From 148c54a3596b2d823346be7795c435f7e5e2eaa9 Mon Sep 17 00:00:00 2001 From: Gao Enhao Date: Fri, 31 Mar 2023 15:44:46 +0800 Subject: [PATCH] [ENH] reform examples --- abl/framework_hed.py | 338 - abl/models/nn.py | 147 - examples/datasets/hed/BK.pl | 83 - examples/datasets/hed/README.md | 4 - examples/datasets/hed/get_hed.py | 130 - examples/datasets/hed/learn_add.pl | 84 - examples/datasets/hwf/README.md | 4 - examples/datasets/hwf/get_hwf.py | 50 - examples/datasets/mnist_add/add.pl | 2 - examples/datasets/mnist_add/get_mnist_add.py | 41 - examples/datasets/mnist_add/test_data.txt | 5000 --- examples/datasets/mnist_add/train_data.txt | 30000 ----------------- examples/hed_example.ipynb | 199 - examples/hwf_example.ipynb | 184 - examples/mnist_add_example.ipynb | 190 - examples/weights/all_weights_here.txt | 0 framework_hed_knn.py | 407 - 17 files changed, 36863 deletions(-) delete mode 100644 abl/framework_hed.py delete mode 100644 abl/models/nn.py delete mode 100644 examples/datasets/hed/BK.pl delete mode 100644 examples/datasets/hed/README.md delete mode 100644 examples/datasets/hed/get_hed.py delete mode 100644 examples/datasets/hed/learn_add.pl delete mode 100644 examples/datasets/hwf/README.md delete mode 100644 examples/datasets/hwf/get_hwf.py delete mode 100644 examples/datasets/mnist_add/add.pl delete mode 100644 examples/datasets/mnist_add/get_mnist_add.py delete mode 100644 examples/datasets/mnist_add/test_data.txt delete mode 100644 examples/datasets/mnist_add/train_data.txt delete mode 100644 examples/hed_example.ipynb delete mode 100644 examples/hwf_example.ipynb delete mode 100644 examples/mnist_add_example.ipynb delete mode 100644 examples/weights/all_weights_here.txt delete mode 100644 framework_hed_knn.py diff --git a/abl/framework_hed.py b/abl/framework_hed.py deleted file mode 100644 index e579cba..0000000 --- a/abl/framework_hed.py +++ /dev/null @@ -1,338 +0,0 @@ -# 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 - -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 = {} - 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 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 - - - - -def train(model, abducer, train_data, test_data, loop_num=50, sample_num=-1, verbose=-1): - train_X, train_Z, train_Y = train_data - test_X, test_Z, test_Y = test_data - - # Set default parameters - if sample_num == -1: - sample_num = len(train_X) - - if verbose < 1: - verbose = loop_num - - char_acc_flag = 1 - if train_Z == None: - char_acc_flag = 0 - train_Z = [None] * len(train_X) - - predict_func = clocker(model.predict) - train_func = clocker(model.train) - abduce_func = clocker(abducer.batch_abduce) - - for loop_idx in range(loop_num): - X, Z, Y = block_sample(train_X, train_Z, train_Y, sample_num, loop_idx) - preds_res = predict_func(X) - abduced_Z = abduce_func(preds_res, Y) - - if ((loop_idx + 1) % verbose == 0) or (loop_idx == loop_num - 1): - res = result_statistics(preds_res['cls'], Z, Y, abducer.kb.logic_forward, char_acc_flag) - INFO('loop: ', loop_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)) - - 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) - - 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, 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, 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, [None] * len(pred_res), [None] * 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.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]) - - 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 - - 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)) - - 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, 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, 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 > 0.95 and false_consist_rule_acc < 0.1: - torch.save(model.cls_list[0].model.state_dict(), "./weights/weights_%d.pth" % equation_len) - break - else: - 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))) - 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/abl/models/nn.py b/abl/models/nn.py deleted file mode 100644 index 7a0f560..0000000 --- a/abl/models/nn.py +++ /dev/null @@ -1,147 +0,0 @@ -# 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 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 - - - -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): - """前向传播函数""" - 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/examples/datasets/hed/BK.pl b/examples/datasets/hed/BK.pl deleted file mode 100644 index 441df4e..0000000 --- a/examples/datasets/hed/BK.pl +++ /dev/null @@ -1,83 +0,0 @@ -:- 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/examples/datasets/hed/README.md b/examples/datasets/hed/README.md deleted file mode 100644 index f48c97d..0000000 --- a/examples/datasets/hed/README.md +++ /dev/null @@ -1,4 +0,0 @@ -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 -``` diff --git a/examples/datasets/hed/get_hed.py b/examples/datasets/hed/get_hed.py deleted file mode 100644 index b317d94..0000000 --- a/examples/datasets/hed/get_hed.py +++ /dev/null @@ -1,130 +0,0 @@ -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/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/examples/datasets/hed/learn_add.pl b/examples/datasets/hed/learn_add.pl deleted file mode 100644 index 35a71c6..0000000 --- a/examples/datasets/hed/learn_add.pl +++ /dev/null @@ -1,84 +0,0 @@ -:- 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), !. - -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 -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -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/examples/datasets/hwf/README.md