full_connected_layer_units=[[hidden1,0.5],[hidden2,0.5]], embedding_dropout_rate=0., nb_epoch=30, earlyStoping_patience=config['earlyStoping_patience'], lr = config['lr'], batch_size = batch_size, embedding_weight_trainable = True, embedding_init_weight=init_weight, ) print (w2v_embedding_cnn.embedding_layer_output.get_weights()[0][1]) w2v_embedding_cnn.print_model_descibe() print('+'*80) # 训练模型 train_loss, train_accuracy, val_loss, val_accuracy = w2v_embedding_cnn.fit((train_X_feature, train_y), (test_X_feature, test_y)) print (w2v_embedding_cnn.embedding_layer_output.get_weights()[0][1]) print('dev:%f,%f' % (train_loss, train_accuracy)) print('val:%f,%f' % (val_loss, val_accuracy)) quit() # train # w2v_embedding_cnn.accuracy((train_X_feature, train_y)) end_time = timeit.default_timer() print('end! Running time:%ds!' % (end_time - start_time)) logging.debug('=' * 20) logging.debug('end! Running time:%ds!' % (end_time - start_time)) logging.debug('=' * 20)
input_dim=feature_encoder.vocabulary_size + 1, word_embedding_dim=config['word_embedding_dim'], input_length=config['sentence_padding_length'], num_labels=len(label_to_index), conv_filter_type=config['conv_filter_type'], k=config['kmax_k'], embedding_dropout_rate=config['embedding_dropout_rate'], output_dropout_rate=config['output_dropout_rate'], nb_epoch=int(config['cnn_nb_epoch']), earlyStoping_patience=config['earlyStoping_patience'], ) rand_embedding_cnn.print_model_descibe() if config['refresh_all_model'] or not os.path.exists(model_file_path): # 训练模型 rand_embedding_cnn.fit((train_X_feature, train_y), (test_X_feature, test_y)) # 保存模型 rand_embedding_cnn.save_model(model_file_path) else: # 从保存的pickle中加载模型 rand_embedding_cnn.model_from_pickle(model_file_path) # -------------- code start : 结束 ------------- if verbose > 2: logging.debug('-' * 20) print '-' * 20 # -------------- region end : 3. 初始化CNN模型并训练 --------------- # -------------- region start : 4. 预测 ------------- if verbose > 1: logging.debug('-' * 20)
class RFAndWordEmbeddingCnnMerge(object): # 如果使用全体数据作为字典,则使用这个变量来存放权重,避免重复加载权重,因为每次加载的权重都是一样的。 train_data_weight = None # 验证数据是一份权重,不包含测试集了 val_data_weight = None def __init__(self, feature_encoder, num_filter, num_labels, n_estimators, word2vec_model_file_path, **kwargs): if kwargs.get('rand_weight', False): # CNN(rand)模式 weight = None elif kwargs['dataset_flag'] == 0: if RFAndWordEmbeddingCnnMerge.train_data_weight is None: # 训练集 RFAndWordEmbeddingCnnMerge.train_data_weight = feature_encoder.to_embedding_weight( word2vec_model_file_path) weight = RFAndWordEmbeddingCnnMerge.train_data_weight else: # kwargs['dataset_flag']>0 if RFAndWordEmbeddingCnnMerge.val_data_weight is None: RFAndWordEmbeddingCnnMerge.val_data_weight = feature_encoder.to_embedding_weight( word2vec_model_file_path) weight = RFAndWordEmbeddingCnnMerge.val_data_weight # print(weight) self.static_w2v_cnn = WordEmbeddingCNN( rand_seed=1377, verbose=kwargs.get('verbose', 0), feature_encoder=feature_encoder, # optimizers='adadelta', optimizers='sgd', # 当使用CNN (rand) 模式的时候使用到了 word_embedding_dim=50, # 设置embedding使用训练好的w2v模型初始化 embedding_init_weight=weight, # 默认设置为训练时embedding层权重不变 embedding_weight_trainable=kwargs.get('embedding_weight_trainable', False), num_labels=num_labels, l1_conv_filter_type=[ [num_filter, 3, -1, 'valid', (-1, 1), 0.5, 'relu', 'none'], [num_filter, 4, -1, 'valid', (-1, 1), 0., 'relu', 'none'], [num_filter, 5, -1, 'valid', (-1, 1), 0., 'relu', 'none'], ], l2_conv_filter_type=[], full_connected_layer_units=[], embedding_dropout_rate=0., nb_epoch=kwargs.get('nb_epoch', 25), batch_size=kwargs.get('batch_size', 