed(train=True) + # train_data, val_data = split_equation(total_train_data, 3, 1) + # test_data = get_hed(train=False) - # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) - cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) + train_data = get_mnist_add(train = True, get_pseudo_label = True) + test_data = get_mnist_add(train = False, get_pseudo_label = True) + + # train_data = get_hwf(train = True, get_pseudo_label = True) + # test_data = get_hwf(train = False, get_pseudo_label = True) + + cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) + # cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - framework_hed.hed_pretrain(kb, cls, recorder) + # framework_hed.hed_pretrain(kb, cls, recorder) criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) - # optimizer = torch.optim.Adam(cls.parameters(), lr=0.00001, betas=(0.9, 0.99)) + # optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) + optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) - base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=10, recorder=recorder) + base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=1, recorder=recorder) model = WABLBasicModel(base_model, kb.pseudo_label_list) # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) @@ -65,8 +71,10 @@ def run_test(): # train_data = get_hwf(train = True, get_pseudo_label = True) # test_data = get_hwf(train = False, get_pseudo_label = True) - model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) - framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) + # model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) + # framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) + + framework_hed.train(model, abducer, train_data, test_data, sample_num=10000, verbose=1) recorder.dump() return True From 2d54e7023d45860f93041ff6f7f42028c4882c6f Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Thu, 16 Mar 2023 09:49:44 +0800 Subject: [PATCH 210/601] Update HED example --- abl/framework_hed.py | 6 ++---- examples/example.py | 42 +++++++++++++++++++++--------------------- 2 files changed, 23 insertions(+), 25 deletions(-) diff --git a/abl/framework_hed.py b/abl/framework_hed.py index ab88318..76c57fa 100644 --- a/abl/framework_hed.py +++ b/abl/framework_hed.py @@ -172,9 +172,6 @@ def abduce_and_train(model, abducer, mapping, train_X_true, select_num): if len(consistent_idx) == 0: return 0, 0, None - if len(mappings) > 1: - INFO('Final mapping is: ', mapping) - INFO('Train pool size is:', len(flatten(consistent_pred_res))) INFO("Start to use abduced pseudo label to train model...") model.train([X[idx] for idx in consistent_idx], remapping_res(consistent_pred_res, mapping)) @@ -214,7 +211,7 @@ def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule, consistent_idx = [] consistent_pred_res = [] for idx in range(len(pred_res)): - if abducer.kb.logic_forward(pred_res[idx]): + if abducer.kb.logic_forward([pred_res[idx]]): consistent_idx.append(idx) consistent_pred_res.append(pred_res[idx]) @@ -296,6 +293,7 @@ def train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len break else: if equation_len == min_len: + INFO('Final mapping is: ', mapping) model.cls_list[0].model.load_state_dict(torch.load("./weights/pretrain_weights.pth")) else: model.cls_list[0].model.load_state_dict(torch.load("./weights/weights_%d.pth" % (equation_len - 1))) diff --git a/examples/example.py b/examples/example.py index 39e8c3f..0f75895 100644 --- a/examples/example.py +++ b/examples/example.py @@ -22,8 +22,8 @@ from abl.models.basic_model import BasicModel, BasicDataset from abl.models.wabl_models import DecisionTree, WABLBasicModel from multiprocessing import Pool -from abl.abducer.abducer_base import AbducerBase -from abl.abducer.kb import add_KB, HWF_KB, prolog_KB +from abl.abducer.abducer_base import AbducerBase, HED_Abducer +from abl.abducer.kb import add_KB, HWF_KB, prolog_KB, HED_prolog_KB from datasets.mnist_add.get_mnist_add import get_mnist_add from datasets.hwf.get_hwf