├── README.md
├── cnn_abstract.py
├── cnn_char.py
├── cnn_char_predict.py
├── cnn_char_punc.py
├── cnn_char_punc_predict.py
├── cnn_char_punc_train.py
├── cnn_char_train.py
├── config
    └── cnn_parameters.json
├── data
    └── 1
├── data_helper.py
├── lstm_example.py
├── model_stacking.py
├── models
    └── 1
├── predict.py
├── result
    └── 1
├── svm.py
└── test.py


/README.md:
--------------------------------------------------------------------------------
 1 | # SMP2018-task1
 2 | 深度学习用于今日日头条用户画像
 3 | 
 4 | 相关文件说明
 5 | <p>-- config/     网络结构的参数配置
 6 | <p>-- data/       原数据，参考SMP2018官网数据下载
 7 | <p>-- models/     训练的模型保存文件夹
 8 | <p>-- results/    最后的输出结果保存文件夹
 9 | 
10 | 
11 | <p>-- data_helper.py 数据预处理过程，生成相应的文件
12 | <p>-- cnn_char.py  以字为单位的cnn网络结构
13 | <p>-- cnn_char_predict.py 以字（char）为单位的cnn在测试集上的预测结果
14 | <p>-- cnn_char_train.py   以字（char）为单位的cnn在训练集上的训练过程
15 | <p>-- cnn_char_punc.py    以字为单位并增加标点符号等的其他特征的cnn网络结构
16 | <p>-- cnn_char_punc_train.py    以字为单位并增加标点符号等的其他特征在训练集上的训练过程
17 | <p>-- cnn_char_punc_predict.py    以字为单位并增加标点符号等的在测试集上的预测结果
18 | 
19 | <p>--lstm_example.py    lstm实现分类在手写数字识别上的简单实例
20 | 
21 | 
22 | <font size =10>机器学习方法<font/>
23 | <p>--svm.py   使用机器学习方法SVM的训练过程     
24 | <p>--predict.py  使用SVM的预测过程
25 | 


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/cnn_abstract.py:
--------------------------------------------------------------------------------
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/cnn_char_predict.py:
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/cnn_char_punc.py:
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  1 | # encoding:utf-8
  2 | """
  3 | @author = 'XXY'
  4 | @contact = '529379497@qq.com'
  5 | @researchFie1d = 'NLP DL ML'
  6 | @date= '2017/12/21 10:18'
  7 | """
  8 | import numpy as np
  9 | import tensorflow as tf
 10 | from tensorflow.contrib import rnn
 11 | 
 12 | class TextCNN(object):
 13 | 	def __init__(self, sequence_length_char, sequence_length_punc, num_classes, vocab_size_char, vocab_size_punc,
 14 | 				 embedding_size, filter_sizes_char, filter_sizes_punc, num_filters, l2_reg_lambda=0.0):
 15 | 		'''
 16 | 		:param sequence_length: 表示文本长度，多少个词
 17 | 		:param num_classes: 	待分类的类别个数
 18 | 		:param vocab_size: 		词库的大小，表示构建的词库有多大
 19 | 		:param embedding_size:  词向量维度大小
 20 | 		:param filter_sizes:	卷积核的尺寸，是一个列表的形式[1,2,3]
 21 | 		:param num_filters:		卷积核的个数
 22 | 		:param l2_reg_lambda:	正则化系数
 23 | 		'''
 24 | 
 25 | 		with tf.name_scope('input'):	# 一个输入的命名空间
 26 | 			self.input_x_char = tf.placeholder(tf.int32, [None, sequence_length_char], name='input_x_char')
 27 | 			self.input_x_punc = tf.placeholder(tf.int32, [None, sequence_length_punc], name='input_x_punc')
 28 | 			self.input_x_fc_feat = tf.placeholder(tf.float32, [None, None], name='input_x_fc_feat')
 29 | 			self.input_y = tf.placeholder(tf.float32, [None, num_classes], name='input_y')
 30 | 
 31 | 		with tf.name_scope('dropout'):
 32 | 			self.dropout_keep_prob = tf.placeholder(tf.float32, name='dropout_keep_prob')
 33 | 
 34 | 		# Keeping track of l2 regularization loss (optional)
 35 | 		l2_loss = tf.constant(0.0)
 36 | 
 37 | 		# Embedding layer
 38 | 		# with tf.device('/gpu:0'), tf.name_scope('embedding'):
 39 | 		with tf.name_scope('embedding-char'):
 40 | 			W = tf.Variable(tf.random_uniform([vocab_size_char, embedding_size], -1.0, 1.0), name='W')
 41 | 			# tf.summary.histogram('embedding',W)							#这个是tensorboard画图的
 42 | 			self.embedded_char = tf.nn.embedding_lookup(W, self.input_x_char)
 43 | 			self.embedded_char_expanded = tf.expand_dims(self.embedded_char, -1)
 44 | 
 45 | 		with tf.name_scope('embedding-punc'):
 46 | 			W = tf.Variable(tf.random_uniform([vocab_size_punc, embedding_size], -1.0, 1.0), name='W')