32), earlyStoping_patience=30, lr=kwargs.get('lr', 1e-2), show_validate_accuracy=True if kwargs.get('verbose', 0) > 0 else False, # output_regularizer=('l2', 0.5), output_constraints=('maxnorm', 3), # 必须设为True,才能取中间结果做特征 save_middle_output=True, ) self.bow_randomforest = BowRandomForest( rand_seed=1377, verbose=kwargs.get('verbose', 0), feature_encoder=feature_encoder, # optimizers='adadelta', n_estimators=n_estimators, min_samples_leaf=1, ) def fit(self, train_data=None, validation_data=None): train_X, train_y = train_data validation_X, validation_y = validation_data self.static_w2v_cnn.fit(train_data, validation_data) train_x_features = self.static_w2v_cnn.get_layer_output(train_X)[4] validation_x_features = self.static_w2v_cnn.get_layer_output( validation_X)[4] return self.bow_randomforest.fit((train_x_features, train_y), (validation_x_features, validation_y))
conv_filter_type=config['conv_filter_type'], k=config['kmax_k'], embedding_dropout_rate=config['embedding_dropout_rate'], output_dropout_rate=config['output_dropout_rate'], nb_epoch=int(config['cnn_nb_epoch']), earlyStoping_patience=config['earlyStoping_patience'], feature_encoder=feature_encoder.vocabulary_size+1, optimizers='sgd', lr= 1e-1, batch_size = 128, ) w2v_embedding_cnn.print_model_descibe() if config['refresh_all_model'] or not os.path.exists(model_file_path): # 训练模型 w2v_embedding_cnn.fit((train_w2v_features, train_y), (test_w2v_features, test_y)) # 保存模型 w2v_embedding_cnn.save_model(model_file_path) else: # 从保存的pickle中加载模型 w2v_embedding_cnn.model_from_pickle(model_file_path) # -------------- code start : 结束 ------------- if verbose > 2: logging.debug('-' * 20) print '-' * 20 # -------------- region end : 3. 初始化CNN模型并训练 --------------- print index_to_label[w2v_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))]
nb_epoch=int(config["cnn_nb_epoch"]), earlyStoping_patience=config["earlyStoping_patience"], lr=config["lr"], batch_size=config["batch_size"], embedding_weight_trainable=True, ) rand_embedding_cnn.print_model_descibe() if config["refresh_all_model"] or not os.path.exists(model_file_path): print ("+" * 80) # 训练模型 print (rand_embedding_cnn.embedding_layer_output.get_weights()[0][1]) train_loss, train_accuracy, val_loss, val_accuracy = rand_embedding_cnn.fit( (train_X_feature, train_y), (test_all_X_feature, test_all_y) ) print (rand_embedding_cnn.embedding_layer_output.get_weights()[0][1]) # y_pred, is_correct, accu, f1 = rand_embedding_cnn.accuracy((test_all_X_feature, test_all_y)) # # print 'F1(macro)为:%f' % (np.average(f1)) # # # train # rand_embedding_cnn.accuracy((train_X_feature, train_y)) print ("dev:%f,%f" % (train_loss, train_accuracy)) print ("val:%f,%f" % (val_loss, val_accuracy)) quit() # 五折 print ("五折") counter = 0
word_embedding_dim=config['word_embedding_dim'], embedding_init_weight=feature_encoder.to_embedding_weight(word2vec_file_path), input_length=config['sentence_padding_length'], num_labels=len(label_to_index), conv_filter_type=config['conv_filter_type'], k=config['kmax_k'], embedding_dropout_rate=config['embedding_dropout_rate'], output_dropout_rate=config['output_dropout_rate'], nb_epoch=int(config['cnn_nb_epoch']), earlyStoping_patience=config['earlyStoping_patience'], ) rand_embedding_cnn.print_model_descibe() if config['refresh_all_model'] or not os.path.exists(model_file_path): # 训练模型 rand_embedding_cnn.fit((feature_encoder.train_padding_index, train_y), (map(feature_encoder.transform_sentence, test_X), test_y)) # 保存模型 rand_embedding_cnn.save_model(model_file_path) else: # 从保存的pickle中加载模型 rand_embedding_cnn.model_from_pickle(model_file_path) # -------------- code start : 结束 ------------- if verbose > 2: logging.debug('-' * 20) print '-' * 20 # -------------- region end : 3. 初始化CNN模型并训练 --------------- # -------------- region start : 4. 预测 ------------- if verbose > 1: logging.debug('-' * 20)
