From c7d6fa81a35f94423ab14d5170a3e1e3d395cd5e Mon Sep 17 00:00:00 2001
From: Newnius <newnius.cn@gmail.com>
Date: Sat, 2 May 2020 17:16:18 +0800
Subject: [PATCH] update

---
 .idea/workspace.xml |  49 ++---
 serve.py            | 445 +++++++++++++++++++-------------------------
 2 files changed, 217 insertions(+), 277 deletions(-)

diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index dd926d0..579c8e2 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -4,7 +4,6 @@
     <list default="true" id="0aedafd8-e57e-462a-beda-65af0b91f3df" name="Default Changelist" comment="">
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/serve.py" beforeDir="false" afterPath="$PROJECT_DIR$/serve.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/train.py" beforeDir="false" afterPath="$PROJECT_DIR$/train.py" afterDir="false" />
     </list>
     <ignored path="$PROJECT_DIR$/out/" />
     <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
@@ -52,7 +51,7 @@
           <entry key="csv" value="9" />
           <entry key="gitignore" value="12" />
           <entry key="md" value="104" />
-          <entry key="py" value="3029" />
+          <entry key="py" value="3773" />
           <entry key="sh" value="5" />
         </counts>
       </usages-collector>
@@ -62,7 +61,7 @@
           <entry key="Dockerfile" value="81" />
           <entry key="Markdown" value="104" />
           <entry key="PLAIN_TEXT" value="21" />
-          <entry key="Python" value="3029" />
+          <entry key="Python" value="3773" />
         </counts>
       </usages-collector>
     </session>
@@ -90,24 +89,26 @@
           </provider>
         </entry>
       </file>
-      <file pinned="false" current-in-tab="false">
+      <file pinned="false" current-in-tab="true">
         <entry file="file://$PROJECT_DIR$/serve.py">
           <provider selected="true" editor-type-id="text-editor">
-            <state relative-caret-position="-625">
-              <caret line="25" lean-forward="true" selection-start-line="25" selection-end-line="25" />
+            <state relative-caret-position="232">
+              <caret line="197" lean-forward="true" selection-start-line="197" selection-end-line="197" />
               <folding>
                 <element signature="e#18#46#0" expanded="true" />
-                <marker date="1588408252097" expanded="true" signature="5911:5913" ph="..." />
+                <marker date="1588410887449" expanded="true" signature="1167:1170" ph="..." />
+                <marker date="1588410887449" expanded="true" signature="3275:3277" ph="..." />
+                <marker date="1588410887449" expanded="true" signature="3988:4720" ph="..." />
               </folding>
             </state>
           </provider>
         </entry>
       </file>
-      <file pinned="false" current-in-tab="true">
+      <file pinned="false" current-in-tab="false">
         <entry file="file://$PROJECT_DIR$/train.py">
           <provider selected="true" editor-type-id="text-editor">
-            <state relative-caret-position="-283">
-              <caret line="58" lean-forward="true" selection-start-line="58" selection-end-line="58" />
+            <state relative-caret-position="294">
+              <caret line="46" selection-start-line="46" selection-end-line="47" selection-end-column="40" />
               <folding>
                 <element signature="e#0#28#0" expanded="true" />
               </folding>
@@ -203,8 +204,8 @@
         <option value="$PROJECT_DIR$/.gitignore" />
         <option value="$PROJECT_DIR$/data/data2.csv" />
         <option value="$PROJECT_DIR$/data/data3.csv" />
-        <option value="$PROJECT_DIR$/serve.py" />
         <option value="$PROJECT_DIR$/train.py" />
+        <option value="$PROJECT_DIR$/serve.py" />
       </list>
     </option>
   </component>
@@ -247,7 +248,7 @@
   <component name="PropertiesComponent">
     <property name="WebServerToolWindowFactoryState" value="false" />
     <property name="aspect.path.notification.shown" value="true" />
-    <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588407897977" />
+    <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588410322728" />
     <property name="go.gopath.indexing.explicitly.defined" value="true" />
     <property name="nodejs_interpreter_path.stuck_in_default_project" value="undefined stuck path" />
     <property name="nodejs_npm_path_reset_for_default_project" value="true" />
@@ -288,12 +289,12 @@
       <option name="presentableId" value="Default" />
       <updated>1588152877746</updated>
       <workItem from="1588152880522" duration="16973000" />
-      <workItem from="1588319878551" duration="31191000" />
+      <workItem from="1588319878551" duration="33678000" />
     </task>
     <servers />
   </component>
   <component name="TimeTrackingManager">
-    <option name="totallyTimeSpent" value="48164000" />
+    <option name="totallyTimeSpent" value="50651000" />
   </component>
   <component name="ToolWindowManager">
     <frame x="0" y="0" width="1280" height="800" extended-state="0" />
@@ -427,23 +428,25 @@
         </state>
       </provider>
     </entry>
-    <entry file="file://$PROJECT_DIR$/serve.py">
+    <entry file="file://$PROJECT_DIR$/train.py">
       <provider selected="true" editor-type-id="text-editor">
-        <state relative-caret-position="-625">
-          <caret line="25" lean-forward="true" selection-start-line="25" selection-end-line="25" />
+        <state relative-caret-position="294">
+          <caret line="46" selection-start-line="46" selection-end-line="47" selection-end-column="40" />
           <folding>
-            <element signature="e#18#46#0" expanded="true" />
-            <marker date="1588408252097" expanded="true" signature="5911:5913" ph="..." />
+            <element signature="e#0#28#0" expanded="true" />
           </folding>
         </state>
       </provider>
     </entry>
-    <entry file="file://$PROJECT_DIR$/train.py">
+    <entry file="file://$PROJECT_DIR$/serve.py">
       <provider selected="true" editor-type-id="text-editor">
-        <state relative-caret-position="-283">
-          <caret line="58" lean-forward="true" selection-start-line="58" selection-end-line="58" />
+        <state relative-caret-position="232">
+          <caret line="197" lean-forward="true" selection-start-line="197" selection-end-line="197" />
           <folding>
-            <element signature="e#0#28#0" expanded="true" />
+            <element signature="e#18#46#0" expanded="true" />
+            <marker date="1588410887449" expanded="true" signature="1167:1170" ph="..." />
+            <marker date="1588410887449" expanded="true" signature="3275:3277" ph="..." />
+            <marker date="1588410887449" expanded="true" signature="3988:4720" ph="..." />
           </folding>
         </state>
       </provider>
diff --git a/serve.py b/serve.py
index 46c1e9b..03f32f9 100644
--- a/serve.py
+++ b/serve.py
@@ -6,10 +6,6 @@ import cgi
 import json
 from urllib import parse
 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
 from pandas import DataFrame
 from pandas import Series
@@ -23,184 +19,163 @@ from keras.layers import LSTM
 from math import sqrt
 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
 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()
-	np.random.seed(config.random_seed)
-	data_gainer = Data(config)
-
-	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)
-
+	if job not in models:
+		models[job] = {
+			'lock': Lock()
+		}
 	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):
 	# 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()