add files

.idea/workspace.xml

@@ -3,6 +3,17 @@
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+      <change beforePath="$PROJECT_DIR$/figure/continue_predict_low_with_pytorch.png" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/figure/predict_high_with_keras.png" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/figure/predict_high_with_pytorch.png" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/figure/predict_high_with_tensorflow.png" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/figure/predict_low_with_keras.png" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/figure/predict_low_with_pytorch.png" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/figure/predict_low_with_tensorflow.png" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/main.py" beforeDir="false" afterPath="$PROJECT_DIR$/main.py" afterDir="false" />
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+      <change beforePath="$PROJECT_DIR$/test.py" beforeDir="false" />
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@@ -23,35 +34,37 @@
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+          <entry key="Python" value="6" />
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+          <entry key="Python" value="522" />
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+      <file pinned="false" current-in-tab="false">
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@@ -59,11 +72,24 @@
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+      <file pinned="false" current-in-tab="true">
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       <file pinned="false" current-in-tab="false">
         <entry file="file://$PROJECT_DIR$/model/model_tensorflow.py">
           <provider selected="true" editor-type-id="text-editor">
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@@ -71,15 +97,6 @@
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@@ -101,9 +118,9 @@
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         <option value="$PROJECT_DIR$/test.py" />
-        <option value="$PROJECT_DIR$/serve.py" />
         <option value="$PROJECT_DIR$/model/model_tensorflow.py" />
         <option value="$PROJECT_DIR$/main.py" />
+        <option value="$PROJECT_DIR$/serve.py" />
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@@ -113,10 +130,9 @@
     <detection-done>true</detection-done>
     <sorting>DEFINITION_ORDER</sorting>
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+  <component name="ProjectFrameBounds" fullScreen="true">
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@@ -137,11 +153,6 @@
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-              <item name="yao-optimizer" type="462c0819:PsiDirectoryNode" />
-              <item name="figure" type="462c0819:PsiDirectoryNode" />
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             <path>
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@@ -157,7 +168,7 @@
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-    <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588158359232" />
+    <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588169466518" />
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@@ -188,15 +199,15 @@
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       <option name="presentableId" value="Default" />
       <updated>1588152877746</updated>
-      <workItem from="1588152880522" duration="4816000" />
+      <workItem from="1588152880522" duration="7941000" />
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     <servers />
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   <component name="TimeTrackingManager">
-    <option name="totallyTimeSpent" value="4816000" />
+    <option name="totallyTimeSpent" value="7941000" />
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-    <frame x="0" y="23" width="1280" height="704" extended-state="6" />
+    <frame x="0" y="0" width="1280" height="800" extended-state="0" />
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@@ -204,10 +215,10 @@
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       <window_info id="Favorites" order="3" side_tool="true" />
       <window_info id="Image Layers" order="4" />
-      <window_info content_ui="combo" id="Project" order="5" visible="true" weight="0.24959612" />
+      <window_info active="true" content_ui="combo" id="Project" order="5" visible="true" weight="0.24959612" />
       <window_info id="Structure" order="6" side_tool="true" weight="0.25" />
       <window_info anchor="bottom" id="Version Control" order="0" />
-      <window_info active="true" anchor="bottom" id="Terminal" order="1" visible="true" weight="0.32843137" />
+      <window_info anchor="bottom" id="Terminal" order="1" weight="0.32843137" />
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       <window_info anchor="bottom" id="Docker" order="4" show_stripe_button="false" />
@@ -240,6 +251,23 @@
     <entry file="file://$PROJECT_DIR$/yao-optimizer.iml">
       <provider selected="true" editor-type-id="text-editor" />
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+    <entry file="file://$PROJECT_DIR$/data/stock_data.csv">
+      <provider selected="true" editor-type-id="text-editor">
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+      </provider>
+    </entry>
+    <entry file="file://$PROJECT_DIR$/model/model_tensorflow.py">
+      <provider selected="true" editor-type-id="text-editor">
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+    </entry>
     <entry file="file://$PROJECT_DIR$/test.py">
       <provider selected="true" editor-type-id="text-editor">
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@@ -250,43 +278,48 @@
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       </provider>
     </entry>
-    <entry file="file://$PROJECT_DIR$/serve.py">
+    <entry file="file://$PROJECT_DIR$/figure/continue_predict_high_with_pytorch.png">
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-      </provider>
     </entry>
-    <entry file="file://$PROJECT_DIR$/data/stock_data.csv">
+    <entry file="file://$PROJECT_DIR$/figure/continue_predict_low_with_pytorch.png">
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+      <provider selected="true" editor-type-id="images" />
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-    <entry file="file://$PROJECT_DIR$/model/model_tensorflow.py">
+    <entry file="file://$PROJECT_DIR$/figure/predict_high_with_keras.png">
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+      <provider selected="true" editor-type-id="images" />
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+    <entry file="file://$PROJECT_DIR$/figure/predict_high_with_tensorflow.png">
+      <provider selected="true" editor-type-id="images" />
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+    <entry file="file://$PROJECT_DIR$/figure/predict_high_with_pytorch.png">
+      <provider selected="true" editor-type-id="images" />
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       <provider selected="true" editor-type-id="text-editor">
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   <component name="masterDetails">
     <states>