b/examples/datasets/hwf/README.md deleted file mode 100644 index b8f3047..0000000 --- a/examples/datasets/hwf/README.md +++ /dev/null @@ -1,4 +0,0 @@ -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 -``` diff --git a/examples/datasets/hwf/get_hwf.py b/examples/datasets/hwf/get_hwf.py deleted file mode 100644 index 87da5cf..0000000 --- a/examples/datasets/hwf/get_hwf.py +++ /dev/null @@ -1,50 +0,0 @@ -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, get_pseudo_label): - 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) - if get_pseudo_label: - imgs_pseudo_label.append(img_path.split('/')[0]) - X.append(imgs) - if get_pseudo_label: - Z.append(imgs_pseudo_label) - 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): - if(train): - file = './datasets/hwf/data/expr_train.json' - else: - file = './datasets/hwf/data/expr_test.json' - - return get_data(file, get_pseudo_label) - -if __name__ == "__main__": - 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]) - diff --git a/examples/datasets/mnist_add/add.pl b/examples/datasets/mnist_add/add.pl deleted file mode 100644 index 96f0869..0000000 --- a/examples/datasets/mnist_add/add.pl +++ /dev/null @@ -1,2 +0,0 @@ -pseudo_label(N) :- between(0, 9, N). -logic_forward([Z1, Z2], Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2. diff --git a/examples/datasets/mnist_add/get_mnist_add.py b/examples/datasets/mnist_add/get_mnist_add.py deleted file mode 100644 index d00c6ce..0000000 --- a/examples/datasets/mnist_add/get_mnist_add.py +++ /dev/null @@ -1,41 +0,0 @@ -import torch -import torchvision -from torch.utils.data import Dataset -from torchvision.transforms import transforms - -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])) - - if get_pseudo_label: - return X, Z, Y - else: - return X, None, Y - -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=train, download=True, transform=transform) - - 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) - - -if __name__ == "__main__": - 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]) - diff --git a/examples/datasets/mnist_add/test_data.txt b/examples/datasets/mnist_add/test_data.txt deleted file mode 100644 index 3987383..0000000 --- a/examples/datasets/mnist_add/test_data.txt +++ /dev/null @@ -1,5000 +0,0 @@ -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 1123 10 -5468 4196 8 -5831 6752 1 -9228 6807 14 -5915 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a/examples/hed_example.ipynb +++ /dev/null @@ -1,199 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "import torch.nn as nn\n", - "import torch\n", - "\n", - "from abl.abducer.abducer_base import HED_Abducer\n", - "from abl.abducer.kb import HED_prolog_KB\n", - "\n", - "from abl.utils.plog import logger\n", - "from abl.models.nn import SymbolNet\n", - "from abl.models.basic_model import BasicModel\n", - "from abl.models.wabl_models import WABLBasicModel\n", - "\n", - "from datasets.hed.get_hed import get_hed, split_equation\n", - "from abl import framework_hed" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize logger\n", - "recorder = logger()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logic Part" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR: /home/gaoeh/ABL-Package/examples/datasets/hed/learn_add.pl:67:9: Syntax error: Operator expected\n" - ] - } - ], - "source": [ - "# Initialize knowledge base and abducer\n", - "kb = HED_prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='./datasets/hed/learn_add.pl')\n", - "abducer = HED_Abducer(kb)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Machine Learning Part" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize necessary component for machine learning part\n", - "cls = SymbolNet(\n", - " num_classes=len(kb.pseudo_label_list),\n", - " image_size=(28, 28, 1),\n", - ")\n", - "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", - "criterion = nn.CrossEntropyLoss()\n", - "optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Pretrain NN classifier\n", - "framework_hed.hed_pretrain(kb, cls, recorder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize BasicModel\n", - "# The function of BasicModel is to wrap NN models into the form of an sklearn estimator\n", - "base_model = BasicModel(\n", - " cls,\n", - " criterion,\n", - " optimizer,\n", - " device,\n", - " save_interval=1,\n", - " save_dir=recorder.save_dir,\n", - " batch_size=32,\n", - " num_epochs=1,\n", - " recorder=recorder,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use WABL model to join two parts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = WABLBasicModel(base_model, kb.pseudo_label_list)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "total_train_data = get_hed(train=True)\n", - "train_data, val_data = split_equation(total_train_data, 3, 1)\n", - "test_data = get_hed(train=False)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train and save" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model, mapping = framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8)\n", - "framework_hed.