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Newnius 2020-05-02 17:16:18 +08:00
parent ee930aa2c5
commit c7d6fa81a3
2 changed files with 217 additions and 277 deletions
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49
.idea/workspace.xml
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@ -4,7 +4,6 @@
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@ -52,7 +51,7 @@
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@ -62,7 +61,7 @@
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@ -90,24 +89,26 @@
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<entry file="file://$PROJECT_DIR$/train.py"> <entry file="file://$PROJECT_DIR$/train.py">
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@ -203,8 +204,8 @@
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<option value="$PROJECT_DIR$/data/data2.csv" /> <option value="$PROJECT_DIR$/data/data2.csv" />
<option value="$PROJECT_DIR$/data/data3.csv" /> <option value="$PROJECT_DIR$/data/data3.csv" />
<option value="$PROJECT_DIR$/serve.py" />
<option value="$PROJECT_DIR$/train.py" /> <option value="$PROJECT_DIR$/train.py" />
<option value="$PROJECT_DIR$/serve.py" />
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@ -247,7 +248,7 @@
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@ -288,12 +289,12 @@
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<servers /> <servers />
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<option name="totallyTimeSpent" value="48164000" /> <option name="totallyTimeSpent" value="50651000" />
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401
serve.py
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@ -6,10 +6,6 @@ import cgi
import json import json
from urllib import parse from urllib import parse
import pandas as pd import pandas as pd
import numpy as np
import os
from sklearn.model_selection import train_test_split
from model_tensorflow import train, predict
import csv import csv
from pandas import DataFrame from pandas import DataFrame
from pandas import Series from pandas import Series
@ -23,184 +19,163 @@ from keras.layers import LSTM
from math import sqrt from math import sqrt
import numpy import numpy
class Config:
feature_columns = list(range(0, 2))
label_columns = [1]
feature_and_label_columns = feature_columns + label_columns
label_in_feature_columns = (lambda x, y: [x.index(i) for i in y])(feature_columns, label_columns)
predict_day = 1
input_size = len(feature_columns)
output_size = len(label_columns)
hidden_size = 128
lstm_layers = 2
dropout_rate = 0.2
time_step = 5
do_train = True
do_predict = True
add_train = False
shuffle_train_data = True
# train_data_rate = 0.95 #comment yqy
train_data_rate = 1 # add yqy
valid_data_rate = 0.15
batch_size = 64
learning_rate = 0.001
epoch = 20
patience = 5
random_seed = 42
do_continue_train = False
continue_flag = ""
if do_continue_train:
shuffle_train_data = False
batch_size = 1
continue_flag = "continue_"
train_data_path = "./data/data.csv"
model_save_path = "./checkpoint/"
figure_save_path = "./figure/"
do_figure_save = False
if not os.path.exists(model_save_path):
os.mkdir(model_save_path)
if not os.path.exists(figure_save_path):
os.mkdir(figure_save_path)
used_frame = "tensorflow"
model_postfix = {"pytorch": ".pth", "keras": ".h5", "tensorflow": ".ckpt"}
model_name = "model_" + continue_flag + used_frame + model_postfix[used_frame]
class Data:
def __init__(self, config):
self.config = config
self.data, self.data_column_name = self.read_data()
self.data_num = self.data.shape[0]
self.train_num = int(self.data_num * self.config.train_data_rate)
self.mean = np.mean(self.data, axis=0)
self.std = np.std(self.data, axis=0) + 0.0001
self.norm_data = (self.data - self.mean) / self.std
self.start_num_in_test = 0
def read_data(self):
init_data = pd.read_csv(self.config.train_data_path,
usecols=self.config.feature_and_label_columns)
return init_data.values, init_data.columns.tolist()
def get_train_and_valid_data(self):
feature_data = self.norm_data[:self.train_num]
label_data = self.norm_data[self.config.predict_day: self.config.predict_day + self.train_num,
self.config.label_in_feature_columns]
if not self.config.do_continue_train:
train_x = [feature_data[i:i + self.config.time_step] for i in range(self.train_num - self.config.time_step)]