class RFAndWordEmbeddingCnnMerge(CnnBaseClass): __version__ = '1.4' # 如果使用全体数据作为字典,则使用这个变量来存放权重,避免重复加载权重,因为每次加载的权重都是一样的。 train_data_weight = None # 验证数据是一份权重,不包含测试集了 val_data_weight = None def __init__(self, feature_encoder, num_filter, num_labels, n_estimators, word2vec_model_file_path, **kwargs): self.static_w2v_cnn = None self.bow_randomforest = None self.feature_encoder = feature_encoder if not kwargs.get('init_model', True): # 不初始化模型,一般在恢复模型时候用 return if kwargs.get('rand_weight', False): # CNN(rand)模式 weight = None elif kwargs['dataset_flag'] == 0: # 训练集 if RFAndWordEmbeddingCnnMerge.train_data_weight is None: # 训练集 RFAndWordEmbeddingCnnMerge.train_data_weight = feature_encoder.to_embedding_weight( word2vec_model_file_path) weight = RFAndWordEmbeddingCnnMerge.train_data_weight else: # kwargs['dataset_flag']>0 if RFAndWordEmbeddingCnnMerge.val_data_weight is None: RFAndWordEmbeddingCnnMerge.val_data_weight = feature_encoder.to_embedding_weight( word2vec_model_file_path) weight = RFAndWordEmbeddingCnnMerge.val_data_weight # print(weight) self.static_w2v_cnn = WordEmbeddingCNN( rand_seed=1377, verbose=kwargs.get('verbose', 0), feature_encoder=feature_encoder, # optimizers='adadelta', optimizers='sgd', # 当使用CNN (rand) 模式的时候使用到了 word_embedding_dim=50, # 设置embedding使用训练好的w2v模型初始化 embedding_init_weight=weight, # 默认设置为训练时embedding层权重不变 embedding_weight_trainable=kwargs.get('embedding_weight_trainable', False), num_labels=num_labels, l1_conv_filter_type=[ [num_filter, 3, -1, 'valid', (-1, 1), 0.5, 'relu', 'none'], [num_filter, 4, -1, 'valid', (-1, 1), 0., 'relu', 'none'], [num_filter, 5, -1, 'valid', (-1, 1), 0., 'relu', 'none'], ], l2_conv_filter_type=[], full_connected_layer_units=[], embedding_dropout_rate=0., nb_epoch=kwargs.get('nb_epoch', 25), batch_size=kwargs.get('batch_size', 32), earlyStoping_patience=30, lr=kwargs.get('lr', 1e-2), show_validate_accuracy=True if kwargs.get('verbose', 0) > 0 else False, # output_regularizer=('l2', 0.5), output_constraints=('maxnorm', 3), # 必须设为True,才能取中间结果做特征 save_middle_output=True, ) self.bow_randomforest = BowRandomForest( rand_seed=1377, verbose=kwargs.get('verbose', 0), feature_encoder=feature_encoder, # optimizers='adadelta', n_estimators=n_estimators, min_samples_leaf=1, ) def fit(self, train_data=None, validation_data=None): train_X, train_y = train_data validation_X, validation_y = validation_data self.static_w2v_cnn.fit(train_data, validation_data) train_x_features = self.static_w2v_cnn.get_layer_output(train_X)[4] validation_x_features = self.static_w2v_cnn.get_layer_output( validation_X)[4] return self.bow_randomforest.fit((train_x_features, train_y), (validation_x_features, validation_y)) def save_model(self, path): """ 保存模型,保存成pickle形式 :param path: 模型保存的路径 :type path: 模型保存的路径 :return: """ model_file = open(path, 'wb') pickle.dump(self.feature_encoder, model_file) pickle.dump(self.static_w2v_cnn, model_file) pickle.dump(self.bow_randomforest, model_file) def model_from_pickle(self, path): ''' 从模型文件中直接加载模型 :param path: :return: RandEmbeddingCNN object ''' model_file = file(path, 'rb') self.feature_encoder = pickle.load(model_file) self.static_w2v_cnn = pickle.load(model_file) self.bow_randomforest = pickle.load(model_file) @staticmethod def get_feature_encoder(**kwargs): """ 获取该分类器的特征编码器 :param kwargs: 可设置参数 [ input_length(*), full_mode(#,False), feature_type(#,word),verbose(#,0)],加*表示必须提供,加#表示可选,不写则默认。 :return: """ assert kwargs.has_key('input_length'), '请提供 input_length 的属性值' from data_processing_util.feature_encoder.onehot_feature_encoder import FeatureEncoder feature_encoder = FeatureEncoder( need_segmented=kwargs.get('need_segmented', True), sentence_padding_length=kwargs['input_length'], verbose=kwargs.get('verbose', 0), full_mode=kwargs.get('full_mode', False), remove_stopword=True, replace_number=True, lowercase=True, zhs2zht=True, remove_url=True, padding_mode='center', add_unkown_word=True, feature_type=kwargs.get('feature_type', 'word'), vocabulary_including_test_set=kwargs.get( 'vocabulary_including_test_set', True), update_dictionary=kwargs.get('update_dictionary', True)) return feature_encoder def batch_predict_bestn(self, sentences, transform_input=False, bestn=1): """ 批量预测句子的类别,对输入的句子进行预测 :param sentences: 测试句子, :type sentences: array-like :param transform_input: 是否转换句子,如果为True,输入原始字符串句子即可,内部已实现转换成字典索引的形式。 :type transform_input: bool :param bestn: 预测,并取出bestn个结果。 :type bestn: int :return: y_pred_result, y_pred_score """ if transform_input: sentences = self.static_w2v_cnn.transform(sentences) # sentences = np.asarray(sentences) # assert len(sentences.shape) == 2, 'shape必须是2维的!' train_x_features = self.static_w2v_cnn.get_layer_output(sentences)[4] # print(train_x_features) # print(train_x_features.shape) return self.bow_randomforest.batch_predict_bestn(train_x_features, transform_input=False, bestn=bestn)
class RFAndWordEmbeddingCnnMerge(CnnBaseClass): __version__ = '1.4' # 如果使用全体数据作为字典,则使用这个变量来存放权重,避免重复加载权重,因为每次加载的权重都是一样的。 train_data_weight = None # 验证数据是一份权重,不包含测试集了 val_data_weight = None def __init__(self, feature_encoder, num_filter, num_labels, n_estimators, word2vec_model_file_path, **kwargs ): self.static_w2v_cnn = None self.bow_randomforest = None self.feature_encoder = feature_encoder if not kwargs.get('init_model', True): # 不初始化模型,一般在恢复模型时候用 return if kwargs.get('rand_weight', False): # CNN(rand)模式 weight = None elif kwargs['dataset_flag'] == 0: # 训练集 if RFAndWordEmbeddingCnnMerge.train_data_weight is None: # 训练集 RFAndWordEmbeddingCnnMerge.train_data_weight = feature_encoder.to_embedding_weight( word2vec_model_file_path) weight = RFAndWordEmbeddingCnnMerge.train_data_weight else: # kwargs['dataset_flag']>0 if RFAndWordEmbeddingCnnMerge.val_data_weight is None: RFAndWordEmbeddingCnnMerge.val_data_weight = feature_encoder.to_embedding_weight( word2vec_model_file_path) weight = RFAndWordEmbeddingCnnMerge.val_data_weight # print(weight) self.static_w2v_cnn = WordEmbeddingCNN( rand_seed=1377, verbose=kwargs.get('verbose', 0), feature_encoder=feature_encoder, # optimizers='adadelta', optimizers='sgd', # 当使用CNN (rand) 模式的时候使用到了 word_embedding_dim=50, # 设置embedding使用训练好的w2v模型初始化 embedding_init_weight=weight, # 默认设置为训练时embedding层权重不变 embedding_weight_trainable=kwargs.get('embedding_weight_trainable', False), num_labels=num_labels, l1_conv_filter_type=[ [num_filter, 3, -1, 'valid', (-1, 1), 0.5, 'relu', 'none'], [num_filter, 4, -1, 'valid', (-1, 1), 0., 'relu', 'none'], [num_filter, 5, -1, 'valid', (-1, 1), 0., 'relu', 'none'], ], l2_conv_filter_type=[], full_connected_layer_units=[], embedding_dropout_rate=0., nb_epoch=kwargs.get('nb_epoch', 25), batch_size=kwargs.get('batch_size', 32), earlyStoping_patience=30, lr=kwargs.get('lr', 1e-2), show_validate_accuracy=True if kwargs.get('verbose', 0) > 0 else False, # output_regularizer=('l2', 0.5), output_constraints=('maxnorm', 3), # 必须设为True,才能取中间结果做特征 save_middle_output=True, ) self.bow_randomforest = BowRandomForest( rand_seed=1377, verbose=kwargs.get('verbose', 