BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/time/train_2.py

import sys
import os
sys.path.append(os.path.abspath("../.."))
import pandas as pd
import re
import psycopg2
from keras.callbacks import ModelCheckpoint
from keras import layers,models,optimizers,losses
from BiddingKG.dl.common.Utils import *
from BiddingKG.dl.common.models import *
from sklearn.metrics import classification_report
from sklearn.utils import shuffle,class_weight
import matplotlib.pyplot as plt

input_shape = (2,30,60)
output_shape = [4]

def getModel():
    '''
    @summary: 时间分类模型
    '''
    L_input = layers.Input(shape=input_shape[1:], dtype='float32')
    R_input = layers.Input(shape=input_shape[1:], dtype='float32')
    L_lstm = layers.Bidirectional(layers.LSTM(32,return_sequences=True,dropout=0.1))(L_input)
    # L_lstm = layers.LSTM(32,return_sequences=True,dropout=0.2)(L_input)
    avg_l = layers.GlobalAveragePooling1D()(L_lstm)
    R_lstm = layers.Bidirectional(layers.LSTM(32,return_sequences=True,dropout=0.1))(R_input)
    # R_lstm = layers.LSTM(32, return_sequences=True, dropout=0.2)(R_input)
    avg_r = layers.GlobalAveragePooling1D()(R_lstm)
    concat = layers.merge([avg_l, avg_r], mode='concat')
    # concat = layers.merge([L_lstm, R_lstm], mode='concat')
    # lstm = layers.LSTM(24,return_sequences=False,dropout=0.2)(concat)
    output = layers.Dense(output_shape[0],activation="softmax")(concat)

    # L_lstm = layers.LSTM(32,return_sequences=True,dropout=0.2)(L_input)
    # avg = layers.GlobalAveragePooling1D()(L_GRU)
    # output = layers.Dense(output_shape[0],activation="softmax")(avg)

    model = models.Model(inputs=[L_input,R_input], outputs=output)
    # model = models.Model(inputs=L_input, outputs=output)
    learn_rate = 0.0005
    model.compile(optimizer=optimizers.Adam(lr=learn_rate),
                  loss=losses.binary_crossentropy,
                  metrics=[precision,recall,f1_score])
    model.summary()
    return model

def getModel_center():
    '''
    @summary: 时间分类模型
    '''
    L_input = layers.Input(shape=input_shape[1:], dtype='float32')
    R_input = layers.Input(shape=input_shape[1:], dtype='float32')
    center_shape = (25, 60)
    C_input = layers.Input(shape=center_shape, dtype='float32')
    L_lstm = layers.Bidirectional(layers.LSTM(32,return_sequences=True,dropout=0.2))(L_input)
    avg_l = layers.GlobalAveragePooling1D()(L_lstm)
    C_lstm = layers.LSTM(32,return_sequences=True,dropout=0.2)(C_input)
    avg_c = layers.GlobalAveragePooling1D()(C_lstm)
    R_lstm = layers.Bidirectional(layers.LSTM(32,return_sequences=True,dropout=0.2))(R_input)
    avg_r = layers.GlobalAveragePooling1D()(R_lstm)
    concat = layers.merge([avg_l, avg_c, avg_r], mode='concat')

    output = layers.Dense(output_shape[0],activation="softmax")(concat)

    model = models.Model(inputs=[L_input,C_input,R_input], outputs=output)
    learn_rate = 0.0005
    model.compile(optimizer=optimizers.Adam(lr=learn_rate),
                  loss=losses.binary_crossentropy,
                  metrics=[precision,recall,f1_score])
    model.summary()
    return model


def training():
    data_load = pd.read_csv("C:\\Users\\admin\\Desktop\\newdata_30.csv", index_col=0)
    test_data = data_load.sample(frac=0.25, random_state=7)
    train_data = data_load.drop(test_data.index, axis=0)
    train_data =train_data.reset_index(drop=True)

    train_x = []
    train_y = []
    for left, right, label in zip(train_data['context_left'], train_data['context_right'], train_data['re_label']):
        y = np.zeros(output_shape)
        y[label] = 1
        left = ''.join(str(left))
        right = ''.join(str(right))
        context = [left, right]
        x = embedding_word(context, shape=input_shape)
        train_x.append(x)
        train_y.append(y)

