# -*- coding:utf-8 -*- """ Author: BigCat """ import time import json import argparse import numpy as np import pandas as pd from config import * from modeling import LstmWithCRFModel, SignalLstmModel, tf from loguru import logger parser = argparse.ArgumentParser() parser.add_argument('--name', default="ssq", type=str, help="选择训练数据: 双色球/大乐透") args = parser.parse_args() pred_key = {} def create_train_data(name, windows): """ 创建训练数据 :param name: 玩法，双色球/大乐透 :param windows: 训练窗口 :return: """ data = pd.read_csv("{}{}".format(name_path[name]["path"], data_file_name)) if not len(data): raise logger.error(" 请执行 get_data.py 进行数据下载！") else: # 创建模型文件夹 if not os.path.exists(model_path): os.mkdir(model_path) logger.info("训练数据已加载! ") data = data.iloc[:, 2:].values logger.info("训练集数据维度: {}".format(data.shape)) x_data, y_data = [], [] for i in range(len(data) - windows - 1): sub_data = data[i:(i+windows+1), :] x_data.append(sub_data[1:]) y_data.append(sub_data[0]) cut_num = 6 if name == "ssq" else 5 return { "red": { "x_data": np.array(x_data)[:, :, :cut_num], "y_data": np.array(y_data)[:, :cut_num] }, "blue": { "x_data": np.array(x_data)[:, :, cut_num:], "y_data": np.array(y_data)[:, cut_num:] } } def train_red_ball_model(name, x_data, y_data): """ 红球模型训练 :param name: 玩法 :param x_data: 训练样本 :param y_data: 训练标签 :return: """ m_args = model_args[name] x_data = x_data - 1 y_data = y_data - 1 data_len = x_data.shape[0] logger.info("特征数据维度: {}".format(x_data.shape)) logger.info("标签数据维度: {}".format(y_data.shape)) with tf.compat.v1.Session() as sess: red_ball_model = LstmWithCRFModel( batch_size=m_args["model_args"]["batch_size"], n_class=m_args["model_args"]["red_n_class"], ball_num=m_args["model_args"]["sequence_len"] if name == "ssq" else m_args["model_args"]["red_sequence_len"], w_size=m_args["model_args"]["windows_size"], embedding_size=m_args["model_args"]["red_embedding_size"], words_size=m_args["model_args"]["red_n_class"], hidden_size=m_args["model_args"]["red_hidden_size"], layer_size=m_args["model_args"]["red_layer_size"] ) train_step = tf.compat.v1.train.AdamOptimizer( learning_rate=m_args["train_args"]["red_learning_rate"], beta1=m_args["train_args"]["red_beta1"], beta2=m_args["train_args"]["red_beta2"], epsilon=m_args["train_args"]["red_epsilon"], use_locking=False, name='Adam' ).minimize(red_ball_model.loss) sess.run(tf.compat.v1.global_variables_initializer()) for epoch in range(m_args["model_args"]["red_epochs"]): for i in range(data_len): _, loss_, pred = sess.run([ train_step, red_ball_model.loss, red_ball_model.pred_sequence ], feed_dict={ "inputs:0": x_data[i:(i+1), :, :], "tag_indices:0": y_data[i:(i+1), :], "sequence_length:0": np.array([m_args["model_args"]["sequence_len"]]*1) \ if name == "ssq" else np.array([m_args["model_args"]["red_sequence_len"]]*1) }) if i % 100 == 0: logger.info("epoch: {}, loss: {}, tag: {}, pred: {}".format( epoch, loss_, y_data[i:(i+1), :][0] + 1, pred[0] + 1) ) pred_key[ball_name[0][0]] = red_ball_model.pred_sequence.name if not os.path.exists(m_args["path"]["red"]): os.makedirs(m_args["path"]["red"]) saver = tf.compat.v1.train.Saver() saver.save(sess, "{}{}.