Update framework.py

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