 47 | 			# tf.summary.histogram('embedding',W)							#这个是tensorboard画图的
 48 | 			self.embedded_punc = tf.nn.embedding_lookup(W, self.input_x_punc)
 49 | 			self.embedded_punc_expanded = tf.expand_dims(self.embedded_punc, -1)
 50 | 
 51 | 
 52 | 			#增加一个维度，变成batch_size*seq_len*em_size*channel(=1)的4维tensor,符合图像的习惯
 53 | 
 54 | 		# Create a convolution + maxpool layer for each filter size
 55 | 
 56 | 		pooled_outputs_char = []
 57 | 		pooled_outputs_punc = []
 58 | 		for i, filter_size_char in enumerate(filter_sizes_char):#比如（0,3），（1,4），（2,5）
 59 | 			with tf.name_scope('conv-char-maxpool-%s' % filter_size_char):				    # 循环一次建立一个名称为如“conv-ma-3”的模块
 60 | 				# Convolution Layer
 61 | 				filter_shape = [filter_size_char, embedding_size, 1, num_filters]	    #卷积核的参数，[高，宽，通道数，卷积核个数]
 62 | 				W_char = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name='W_char')	#卷积核的初始化
 63 | 
 64 | 				# tf.summary.histogram('convW-%s' % filter_size, W)				    #tensorboard画图
 65 | 				b_char = tf.Variable(tf.constant(0.1, shape=[num_filters]), name='b_char')    # 偏置b，维度为卷积核个数的tensor
 66 | 				# tf.summary.histogram('convb-%s' % filter_size,b)				    #tensorboard画图
 67 | 				conv_char = tf.nn.conv2d(				#卷积运算
 68 | 					self.embedded_char_expanded,	#输入特征矩阵
 69 | 					W_char,								#初始化的卷积核矩阵
 70 | 					strides=[1, 1, 1, 1],			#划窗移动距离[1, 横向距离， 纵向距离, 1]
 71 | 					padding='VALID',				#边缘是否补0
 72 | 					name='conv_char')
 73 | 
 74 | 				h_char = tf.nn.relu(tf.nn.bias_add(conv_char, b_char), name='relu')	#卷积之后使用relu（）激活函数去线性化
 75 | 
 76 | 				# Maxpooling over the outputs
 77 | 				pooled = tf.nn.max_pool(			#池化运算
 78 | 					h_char,								#卷积后的输入矩阵
 79 | 					ksize=[1, sequence_length_char - filter_size_char + 1, 1, 1],
 80 | 					strides=[1, 1, 1, 1],
 81 | 					padding='VALID',
 82 | 					name='pool_char')
 83 | 				pooled_outputs_char.append(pooled)
 84 | 
 85 | 		for i, filter_size in enumerate(filter_sizes_punc):  # 比如（0,3），（1,4），（2,5）
 86 | 			with tf.name_scope('conv-punc-maxpool-%s' % filter_size):				    # 循环一次建立一个名称为如“conv-ma-3”的模块
 87 | 				# Convolution Layer
 88 | 				filter_shape = [filter_size, embedding_size, 1, num_filters]	    #卷积核的参数，[高，宽，通道数，卷积核个数]
 89 | 				W_punc = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name='W_punc')	#卷积核的初始化
 90 | 				# tf.summary.histogram('convW-%s' % filter_size, W)				    #tensorboard画图
 91 | 				b_punc = tf.Variable(tf.constant(0.1, shape=[num_filters]), name='b_punc')    # 偏置b，维度为卷积核个数的tensor
 92 | 				# tf.summary.histogram('convb-%s' % filter_size,b)				    #tensorboard画图
 93 | 				conv_punc = tf.nn.conv2d(				#卷积运算
 94 | 					self.embedded_punc_expanded,	#输入特征矩阵
 95 | 					W_punc,								#初始化的卷积核矩阵
 96 | 					strides=[1, 1, 1, 1],			#划窗移动距离[1, 横向距离， 纵向距离, 1]
 97 | 					padding='VALID',				#边缘是否补0
 98 | 					name='conv_punc')
 99 | 				h_punc = tf.nn.relu(tf.nn.bias_add(conv_punc, b_punc), name='relu')	#卷积之后使用relu（）激活函数去线性化
100 | 
101 | 				# Maxpooling over the outputs
102 | 				pooled = tf.nn.max_pool(			#池化运算
103 | 					h_punc,								#卷积后的输入矩阵
104 | 					ksize=[1, sequence_length_punc - filter_size + 1, 1, 1],
105 | 					strides=[1, 1, 1, 1],
106 | 					padding='VALID',
107 | 					name='pool_punc')
108 | 				pooled_outputs_punc.append(pooled)
109 | 
110 | 		# with tf.name_scope('lstm'):
111 | 		# 	lstm_cell = rnn.BasicLSTMCell(num_units=128, forget_bias=1.0, state_is_tuple=True)
112 | 		#
113 | 		# 	# **步骤3：添加 dropout layer, 一般只设置 output_keep_prob
114 | 		# 	lstm_cell = rnn.DropoutWrapper(cell=lstm_cell, input_keep_prob=1.0, output_keep_prob=0.5)
115 | 		#
116 | 		# 	# **步骤4：调用 MultiRNNCell 来实现多层 LSTM
117 | 		# 	mlstm_cell = rnn.MultiRNNCell([lstm_cell] * 1, state_is_tuple=True)
118 | 		#
