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445
serve.py
445
serve.py
@ -6,10 +6,6 @@ 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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import csv
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from pandas import DataFrame
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from pandas import Series
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@ -23,184 +19,163 @@ from keras.layers import LSTM
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from math import sqrt
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import numpy
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class Config:
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feature_columns = list(range(0, 2))
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label_columns = [1]
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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 = 5
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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 #comment yqy
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train_data_rate = 1 # add yqy
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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/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 = "tensorflow"
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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) + 0.0001
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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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# add yqy
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def get_test_data_yqy(self, test_data_yqy=None):
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if test_data_yqy is None:
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test_data_yqy = []
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# test_data_yqy=test_data_yqy[1:21]
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feature_data = (test_data_yqy - self.mean) / self.std
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test_x = [feature_data]
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return np.array(test_x)
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# add end
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def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
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label_norm_data = (origin_data - mean_yqy) / std_yqy
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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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print("dsa")
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# label_norm_data=label_norm_data[:,1]
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label_name = 'high'
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label_column_num = 3
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loss = \
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np.mean((label_norm_data[config.predict_day:, 1:2] - 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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print("2")
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print(label_norm_data[:, 1:2])
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label_data = label_norm_data[:, 1:2] * std_yqy[1:2] + mean_yqy[1:2]
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print(label_data)
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print(predict_norm_data)
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predict_data = predict_norm_data * std_yqy[config.label_in_feature_columns] + mean_yqy[
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config.label_in_feature_columns]
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print(predict_data)
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print(label_data[:, -1])
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print(predict_data[:, -1])
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PORT_NUMBER = 8080
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lock = Lock()
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config = Config()
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models = {}
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def train_models():
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# frame a sequence as a supervised learning problem
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def timeseries_to_supervised(data, lag=1):
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df = DataFrame(data)
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columns = [df.shift(i) for i in range(1, lag + 1)]
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columns.append(df)
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df = concat(columns, axis=1)
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df = df.drop(0)
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return df
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# create a differenced series
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def difference(dataset, interval=1):
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diff = list()
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for i in range(interval, len(dataset)):
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value = dataset[i] - dataset[i - interval]
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diff.append(value)
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return Series(diff)
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# invert differenced value
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def inverse_difference(history, yhat, interval=1):
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return yhat + history[-interval]
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# inverse scaling for a forecasted value
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def invert_scale(scaler, X, yhat):
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new_row = [x for x in X] + [yhat]
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array = numpy.array(new_row)
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array = array.reshape(1, len(array))
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inverted = scaler.inverse_transform(array)
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return inverted[0, -1]