{}".format(m_args["path"]["red"], red_ball_model_name, extension)) def train_blue_ball_model(name, x_data, y_data): """ 蓝球模型训练 :param name: 玩法 :param x_data: 训练样本 :param y_data: 训练标签 :return: """ m_args = model_args[name] x_data = x_data - 1 data_len = x_data.shape[0] if name == "ssq": x_data = x_data.reshape(len(x_data), m_args["model_args"]["windows_size"]) y_data = tf.keras.utils.to_categorical(y_data - 1, num_classes=m_args["model_args"]["blue_n_class"]) logger.info("特征数据维度: {}".format(x_data.shape)) logger.info("标签数据维度: {}".format(y_data.shape)) with tf.compat.v1.Session() as sess: if name == "ssq": blue_ball_model = SignalLstmModel( batch_size=m_args["model_args"]["batch_size"], n_class=m_args["model_args"]["blue_n_class"], w_size=m_args["model_args"]["windows_size"], embedding_size=m_args["model_args"]["blue_embedding_size"], hidden_size=m_args["model_args"]["blue_hidden_size"], outputs_size=m_args["model_args"]["blue_n_class"], layer_size=m_args["model_args"]["blue_layer_size"] ) else: blue_ball_model = LstmWithCRFModel( batch_size=m_args["model_args"]["batch_size"], n_class=m_args["model_args"]["blue_n_class"], ball_num=m_args["model_args"]["blue_sequence_len"], w_size=m_args["model_args"]["windows_size"], embedding_size=m_args["model_args"]["blue_embedding_size"], words_size=m_args["model_args"]["blue_n_class"], hidden_size=m_args["model_args"]["blue_hidden_size"], layer_size=m_args["model_args"]["blue_layer_size"] ) train_step = tf.compat.v1.train.AdamOptimizer( learning_rate=m_args["train_args"]["blue_learning_rate"], beta1=m_args["train_args"]["blue_beta1"], beta2=m_args["train_args"]["blue_beta2"], epsilon=m_args["train_args"]["blue_epsilon"], use_locking=False, name='Adam' ).minimize(blue_ball_model.loss) sess.run(tf.compat.v1.global_variables_initializer()) for epoch in range(m_args["model_args"]["blue_epochs"]): for i in range(data_len): if name == "ssq": _, loss_, pred = sess.run([ train_step, blue_ball_model.loss, blue_ball_model.pred_label ], feed_dict={ "inputs:0": x_data[i:(i+1), :], "tag_indices:0": y_data[i:(i+1), :], }) if i % 100 == 0: logger.info("epoch: {}, loss: {}, tag: {}, pred: {}".format( epoch, loss_, np.argmax(y_data[i:(i+1), :][0]) + 1, pred[0] + 1) ) else: _, loss_, pred = sess.run([ train_step, blue_ball_model.loss, blue_ball_model.pred_sequence ], feed_dict={ "inputs:0": x_data[i:(i + 1), :, :], "tag_indices:0": y_data[i:(i + 1), :], "sequence_length:0": np.array([m_args["model_args"]["blue_sequence_len"]] * 1) }) if i % 100 == 0: logger.info("epoch: {}, loss: {}, tag: {}, pred: {}".format( epoch, loss_, y_data[i:(i + 1), :][0] + 1, pred[0] + 1) ) pred_key[ball_name[1][0]] = blue_ball_model.pred_label.name if name == "ssq" else blue_ball_model.pred_sequence.name if not os.path.exists(m_args["path"]["blue"]): os.mkdir(m_args["path"]["blue"]) saver = tf.compat.v1.train.Saver() saver.save(sess, "{}{}.{}".format(m_args["path"]["blue"], blue_ball_model_name, extension)) def run(name): """ 执行训练 :param name: 玩法 :return: """ logger.info("正在创建【{}】数据集...".format(name_path[name]["name"])) train_data = create_train_data(args.name, model_args[name]["model_args"]["windows_size"]) logger.info("开始训练【{}】红球模型...".format(name_path[name]["name"])) start_time = time.time() train_red_ball_model(name, x_data=train_data["red"]["x_data"], y_data=train_data["red"]["y_data"]) logger.info("训练耗时: {}".format(time.time() - start_time)) tf.compat.v1.reset_default_graph() # 重置网络图 logger.info("开始训练【{}】蓝球模型...".format(name_path[name]["name"])) start_time = time.time() train_blue_ball_model(name, x_data=train_data["blue"]["x_data"], y_data=train_data["blue"]["y_data"]) logger.info("训练耗时: {}".format(time.time() - start_time)) # 保存预测关键结点名 with open("{}/{}/{}".format(model_path, name, pred_key_name), "w") as f: json.dump(pred_key, f) if __name__ == '__main__': if not args.name: raise Exception("玩法名称不能为空！") else: run(args.name)