119 | 		# 	# **步骤5：用全零来初始化state
120 | 		# 	init_state = mlstm_cell.zero_state(tf.placeholder(tf.int32, name='init_state'), dtype=tf.float32)
121 | 		# 	#
122 | 		# 	# #**步骤6：方法一，调用 dynamic_rnn() 来让我们构建好的网络运行起来
123 | 		# 	# # ** 当 time_major==False 时， outputs.shape = [batch_size, timestep_size, hidden_size]
124 | 		# 	# # ** 所以，可以取 h_state = outputs[:, -1, :] 作为最后输出
125 | 		# 	# # ** state.shape = [layer_num, 2, batch_size, hidden_size],
126 | 		# 	# # ** 或者，可以取 h_state = state[-1][1] 作为最后输出
127 | 		# 	# # ** 最后输出维度是 [batch_size, hidden_size]
128 | 		# 	outputs, state = tf.nn.dynamic_rnn(mlstm_cell, inputs=self.embedded_punc, initial_state=init_state, time_major=False)
129 | 		# 	h_state = outputs[:, -1, :]  # 或者 h_state = state[-1][1]
130 | 		#
131 | 		# 	# *************** 为了更好的理解 LSTM 工作原理，我们把上面 步骤6 中的函数自己来实现 ***************
132 | 		# 	# 通过查看文档你会发现， RNNCell 都提供了一个 __call__()函数（见最后附），我们可以用它来展开实现LSTM按时间步迭代。
133 | 		# 	# **步骤6：方法二，按时间步展开计算
134 | 		# 	# outputs = list()
135 | 		# 	# state = init_state
136 | 		# 	# with tf.variable_scope('RNN'):
137 | 		# 	# 	for timestep in range(timestep_size):
138 | 		# 	# 		if timestep > 0:
139 | 		# 	# 			tf.get_variable_scope().reuse_variables()
140 | 		# 	# 		# 这里的state保存了每一层 LSTM 的状态
141 | 		# 	# 		(cell_output, state) = mlstm_cell(X[:, timestep, :], state)
142 | 		# 	# 		outputs.append(cell_output)
143 | 		# 	# h_state = outputs[-1]
144 | 		#
145 | 		# 	# with tf.name_scope("lstm"):
146 | 		# 	# 	lstm_cell = tf.contrib.rnn.BasicLSTMCell(128)
147 | 		# 	# 	init_state = lstm_cell.zero_state(batch_size=, dtype=tf.float32)
148 | 		# 	# 	outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, self.input_x_punc, initial_state=init_state, time_major=False)
149 | 		# 	# 	# results = tf.matmul(final_state[1], weights['out']) + biases['out']
150 | 		num_filters_total_char = num_filters * len(filter_sizes_char)                   #每种卷积核个数与卷积种类的积
151 | 		num_filters_total_punc = num_filters * len(filter_sizes_punc)					#每种卷积核个数与卷积种类的积
152 | 
153 | 		self.h_pool_char = tf.concat(pooled_outputs_char,3)	# 将outputs在第4个维度上拼接，如本来是128*1*1*64的结果3个，拼接后为128*1*1*192的tensor
154 | 		self.h_pool_flat_char = tf.reshape(self.h_pool_char, [-1, num_filters_total_char]) #  将最后结果reshape为128*192的tensor
155 | 
156 | 		self.h_pool_punc = tf.concat(pooled_outputs_punc,3)  # 将outputs在第4个维度上拼接，如本来是128*1*1*64的结果3个，拼接后为128*1*1*192的tensor
157 | 		self.h_pool_flat_punc = tf.reshape(self.h_pool_punc, [-1, num_filters_total_punc])  # 将最后结果reshape为128*192的tensor
158 | 
159 | 		self.fc_vec = tf.concat([self.h_pool_flat_char, self.h_pool_flat_punc], 1)
160 | 
161 | 		with tf.name_scope('dense_layer1'):
162 | 			W_dense1 = tf.get_variable(
163 | 				'W_dense1',
164 | 				shape=[num_filters_total_char + num_filters_total_punc, 256],
165 | 				initializer=tf.contrib.layers.xavier_initializer())
166 | 			b_dense1 = tf.Variable(tf.constant(0.1, shape=[256]), name='b_dense1')
167 | 			self.dense1 = tf.nn.xw_plus_b(self.fc_vec, W_dense1, b_dense1, name='dense1')
168 | 			self.dense_feat = tf.concat([self.dense1, self.input_x_fc_feat], 1)
169 | 
170 | 		# Add dropout
171 | 		with tf.name_scope('dropout1'):				# 添加一个"dropout"的模块，里面一个操作，输出为dropout过后的128*192的tensor
172 | 			self.h_drop_1 = tf.nn.dropout(self.dense_feat, self.dropout_keep_prob)
173 | 		#
174 | 		# with tf.name_scope('dense_layer2'):
175 | 		# 	W_dense2 = tf.get_variable(
176 | 		# 		'W_dense2',
177 | 		# 		shape=[256, 64],
178 | 		# 		initializer=tf.contrib.layers.xavier_initializer())
179 | 		# 	b_dense2 = tf.Variable(tf.constant(0.1, shape=[64]), name='b')
180 | 		# 	self.dense2 = tf.nn.xw_plus_b(self.dense1, W_dense2, b_dense2, name='dense1')
181 | 
182 | 		# # Add dropout