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# fit an LSTM network to training data
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def fit_lstm(train, batch_size2, nb_epoch, neurons):
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X, y = train[:, 0:-1], train[:, -1]
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X = X.reshape(X.shape[0], 1, X.shape[1])
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model = Sequential()
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model.add(LSTM(neurons, batch_input_shape=(batch_size2, X.shape[1], X.shape[2]), stateful=True))
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model.add(Dense(1))
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model.compile(loss='mean_squared_error', optimizer='adam')
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for i in range(nb_epoch):
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model.fit(X, y, epochs=1, batch_size=batch_size2, verbose=0, shuffle=False)
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# loss = model.evaluate(X, y)
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# print("Epoch {}/{}, loss = {}".format(i, nb_epoch, loss))
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model.reset_states()
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return model
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def train_models(job):
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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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print(train_X, valid_X, train_Y, valid_Y)
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print(train_X.shape[0])
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if train_X.shape[0] < 500:
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config.batch_size = 32
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if train_X.shape[0] < 200:
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config.batch_size = 16
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train(config, train_X, train_Y, valid_X, valid_Y)
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if job not in models:
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models[job] = {
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'lock': Lock()
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}
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lock.release()
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models[job]['lock'].acquire()
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# load dataset
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series = read_csv('./data/' + job + '.csv', header=0, index_col=0, squeeze=True)
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# transform data to be stationary
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raw_values = series.values
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diff_values = difference(raw_values, 1)
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# transform data to be supervised learning
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lag = 4
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supervised = timeseries_to_supervised(diff_values, lag)
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supervised_values = supervised.values
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batch_size = 32
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if supervised_values.shape[0] < 100:
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batch_size = 16
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if supervised_values.shape[0] < 60:
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batch_size = 8
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# split data into train and test-sets
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train = supervised_values
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# transform the scale of the data
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# scale data to [-1, 1]
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# fit scaler
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scaler = MinMaxScaler(feature_range=(-1, 1))
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scaler = scaler.fit(train)
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# transform train
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train = train.reshape(train.shape[0], train.shape[1])
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train_scaled = scaler.transform(train)
|
||||
|
||||
# fit the model
|
||||
t1 = train.shape[0] % batch_size
|
||||
|
||||
train_trimmed = train_scaled[t1:, :]
|
||||
model = fit_lstm(train_trimmed, batch_size, 30, 4)
|
||||
|
||||
models[job] = {
|
||||
'model': model,
|
||||
'scaler': scaler,
|
||||
'batch_size': batch_size
|
||||
}
|
||||
|
||||
models[job]['lock'].release()
|
||||
|
||||
|
||||
def predict(job, seq):
|
||||
if job not in models:
|
||||
return -1, False
|
||||
|
||||
# load dataset
|
||||
data = {
|
||||
'seq': seq,
|
||||
'value': 0,
|
||||
}
|
||||
|
||||
df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value'])
|
||||
df = df.tail(models[job]['batch_size'] * 2 - 1)
|
||||
df = df.append(data, ignore_index=True)
|
||||
|
||||
# transform data to be stationary
|
||||
raw_values = df.values
|
||||
diff_values = difference(raw_values, 1)[models[job]['batch_size']:]
|
||||
# transform data to be supervised learning
|
||||
lag = 4
|
||||
supervised = timeseries_to_supervised(diff_values, lag)
|
||||
supervised_values = supervised.values
|
||||
|
||||
batch_size = models[job]['batch_size']
|
||||
|
||||
test = supervised_values
|
||||
|
||||
test = test.reshape(test.shape[0], test.shape[1])
|
||||
test_scaled = models[job]['scaler'].transform(test)
|
||||
|
||||
# forecast the entire training dataset to build up state for forecasting
|
||||
test_reshaped = test_scaled[:, 0:-1]
|
||||
test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag)
|
||||
output = models[job]['model'].predict(test_reshaped, batch_size=batch_size)
|
||||
predictions = list()
|
||||
for i in range(len(output)):
|
||||
yhat = output[i, 0]
|
||||
X = test_scaled[i, 0:-1]
|
||||
# invert scaling
|
||||
yhat = invert_scale(models[job]['scaler'], X, yhat)
|
||||
# invert differencing
|
||||
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
|
||||