import get_hwf from datasets.hed.get_hed import get_hed, split_equation @@ -32,37 +32,37 @@ from abl import framework_hed def run_test(): - kb = add_KB() - # kb = HWF_KB(True) - abducer = AbducerBase(kb, 'confidence') + # kb = add_KB() + # kb = HWF_KB() + # abducer = AbducerBase(kb, 'confidence') - # kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') - # abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True) + kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') + abducer = HED_Abducer(kb) recorder = logger() - # total_train_data = get_hed(train=True) - # train_data, val_data = split_equation(total_train_data, 3, 1) - # test_data = get_hed(train=False) + total_train_data = get_hed(train=True) + train_data, val_data = split_equation(total_train_data, 3, 1) + test_data = get_hed(train=False) - train_data = get_mnist_add(train = True, get_pseudo_label = True) - test_data = get_mnist_add(train = False, get_pseudo_label = True) + # train_data = get_mnist_add(train = True, get_pseudo_label = True) + # test_data = get_mnist_add(train = False, get_pseudo_label = True) # train_data = get_hwf(train = True, get_pseudo_label = True) # test_data = get_hwf(train = False, get_pseudo_label = True) - cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) - # cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) + # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) + cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - # framework_hed.hed_pretrain(kb, cls, recorder) + framework_hed.hed_pretrain(kb, cls, recorder) criterion = nn.CrossEntropyLoss() - # optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) - optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) + optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) + # optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) - base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=1, recorder=recorder) + base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=10, recorder=recorder) model = WABLBasicModel(base_model, kb.pseudo_label_list) # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) @@ -71,10 +71,10 @@ def run_test(): # train_data = get_hwf(train = True, get_pseudo_label = True) # test_data = get_hwf(train = False, get_pseudo_label = True) - # model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) - # framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) + model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) + framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) - framework_hed.train(model, abducer, train_data, test_data, sample_num=10000, verbose=1) + # framework_hed.train(model, abducer, train_data, test_data, sample_num=10000, verbose=1) recorder.dump() return True From af84354bdacc0ab93c9d1d150420c76a679051bc Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Fri, 17 Mar 2023 11:33:54 +0800 Subject: [PATCH 211/601] Parallelizing batch_abduce --- abl/abducer/abducer_base.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 4597292..96161a4 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -12,6 +12,7 @@ import abc import numpy as np +import multiprocessing from zoopt import Dimension, Objective, Parameter, Opt from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist @@ -101,9 +102,20 @@ class AbducerBase(abc.ABC): candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates) return candidate - def batch_abduce(self, Z, Y, max_address=-1, require_more_address=0): - return [self.abduce((z, prob, y), max_address, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] + # def batch_abduce(self, Z, Y, max_address=-1, require_more_address=0): + # return [self.abduce((z, prob, y), max_address, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)] + + def _batch_abduce_helper(self, args): + z, prob, y, max_address, require_more_address = args + return self.abduce((z, prob, y), max_address, require_more_address) + def batch_abduce(self, Z, Y, max_address=-1, require_more_address=0): + pool = multiprocessing.Pool(processes=len(Z)) + results = pool.map(self._batch_abduce_helper, [(z, prob, y, max_address, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)]) + pool.close() + pool.join() + return results + def __call__(self, Z, Y, max_address=-1, require_more_address=0): return self.batch_abduce(Z, Y, max_address, require_more_address) From 953a88d15a90adca9d970c08f04ccf9b92d198f9 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Fri, 17 Mar 2023 14:25:35 +0800 Subject: [PATCH 212/601] Examples for HWF --- examples/datasets/hwf/get_hwf.py | 8 +++---- examples/example.py | 41 +++++++++++++------------------- 2 files changed, 21 insertions(+), 28 deletions(-) diff --git a/examples/datasets/hwf/get_hwf.py b/examples/datasets/hwf/get_hwf.py index b478a15..87da5cf 100644 --- a/examples/datasets/hwf/get_hwf.py +++ b/examples/datasets/hwf/get_hwf.py @@ -7,7 +7,7 @@ img_transform = transforms.Compose([ transforms.Normalize((0.5,), (1,)) ]) -def get_data(file, get_pseudo_label, precision_num = 2): +def get_data(file, get_pseudo_label): X = [] if get_pseudo_label: Z = [] @@ -27,20 +27,20 @@ def get_data(file, get_pseudo_label, precision_num = 2): X.append(imgs) if get_pseudo_label: Z.append(imgs_pseudo_label) - Y.append(round(data[idx]['res'], precision_num)) + Y.append(data[idx]['res']) if get_pseudo_label: return X, Z, Y else: return X, None, Y -def get_hwf(train = True, get_pseudo_label = False, precision_num = 2): +def get_hwf(train = True, get_pseudo_label = False): if(train): file = './datasets/hwf/data/expr_train.json' else: file = './datasets/hwf/data/expr_test.json' - return get_data(file, get_pseudo_label, precision_num) + return get_data(file, get_pseudo_label) if __name__ == "__main__": train_X, train_Y = get_hwf(train = True) diff --git a/examples/example.py b/examples/example.py index 0f75895..df101e2 100644 --- a/examples/example.py +++ b/examples/example.py @@ -33,48 +33,41 @@ from abl import framework_hed def run_test(): # kb = add_KB() - # kb = HWF_KB() - # abducer = AbducerBase(kb, 'confidence') + kb = HWF_KB(GKB_flag=True) + abducer = AbducerBase(kb, 'confidence') - kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') - abducer = HED_Abducer(kb) + # kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl') + # abducer = HED_Abducer(kb) recorder = logger() - total_train_data = get_hed(train=True) - train_data, val_data = split_equation(total_train_data, 3, 1) - test_data = get_hed(train=False) + # total_train_data = get_hed(train=True) + # train_data, val_data = split_equation(total_train_data, 3, 1) + # test_data = get_hed(train=False) - # train_data = get_mnist_add(train = True, get_pseudo_label = True) - # test_data = get_mnist_add(train = False, get_pseudo_label = True) + # train_data = get_mnist_add(train=True, get_pseudo_label=True) + # test_data = get_mnist_add(train=False, get_pseudo_label=True) - # train_data = get_hwf(train = True, get_pseudo_label = True) - # test_data = get_hwf(train = False, get_pseudo_label = True) + train_data = get_hwf(train=True, get_pseudo_label=True) + test_data = get_hwf(train=False, get_pseudo_label=True) # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) - cls = SymbolNet(num_classes=len(kb.pseudo_label_list)) + cls = SymbolNet(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:])) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - framework_hed.hed_pretrain(kb, cls, recorder) + # framework_hed.hed_pretrain(kb, cls, recorder) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6) # optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99)) - - base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=10, recorder=recorder) + base_model = BasicModel(cls, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, batch_size=32, num_epochs=1, recorder=recorder) model = WABLBasicModel(base_model, kb.pseudo_label_list) - - # train_X, train_Z, train_Y = get_mnist_add(train = True, get_pseudo_label = True) - # test_X, test_Z, test_Y = get_mnist_add(train = False, get_pseudo_label = True) - - # train_data = get_hwf(train = True, get_pseudo_label = True) - # test_data = get_hwf(train = False, get_pseudo_label = True) - model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) - framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) + # model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8) + # framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8) - # framework_hed.train(model, abducer, train_data, test_data, sample_num=10000, verbose=1) + framework_hed.train(model, abducer, train_data, test_data, sample_num=-1, verbose=1) recorder.dump() return True From ee25a2c74e40d87a7afca5faa9579885fed3966e Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Fri, 17 Mar 2023 14:35:58 +0800 Subject: [PATCH 213/601] Modify filter_data --- abl/framework_hed.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/abl/framework_hed.py b/abl/framework_hed.py index 76c57fa..c8d1e2a 100644 --- a/abl/framework_hed.py +++ b/abl/framework_hed.py @@ -52,9 +52,9 @@ def result_statistics(pred_Z, Z, Y, logic_forward, char_acc_flag): def filter_data(X, abduced_Z): finetune_Z = [] finetune_X = [] - for abduced_x, abduced_z in zip(X, abduced_Z): - if abduced_z is not []: - finetune_X.append(abduced_x) + for x, abduced_z in zip(X, abduced_Z): + if len(abduced_z) > 0: + finetune_X.append(x) finetune_Z.append(abduced_z) return finetune_X, finetune_Z @@ -84,7 +84,6 @@ def train(model, abducer, train_data, test_data, epochs=50, sample_num=-1, verbo for epoch_idx in range(epochs): X, Z, Y = block_sample(train_X, train_Z, train_Y, sample_num, epoch_idx) preds_res = predict_func(X) - # input() abduced_Z = abduce_func(preds_res, Y) if ((epoch_idx + 1) % verbose == 0) or (epoch_idx == epochs - 1): From 33b5e25d3ff59c348300b9559f022d0463e44acb Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Mon, 20 Mar 2023 20:35:18 +0800 Subject: [PATCH 214/601] add test code for BasicModel --- tests/test_models.py | 75 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 tests/test_models.py diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 0000000..b01b8c3 --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,75 @@ +import sys + +sys.path.insert(0, sys.path[0] + "/../") + +import os +import pytest +import torch +import torch.nn as nn +import numpy as np + +from abl.models.nn import LeNet5, SymbolNet +from abl.models.basic_model import BasicModel + + +class TestBasicModel(object): + @pytest.mark.parametrize("num_classes", [4, 10]) + @pytest.mark.parametrize("image_size", [(28, 28, 1), (45, 45, 1)]) + @pytest.mark.parametrize("cls", [LeNet5, SymbolNet]) + @pytest.mark.parametrize("criterion", [nn.CrossEntropyLoss]) + @pytest.mark.parametrize("optimizer", [torch.optim.RMSprop]) + @pytest.mark.parametrize("device", [torch.device("cpu"), torch.device("cuda:0")]) + def test_models(self, num_classes, image_size, cls, criterion, optimizer, device): + cls = cls(num_classes=num_classes, image_size=image_size) + criterion = criterion() + optimizer = optimizer(cls.parameters(), lr=0.001) + + self.num_classes = num_classes + self.image_size = image_size + self.model = BasicModel(cls, criterion, optimizer, device) + + self.data_X = [ + np.random.rand(image_size[2], image_size[0], image_size[1]).astype( + np.float32 + ) + for i in range(5) + ] + self.data_y = np.random.randint(0, num_classes, (5,)) + + self._test_fit() + self._test_predict() + self._test_predict_proba() + self._test_val() + self._test_save() + self._test_load() + + def _test_fit(self): + self.model.fit(X=self.data_X, y=self.data_y) + + def _test_predict(self): + predict_result = self.model.predict(X=self.data_X) + assert predict_result.dtype == int + assert predict_result.shape == (5,) + assert (0 <= predict_result).all() and (predict_result < self.num_classes).all() + + def _test_predict_proba(self): + predict_result = self.model.predict_proba(X=self.data_X) + assert predict_result.dtype == np.float32 + assert predict_result.shape == (5, self.num_classes) + assert (0 <= predict_result).all() and (predict_result <= 1).all() + + def _test_val(self): + accuracy = self.model.val(X=self.data_X, y=self.data_y) + assert type(accuracy) == float + assert 0 <= accuracy <= 1 + + def _test_save(self): + self.model.save(1, "results/test_models") + assert os.path.exists("results/test_models/1_net.pth") + assert os.path.exists("results/test_models/1_opt.pth") + os.remove("results/test_models/1_net.pth") + os.remove("results/test_models/1_opt.pth") + + def _test_load(self): + self.model.save(1, "results/test_models") + self.model.load(1, "results/test_models") From 1ac226676b5d89b21adecb2fe53794356c6ead38 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Mon, 20 Mar 2023 22:07:15 +0800 Subject: [PATCH 215/601] add build-and-test workflow and related files --- .coveragerc | 8 ++++++ .github/workflows/build-and-test.yaml | 35 +++++++++++++++++++++++++++ requirements.txt | 6 +++++ 3 files changed, 49 insertions(+) create mode 100644 .coveragerc create mode 100644 .github/workflows/build-and-test.yaml create mode 100644 requirements.txt diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..5f18aae --- /dev/null +++ b/.coveragerc @@ -0,0 +1,8 @@ +[report] +show_missing = True + +[run] +disable_warnings = include-ignored +include = */abl/* +omit = + */abl/__init__.py \ No newline at end of file diff --git a/.github/workflows/build-and-test.yaml b/.github/workflows/build-and-test.yaml new file mode 100644 index 0000000..45b2bd2 --- /dev/null +++ b/.github/workflows/build-and-test.yaml @@ -0,0 +1,35 @@ +name: ABL-Package-CI + +on: + push: + branches: [ main, Dev ] + pull_request: + branches: [ main ] + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest] + python-version: [3.8, 3.9] + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Display python version + run: python -c "import sys; print(sys.version)" + - name: Install package dependencies + run: | + python -m pip install --upgrade pip + pip install -r ./requirements.txt + - name: Run tests + run: | + pytest --cov-config=.coveragerc --cov-report=xml --cov=abl ./tests + - name: Publish code coverage + uses: codecov/codecov-action@v1 + with: + token: ${{ secrets.CODECOV_TOKEN }} + file: ./coverage.xml \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..1c7dab4 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +numpy +pyswip==0.2.9 +pytest-cov +torch +torchvision +torchaudio \ No newline at end of file From b854ffd282d06da0ed77ad6577db13d4c4acbaef Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Mon, 20 Mar 2023 22:26:41 +0800 Subject: [PATCH 216/601] fix bug in BasicModel test --- .github/workflows/build-and-test.yaml | 6 +++++- abl/models/basic_model.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-and-test.yaml b/.github/workflows/build-and-test.yaml index 45b2bd2..0e38b25 100644 --- a/.github/workflows/build-and-test.yaml +++ b/.github/workflows/build-and-test.yaml @@ -11,7 +11,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, windows-latest] + os: ubuntu-latest python-version: [3.8, 3.9] steps: - uses: actions/checkout@v2 @@ -25,6 +25,10 @@ jobs: run: | python -m pip install --upgrade pip pip install -r ./requirements.txt + - uses: Jimver/cuda-toolkit@v0.2.10 + id: cuda-toolkit + with: + cuda: '12.1.0' - name: Run tests run: | pytest --cov-config=.coveragerc --cov-report=xml --cov=abl ./tests diff --git a/abl/models/basic_model.py b/abl/models/basic_model.py index 8c137e2..9050365 100644 --- a/abl/models/basic_model.py +++ b/abl/models/basic_model.py @@ -251,7 +251,7 @@ class BasicModel: def save(self, epoch_id, save_dir): recorder = self.recorder if not os.path.exists(save_dir): - os.mkdir(save_dir) + os.makedirs(save_dir) recorder.print("Saving model and opter") save_path = os.path.join(save_dir, str(epoch_id) + "_net.pth") torch.save(self.model.state_dict(), save_path) From c844d8ffe549b35bd92abfe458801bfc7374ae91 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Mon, 20 Mar 2023 22:31:16 +0800 Subject: [PATCH 217/601] fix bug in BasicModel test --- .github/workflows/build-and-test.