train_y = [label_data[i:i + self.config.time_step] for i in range(self.train_num - self.config.time_step)]
else:
train_x = [
feature_data[start_index + i * self.config.time_step: start_index + (i + 1) * self.config.time_step]
for start_index in range(self.config.time_step)
for i in range((self.train_num - start_index) // self.config.time_step)]
train_y = [
label_data[start_index + i * self.config.time_step: start_index + (i + 1) * self.config.time_step]
for start_index in range(self.config.time_step)
for i in range((self.train_num - start_index) // self.config.time_step)]
train_x, train_y = np.array(train_x), np.array(train_y)
train_x, valid_x, train_y, valid_y = train_test_split(train_x, train_y, test_size=self.config.valid_data_rate,
random_state=self.config.random_seed,
shuffle=self.config.shuffle_train_data)
return train_x, valid_x, train_y, valid_y
def get_test_data(self, return_label_data=False):
feature_data = self.norm_data[self.train_num:]
self.start_num_in_test = feature_data.shape[0] % self.config.time_step
time_step_size = feature_data.shape[0] // self.config.time_step
test_x = [feature_data[self.start_num_in_test + i * self.config.time_step: self.start_num_in_test + (
i + 1) * self.config.time_step]
for i in range(time_step_size)]
if return_label_data:
label_data = self.norm_data[self.train_num + self.start_num_in_test:, self.config.label_in_feature_columns]
return np.array(test_x), label_data
return np.array(test_x)
# add yqy
def get_test_data_yqy(self, test_data_yqy=None):
if test_data_yqy is None:
test_data_yqy = []
# test_data_yqy=test_data_yqy[1:21]
feature_data = (test_data_yqy - self.mean) / self.std
test_x = [feature_data]
return np.array(test_x)
# add end
def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
label_norm_data = (origin_data - mean_yqy) / std_yqy
assert label_norm_data.shape[0] == predict_norm_data.shape[
0], "The element number in origin and predicted data is different"
print("dsa")
# label_norm_data=label_norm_data[:,1]
label_name = 'high'
label_column_num = 3
loss = \
np.mean((label_norm_data[config.predict_day:, 1:2] - predict_norm_data[:-config.predict_day]) ** 2, axis=0)
print("The mean squared error of stock {} is ".format(label_name), loss)
# label_X = range(origin_data.data_num - origin_data.train_num - origin_data.start_num_in_test)
# predict_X = [x + config.predict_day for x in label_X]
print("2")
print(label_norm_data[:, 1:2])
label_data = label_norm_data[:, 1:2] * std_yqy[1:2] + mean_yqy[1:2]
print(label_data)
print(predict_norm_data)
predict_data = predict_norm_data * std_yqy[config.label_in_feature_columns] + mean_yqy[
config.label_in_feature_columns]
print(predict_data)
print(label_data[:, -1])
print(predict_data[:, -1])
PORT_NUMBER = 8080 PORT_NUMBER = 8080
lock = Lock() lock = Lock()
config = Config() models = {}
def train_models(): # frame a sequence as a supervised learning problem
def timeseries_to_supervised(data, lag=1):
df = DataFrame(data)
columns = [df.shift(i) for i in range(1, lag + 1)]
columns.append(df)
df = concat(columns, axis=1)
df = df.drop(0)
return df
# create a differenced series
def difference(dataset, interval=1):
diff = list()
for i in range(interval, len(dataset)):
value = dataset[i] - dataset[i - interval]
diff.append(value)
return Series(diff)
# invert differenced value
def inverse_difference(history, yhat, interval=1):
return yhat + history[-interval]
# inverse scaling for a forecasted value
def invert_scale(scaler, X, yhat):
new_row = [x for x in X] + [yhat]
array = numpy.array(new_row)
array = array.reshape(1, len(array))
inverted = scaler.inverse_transform(array)
return inverted[0, -1]
# fit an LSTM network to training data
def fit_lstm(train, batch_size2, nb_epoch, neurons):
X, y = train[:, 0:-1], train[:, -1]
X = X.reshape(X.shape[0], 1, X.shape[1])
model = Sequential()
model.add(LSTM(neurons, batch_input_shape=(batch_size2, X.shape[1], X.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
for i in range(nb_epoch):
model.fit(X, y, epochs=1, batch_size=batch_size2, verbose=0, shuffle=False)
# loss = model.evaluate(X, y)
# print("Epoch {}/{}, loss = {}".format(i, nb_epoch, loss))
model.reset_states()
return model
def train_models(job):
lock.acquire() lock.acquire()
np.random.seed(config.random_seed) if job not in models:
data_gainer = Data(config) models[job] = {