# store forecast
|
||||
predictions.append(yhat)
|
||||
# report performance
|
||||
|
||||
rmse = sqrt(mean_squared_error(raw_values[-batch_size:], predictions))
|
||||
print(predictions, raw_values[-batch_size:])
|
||||
return 1, True
|
||||
|
||||
|
||||
class MyHandler(BaseHTTPRequestHandler):
|
||||
# Handler for the GET requests
|
||||
@ -216,42 +191,14 @@ class MyHandler(BaseHTTPRequestHandler):
|
||||
|
||||
elif req.path == "/predict":
|
||||
try:
|
||||
data = {
|
||||
'job': query.get('job')[0],
|
||||
'model': query.get('model')[0],
|
||||
'time': query.get('time')[0],
|
||||
'utilGPU': query.get('utilGPU')[0],
|
||||
'utilCPU': query.get('utilCPU')[0],
|
||||
'pre': 0,
|
||||
'main': 0,
|
||||
'post': 0
|
||||
}
|
||||
|
||||
data = {
|
||||
'seq': query.get('job')[0],
|
||||
'value': query.get('model')[0],
|
||||
}
|
||||
|
||||
with open(config.train_data_path, 'r') as f:
|
||||
df = pd.read_csv(config.train_data_path, usecols=['seq', 'value'])
|
||||
df = df.tail(config.time_step - 1)
|
||||
df = df.append(data, ignore_index=True)
|
||||
df.to_csv('./data/test_data.csv', index=False)
|
||||
|
||||
np.random.seed(config.random_seed)
|
||||
data_gainer = Data(config)
|
||||
test_data_yqy = pd.read_csv("./data/test_data.csv", usecols=list(range(0, 2)))
|
||||
test_data_values = test_data_yqy.values[:]
|
||||
test_X = data_gainer.get_test_data_yqy(test_data_values)
|
||||
pred_result = predict(config, test_X)
|
||||
|
||||
mean = Data(config).mean
|
||||
std = Data(config).std
|
||||
draw_yqy(config, test_data_values, pred_result, mean, std)
|
||||
job = query.get('job')[0],
|
||||
seq = query.get('model')[0]
|
||||
|
||||
predict(job, seq)
|
||||
msg = {'code': 1, 'error': "container not exist"}
|
||||
except Exception as e:
|
||||
msg = {'code': 2, 'error': str(e)}
|
||||
|
||||
self.send_response(200)
|
||||
self.send_header('Content-type', 'application/json')
|
||||
self.end_headers()
|
||||
@ -260,27 +207,26 @@ class MyHandler(BaseHTTPRequestHandler):
|
||||
elif req.path == "/feed":
|
||||
try:
|
||||
job = query.get('job')[0]
|
||||
model = query.get('model')[0]
|
||||
time = query.get('time')[0]
|
||||
utilGPU = query.get('utilGPU')[0]
|
||||
utilCPU = query.get('utilCPU')[0]
|
||||
pre = query.get('pre')[0]
|
||||
main = query.get('main')[0]
|
||||
post = query.get('post')[0]
|
||||
|
||||
seq = query.get('seq')[0]
|
||||
value = query.get('value')[0]
|
||||
|
||||
with open(config.train_data_path, 'a+', newline='') as csvfile:
|
||||
if int(seq) == 1:
|
||||
with open('./data/' + job + '.csv', 'w', newline='') as csvfile:
|
||||
spamwriter = csv.writer(
|
||||
csvfile, delimiter=',',
|
||||
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||
)
|
||||
spamwriter.writerow(["seq", "value"])
|
||||
|
||||
with open('./data/' + job + '.csv', 'a+', newline='') as csvfile:
|
||||
spamwriter = csv.writer(
|
||||
csvfile, delimiter=',',
|
||||
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||
)
|
||||
# spamwriter.writerow([job, model, time, utilGPU, utilCPU, pre, main, post])
|
||||
spamwriter.writerow([seq, value])
|
||||
msg = {'code': 1, 'error': "container not exist"}
|
||||
msg = {'code': 0, 'error': ""}
|
||||
except Exception as e:
|
||||
msg = {'code': 2, 'error': str(e)}
|
||||
msg = {'code': 1, 'error': str(e)}
|
||||
self.send_response(200)
|
||||
self.send_header('Content-type', 'application/json')
|
||||
self.end_headers()
|
||||
@ -290,9 +236,9 @@ class MyHandler(BaseHTTPRequestHandler):
|
||||
try:
|
||||
t = Thread(target=train_models, name='train_models', args=())
|
||||
t.start()
|
||||
msg = {'code': 1, 'error': "container not exist"}
|
||||
msg = {'code': 0, 'error': ""}
|
||||
except Exception as e:
|
||||
msg = {'code': 2, 'error': str(e)}
|
||||
msg = {'code': 1, 'error': str(e)}
|
||||
self.send_response(200)
|
||||
self.send_header('Content-type', 'application/json')
|
||||
self.end_headers()
|
||||
@ -301,32 +247,31 @@ class MyHandler(BaseHTTPRequestHandler):
|
||||
else:
|
||||
self.send_error(404, 'File Not Found: %s' % self.path)
|
||||
|
||||
# Handler for the POST requests
|
||||
def do_POST(self):
|
||||
if self.path == "/train2":
|
||||
form = cgi.FieldStorage(
|
||||
fp=self.rfile,
|
||||
headers=self.headers,
|
||||
environ={
|
||||
'REQUEST_METHOD': 'POST',
|
||||
'CONTENT_TYPE': self.headers['Content-Type'],
|
||||
})
|
||||
try:
|
||||
job = form.getvalue('job')[0]
|
||||
data = form.getvalue('records')[0]
|
||||
records = json.load(data)
|
||||
t = Thread(target=train_models(), name='train_models', args=(job, records,))
|
||||
t.start()
|
||||
msg = {"code": 0, "error": ""}
|
||||
except Exception as e:
|
||||
msg = {"code": 1, "error": str(e)}
|
||||
self.send_response(200)
|
||||
self.send_header('Content-type', 'application/json')
|
||||
self.end_headers()
|
||||
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||
# Handler for the POST requests
|
||||
def do_POST(self):
|
||||
if self.path == "/train2":
|
||||
form = cgi.FieldStorage(
|
||||
fp=self.rfile,
|
||||
headers=self.headers,
|
||||
environ={
|
||||
'REQUEST_METHOD': 'POST',
|
||||
'CONTENT_TYPE': self.headers['Content-Type'],
|
||||
})
|
||||
try:
|
||||
job = form.getvalue('job')[0]
|
||||
seq = form.getvalue('seq')[0]
|
||||
t = Thread(target=train_models(), name='train_models', args=(job, seq,))
|
||||
t.start()
|
||||
msg = {"code": 0, "error": ""}
|
||||
except Exception as e:
|
||||
msg = {"code": 1, "error": str(e)}
|
||||
self.send_response(200)
|
||||
self.send_header('Content-type', 'application/json')
|
||||
self.end_headers()
|
||||
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||
|
||||
else:
|
||||
self.send_error(404, 'File Not Found: %s' % self.path)
|
||||
else:
|
||||
self.send_error(404, 'File Not Found: %s' % self.path)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
@ -336,14 +281,6 @@ if __name__ == '__main__':
|
||||
server = HTTPServer(('', PORT_NUMBER), MyHandler)
|
||||
print('Started http server on port ', PORT_NUMBER)
|
||||
|
||||
with open(config.train_data_path, 'w', newline='') as csvfile:
|
||||
spamwriter = csv.writer(
|
||||
csvfile, delimiter=',',
|
||||
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||
)
|
||||
#spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"])
|
||||
spamwriter.writerow(["seq", "value"])
|
||||
|
||||
# Wait forever for incoming http requests
|
||||
server.serve_forever()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user