[MNT] remove examples/share_example.py and examples/nonshare_example.py

examples/nonshare_example.py

@@ -1,97 +0,0 @@
-# coding: utf-8
-#================================================================#
-#   Copyright (C) 2021 Freecss All rights reserved.
-#   
-#   File Name     ：nonshare_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
-
-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 ClsKB, RegKB
-
-def run_test(params):
-    code_len, times, code_num, share, model_type, need_prob, letter_num = params
-
-    if share:
-        result_dir = "share_result"
-    else:
-        result_dir = "non_share_result"
-
-    recoder_file_path = f"{result_dir}/random_{times}_{code_len}_{code_num}_{model_type}_{need_prob}.pk"
-
-    words = code_generator(code_len, code_num, letter_num)
-    kb = ClsKB(words)
-    abducer = AbducerBase(kb, dist_func = "confidence", pred_res_parse = lambda x : x["prob"])
-
-    label_lists = [[] for _ in range(code_len)]
-    for widx, word in enumerate(words):
-        for cidx, c in enumerate(word):
-            label_lists[cidx].append(c)
-
-    if share:
-        label_lists = [sum(label_lists, [])]
-
-    recoder = logger()
-    recoder.set_savefile("test.log")
-    for idx, err in enumerate(range(15, 41)):
-        start = time.process_time()
-        err = err / 40.
-        if 1 - err < (1. / letter_num):
-            break
-
-        print("Start expriment", idx)
-        if model_type == "KNN":
-            model = KNN(code_len, label_lists = label_lists, share=share)
-        elif model_type == "DT":
-            model = DecisionTree(code_len, label_lists = label_lists, share=share)
-
-        pre_X, pre_Y = generate_data_via_codes(words, err, letter_num)
-        X, Y = generate_data_via_codes(words, 0, letter_num)
-
-        str_words = ["".join(str(c) for c in word) for word in words]
-
-        recoder.print(str_words)
-
-        model.train(pre_X, pre_Y)
-        abl_epoch = 30
-        res = framework.train(model, abducer, X, Y, sample_num = 10000, verbose = 1)
-        print("Initial data accuracy:", 1 - err)
-        print("Abd word accuracy:    ", res["accuracy_word"] * 1.0 / res["total_word"])
-        print("Abd char accuracy:    ", res["accuracy_abd_char"] * 1.0 / res["total_abd_char"])
-        print("Ori char accuracy:    ", res["accuracy_ori_char"] * 1.0 / res["total_ori_char"])
-        print("End expriment", idx)
-        print()
-
-    recoder.dump(open(recoder_file_path, "wb"))
-    return True
-
-if __name__ == "__main__":
-    os.system("mkdir share_result")
-    os.system("mkdir non_share_result")
-    
-    for times in range(5):
-        for code_num in [32, 64, 128]:
-            params = [11, times, code_num, False, "KNN", True, 2]
-            run_test(params)
-
-            params = [11, times, code_num, False, "KNN", False, 2]
-            run_test(params)
-
-    #params = [11, 0, 32, False, "DT", False, 2]
-    #run_test(params)
-

examples/share_example.py

@@ -1,96 +0,0 @@
-# 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
-
-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 ClsKB, RegKB
-
-def run_test(params):
-    code_len, times, code_num, share, model_type, need_prob, letter_num = params
-
-    if share:
-        result_dir = "share_result"
-    else:
-        result_dir = "non_share_result"
-
-    recoder_file_path = f"{result_dir}/random_{times}_{code_len}_{code_num}_{model_type}_{need_prob}.pk"#
-
-    words = code_generator(code_len, code_num, letter_num)
-    kb = ClsKB(words)
-    abducer = AbducerBase(kb)
-
-    label_lists = [[] for _ in range(code_len)]
-    for widx, word in enumerate(words):
-        for cidx, c in enumerate(word):
-            label_lists[cidx].append(c)
-
-    if share:
-        label_lists = [sum(label_lists, [])]
-
-    recoder = logger()
-    recoder.set_savefile("test.log")
-    for idx, err in enumerate(range(0, 41)):
-        print("Start expriment", idx)
-        start = time.process_time()
-        err = err / 40.
-        if 1 - err < (1. / letter_num):
-            break
-        if model_type == "KNN":
-            model = KNN(code_len, label_lists = label_lists, share=share)
-        elif model_type == "DT":
-            model = DecisionTree(code_len, label_lists = label_lists, share=share)
-
-        pre_X, pre_Y = generate_data_via_codes(words, err, letter_num)
-        X, Y = generate_data_via_codes(words, 0, letter_num)
-
-        str_words = ["".join(str(c) for c in word) for word in words]
-
-        recoder.print(str_words)
-
-        model.train(pre_X, pre_Y)
-        abl_epoch = 30
-        res = framework.train(model, abducer, X, Y, sample_num = 10000, verbose = 1)
-        print("Initial data accuracy:", 1 - err)
-        print("Abd word accuracy:    ", res["accuracy_word"] * 1.0 / res["total_word"])
-        print("Abd char accuracy:    ", res["accuracy_abd_char"] * 1.0 / res["total_abd_char"])
-        print("Ori char accuracy:    ", res["accuracy_ori_char"] * 1.0 / res["total_ori_char"])
-        print("End expriment", idx)
-        print()
-
-    recoder.dump(open(recoder_file_path, "wb"))
-    return True
-
-if __name__ == "__main__":
-    os.system("mkdir share_result")
-    os.system("mkdir non_share_result")
-    
-    for times in range(5):
-        for code_num in [32, 64, 128]:
-            params = [11, times, code_num, True, "KNN", True, 2]
-            run_test(params)
-
-            params = [11, times, code_num, True, "KNN", False, 2]
-            run_test(params)
-
-    #params = [11, 0, 32, True, "DT", True, 2]
-    #run_test(params)
-