183 | 		# with tf.name_scope('dropout2'):				# 添加一个"dropout"的模块，里面一个操作，输出为dropout过后的128*192的tensor
184 | 		# 	self.h_drop_2 = tf.nn.dropout(self.dense2, self.dropout_keep_prob)		# 使用dropout机制防止过拟合
185 | 
186 | 
187 | 		# Final (unnormalized) scores and predictions
188 | 		with tf.name_scope('output'):	#全连接操作，到输出层，注意这里用的是get_variables
189 | 			W_output = tf.Variable(
190 | 				tf.random_normal([256+200, num_classes], stddev=0.35),
191 | 				name = "weights"
192 | 			)
193 | 			b_output = tf.Variable(tf.constant(0.1, shape=[num_classes]), name='b')		#输出层的偏置
194 | 			l2_loss += tf.nn.l2_loss(W_output)				                            #对全连接层的W使用l2_loss正则
195 | 			l2_loss += tf.nn.l2_loss(b_output)				                            #对全连接层的b使用l2_loss正则
196 | 			self.scores = tf.nn.xw_plus_b(self.h_drop_1, W_output, b_output, name='scores')# 相当于tf.nn.matmul(self.h_drop, W) + b
197 | 			self.predictions = tf.argmax(self.scores, 1, name='predictions')		     # 转换成one-hot的编码形式
198 | 
199 | 		# Calculate mean cross-entropy loss
200 | 		with tf.name_scope('loss'):#定义一个”loss“的模块
201 | 			losses = tf.nn.softmax_cross_entropy_with_logits(labels = self.input_y, logits = self.scores) #  交叉熵损失函数
202 | 			self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss	#计算loss（包含正则化系数）
203 | 			# tf.summary.scalar('loss',self.loss)	#tensorboard 画图形式
204 | 
205 | 		# Accuracy
206 | 		with tf.name_scope('accuracy'):
207 | 			correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
208 | 			self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, 'float'), name='accuracy')
209 | 			# operation2，计算均值即为准确率，名称”accuracy“
210 | 
211 | 		with tf.name_scope('correct'):
212 | 			correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
213 | 			self.num_correct = tf.reduce_sum(tf.cast(correct_predictions, 'float'), name='num_correct')


--------------------------------------------------------------------------------
/cnn_char_punc_predict.py:
--------------------------------------------------------------------------------
  1 | # encoding:utf-8
  2 | """
  3 | @author = 'XXY'
  4 | @contact = '529379497@qq.com'
  5 | @researchFie1d = 'NLP DL ML'
  6 | @date= '2017/12/21 10:18'
  7 | """
  8 | import json, os
  9 | import jieba.posseg as pseg
 10 | import logging
 11 | from data_helper import *
 12 | import numpy as np
 13 | from sklearn.model_selection import train_test_split
 14 | import pandas as pd
 15 | import tensorflow as tf
 16 | from tensorflow.contrib import learn
 17 | from sklearn.metrics import classification_report, accuracy_score
 18 | 
 19 | logging.getLogger().setLevel(logging.INFO)
 20 | 
 21 | 
 22 | def make_submission(file, prediction, encoding):
 23 | 	valid_id = []
 24 | 	label2int = {u"人类作者": 0, u"机器作者": 1, u"机器翻译": 2, u"自动摘要": 3}
 25 | 	int2label = {0.0: u"人类作者", 1.0: u"机器作者", 2.0: u"机器翻译", 3.0: u"自动摘要"}
 26 | 
 27 | 	for line in open('./data/validation.txt'):
 28 | 		text = json.loads(line.strip())
 29 | 		valid_id.append(text['id'])
 30 | 	result = pd.DataFrame({'id': valid_id, 'label': prediction})
 31 | 	result['label'] = result['label'].apply(lambda x: int2label[x])
 32 | 	print(result.head())
 33 | 	result.to_csv(file, header=None, index=None, encoding=encoding)
 34 | 
 35 | 
 36 | def predict_unseen_data():
 37 | 	X_char = []
 38 | 	X_punc = []
 39 | 	y = []
 40 | 
 41 | 	# 读取字符特征
 42 | 	print "读取char特征"
 43 | 	with open('./data/validation_char.txt') as f:
 44 | 		for line in f:
 45 | 			temp = line.strip().split('\t')
 46 | 			text = temp[0][1:-1].split(',')
 47 | 			label = temp[1]
 48 | 			X_char.append(text)
 49 | 			y.append(label)
 50 | 
 51 | 	# 读取标点符号结构特征
 52 | 	print "读取punc特征"
 53 | 	with open('./data/validation_punc.txt') as f:
 54 | 		for line in f:
 55 | 			temp = line.strip().split('\t')