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8)\n", - "\n", - "recorder.dump()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ABL", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/hwf_example.ipynb b/examples/hwf_example.ipynb deleted file mode 100644 index 894413c..0000000 --- a/examples/hwf_example.ipynb +++ /dev/null @@ -1,184 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "import torch.nn as nn\n", - "import torch\n", - "\n", - "from abl.abducer.abducer_base import AbducerBase\n", - "from abl.abducer.kb import HWF_KB\n", - "\n", - "from abl.utils.plog import logger\n", - "from abl.models.nn import SymbolNet\n", - "from abl.models.basic_model import BasicModel\n", - "from abl.models.wabl_models import WABLBasicModel\n", - "\n", - "from datasets.hwf.get_hwf import get_hwf\n", - "from abl import framework_hed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize logger\n", - "recorder = logger()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logic Part" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize knowledge base and abducer\n", - "kb = HWF_KB(GKB_flag=True)\n", - "abducer = AbducerBase(kb)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Machine Learning Part" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize necessary component for machine learning part\n", - "cls = SymbolNet(num_classes=len(kb.pseudo_label_list), image_size=(45, 45, 1))\n", - "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", - "criterion = nn.CrossEntropyLoss()\n", - "optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize BasicModel\n", - "# The function of BasicModel is to wrap NN models into the form of an sklearn estimator\n", - "base_model = BasicModel(\n", - " cls,\n", - " criterion,\n", - " optimizer,\n", - " device,\n", - " save_interval=1,\n", - " save_dir=recorder.save_dir,\n", - " batch_size=32,\n", - " num_epochs=1,\n", - " recorder=recorder,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use WABL model to join two parts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize WABL model\n", - "# The main function of the WABL model is to serialize data and \n", - "# provide a unified interface for different machine learning models\n", - "model = WABLBasicModel(base_model, kb.pseudo_label_list)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get training and testing data\n", - "train_data = get_hwf(train=True, get_pseudo_label=True)\n", - "test_data = get_hwf(train=False, get_pseudo_label=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train and save" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train model\n", - "framework_hed.train(\n", - " model, abducer, train_data, test_data, loop_num=15, sample_num=5000, verbose=1\n", - ")\n", - "\n", - "# Save results\n", - "recorder.dump()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ABL", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/mnist_add_example.ipynb b/examples/mnist_add_example.ipynb deleted file mode 100644 index 49894c6..0000000 --- a/examples/mnist_add_example.ipynb +++ /dev/null @@ -1,190 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "import torch.nn as nn\n", - "import torch\n", - "\n", - "from abl.abducer.abducer_base import AbducerBase\n", - "from abl.abducer.kb import add_KB\n", - "\n", - "from abl.utils.plog import logger\n", - "from abl.models.nn import LeNet5\n", - "from abl.models.basic_model import BasicModel\n", - "from abl.models.wabl_models import WABLBasicModel\n", - "\n", - "from datasets.mnist_add.get_mnist_add import get_mnist_add\n", - "from abl import framework_hed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize logger\n", - "recorder = logger()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logic Part" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize knowledge base and abducer\n", - "kb = add_KB(GKB_flag=True)\n", - "abducer = AbducerBase(kb, dist_func=\"confidence\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Machine Learning Part" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize necessary component for machine learning part\n", - "cls = LeNet5(num_classes=len(kb.pseudo_label_list))\n", - "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", - "criterion = nn.CrossEntropyLoss()\n", - "optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize BasicModel\n", - "# The function of BasicModel is to wrap NN models into the form of an sklearn estimator\n", - "base_model = BasicModel(\n", - " cls,\n", - " criterion,\n", - " optimizer,\n", - " device,\n", - " save_interval=1,\n", - " save_dir=recorder.save_dir,\n", - " batch_size=32,\n", - " num_epochs=1,\n", - " recorder=recorder,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use WABL model to join two parts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize WABL model\n", - "# The main function of the WABL model is to serialize data and \n", - "# provide a unified interface for different machine learning models\n", - "model = WABLBasicModel(base_model, kb.pseudo_label_list)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get training and testing data\n", - "train_X, train_Z, train_Y = get_mnist_add(train=True, get_pseudo_label=True)\n", - "test_X, test_Z, test_Y = get_mnist_add(train=False, get_pseudo_label=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train and save" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train model\n", - "framework_hed.train(\n", - " model,\n", - " abducer,\n", - " (train_X, train_Z, train_Y),\n", - " (test_X, test_Z, test_Y),\n", - " loop_num=15,\n", - " sample_num=5000,\n", - " verbose=1,\n", - ")\n", - "\n", - "# Save results\n", - "recorder.dump()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ABL", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/weights/all_weights_here.txt b/examples/weights/all_weights_here.txt deleted file mode 100644 index e69de29..0000000 diff --git a/framework_hed_knn.py b/framework_hed_knn.py deleted file mode 100644 index a5e5506..0000000 --- a/framework_hed_knn.py +++ /dev/null @@ -1,407 +0,0 @@ -# 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