0), feature_encoder=feature_encoder, # optimizers='adadelta', n_estimators=n_estimators, min_samples_leaf=1, ) def fit(self, train_data=None, validation_data=None): train_X, train_y = train_data validation_X, validation_y = validation_data self.static_w2v_cnn.fit(train_data, validation_data) train_x_features = self.static_w2v_cnn.get_layer_output(train_X)[4] validation_x_features = self.static_w2v_cnn.get_layer_output(validation_X)[4] return self.bow_randomforest.fit((train_x_features, train_y), (validation_x_features, validation_y)) def save_model(self, path): """ 保存模型,保存成pickle形式 :param path: 模型保存的路径 :type path: 模型保存的路径 :return: """ model_file = open(path, 'wb') pickle.dump(self.feature_encoder, model_file) pickle.dump(self.static_w2v_cnn, model_file) pickle.dump(self.bow_randomforest, model_file) def model_from_pickle(self, path): ''' 从模型文件中直接加载模型 :param path: :return: RandEmbeddingCNN object ''' model_file = file(path, 'rb') self.feature_encoder = pickle.load(model_file) self.static_w2v_cnn = pickle.load(model_file) self.bow_randomforest = pickle.load(model_file) @staticmethod def get_feature_encoder(**kwargs): """ 获取该分类器的特征编码器 :param kwargs: 可设置参数 [ input_length(*), full_mode(#,False), feature_type(#,word),verbose(#,0)],加*表示必须提供,加#表示可选,不写则默认。 :return: """ assert kwargs.has_key('input_length'), '请提供 input_length 的属性值' from data_processing_util.feature_encoder.onehot_feature_encoder import FeatureEncoder feature_encoder = FeatureEncoder( need_segmented=kwargs.get('need_segmented', True), sentence_padding_length=kwargs['input_length'], verbose=kwargs.get('verbose', 0), full_mode=kwargs.get('full_mode', False), remove_stopword=True, replace_number=True, lowercase=True, zhs2zht=True, remove_url=True, padding_mode='center', add_unkown_word=True, feature_type=kwargs.get('feature_type', 'word'), vocabulary_including_test_set=kwargs.get('vocabulary_including_test_set', True), update_dictionary=kwargs.get('update_dictionary', True) ) return feature_encoder def batch_predict_bestn(self, sentences, transform_input=False, bestn=1): """ 批量预测句子的类别,对输入的句子进行预测 :param sentences: 测试句子, :type sentences: array-like :param transform_input: 是否转换句子,如果为True,输入原始字符串句子即可,内部已实现转换成字典索引的形式。 :type transform_input: bool :param bestn: 预测,并取出bestn个结果。 :type bestn: int :return: y_pred_result, y_pred_score """ if transform_input: sentences = self.static_w2v_cnn.transform(sentences) # sentences = np.asarray(sentences) # assert len(sentences.shape) == 2, 'shape必须是2维的!' train_x_features = self.static_w2v_cnn.get_layer_output(sentences)[4] # print(train_x_features) # print(train_x_features.shape) return self.bow_randomforest.batch_predict_bestn(train_x_features, transform_input=False, bestn=bestn)
full_connected_layer_units=config['full_connected_layer_units'], embedding_dropout_rate=config['embedding_dropout_rate'], output_dropout_rate=config['output_dropout_rate'], nb_epoch=int(config['cnn_nb_epoch']), earlyStoping_patience=config['earlyStoping_patience'], lr = config['lr'], batch_size = config['batch_size'], ) rand_embedding_cnn.print_model_descibe() if config['refresh_all_model'] or not os.path.exists(model_file_path): print('+'*80) # 训练模型 rand_embedding_cnn.fit((train_X_feature, train_y), (test_all_X_feature, test_all_y)) y_pred, is_correct, accu, f1 = rand_embedding_cnn.accuracy((test_all_X_feature, test_all_y)) print 'F1(macro)为:%f' % (np.average(f1)) # train rand_embedding_cnn.accuracy((train_X_feature, train_y)) # 五折 print('五折') counter = 0 for dev_X,dev_y,val_X,val_y in data_util.get_k_fold_data(k=5, data=(train_X_feature, train_y)): counter += 1 # quit()