README.md

@@ -1,21 +0,0 @@
-## Predict stock with LSTM
-
-This project includes training and predicting processes with LSTM for stock data. The characteristics is as fellow: 
-
-- Concise and modular
-- Support three mainstream deep learning frameworks of pytorch, keras and tensorflow
-- Parameters, models and frameworks can be highly customized and modified
-- Supports incremental training
-- Support predicting multiple indicators at the same time
-- Support predicting any number of days in the future
-
-
-Chinese introduction can refer to : <https://blog.csdn.net/songyunli1111/article/details/78513811>
-
-
-
-The simultaneous predict results for stock high and low price with pytorch show as follow:
-
-![predict_high_with_pytorch](https://github.com/hichenway/stock_predict_with_LSTM/blob/master/figure/predict_high_with_pytorch.png)
-
-![predict_low_with_pytorch](https://github.com/hichenway/stock_predict_with_LSTM/blob/master/figure/predict_low_with_pytorch.png)

main.py

@@ -4,8 +4,7 @@ import os
 from sklearn.model_selection import train_test_split
 
 frame = "tensorflow"
-from model.model_tensorflow import train, predict
+from model_tensorflow import train, predict
-
 
 class Config:
 	feature_columns = list([2,5])
@@ -139,24 +138,6 @@ def draw(config, origin_data, predict_norm_data):
 	print(label_data)
 	print(predict_data)
 
-	'''
-	for i in range(label_column_num):
-		plt.figure(i + 1)
-		plt.plot(label_X, label_data[:, i], label='label')
-		plt.plot(predict_X, predict_data[:, i], label='predict')
-		plt.legend(loc='upper right')
-		plt.xlabel("Day")
-		plt.ylabel("Price")
-		plt.title("Predict stock {} price with {}".format(label_name[i], config.used_frame))
-		print("The predicted stock {} for the next {} day(s) is: ".format(label_name[i], config.predict_day),
-		      np.squeeze(predict_data[-config.predict_day:, i]))
-		if config.do_figure_save:
-			plt.savefig(config.figure_save_path + "{}predict_{}_with_{}.png".format(config.continue_flag, label_name[i],
-			                                                                        config.used_frame))
-
-	plt.show()
-	'''
-
 
 def main(config):
 	np.random.seed(config.random_seed)

model/model_keras.py

@@ -1,35 +0,0 @@
-from keras.layers import Input, Dense, LSTM
-from keras.models import Model
-from keras.callbacks import ModelCheckpoint, EarlyStopping
-
-
-def get_keras_model(config):
-	input1 = Input(shape=(config.time_step, config.input_size))
-	lstm = input1
-	for i in range(config.lstm_layers):
-		lstm = LSTM(units=config.hidden_size, dropout=config.dropout_rate, return_sequences=True)(lstm)
-	output = Dense(config.output_size)(lstm)
-	model = Model(input1, output)
-	model.compile(loss='mse', optimizer='adam')
-	return model
-
-
-def train(config, train_X, train_Y, valid_X, valid_Y):
-	model = get_keras_model(config)
-	model.summary()
-	if config.add_train:
-		model.load_weights(config.model_save_path + config.model_name)
-
-	check_point = ModelCheckpoint(filepath=config.model_save_path + config.model_name, monitor='val_loss',
-	                              save_best_only=True, mode='auto')
-	early_stop = EarlyStopping(monitor='val_loss', patience=config.patience, mode='auto')
-	model.fit(train_X, train_Y, batch_size=config.batch_size, epochs=config.epoch, verbose=2,
-	          validation_data=(valid_X, valid_Y), callbacks=[check_point, early_stop])
-
-
-def predict(config, test_X):
-	model = get_keras_model(config)
-	model.load_weights(config.model_save_path + config.model_name)
-	result = model.predict(test_X, batch_size=1)
-	result = result.reshape((-1, config.output_size))
-	return result