 56 | 			text = temp[0][1:-1].split(',')
 57 | 			X_punc.append(text)
 58 | 
 59 | 	print "读取全连接层特征"
 60 | 	X_validation_fc_feat = pd.read_csv('./data/validation_fc_feat_norm_200.txt', sep=',')
 61 | 	print"数据加载完毕！"
 62 | 
 63 | 
 64 | 	X_validation_char = np.array(X_char)
 65 | 	X_validation_punc = np.array(X_punc)
 66 | 	X_validation_fc_feat = X_validation_fc_feat.values
 67 | 	params = json.loads(open('./config/cnn_parameters.json').read())
 68 | 	print "validation数据大小：", X_validation_char.shape, X_validation_punc.shape
 69 | 	print "validation集加载完毕！"
 70 | 
 71 | 	checkpoint_dir = 'models/cnn_models/trained_model_1528419951/'
 72 | 	if not checkpoint_dir.endswith('/'):
 73 | 		checkpoint_dir += '/'
 74 | 	checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir + 'checkpoints')  # 加载最近保存的模型
 75 | 	# checkpoint_file = '/home/h325/data/Xxy/SMP_NEW/models/cnn_models/trained_model_1528257857/checkpoints/model-12400'
 76 | 	logging.critical('Loaded the trained model: {}'.format(checkpoint_file))
 77 | 
 78 | 	graph = tf.Graph()
 79 | 	with graph.as_default():
 80 | 		session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
 81 | 		sess = tf.Session(config=session_conf)
 82 | 
 83 | 		with sess.as_default():
 84 | 			saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
 85 | 			saver.restore(sess, checkpoint_file)
 86 | 			input_x_char = graph.get_operation_by_name("input/input_x_char").outputs[0]
 87 | 			input_x_punc = graph.get_operation_by_name("input/input_x_punc").outputs[0]
 88 | 			input_x_fc_feat = graph.get_operation_by_name('input/input_x_fc_feat').outputs[0]
 89 | 			dropout_keep_prob = graph.get_operation_by_name("dropout/dropout_keep_prob").outputs[0]
 90 | 			predictions = graph.get_operation_by_name("output/predictions").outputs[0]
 91 | 
 92 | 			dev_predictions = []
 93 | 			for i in range(int(len(X_validation_char) / params['batch_size']) + 1):
 94 | 				start_index = i * params['batch_size']
 95 | 				end_index = min((i + 1) * params['batch_size'], len(X_validation_char))
 96 | 				X_validation_char_batch = X_validation_char[start_index: end_index]
 97 | 				X_validation_punc_batch = X_validation_punc[start_index: end_index]
 98 | 				X_validation_fc_feat_batch = X_validation_fc_feat[start_index: end_index]
 99 | 				prediction= sess.run(predictions, {input_x_char: X_validation_char_batch, input_x_punc:X_validation_punc_batch,
100 | 				                                   input_x_fc_feat:X_validation_fc_feat_batch, dropout_keep_prob: 1.0})
101 | 				dev_predictions = np.concatenate([dev_predictions, prediction])
102 | 
103 | 			# for test_batch in test_batches:
104 | 			# 	X_test_batch = test_batch
105 | 			# 	prediction= sess.run(predictions, {input_x1: X_test_batch, dropout_keep_prob: 1.0})
106 | 			# 	dev_predictions = np.concatenate([dev_predictions, prediction])
107 | 			make_submission('results/cnn_char_punc_fc_feat_result4.csv', dev_predictions, encoding='utf-8')
108 | 			logging.critical('The prediction is complete')
109 | if __name__ == '__main__':
110 | 	predict_unseen_data()
111 | 


--------------------------------------------------------------------------------
/cnn_char_punc_train.py:
--------------------------------------------------------------------------------
  1 | # encoding:utf-8
  2 | """
  3 | @author = 'XXY'
  4 | @contact = '529379497@qq.com'
  5 | @researchFie1d = 'NLP DL ML'
  6 | @date= '2017/12/21 10:18'
  7 | """
  8 | import os
  9 | import json
 10 | import time
 11 | import logging
 12 | import pandas as pd
 13 | import numpy as np
 14 | import tensorflow as tf
 15 | from cnn_char_punc import TextCNN
 16 | from sklearn.model_selection import train_test_split
 17 | from sklearn.metrics import classification_report
