YAO/YAO-optimizer
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update

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Newnius
2020-05-01 22:52:25 +08:00
parent 3310cdb417
commit e90d5f499f
2 changed files with 35 additions and 22 deletions
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29
serve.py
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@@ -16,8 +16,8 @@ from io import StringIO
class Config:
feature_columns = list(range(0, 8))
label_columns = [5, 6, 7]
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)
@@ -148,7 +148,7 @@ def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
label_column_num = 3
loss = \
np.mean((label_norm_data[config.predict_day:, 5:8] - predict_norm_data[:-config.predict_day]) ** 2, axis=0)
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)
@@ -156,8 +156,8 @@ def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
print("2")
print(label_norm_data[:, 5:8])
label_data = label_norm_data[:, 5:8] * std_yqy[5:8] + mean_yqy[5:8]
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)
@@ -213,16 +213,20 @@ class MyHandler(BaseHTTPRequestHandler):
'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=['job', 'model', 'time', 'utilCPU', 'utilGPU', 'pre', 'main', 'post'])
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, 8)))
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)
@@ -250,12 +254,16 @@ class MyHandler(BaseHTTPRequestHandler):
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:
spamwriter = csv.writer(
csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL
)
spamwriter.writerow([job, model, time, utilGPU, utilCPU, pre, main, post])
# spamwriter.writerow([job, model, time, utilGPU, utilCPU, pre, main, post])
spamwriter.writerow([seq, value])
msg = {'code': 1, 'error': "container not exist"}
except Exception as e:
msg = {'code': 2, 'error': str(e)}
@@ -319,7 +327,8 @@ if __name__ == '__main__':
csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL
)
spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"])
#spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"])
spamwriter.writerow(["seq", "value"])
# Wait forever for incoming http requests
server.serve_forever()