ABLkit/framework.py

# 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.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:
        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[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 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(Y, pseudo_Z, logic_forward):
    abl_acc = 0
    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
    
    
# 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:
            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 = 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)

    epochs = 50
    
    # Abductive learning train process
    for epoch_idx in range(epochs):
        X, Z, Y = block_sample(X, Z, Y, sample_num, epoch_idx)
        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)
        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_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):
#     # 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