BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/product/predict.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
@author: bidikeji
@time: 2023/3/27 10:19
"""
from BiddingKG.dl.product.product_model import Product_Model
import os
import re
import time
import pandas as pd
import tensorflow as tf
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
def predict():
    ckpt_path = "model"
    import json
    with tf.Session() as sess:
        model = Product_Model()
        sess.run(tf.global_variables_initializer())
        ckpt = tf.train.get_checkpoint_state(ckpt_path)

        # model.saver.restore(sess, os.path.dirname(__file__) + '/model/ner_epoch16_f10.8000_loss1.0775.ckpt')
        # model.saver.restore(sess, os.path.dirname(__file__) + '/model/ner_epoch7_f10.7998_loss1.0508.ckpt')
        model.saver.restore(sess, os.path.dirname(__file__) + '/model/ner_epoch22_f10.7923_loss1.1039.ckpt') # 整理数据后再次训练
        # model.saver.restore(sess, os.path.dirname(__file__) + '/model/ner_epoch18_f10.8000_loss1.1276.ckpt') # 新
        # model.saver.restore(sess, os.path.dirname(__file__) + '/model/ner_epoch5_f10.6855_loss1.3800.ckpt') # 旧
        t1 = time.time()

        print(model.logits, model.lengths, model.trans, model.dropout, model.char_inputs)
        # df = pd.read_csv(os.path.dirname(__file__) + '/data/df_test.csv') #../test/
        df = pd.read_excel(os.path.dirname(__file__) + '/data/df_test_pred.xlsx')
        print('公告数量：', len(df))
        df.fillna('', inplace=True)
        # df = pd.read_excel('data/所有产品标注数据筛选测试数据2021-12-01_pred.xlsx')
        df.reset_index(drop=True, inplace=True)
        rs = []
        for i in df.index:
            text = df.loc[i, 'text']
            # result = model.evaluate_line(sess, text)
            # print(result[0][1])
            # rs.append(json.dumps(result[0][1], ensure_ascii=False))

            tmp = []
            for line in text.split('。'):
                # line = re.sub('[^\w]', '', line)
                # if len(line) < 5:
                #     continue
                result = model.evaluate_line(sess, line)
                # print(result[0][1])
                tmp.extend(result[0][1])
            rs.append(json.dumps(tmp, ensure_ascii=False))
        df['predict_new'] = pd.Series(rs)
        df.to_excel(os.path.dirname(__file__) + '/data/df_test_pred.xlsx', index=False)
        print('耗时： ', time.time()-t1)
        return df

def 统计准确率(df):
    import json
    # df = pd.read_excel(os.path.dirname(__file__) + '/data/df_test_pred.xlsx')
    df['pr'] = df['predict_new'].apply(lambda x:set([it[0] for it in json.loads(x)]))
    df['lb'] = df['lbset'].apply(lambda x: set(json.loads(x)))
    df['pos'] = df.apply(lambda x:1 if x['pr']==x['lb'] else 0, axis=1)
    eq = lb = pr = 0
    for i in df.index:
        pred = df.loc[i, 'pr']
        label = df.loc[i, 'lb']
        lb += len(label)
        pr += len(pred)
        eq += len(pred&label)
    acc = eq/pr
    recall = eq/lb
    f1 = acc*recall*2/(acc+recall)
    print('准确率：%.4f，召回率：%.4f，F1：%.4f'%(acc, recall, f1))  # 准确率：0.6489，召回率：0.8402，F1：0.7323
    # df.to_excel(os.path.dirname(__file__) + '/data/df_test_pred.xlsx')

if __name__ == "__main__":
    df = predict()
    统计准确率(df)