    test_x = []
    test_y = []
    for left, right, label in zip(test_data['context_left'], test_data['context_right'], test_data['re_label']):
        y = np.zeros(output_shape)
        y[label] = 1
        left = ''.join(str(left))
        right = ''.join(str(right))
        context = [left, right]
        x = embedding_word(context, shape=input_shape)
        test_x.append(x)
        test_y.append(y)

    train_y, test_y = (np.array(train_y), np.array(test_y))
    train_x, test_x = (np.array(train_x), np.array(test_x))
    train_x, test_x = (np.transpose(train_x, (1, 0, 2, 3)), np.transpose(test_x, (1, 0, 2, 3)))

    model = getModel()
    epochs = 100
    batch_size = 256
    checkpoint = ModelCheckpoint("model_label_time_classify.model.hdf5", monitor="val_loss", verbose=1,
                                 save_best_only=True, mode='min')
    # cw = class_weight.compute_class_weight('auto',np.unique(np.argmax(train_y,axis=1)),np.argmax(train_y,axis=1))
    # cw = dict(enumerate(cw))
    history = model.fit(
        x=[train_x[0], train_x[1]],
        y=train_y,
        validation_data=([test_x[0], test_x[1]], test_y),
        epochs=epochs,
        batch_size=batch_size,
        shuffle=True,
        callbacks=[checkpoint],
        class_weight='auto'
    )
    plot_loss(history=history)
    load_model = models.load_model("model_label_time_classify.model.hdf5",
                                   custom_objects={'precision': precision, 'recall': recall, 'f1_score': f1_score})
    y_pre = load_model.predict([test_x[0], test_x[1]])
    # y_pre = load_model.predict(test_x[0])
    # 各类别预测评估
    res1 = classification_report(np.argmax(test_y, axis=1), np.argmax(y_pre, axis=1))
    print(res1)
    y_pre2 = load_model.predict([train_x[0], train_x[1]])
    # y_pre2 = load_model.predict(train_x[0])
    res2 = classification_report(np.argmax(train_y, axis=1), np.argmax(y_pre2, axis=1))
    print(res2)

def training_center():
    data_load = pd.read_csv("C:\\Users\\admin\\Desktop\\newdata.csv", index_col=0)
    test_data = data_load.sample(frac=0.25, random_state=7)
    train_data = data_load.drop(test_data.index, axis=0)
    train_data =train_data.reset_index(drop=True)

    train_x = []
    train_y = []
    for left, center, right, label in zip(train_data['context_left'], train_data['entity_time'], train_data['context_right'], train_data['re_label']):
        y = np.zeros(output_shape)
        y[label] = 1
        left = ''.join(str(left))
        right = ''.join(str(right))
        center = ''.join(str(center))
        context = [left,center, right]
        x = embedding_word(context, shape=(3,25,60))
        train_x.append(x)
        train_y.append(y)

    test_x = []
    test_y = []
    for left, center, right, label in zip(test_data['context_left'], train_data['entity_time'], test_data['context_right'], test_data['re_label']):
        y = np.zeros(output_shape)
        y[label] = 1
        left = ''.join(str(left))
        right = ''.join(str(right))
        center = ''.join(str(center))
        context = [left, center, right]
        x = embedding_word(context, shape=(3,25,60))
        test_x.append(x)
        test_y.append(y)

    train_y, test_y = (np.array(train_y), np.array(test_y))
    train_x, test_x = (np.array(train_x), np.array(test_x))
    train_x, test_x = (np.transpose(train_x, (1, 0, 2, 3)), np.transpose(test_x, (1, 0, 2, 3)))