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-and-test.yaml b/.github/workflows/build-and-test.yaml index 0e38b25..2c14f61 100644 --- a/.github/workflows/build-and-test.yaml +++ b/.github/workflows/build-and-test.yaml @@ -11,7 +11,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: ubuntu-latest + os: [ubuntu-latest] python-version: [3.8, 3.9] steps: - uses: actions/checkout@v2 From 4af9a0bf6b12511248374cd157ee4254b38eb7b0 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Tue, 21 Mar 2023 09:13:43 +0800 Subject: [PATCH 218/601] add zoopt in requirements --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1c7dab4..78a9c6f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,5 @@ pyswip==0.2.9 pytest-cov torch torchvision -torchaudio \ No newline at end of file +torchaudio +zoopt \ No newline at end of file From 9db37dececbd906113557101cd9cf027c6339f3d Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Tue, 21 Mar 2023 10:02:13 +0800 Subject: [PATCH 219/601] remove cuda related test --- .github/workflows/build-and-test.yaml | 4 ---- tests/test_models.py | 2 +- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/.github/workflows/build-and-test.yaml b/.github/workflows/build-and-test.yaml index 2c14f61..31dc9ec 100644 --- a/.github/workflows/build-and-test.yaml +++ b/.github/workflows/build-and-test.yaml @@ -25,10 +25,6 @@ jobs: run: | python -m pip install --upgrade pip pip install -r ./requirements.txt - - uses: Jimver/cuda-toolkit@v0.2.10 - id: cuda-toolkit - with: - cuda: '12.1.0' - name: Run tests run: | pytest --cov-config=.coveragerc --cov-report=xml --cov=abl ./tests diff --git a/tests/test_models.py b/tests/test_models.py index b01b8c3..8ea4383 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -18,7 +18,7 @@ class TestBasicModel(object): @pytest.mark.parametrize("cls", [LeNet5, SymbolNet]) @pytest.mark.parametrize("criterion", [nn.CrossEntropyLoss]) @pytest.mark.parametrize("optimizer", [torch.optim.RMSprop]) - @pytest.mark.parametrize("device", [torch.device("cpu"), torch.device("cuda:0")]) + @pytest.mark.parametrize("device", [torch.device("cpu")]) def test_models(self, num_classes, image_size, cls, criterion, optimizer, device): cls = cls(num_classes=num_classes, image_size=image_size) criterion = criterion() From 3a965b80138df25728e5519d97f5903aefb3f125 Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Tue, 21 Mar 2023 10:11:50 +0800 Subject: [PATCH 220/601] Modify pool --- abl/abducer/abducer_base.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/abl/abducer/abducer_base.py b/abl/abducer/abducer_base.py index 96161a4..441223f 100644 --- a/abl/abducer/abducer_base.py +++ b/abl/abducer/abducer_base.py @@ -10,9 +10,10 @@ # # ================================================================# +import os import abc import numpy as np -import multiprocessing +from multiprocessing import Pool from zoopt import Dimension, Objective, Parameter, Opt from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist @@ -110,10 +111,8 @@ class AbducerBase(abc.ABC): return self.abduce((z, prob, y), max_address, require_more_address) def batch_abduce(self, Z, Y, max_address=-1, require_more_address=0): - pool = multiprocessing.Pool(processes=len(Z)) - results = pool.map(self._batch_abduce_helper, [(z, prob, y, max_address, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)]) - pool.close() - pool.join() + with Pool(processes=os.cpu_count()) as pool: + results = pool.map(self._batch_abduce_helper, [(z, prob, y, max_address, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)]) return results def __call__(self, Z, Y, max_address=-1, require_more_address=0): From df4d48c04accef6b5191e7ddb1e18c8a03f4a5ae Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Tue, 21 Mar 2023 10:26:22 +0800 Subject: [PATCH 221/601] add CI token to readme.md --- readme.md | 1 + 1 file changed, 1 insertion(+) diff --git a/readme.md b/readme.md index 512a68f..4181afb 100644 --- a/readme.md +++ b/readme.md @@ -1,5 +1,6 @@ [![flake8 Lint](https://github.com/AbductiveLearning/ABL-Package/actions/workflows/lint.yml/badge.svg?branch=Dev)](https://github.com/AbductiveLearning/ABL-Package/actions/workflows/lint.yml) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![ABL-Package-CI](https://github.com/AbductiveLearning/ABL-Package/actions/workflows/build-and-test.yaml/badge.svg?branch=Dev)](https://github.com/AbductiveLearning/ABL-Package/actions/workflows/build-and-test.yaml) # ABL Package From c5aeb045aa1f93ba30b67c02cf6f4f292233088f Mon Sep 17 00:00:00 2001 From: troyyyyy Date: Tue, 21 Mar 2023 12:57:03 +0800 Subject: [PATCH 222/601] add docs --- docs/ABL/Abductive Learning.rst | 5 + docs/ABL/Quick Start.rst | 52 + 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b/docs/ABL/Abductive Learning.rst @@ -0,0 +1,5 @@ +----------------------------- +Abductive Learning +----------------------------- + +What is ABL \ No newline at end of file diff --git a/docs/ABL/Quick Start.rst b/docs/ABL/Quick Start.rst new file mode 100644 index 0000000..4c7da0a --- /dev/null +++ b/docs/ABL/Quick Start.rst @@ -0,0 +1,52 @@ +--------------- +Quick Start +--------------- + +How to use + +.. contents:: Table of Contents + +Required packages +----------------- + +This package requires the following packages: + +- Python version above 3.x +- ``numpy`` http://www.numpy.org + +The easiest way to get these is to use +`pip `__ or +`conda `__ environment +manager. Typing the following command in your terminal will install all +required packages in your Python environment. + +.. code:: console + + $ conda install numpy + +or + +.. code:: console + + $ pip install numpy + +Getting and installing ABL +---------------------------- + +The easiest way to install ABL is to type ``pip install ABL`` in you +terminal/command line. + +If you want to install ABL by source code, download this project and +sequentially run following commands in your terminal/command line. + +.. code:: console + + $ python setup.py build + $ python setup.py install + +A quick example +--------------- + +Show an example. + +More examples are available in the **Example** part. diff --git a/docs/Examples/MNISTadd.rst b/docs/Examples/MNISTadd.rst new file mode 100644 index 0000000..c958334 --- /dev/null +++ b/docs/Examples/MNISTadd.rst @@ -0,0 +1,3 @@ +------------------------------------- +MNISTadd +------------------------------------- diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..9a4183c --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = ABL +SOURCEDIR = . +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/README.rst b/docs/README.rst new file mode 100644 index 0000000..18600c4 --- /dev/null +++ b/docs/README.rst @@ -0,0 +1,27 @@ +ABL +^^^^^^ + +ABL(Abductive Learning) is ... + +Installation +------------- + +ABL is distributed on PyPI and can be installed with ``pip``: + +.. code:: 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z5l4BanylG$7>}`B=QKSEp+`uG3rS0apo?l#$qU)xUJwftmCLtbQ4q>ljP}2?A91{2 zo&RXkF!V!{fxbCNLHG`PvqU1d{8-G-Qq!{}a(Cg&!W0u5u`E03yrmBL=&SP|76!|0 zwTB3XVla`TDez{_dtq@KX zI9W|-!|NM>?V^b&?W)8=@OWdyqQhM-k_X#w$xc86)a{JfsQt8+WC*rps{D|z<#in5 z2;@~U*`6Pc`-!})dTIsG(-agKilkGA6dLSbzx7R+v=ff3h3S=~oc_&xdgLonQF~0o z9;afY*Ffg?nQ$E}))HihM6y-6Ysy`OrE3-$T5P~f&q-%;JDPcdOwHmrmEcyxSJB^& zT$ESyS&Y<yfn7%3X`$8p49_)0BgiwFxo$I1bcN4ch5SnLz={DkR(+ zAq;YCxTMe`iiZpRxC@eo#Ymcv>b~VTLD--M+sNV>RA3YY>r7s@(CgTwF5!-e$^`DF zc<>9^&F+N|ug)k%wlld(y%TkBqd1;I6r?bs5yh7yKZKdmHhpK)g;EGDfx9Mz4H$+# zYF?U?rZ+h_fnv5%(U{{~xFY$4M4G9piy`!FsE+CT-sF48{mFOnU>~s$C*M}BZ>ZL1 z?9*7bR8KJrupL~z=)Py{qch#@jD7YZW^X9D9^PgjvODS6kJ+c}FR|RLI$y86X>o** z+78rBB`VGHe(AQWlC&Q#un;Wn(07V>(p_I&5Ei{#b2GN`RrR1tbMphSLFABpmlx;C zzJO~1ge*`` ijg0Rh + + + + + + Abductive Learning — ABL 0.1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Abductive Learningïƒ

