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49
main.py
49
main.py
@ -4,7 +4,6 @@ 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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@ -31,7 +30,7 @@ class Config:
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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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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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@ -47,11 +46,11 @@ class Config:
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batch_size = 1
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continue_flag = "continue_"
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#comment yqy
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# comment yqy
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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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#comment end
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# comment end
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# add yqy
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train_data_path = "./data/stock_data_30.csv"
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# model_save_path = "./checkpoint/30/"
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@ -129,10 +128,10 @@ class Data:
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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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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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# add end
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def draw(config, origin_data, predict_norm_data):
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@ -161,38 +160,42 @@ def draw(config, origin_data, predict_norm_data):
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print(predict_data)
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||||
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||||
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||||
def draw_yqy(config, origin_data, predict_norm_data,mean_yqy,std_yqy):# 这里origin_data等同于test_data_values_yqy
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||||
def draw_yqy(config, origin_data, predict_norm_data, mean_yqy, std_yqy): # 这里origin_data等同于test_data_values_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[0], "The element number in origin and predicted data is different"
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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_norm_data=label_norm_data[:,1]
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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 = 1
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loss = np.mean((label_norm_data[config.predict_day:][:,1] - predict_norm_data[:-config.predict_day][0:]) ** 2, axis=0)[1]
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loss = \
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np.mean((label_norm_data[config.predict_day:][:, 1] - predict_norm_data[:-config.predict_day][0:]) ** 2, axis=0)[1]
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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[:,1] * std_yqy[1]+ mean_yqy[1]
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label_data = label_norm_data[:, 1] * std_yqy[1] + mean_yqy[1]
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||||
predict_data = predict_norm_data * std_yqy[1]+ mean_yqy[1]
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predict_data = predict_norm_data * std_yqy[1] + mean_yqy[1]
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print(label_data)
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print(predict_data)
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# print(label_data[-1])
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# print(predict_data[-1][0])
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# print(label_data[-1])
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# print(predict_data[-1][0])
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||||
def main(config):
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np.random.seed(config.random_seed)
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data_gainer = Data(config)
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# add yqy
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mean_yqy=Data(config).mean
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std_yqy=Data(config).std
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#add end
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mean_yqy = Data(config).mean
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std_yqy = Data(config).std
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||||
# add end
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||||
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||||
if config.do_train:
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||||
train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
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||||
@ -200,15 +203,15 @@ def main(config):
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if config.do_predict:
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||||
# add yqy
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||||
test_data_yqy = pd.read_csv("./data/test_data.csv",usecols=list([2, 5]))
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||||
test_data_values_yqy=test_data_yqy.values[:]
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||||
test_data_yqy = pd.read_csv("./data/test_data.csv", usecols=list([2, 5]))
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||||
test_data_values_yqy = test_data_yqy.values[:]
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||||
# test_data_yqy=[104.3,104.39]
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||||
test_X =data_gainer.get_test_data_yqy(test_data_values_yqy)
