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@ -181,7 +181,7 @@
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<component name="PropertiesComponent">
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<property name="WebServerToolWindowFactoryState" value="false" />
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<property name="aspect.path.notification.shown" value="true" />
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<property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588169908975" />
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<property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588169942720" />
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<property name="go.gopath.indexing.explicitly.defined" value="true" />
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<property name="nodejs_interpreter_path.stuck_in_default_project" value="undefined stuck path" />
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<property name="nodejs_npm_path_reset_for_default_project" value="true" />
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@ -190,6 +190,12 @@
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<property name="project.structure.side.proportion" value="0.0" />
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<property name="settings.editor.selected.configurable" value="http.proxy" />
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</component>
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<component name="RecentsManager">
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<key name="MoveFile.RECENT_KEYS">
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<recent name="$PROJECT_DIR$" />
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<recent name="$PROJECT_DIR$/model" />
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</key>
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</component>
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<component name="RunDashboard">
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<option name="ruleStates">
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<list>
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8
main.py
8
main.py
@ -4,6 +4,8 @@ import os
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from sklearn.model_selection import train_test_split
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from model_tensorflow import train, predict
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frame = "tensorflow"
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class Config:
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feature_columns = list([2, 5])
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@ -51,9 +53,9 @@ class Config:
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if not os.path.exists(figure_save_path):
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os.mkdir(figure_save_path)
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#used_frame = frame
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#model_postfix = {"pytorch": ".pth", "keras": ".h5", "tensorflow": ".ckpt"}
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#model_name = "model_" + continue_flag + used_frame + model_postfix[used_frame]
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used_frame = frame
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model_postfix = {"pytorch": ".pth", "keras": ".h5", "tensorflow": ".ckpt"}
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model_name = "model_" + continue_flag + used_frame + model_postfix[used_frame]
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class Data:
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166
serve.py
166
serve.py
@ -5,14 +5,159 @@ from http.server import BaseHTTPRequestHandler, HTTPServer
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import cgi
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import json
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from urllib import parse
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import pandas as pd
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import numpy as np
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import os
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from sklearn.model_selection import train_test_split
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from model_tensorflow import train, predict
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class Config:
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feature_columns = list([2, 5])
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label_columns = [5]
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feature_and_label_columns = feature_columns + label_columns
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label_in_feature_columns = (lambda x, y: [x.index(i) for i in y])(feature_columns, label_columns)
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predict_day = 1
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input_size = len(feature_columns)
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output_size = len(label_columns)
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hidden_size = 128
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lstm_layers = 2
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dropout_rate = 0.2
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time_step = 20
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do_train = True
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do_predict = True
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add_train = False
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shuffle_train_data = True
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train_data_rate = 0.95
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valid_data_rate = 0.15
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batch_size = 64
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learning_rate = 0.001
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epoch = 20
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patience = 5
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random_seed = 42
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do_continue_train = False
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continue_flag = ""
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if do_continue_train:
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shuffle_train_data = False
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batch_size = 1
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continue_flag = "continue_"
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train_data_path = "./data/stock_data.csv"
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model_save_path = "./checkpoint/"
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figure_save_path = "./figure/"
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do_figure_save = False
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if not os.path.exists(model_save_path):
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os.mkdir(model_save_path)
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if not os.path.exists(figure_save_path):
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os.mkdir(figure_save_path)
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used_frame = frame
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model_postfix = {"pytorch": ".pth", "keras": ".h5", "tensorflow": ".ckpt"}
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model_name = "model_" + continue_flag + used_frame + model_postfix[used_frame]
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class Data:
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def __init__(self, config):
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self.config = config
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self.data, self.data_column_name = self.read_data()
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self.data_num = self.data.shape[0]
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self.train_num = int(self.data_num * self.config.train_data_rate)
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self.mean = np.mean(self.data, axis=0)
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self.std = np.std(self.data, axis=0)
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self.norm_data = (self.data - self.mean) / self.std
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self.start_num_in_test = 0
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def read_data(self):
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init_data = pd.read_csv(self.config.train_data_path,