    model = getModel_center()
    epochs = 70
    batch_size = 256
    checkpoint = ModelCheckpoint("model_label_time_classify.model.hdf5", monitor="val_loss", verbose=1,
                                 save_best_only=True, mode='min')
    # cw = class_weight.compute_class_weight('auto',np.unique(np.argmax(train_y,axis=1)),np.argmax(train_y,axis=1))
    # cw = dict(enumerate(cw))
    history = model.fit(
        x=[train_x[0], train_x[1], train_x[2]],
        y=train_y,
        validation_data=([test_x[0], test_x[1], test_x[2]], test_y),
        # validation_data=(test_x[0],test_y),
        epochs=epochs,
        batch_size=batch_size,
        shuffle=True,
        callbacks=[checkpoint],
        class_weight='auto'
    )
    plot_loss(history = history)
    load_model = models.load_model("model_label_time_classify.model.hdf5",
                                   custom_objects={'precision': precision, 'recall': recall, 'f1_score': f1_score})
    y_pre = load_model.predict([test_x[0], test_x[1], test_x[2]])
    # y_pre = load_model.predict(test_x[0])
    # 各类别预测评估
    res1 = classification_report(np.argmax(test_y, axis=1), np.argmax(y_pre, axis=1))
    print(res1)
    y_pre2 = load_model.predict([train_x[0], train_x[1], train_x[2]])
    # y_pre2 = load_model.predict(train_x[0])
    res2 = classification_report(np.argmax(train_y, axis=1), np.argmax(y_pre2, axis=1))
    print(res2)

def predict():
    model1 = models.load_model("model_label_time_classify.model.hdf5",custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
    data_load = pd.read_csv("C:\\Users\\admin\\Desktop\\newdata_30.csv", index_col=0)
    test_x = []
    test_y = []
    for left, right, label in zip(data_load['context_left'], data_load['context_right'], data_load['re_label']):
        y = np.zeros(output_shape)
        y[label] = 1
        left = ''.join(str(left))
        right = ''.join(str(right))
        context = [left, right]
        x = embedding_word(context, shape=input_shape)
        test_x.append(x)
        test_y.append(y)
    test_x = np.transpose(np.array(test_x), (1, 0, 2, 3))
    pre_y = model1.predict([test_x[0],test_x[1]])
    data_load['pre'] = [np.argmax(item) for item in pre_y]
    error_data = data_load[data_load['re_label']!=data_load['pre']]
    # print(error_data.info())
    error_data.to_csv("C:\\Users\\admin\\Desktop\\test\\error4-0.2-0.6_30.csv")

def predict_center():
    model1 = models.load_model("model_label_time_classify.model.hdf5",custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
    data_load = pd.read_csv("C:\\Users\\admin\\Desktop\\newdata.csv", index_col=0)
    test_x = []
    test_y = []
    for left, center, right, label in zip(data_load['context_left'],data_load['entity_time'], data_load['context_right'], data_load['re_label']):
        y = np.zeros(output_shape)
        y[label] = 1
        left = ''.join(str(left))
        right = ''.join(str(right))
        center = ''.join(str(center))
        context = [left, center, right]
        x = embedding_word(context, shape=(3, 25, 60))
        test_x.append(x)
        test_y.append(y)
    test_x = np.transpose(np.array(test_x), (1, 0, 2, 3))
    pre_y = model1.predict([test_x[0],test_x[1],test_x[2]])
    data_load['pre'] = [np.argmax(item) for item in pre_y]
    error_data = data_load[data_load['re_label']!=data_load['pre']]
    # print(error_data.info())
    error_data.to_csv("C:\\Users\\admin\\Desktop\\test\\error_center.csv")

def plot_loss(history):
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('Model loss')
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Test'], loc='upper left')
    plt.show()

if __name__ == '__main__':
    # getModel()
    # getModel_center()
    # training()
    # training_center()
    # predict()
    # predict_center()
    model1 = models.load_model("model_label_time_classify.model.hdf5",
                               custom_objects={'precision': precision, 'recall': recall, 'f1_score': f1_score})
    test_x = []
    test_y = []
    left = '8675.20元人民币，(3)服务期限：'
    right = '(4)质量：符合竞争性磋商文件规定的质'
    context = [left, right]
    x = embedding_word(context, shape=input_shape)
    test_x.append(x)
    test_x = np.transpose(np.array(test_x), (1, 0, 2, 3))
    pre_y = model1.predict([test_x[0],test_x[1]])
    rs = [np.argmax(item) for item in pre_y]
    print(pre_y, rs)
    pass