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||||
test_X = data_gainer.get_test_data_yqy(test_data_values_yqy)
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||||
# add end
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||||
# test_X, test_Y = data_gainer.get_test_data(return_label_data=True)# comment yqy
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||||
pred_result = predict(config, test_X)
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||||
# draw(config, data_gainer, pred_result)# comment yqy
|
||||
draw_yqy(config, test_data_values_yqy, pred_result,mean_yqy,std_yqy)
|
||||
draw_yqy(config, test_data_values_yqy, pred_result, mean_yqy, std_yqy)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
109
serve.py
109
serve.py
@ -11,11 +11,13 @@ import os
|
||||
from sklearn.model_selection import train_test_split
|
||||
from model_tensorflow import train, predict
|
||||
import csv
|
||||
from collections import deque
|
||||
from StringIO import StringIO
|
||||
|
||||
|
||||
class Config:
|
||||
feature_columns = list(range(0, 6))
|
||||
label_columns = [3, 4, 5]
|
||||
feature_columns = list([2, 5])
|
||||
label_columns = [5]
|
||||
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)
|
||||
|
||||
@ -34,7 +36,8 @@ class Config:
|
||||
add_train = False
|
||||
shuffle_train_data = True
|
||||
|
||||
train_data_rate = 0.95
|
||||
# train_data_rate = 0.95 #comment yqy
|
||||
train_data_rate = 1 # add yqy
|
||||
valid_data_rate = 0.15
|
||||
|
||||
batch_size = 64
|
||||
@ -50,10 +53,10 @@ class Config:
|
||||
batch_size = 1
|
||||
continue_flag = "continue_"
|
||||
|
||||
train_data_path = "./data.csv"
|
||||
test_data_path = "./test.csv"
|
||||
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)
|
||||
@ -74,17 +77,14 @@ class Data:
|
||||
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)
|
||||
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
|
||||
)
|
||||
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):
|
||||
@ -112,22 +112,6 @@ class Data:
|
||||
return train_x, valid_x, train_y, valid_y
|
||||
|
||||
def get_test_data(self, return_label_data=False):
|
||||
init_data = pd.read_csv(
|
||||
self.config.test_data_path,
|
||||
usecols=self.config.feature_and_label_columns
|
||||
)
|
||||
norm_data = (init_data - self.mean) / self.std
|
||||
feature_data = norm_data[:]
|
||||
time_step_size = feature_data.shape[0] // self.config.time_step
|
||||
|
||||
test_x = [feature_data[0 + i * self.config.time_step: 0 + (
|
||||
i + 1) * self.config.time_step]
|
||||
for i in range(time_step_size)]
|
||||
if return_label_data:
|
||||
label_data = norm_data[0:, self.config.label_in_feature_columns]
|
||||
return np.array(test_x), label_data
|
||||
return np.array(test_x)
|
||||
|
||||
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
|
||||
@ -140,31 +124,46 @@ class Data:
|
||||
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)
|
||||
|
||||
def draw(config, origin_data, predict_norm_data):
|
||||
label_norm_data = origin_data.norm_data[origin_data.train_num + origin_data.start_num_in_test:,
|
||||
config.label_in_feature_columns]
|
||||
|
||||
# add end
|
||||
|
||||
|
||||
def draw_yqy(config, origin_data, predict_norm_data, mean_yqy, std_yqy): # 这里origin_data等同于test_data_values_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"
|
||||
|
||||
label_name = [origin_data.data_column_name[i] for i in config.label_in_feature_columns]
|
||||
label_column_num = len(config.label_columns)
|
||||
# label_norm_data=label_norm_data[:,1]
|
||||
label_name = 'high'
|
||||
label_column_num = 1
|
||||
|
||||
loss = np.mean((label_norm_data[config.predict_day:] - predict_norm_data[:-config.predict_day]) ** 2, axis=0)
|
||||
loss = \
|
||||
np.mean((label_norm_data[config.predict_day:][:, 1] - predict_norm_data[:-config.predict_day][0:]) ** 2,
|
||||
axis=0)[1]
|
||||
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]
|
||||
# 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]
|
||||
|
||||
label_data = label_norm_data * origin_data.std[config.label_in_feature_columns] + \
|
||||
origin_data.mean[config.label_in_feature_columns]
|
||||
label_data = label_norm_data[:, 1] * std_yqy[1] + mean_yqy[1]
|
||||
|
||||
predict_data = predict_norm_data * origin_data.std[config.label_in_feature_columns] + \
|
||||
origin_data.mean[config.label_in_feature_columns]
|
||||
predict_data = predict_norm_data * std_yqy[1] + mean_yqy[1]
|
||||
|
||||
print(label_data)
|
||||
print(predict_data)
|
||||
|
||||
print(label_data[-1])
|
||||
print(predict_data[-1])
|
||||
|
||||
|
||||
PORT_NUMBER = 8080
|
||||
lock = Lock()
|
||||
@ -199,15 +198,35 @@ class MyHandler(BaseHTTPRequestHandler):
|
||||
|
||||
elif req.path == "/predict":
|
||||
try:
|
||||
job = query.get('job')[0]
|
||||
gpu_model = query.get('gpu_model')[0]
|
||||
time = query.get('time')[0]
|
||||
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
|
||||
}
|
||||
|
||||
with open(config.train_data_path, 'r') as f:
|
||||
q = deque(f, config.time_step - 1)
|
||||
df = pd.read_csv(StringIO(''.join(q)), usecols=config.feature_and_label_columns)
|
||||
|
||||
df.append(data, ignore_index=True)
|
||||
df.to_csv('./data/test_data.csv')
|
||||
|
||||
np.random.seed(config.random_seed)
|
||||
data_gainer = Data(config)
|
||||
test_X, test_Y = data_gainer.get_test_data(return_label_data=True)
|
||||
print(test_X, test_Y)
|
||||
test_data_yqy = pd.read_csv("./data/test_data.csv", usecols=list(range(0, 8)))
|
||||
test_data_values = test_data_yqy.values[:]
|
||||
test_X = data_gainer.get_test_data_yqy(test_data_values)
|
||||
pred_result = predict(config, test_X)
|
||||
print(pred_result)
|
||||
draw(config, data_gainer, pred_result)
|
||||
|
||||
mean = Data(config).mean
|
||||
std = Data(config).std
|
||||
draw_yqy(config, test_data_values, pred_result, mean, std)
|
||||
|
||||
msg = {'code': 1, 'error': "container not exist"}
|
||||
except Exception as e:
|
||||
msg = {'code': 2, 'error': str(e)}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user