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usecols=self.config.feature_and_label_columns)
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return init_data.values, init_data.columns.tolist()
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def get_train_and_valid_data(self):
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feature_data = self.norm_data[:self.train_num]
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label_data = self.norm_data[self.config.predict_day: self.config.predict_day + self.train_num,
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self.config.label_in_feature_columns]
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if not self.config.do_continue_train:
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train_x = [feature_data[i:i + self.config.time_step] for i in range(self.train_num - self.config.time_step)]
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train_y = [label_data[i:i + self.config.time_step] for i in range(self.train_num - self.config.time_step)]
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else:
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train_x = [
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feature_data[start_index + i * self.config.time_step: start_index + (i + 1) * self.config.time_step]
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for start_index in range(self.config.time_step)
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for i in range((self.train_num - start_index) // self.config.time_step)]
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train_y = [
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label_data[start_index + i * self.config.time_step: start_index + (i + 1) * self.config.time_step]
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for start_index in range(self.config.time_step)
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for i in range((self.train_num - start_index) // self.config.time_step)]
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train_x, train_y = np.array(train_x), np.array(train_y)
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train_x, valid_x, train_y, valid_y = train_test_split(train_x, train_y, test_size=self.config.valid_data_rate,
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random_state=self.config.random_seed,
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shuffle=self.config.shuffle_train_data)
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return train_x, valid_x, train_y, valid_y
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def get_test_data(self, return_label_data=False):
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feature_data = self.norm_data[self.train_num:]
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self.start_num_in_test = feature_data.shape[0] % self.config.time_step
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time_step_size = feature_data.shape[0] // self.config.time_step
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test_x = [feature_data[self.start_num_in_test + i * self.config.time_step: self.start_num_in_test + (
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i + 1) * self.config.time_step]
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for i in range(time_step_size)]
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if return_label_data:
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label_data = self.norm_data[self.train_num + self.start_num_in_test:, self.config.label_in_feature_columns]
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return np.array(test_x), label_data
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return np.array(test_x)
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def draw(config, origin_data, predict_norm_data):
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label_norm_data = origin_data.norm_data[origin_data.train_num + origin_data.start_num_in_test:,
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config.label_in_feature_columns]
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assert label_norm_data.shape[0] == predict_norm_data.shape[
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0], "The element number in origin and predicted data is different"
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label_name = [origin_data.data_column_name[i] for i in config.label_in_feature_columns]
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label_column_num = len(config.label_columns)
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loss = np.mean((label_norm_data[config.predict_day:] - predict_norm_data[:-config.predict_day]) ** 2, axis=0)
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print("The mean squared error of stock {} is ".format(label_name), loss)
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label_X = range(origin_data.data_num - origin_data.train_num - origin_data.start_num_in_test)
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predict_X = [x + config.predict_day for x in label_X]
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label_data = label_norm_data * origin_data.std[config.label_in_feature_columns] + \
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origin_data.mean[config.label_in_feature_columns]
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predict_data = predict_norm_data * origin_data.std[config.label_in_feature_columns] + \
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origin_data.mean[config.label_in_feature_columns]
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print(label_data)
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print(predict_data)
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PORT_NUMBER = 8000
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lock = Lock()
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config = Config()
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frame = "tensorflow"
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def train_models():
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lock.acquire()
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np.random.seed(config.random_seed)
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data_gainer = Data(config)
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train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
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train(config, train_X, train_Y, valid_X, valid_Y)
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lock.release()
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@ -34,6 +179,25 @@ class MyHandler(BaseHTTPRequestHandler):
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job = query.get('job')[0]
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gpu_model = query.get('gpu_model')[0]
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time = query.get('time')[0]
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data_gainer = Data(config)
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test_X, test_Y = data_gainer.get_test_data(return_label_data=True)
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pred_result = predict(config, test_X)
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draw(config, data_gainer, pred_result)
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msg = {'code': 1, 'error': "container not exist"}
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except Exception as e:
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msg = {'code': 2, 'error': str(e)}
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self.send_response(200)
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self.send_header('Content-type', 'application/json')
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self.end_headers()
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self.wfile.write(bytes(json.dumps(msg), "utf-8"))
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elif req.path == "/train":
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try:
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job = query.get('job')[0]
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gpu_model = query.get('gpu_model')[0]
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time = query.get('time')[0]
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t = Thread(target=train_models(), name='train_models', args=(job,))
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t.start()
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msg = {'code': 1, 'error': "container not exist"}
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except Exception as e:
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msg = {'code': 2, 'error': str(e)}
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