+

What is ABL

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/ABL/Quick Start.html b/docs/build/html/ABL/Quick Start.html new file mode 100644 index 0000000..c5791c2 --- /dev/null +++ b/docs/build/html/ABL/Quick Start.html @@ -0,0 +1,171 @@ + + + + + + + Quick Start — ABL 0.1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Quick Startïƒ

+

How to use

+ +
+

Required packagesïƒ

+

This package requires the following packages:

+ +

The easiest way to get these is to use +pip or +conda environment +manager. Typing the following command in your terminal will install all +required packages in your Python environment.

+
$ conda install numpy
+
+
+

or

+
$ pip install numpy
+
+
+
+
+

Getting and installing ABLïƒ

+

The easiest way to install ABL is to type pip install ABL in you +terminal/command line.

+

If you want to install ABL by source code, download this project and +sequentially run following commands in your terminal/command line.

+
$ python setup.py build
+$ python setup.py install
+
+
+
+
+

A quick exampleïƒ

+

Show an example.

+

More examples are available in the Example part.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/Examples/MNISTadd.html b/docs/build/html/Examples/MNISTadd.html new file mode 100644 index 0000000..646b64d --- /dev/null +++ b/docs/build/html/Examples/MNISTadd.html @@ -0,0 +1,115 @@ + + + + + + + MNISTadd — ABL 0.1.0 documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

MNISTaddïƒ

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/README.html b/docs/build/html/README.html new file mode 100644 index 0000000..a3c89cf --- /dev/null +++ b/docs/build/html/README.html @@ -0,0 +1,129 @@ + + + + + + + ABL — ABL 0.1.0 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

ABLïƒ

+

ABL(Abductive Learning) is …

+
+

Installationïƒ

+

ABL is distributed on PyPI and can be installed with pip:

+
$ pip install ABL
+
+
+

Alternatively, to install ABL by source code, download this project and sequentially run following commands in your terminal/command line.

+
$ python setup.py build
+$ python setup.py install
+
+
+
+
+

Releasesïƒ

+

release 0.1

+
+
+ + +
+
+
+ +
+ +
+

© Copyright Yu-Xuan Huang, Wen-Chao Hu, En-Hao Gao.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_sources/ABL/Abductive Learning.rst.txt b/docs/build/html/_sources/ABL/Abductive Learning.rst.txt new file mode 100644 index 0000000..47537b3 --- /dev/null +++ b/docs/build/html/_sources/ABL/Abductive Learning.rst.txt @@ -0,0 +1,5 @@ +----------------------------- +Abductive Learning +----------------------------- + +What is ABL \ No newline at end of file diff --git a/docs/build/html/_sources/ABL/Quick Start.rst.txt b/docs/build/html/_sources/ABL/Quick Start.rst.txt new file mode 100644 index 0000000..4c7da0a --- /dev/null +++ b/docs/build/html/_sources/ABL/Quick Start.rst.txt @@ -0,0 +1,52 @@ +--------------- +Quick Start +--------------- + +How to use + +.. contents:: Table of Contents + +Required packages +----------------- + +This package requires the following packages: + +- Python version above 3.x +- ``numpy`` http://www.numpy.org + +The easiest way to get these is to use +`pip `__ or +`conda `__ environment +manager. Typing the following command in your terminal will install all +required packages in your Python environment. + +.. code:: console + + $ conda install numpy + +or + +.. code:: console + + $ pip install numpy + +Getting and installing ABL +---------------------------- + +The easiest way to install ABL is to type ``pip install ABL`` in you +terminal/command line. + +If you want to install ABL by source code, download this project and +sequentially run following commands in your terminal/command line. + +.. code:: console + + $ python setup.py build + $ python setup.py install + +A quick example +--------------- + +Show an example. + +More examples are available in the **Example** part. diff --git a/docs/build/html/_sources/Examples/MNISTadd.rst.txt b/docs/build/html/_sources/Examples/MNISTadd.rst.txt new file mode 100644 index 0000000..c958334 --- /dev/null +++ b/docs/build/html/_sources/Examples/MNISTadd.rst.txt @@ -0,0 +1,3 @@ +------------------------------------- +MNISTadd +------------------------------------- diff --git a/docs/build/html/_sources/README.rst.txt b/docs/build/html/_sources/README.rst.txt new file mode 100644 index 0000000..18600c4 --- /dev/null +++ b/docs/build/html/_sources/README.rst.txt @@ -0,0 +1,27 @@ +ABL +^^^^^^ + +ABL(Abductive Learning) is ... + +Installation +------------- + +ABL is distributed on PyPI and can be installed with ``pip``: + +.. code:: console + + $ pip install ABL + +Alternatively, to install ABL by source code, download this project and sequentially run following commands in your terminal/command line. + +.. code:: console + + $ python setup.py build + $ python setup.py install + + +Releases +-------- +`release 0.1`_ + +.. _release 0.1: https://github.com/AbductiveLearning/ABL-Package/releases/tag/v0.1 \ No newline at end of file diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt new file mode 100644 index 0000000..6acedc2 --- /dev/null +++ b/docs/build/html/_sources/index.rst.txt @@ -0,0 +1,15 @@ +.. include:: README.rst + +.. toctree:: + :caption: Tutorial of ABL + :maxdepth: 2 + + ABL/Abductive Learning.rst + ABL/Quick Start.rst + +.. toctree:: + :caption: Examples + :maxdepth: 2 + + Examples/MNISTadd.rst + diff --git a/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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