[MNT] delete framework since it has been replaced by bridge

abl/framework.py

@@ -1,108 +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   ：
-#
-# ================================================================#
-
-from .utils.plog import INFO, clocker
-from .utils.utils import block_sample, float_parameter
-
-
-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, epochs=50, sample=-1, verbose=-1):
-    train_X, train_Z, train_Y = train_data
-
-    # Set default parameters
-    sample_num = float_parameter(sample, len(train_X))
-    part_num = (len(train_X) - 1) // sample_num + 1
-
-    if verbose < 1:
-        verbose = epochs
-
-    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 epoch in range(epochs):
-        for seg_idx in range(part_num):
-            X, Z, Y = block_sample(train_X, train_Z, train_Y, sample_num, seg_idx)
-            INFO("epoch:", epoch + 1, ", seg_idx:", seg_idx + 1, "/", part_num, ", data num:", len(X))
-            
-            preds_res = predict_func(X)
-            abduced_Z = abduce_func(preds_res, Y)
-
-            ## TODO: change verbose
-            if ((seg_idx + 1) % verbose == 0) or (seg_idx == epochs - 1):
-                pseudo_label = [[abducer.mapping[label] for label in formula] for formula in preds_res['label']]
-                res = result_statistics(pseudo_label, Z, Y, abducer.kb.logic_forward, char_acc_flag)
-                INFO("seg: ", seg_idx + 1, " ", res)
-
-            finetune_X, finetune_Z = filter_data(X, abduced_Z)
-            finetune_Z = [[abducer.remapping[symbol] for symbol in formula] for formula in finetune_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 model
-
-## TODO: test
-def test(model, abducer, test_data):
-    test_X, test_Z, test_Y = test_data
-    predict_func = clocker(model.predict)
-    preds_res = predict_func(test_X)
-    
-    char_acc_flag = 1
-    if test_Z == None:
-        char_acc_flag = 0
-        test_Z = [None] * len(test_X)
-        
-    res = result_statistics(preds_res["cls"], test_Z, test_Y, abducer.kb.logic_forward, char_acc_flag)
-    INFO(res)
-
-if __name__ == "__main__":
-    pass