 18 | from data_helper import batch_iter
 19 | 
 20 | logging.getLogger().setLevel(logging.INFO)
 21 | 
 22 | 
 23 | def train():
 24 | 	X_char = []
 25 | 	X_punc = []
 26 | 	y = []
 27 | 	word2id = json.loads(open('./data/word2id.json').read())
 28 | 	punc2id = json.loads(open('./data/punc2id.json').read())
 29 | 
 30 | 	print "读取char特征"
 31 | 	with open('./data/training_char.txt') as f:
 32 | 		for line in f:
 33 | 			temp = line.strip().split('\t')
 34 | 			text = temp[0][1:-1].split(',')
 35 | 			label = temp[1]
 36 | 			X_char.append(text)
 37 | 			y.append(label)
 38 | 
 39 | 	print "读取punc特征"
 40 | 	with open('./data/training_punc.txt') as f:
 41 | 		for line in f:
 42 | 			temp = line.strip().split('\t')
 43 | 			text = temp[0][1:-1].split(',')
 44 | 			X_punc.append(text)
 45 | 	print "读取全连接层特征"
 46 | 	X_df_fc = pd.read_csv('./data/training_fc_feat_norm_200.txt', sep=',')
 47 | 
 48 | 	print"数据加载完毕！"
 49 | 
 50 | 	labels = sorted(list(set(y)))
 51 | 	one_hot = np.zeros((len(labels), len(labels)), int)
 52 | 	np.fill_diagonal(one_hot, 1)
 53 | 	label_dict = dict(zip(labels, one_hot))
 54 | 	y = [label_dict[i] for i in y]
 55 | 
 56 | 	X_char = np.array(X_char)
 57 | 	X_punc = np.array(X_punc)
 58 | 	X_fc_feat = X_df_fc.values
 59 | 	y = np.array(y)
 60 | 
 61 | 	parameter_file = './config/cnn_parameters.json'
 62 | 	params = json.loads(open(parameter_file).read())
 63 | 	print"所有训练数据的大小：", X_char.shape, X_punc.shape
 64 | 	X_train_char, X_dev_char, X_train_punc, X_dev_punc, X_train_fc_feat, X_dev_fc_feat, y_train, y_dev = \
 65 | 		train_test_split(X_char, X_punc, X_fc_feat, y, random_state=10, test_size=0.1)
 66 | 	print"训练数据大小：", X_train_char.shape, X_train_punc.shape, X_train_fc_feat.shape
 67 | 	print"验证数据大小：", X_dev_char.shape, X_dev_punc.shape, X_dev_fc_feat.shape
 68 | 
 69 | 	graph = tf.Graph()
 70 | 	with graph.as_default():
 71 | 		session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
 72 | 		sess = tf.Session(config=session_conf)
 73 | 		with sess.as_default():
 74 | 			cnn = TextCNN(
 75 | 				sequence_length_char = X_char.shape[1],
 76 | 				sequence_length_punc = X_punc.shape[1],
 77 | 				num_classes=len(labels),
 78 | 				vocab_size_char=len(word2id),
 79 | 				vocab_size_punc=len(punc2id),
 80 | 				embedding_size=params['embedding_dim'],
 81 | 				filter_sizes_char=list(map(int, params['filter_sizes_char'].split(","))),
 82 | 				filter_sizes_punc=list(map(int, params['filter_sizes_punc'].split(","))),
 83 | 				num_filters=params['num_filters'],
 84 | 				l2_reg_lambda=params['l2_reg_lambda']
 85 | 			)
 86 | 
 87 | 			global_step = tf.Variable(0, name="global_step", trainable=False)
 88 | 			optimizer = tf.train.AdamOptimizer(1e-3)
 89 | 			grads_and_vars = optimizer.compute_gradients(cnn.loss)
 90 | 			train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
 91 | 			timestamp = str(int(time.time()))
 92 | 			out_dir = os.path.abspath(os.path.join("models", "cnn_models", "trained_model_" + timestamp))
 93 | 
 94 | 			checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
 95 | 			checkpoint_prefix = os.path.join(checkpoint_dir, "model")
 96 | 			if not os.path.exists(checkpoint_dir):
 97 | 				os.makedirs(checkpoint_dir)
 98 | 			saver = tf.train.Saver(tf.global_variables())
 99 | 
100 | 
101 | 			def train_step(input_x_char, input_x_punc, input_x_fc_feat, y_train):
102 | 				feed_dict = {
103 | 					cnn.input_x_char: input_x_char,
104 | 					cnn.input_x_punc: input_x_punc,
105 | 					cnn.input_x_fc_feat: input_x_fc_feat,
106 | 					cnn.input_y: y_train,
107 | 					cnn.dropout_keep_prob: params['dropout_keep_prob']
108 | 				}
109 | 				_, step, loss, acc, prediction = sess.run([train_op, global_step, cnn.loss, cnn.accuracy, cnn.predictions], feed_dict)