model/model_pytorch.py

@@ -1,98 +0,0 @@
-import torch
-from torch.nn import Module, LSTM, Linear
-from torch.utils.data import DataLoader, TensorDataset
-import numpy as np
-
-
-class Net(Module):
-	def __init__(self, config):
-		super(Net, self).__init__()
-		self.lstm = LSTM(input_size=config.input_size, hidden_size=config.hidden_size,
-		                 num_layers=config.lstm_layers, batch_first=True, dropout=config.dropout_rate)
-		self.linear = Linear(in_features=config.hidden_size, out_features=config.output_size)
-
-	def forward(self, x, hidden=None):
-		lstm_out, hidden = self.lstm(x, hidden)
-		linear_out = self.linear(lstm_out)
-		return linear_out, hidden
-
-
-def train(config, train_X, train_Y, valid_X, valid_Y):
-	train_X, train_Y = torch.from_numpy(train_X).float(), torch.from_numpy(train_Y).float()
-	train_loader = DataLoader(TensorDataset(train_X, train_Y), batch_size=config.batch_size)
-
-	valid_X, valid_Y = torch.from_numpy(valid_X).float(), torch.from_numpy(valid_Y).float()
-	valid_loader = DataLoader(TensorDataset(valid_X, valid_Y), batch_size=config.batch_size)
-
-	model = Net(config)
-	if config.add_train:
-		model.load_state_dict(torch.load(config.model_save_path + config.model_name))
-	optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
-	criterion = torch.nn.MSELoss()
-
-	valid_loss_min = float("inf")
-	bad_epoch = 0
-	for epoch in range(config.epoch):
-		print("Epoch {}/{}".format(epoch, config.epoch))
-		model.train()
-		train_loss_array = []
-		hidden_train = None
-		for i, _data in enumerate(train_loader):
-			_train_X, _train_Y = _data
-			optimizer.zero_grad()
-			pred_Y, hidden_train = model(_train_X, hidden_train)
-
-			if not config.do_continue_train:
-				hidden_train = None
-			else:
-				h_0, c_0 = hidden_train
-				h_0.detach_(), c_0.detach_()
-				hidden_train = (h_0, c_0)
-			loss = criterion(pred_Y, _train_Y)
-			loss.backward()
-			optimizer.step()
-			train_loss_array.append(loss.item())
-
-		model.eval()
-		valid_loss_array = []
-		hidden_valid = None
-		for _valid_X, _valid_Y in valid_loader:
-			pred_Y, hidden_valid = model(_valid_X, hidden_valid)
-			if not config.do_continue_train: hidden_valid = None
-			loss = criterion(pred_Y, _valid_Y)
-			valid_loss_array.append(loss.item())
-
-		valid_loss_cur = np.mean(valid_loss_array)
-		print("The train loss is {:.4f}. ".format(np.mean(train_loss_array)),
-		      "The valid loss is {:.4f}.".format(valid_loss_cur))
-
-		if valid_loss_cur < valid_loss_min:
-			valid_loss_min = valid_loss_cur
-			bad_epoch = 0
-			torch.save(model.state_dict(), config.model_save_path + config.model_name)
-		else:
-			bad_epoch += 1
-			if bad_epoch >= config.patience:
-				print(" The training stops early in epoch {}".format(epoch))
-				break
-
-
-def predict(config, test_X):
-	test_X = torch.from_numpy(test_X).float()
-	test_set = TensorDataset(test_X)
-	test_loader = DataLoader(test_set, batch_size=1)
-
-	model = Net(config)
-	model.load_state_dict(torch.load(config.model_save_path + config.model_name))
-
-	result = torch.Tensor()
-
-	model.eval()
-	hidden_predict = None
-	for _data in test_loader:
-		data_X = _data[0]
-		pred_X, hidden_predict = model(data_X, hidden_predict)
-		cur_pred = torch.squeeze(pred_X, dim=0)
-		result = torch.cat((result, cur_pred), dim=0)
-
-	return result.detach().numpy()

requirements.txt

@@ -6,4 +6,3 @@ tensorflow==1.15.2
 matplotlib>=3.0.2
 numpy>=1.14.6
 scipy>=1.1.0
-torch>=1.1.0

serve.py

@@ -1,87 +1,89 @@
-import tensorflow as tf
+#!/usr/bin/python
-import numpy as np
+from threading import Thread
-import tushare as ts
+from threading import Lock
-import pandas as pd
+from http.server import BaseHTTPRequestHandler, HTTPServer
-from sklearn.preprocessing import MinMaxScaler
+import cgi
+import json
+from urllib import parse
 