'lock': Lock()
train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data() }
print(train_X, valid_X, train_Y, valid_Y)
print(train_X.shape[0])
if train_X.shape[0] < 500:
config.batch_size = 32
if train_X.shape[0] < 200:
config.batch_size = 16
train(config, train_X, train_Y, valid_X, valid_Y)
lock.release() lock.release()
models[job]['lock'].acquire()
# load dataset
series = read_csv('./data/' + job + '.csv', header=0, index_col=0, squeeze=True)
# transform data to be stationary
raw_values = series.values
diff_values = difference(raw_values, 1)
# transform data to be supervised learning
lag = 4
supervised = timeseries_to_supervised(diff_values, lag)
supervised_values = supervised.values
batch_size = 32
if supervised_values.shape[0] < 100:
batch_size = 16
if supervised_values.shape[0] < 60:
batch_size = 8
# split data into train and test-sets
train = supervised_values
# transform the scale of the data
# scale data to [-1, 1]
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
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): class MyHandler(BaseHTTPRequestHandler):
# Handler for the GET requests # Handler for the GET requests
@ -216,42 +191,14 @@ class MyHandler(BaseHTTPRequestHandler):
elif req.path == "/predict": elif req.path == "/predict":
try: try:
data = { job = query.get('job')[0],
'job': query.get('job')[0], seq = query.get('model')[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)
predict(job, seq)
msg = {'code': 1, 'error': "container not exist"} msg = {'code': 1, 'error': "container not exist"}
except Exception as e: except Exception as e:
msg = {'code': 2, 'error': str(e)} msg = {'code': 2, 'error': str(e)}
self.send_response(200) self.send_response(200)
self.send_header('Content-type', 'application/json') self.send_header('Content-type', 'application/json')
self.end_headers() self.end_headers()
@ -260,27 +207,26 @@ class MyHandler(BaseHTTPRequestHandler):
elif req.path == "/feed": elif req.path == "/feed":
try: try:
job = query.get('job')[0] 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] seq = query.get('seq')[0]
value = query.get('value')[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( spamwriter = csv.writer(
csvfile, delimiter=',', csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL quotechar='|', quoting=csv.QUOTE_MINIMAL
) )
# spamwriter.writerow([job, model, time, utilGPU, utilCPU, pre, main, post])
spamwriter.writerow([seq, value]) spamwriter.writerow([seq, value])
msg = {'code': 1, 'error': "container not exist"} msg = {'code': 0, 'error': ""}
except Exception as e: except Exception as e:
msg = {'code': 2, 'error': str(e)} msg = {'code': 1, 'error': str(e)}
self.send_response(200) self.send_response(200)
self.send_header('Content-type', 'application/json') self.send_header('Content-type', 'application/json')
self.end_headers() self.end_headers()
@ -290,9 +236,9 @@ class MyHandler(BaseHTTPRequestHandler):
try: try:
t = Thread(target=train_models, name='train_models', args=()) t = Thread(target=train_models, name='train_models', args=())
t.start() t.start()
msg = {'code': 1, 'error': "container not exist"} msg = {'code': 0, 'error': ""}
except Exception as e: except Exception as e:
msg = {'code': 2, 'error': str(e)} msg = {'code': 1, 'error': str(e)}
self.send_response(200) self.send_response(200)
self.send_header('Content-type', 'application/json') self.send_header('Content-type', 'application/json')
self.end_headers() self.end_headers()
@ -313,9 +259,8 @@ class MyHandler(BaseHTTPRequestHandler):
}) })
try: try:
job = form.getvalue('job')[0] job = form.getvalue('job')[0]
data = form.getvalue('records')[0] seq = form.getvalue('seq')[0]
records = json.load(data) t = Thread(target=train_models(), name='train_models', args=(job, seq,))
t = Thread(target=train_models(), name='train_models', args=(job, records,))
t.start() t.start()
msg = {"code": 0, "error": ""} msg = {"code": 0, "error": ""}
except Exception as e: except Exception as e:
@ -336,14 +281,6 @@ if __name__ == '__main__':
server = HTTPServer(('', PORT_NUMBER), MyHandler) server = HTTPServer(('', PORT_NUMBER), MyHandler)
print('Started http server on port ', PORT_NUMBER) 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 # Wait forever for incoming http requests
server.serve_forever() server.serve_forever()