110 | 				print("After training {} step loss is: {}, accuracy is {}".format(step, loss, acc))
111 | 
112 | 			def test_step(input_x_char, input_x_punc, input_x_fc_feat, y_test):
113 | 				feed_dict = {
114 | 					cnn.input_x_char: input_x_char,
115 | 					cnn.input_x_punc: input_x_punc,
116 | 					cnn.input_x_fc_feat: input_x_fc_feat,
117 | 					cnn.input_y: y_test,
118 | 					cnn.dropout_keep_prob: params['dropout_keep_prob']
119 | 				}
120 | 				step, loss, acc, num_correct, prediction = sess.run(
121 | 					[global_step, cnn.loss, cnn.accuracy, cnn.num_correct, cnn.predictions], feed_dict)
122 | 				return num_correct, prediction, loss, acc
123 | 
124 | 
125 | 			# 下面开始训练过程
126 | 			# Save the word_to_id map since predict.py needs it
127 | 
128 | 			sess.run(tf.global_variables_initializer())
129 | 			# 对训练集分batch
130 | 			train_batches = batch_iter(zip(X_train_char, X_train_punc, X_train_fc_feat, y_train), params['batch_size'], params['num_epochs'])
131 | 
132 | 			X_dev = zip(X_dev_char, X_dev_punc, X_dev_fc_feat, y_dev)
133 | 			best_accuracy, best_at_step = 0, 0
134 | 
135 | 			for train_batch in train_batches:
136 | 				'''
137 | 				对labels（y_test_batches）进行one-hot编码操作
138 | 				'''
139 | 				X_train_char_batch, X_train_punc_batch, X_train_fc_feat_batch, y_train_batch = zip(*train_batch)
140 | 				train_step(X_train_char_batch, X_train_punc_batch, X_train_fc_feat_batch, y_train_batch)
141 | 				current_step = tf.train.global_step(sess, global_step)
142 | 				if current_step % params['evaluate_every'] == 0:  # 多少步评估一次
143 | 					total_dev_correct = 0
144 | 					dev_predictions = []
145 | 
146 | 					for i in range(int(len(X_dev) / params['batch_size']) + 1):
147 | 						start_index = i * params['batch_size']
148 | 						end_index = min((i + 1) * params['batch_size'], len(X_dev))
149 | 						X_dev_batch = X_dev[start_index: end_index]
150 | 						X_dev_batch_char, X_dev_batch_punc, X_dev_batch_fc_feat, y_test_batch = zip(*X_dev_batch)
151 | 						num_dev_correct, dev_prediction, loss, acc = test_step(X_dev_batch_char, X_dev_batch_punc, X_dev_batch_fc_feat, y_test_batch)
152 | 						total_dev_correct += num_dev_correct
153 | 						dev_predictions = np.concatenate([dev_predictions, dev_prediction])
154 | 					print "最后预测结果：", dev_predictions
155 | 					print "长度为：", len(dev_predictions)
156 | 					print "最后预测结果：", dev_predictions
157 | 					dev_accuracy = float(total_dev_correct) / len(y_dev)
158 | 					logging.critical('Loss on dev set is:{}, Accuracy on dev set: {}'.format(loss, dev_accuracy))
159 | 					if dev_accuracy >= best_accuracy:
160 | 						best_accuracy, best_at_step = dev_accuracy, current_step
161 | 						path = saver.save(sess, checkpoint_prefix, global_step=current_step)
162 | 						logging.critical('Saved model at {} at step {}'.format(path, best_at_step))
163 | 						logging.critical('Best accuracy is {} at step {}'.format(best_accuracy, best_at_step))
164 | 
165 | if __name__ == '__main__':
166 | 	train()
167 | 


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/cnn_char_train.py:
--------------------------------------------------------------------------------
1 | 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/config/cnn_parameters.json:
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 1 | {
 2 |   "embedding_dim":128,
 3 |   "filter_sizes_char": "1,2,3,5",
 4 |   "filter_sizes_punc": "10,20,50",
 5 |   "num_filters": 100,
 6 |   "dropout_keep_prob":0.5,
 7 |   "l2_reg_lambda":0,
 8 |   "batch_size": 128,
 9 |   "num_epochs": 64,
10 |   "evaluate_every": 100,
11 |   "checkpoint_every": 100
12 | }


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/data/1:
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2 | 


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/data_helper.py:
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/result/1:
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1 | 
2 | 


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/svm.py:
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 1 | """
 2 | @author = 'XXY'
 3 | @contact = '529379497@qq.com'
 4 | @researchFie1d = 'NLP DL ML'
 5 | @date= '2017/12/21 10:18'
 6 | """
 7 | from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF
 8 | import json
 9 | from sklearn.metrics import classification_report
10 | import numpy as np
11 | import pandas as pd
12 | # from predictor import data
13 | from sklearn.svm import LinearSVC
14 | from sklearn.externals import joblib
15 | import pickle
16 | from data_helper import get_data
17 | from gensim.models.word2vec import Word2Vec
18 | import thulac
19 | from sklearn.model_selection import train_test_split
20 | 
21 | dim = 5000
22 | def cut_text(alltext):
23 |     # 分词
24 |     count = 0
25 |     cut = thulac.thulac(seg_only=True)
26 |     train_text = []
27 |     for text in alltext:
28 |         count += 1
29 |         if count % 2000 == 0:
30 |             print(count)
31 |         train_text.append(cut.cut(text, text=True))
32 |     return train_text
33 | 
34 | 
35 | def train_tfidf(train_data):
36 |     tfidf = TFIDF(
37 |         min_df=5,
38 |         max_features=dim,
39 |         ngram_range=(1, 2),
40 |         use_idf=1,
41 |         smooth_idf=1
42 |     )
43 |     tfidf.fit(train_data)
44 |     return tfidf
45 | 
46 | 
47 | def train_word2vec(train_data):
48 |     model = Word2Vec(train_data, size=128, window=5, min_count=5, workers=4)
49 |     return model
50 | 
51 | 
52 | def train_SVC(vec, label):
53 |     SVC = LinearSVC(C=100)
54 |     SVC.fit(vec, label)
55 |     return SVC
56 | 
57 | 
58 | def get_word2vec(content):
59 |     word2vec = Word2Vec.load('predictor/model/wiki.zh.seg_200d.model')
60 |     res = np.zeros([200])
61 |     count = 0
62 |     # word_list = content.split()
63 |     for word in content:
64 |         if word in word2vec:
65 |             res += word2vec[word]
66 |             count += 1
67 |     return pd.Series(res / count)
68 | 
69 | 
70 | if __name__ == '__main__':
71 |     print('reading...')
72 |     all_text, y, label, label_to_int, int_tolabel = get_data(file='./data/train_data.csv')
73 |     print('cut text...')
74 |     train_data = cut_text(all_text)
75 |     # train_data = [line.split() for line in train_data]
76 |     print('get tfidf...')
77 |     tfidf = train_tfidf(train_data)
78 |     print('saving tfidf model')
79 |     joblib.dump(tfidf, 'model/tfidf_5000.model')
80 | 
81 |     X_train, X_dev, y_train, y_dev = train_test_split(train_data, y, random_state=10, test_size=0.1)
82 |     train_vec = tfidf.transform(X_train)
83 |     test_vec = tfidf.transform(X_dev)
84 |     print('training SVC')
85 |     svm = train_SVC(train_vec, y_train)
86 |     y_pre = svm.predict(test_vec)
87 |     print(classification_report(y_dev, y_pre))
88 |     print("saving svm model")
89 |     joblib.dump(svm, 'model/svm_5000.model')
90 | 


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/test.py:
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1 | #encoding:utf-8
2 | import numpy as np
3 | from sklearn.cross_validation import KFold
4 | X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
5 | y = np.array([1, 2, 3, 4])
6 | kf = KFold(4, n_folds=3)
7 | for train_index, test_index in kf:
8 |     print("TRAIN:", train_index, "TEST:", test_index)
9 |     print X[train_index], y[train_index]


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