machine_learn/mix/lstm_predict_by_day.py

# -*- encoding:utf-8 -*-
import numpy as np
from keras.models import load_model
import joblib


def read_data(path):
    day_lines = {}
    with open(path) as f:
        for line in f.readlines()[:]:
            line = eval(line.strip())
            date = str(line[-1][-1])
            if date in day_lines:
                day_lines[date].append(line)
            else:
                day_lines[date] = [line]
    # print(len(day_lines['20191230']))
    return day_lines


def predict(file_path='', model_path='15min_dnn_seq'):
    day_lines = read_data(file_path)
    print('数据读取完毕')

    model=load_model(model_path)
    print('模型加载完毕')

    items = sorted(day_lines.keys())
    for key in items:
        # print(day)
        lines = day_lines[key]

        up_num = 0
        down_num = 0
        size = len(lines[0])
        x0 = 0
        x1 = 0
        x2 = 0
        x3 = 0
        x4 = 0

        for line in lines:
            train_x = np.array([line[:size - 1]])
            train_x_a = train_x[:,:18*24]
            train_x_a = train_x_a.reshape(train_x.shape[0], 18, 24)
            # train_x_b = train_x[:, 18*18:18*18+2*18]
            # train_x_b = train_x_b.reshape(train_x.shape[0], 18, 2, 1)
            train_x_c = train_x[:,18*24:]

            result = model.predict([train_x_c, train_x_a])

            if result[0][3] + result[0][4] > 0.5:
                down_num = down_num + 1
            elif result[0][1] + result[0][0] > 0.5:
                up_num = up_num + 0.6
            # else:
            #     up_num = up_num + 0.4 # 乐观调大 悲观调小
            #     down_num = down_num + 0.6

            # if result[0][0] > 0.5:
            #     x0 = x0 + 1
            # if result[0][1] > 0.5:
            #     x1 = x1 + 1
            # if result[0][2] > 0.5:
            #     x2 = x3 + 1
            # if result[0][3] > 0.5:
            #     x3 = x3 + 1
            # if result[0][4] > 0.5:
            #     x4 = x4 + 1

        print(key, int(up_num), int(down_num), (down_num*1.2 + 2)/(up_num*1.2 + 2), )
        # print(key, x0, x1, x2,x3,x4)


if __name__ == '__main__':
    # predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock9_18_20200220.log', model_path='18d_dnn_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock9_18_2.log', model_path='18d_dnn_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock16_18d_20200310.log', model_path='16_18d_mix_seq')
    predict(file_path='D:\\data\\quantization\\stock16_18d_20191225_20200310.log', model_path='16_18d_lstm_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock9_18_4.log', model_path='18d_dnn_seq.h5')