-timesteps = seq_length = 7
+PORT_NUMBER = 8000
-data_dim = 5
-output_dim = 1
 
-stock_data = ts.get_k_data('600000', start='2015-01-01', end='2017-12-01')
+lock = Lock()
-xy = stock_data[['open', 'close', 'high', 'low', 'volume']]
-
-# xy_new = pd.DataFrame()
-# scaler = MinMaxScaler()
-
-# scaler.fit(xy)
-# t = scaler.transform(xy)
-
-# for col in xy.columns:
-#	xy_new.ix[:, col] = t[col]
-
-x = xy
-y = xy[['close']]
-dataX = []
-dataY = []
-for i in range(0, len(y) - seq_length):
-	_x = x[i:i + seq_length]
-	_y = y.loc[i + seq_length]
-	#print(_x, "->", _y)
-	dataX.append(_x)
-	dataY.append(_y)
-
-x_real = np.vstack(dataX).reshape(-1, seq_length, data_dim)
-y_real = np.vstack(dataY).reshape(-1, output_dim)
-print(x_real.shape)
-print(y_real.shape)
-dataX = x_real
-dataY = y_real
-
-train_size = int(len(dataY) * 0.7)
-test_size = len(dataY) - train_size
-trainX, testX = np.array(dataX[0:train_size]), np.array(dataX[train_size:len(dataX)])
-trainY, testY = np.array(dataY[0:train_size]), np.array(dataY[train_size:len(dataY)])
-
-X = tf.placeholder(tf.float32, [None, seq_length, data_dim])
-Y = tf.placeholder(tf.float32, [None, 1])
 
 
-def add_layer(inputs, in_size, out_size, activation_function=None):
+def train_models():
-	inputs = tf.reshape(inputs, [-1, in_size])
+	lock.acquire()
-	Weights = tf.Variable(tf.random_normal([in_size, out_size]))
+
-	biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
+	lock.release()
-	Wx_plus_b = tf.matmul(inputs, Weights) + biases
-	if activation_function is None:
-		outputs = Wx_plus_b
-	else:
-		outputs = activation_function(Wx_plus_b)
-	return outputs
 
 
-outsize_first = 5
+class MyHandler(BaseHTTPRequestHandler):
-l1 = add_layer(X, data_dim, outsize_first, activation_function=tf.nn.relu)
+	# Handler for the GET requests
-l1_output = tf.reshape(l1, [-1, seq_length, outsize_first])
+	def do_GET(self):
+		req = parse.urlparse(self.path)
+		query = parse.parse_qs(req.query)
 
-cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=output_dim, state_is_tuple=True)
+		if req.path == "/ping":
-outputs, _states = tf.nn.dynamic_rnn(cell, l1_output, dtype=tf.float32)
+			self.send_response(200)
-Y_pred = outputs[:, -1]
+			self.send_header('Content-type', 'application/json')
+			self.end_headers()
+			self.wfile.write(bytes("pong", "utf-8"))
 
-loss = tf.reduce_sum(tf.square(Y_pred - Y))
+		elif req.path == "/predict":
+			try:
+				job = query.get('job')[0]
+				gpu_model = query.get('gpu_model')[0]
+				time = query.get('time')[0]
+				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()
+			self.wfile.write(bytes(json.dumps(msg), "utf-8"))
 
-optimizer = tf.train.GradientDescentOptimizer(0.01)
+		else:
-train = optimizer.minimize(loss)
+			self.send_error(404, 'File Not Found: %s' % self.path)
 
-sess = tf.Session()
+	# Handler for the POST requests
-sess.run(tf.global_variables_initializer())
+	def do_POST(self):
-for i in range(100):
+		if self.path == "/train":
-	_, l = sess.run(
+			form = cgi.FieldStorage(
-		[train, loss],
+				fp=self.rfile,
-		feed_dict={X: trainX, Y: trainY}
+				headers=self.headers,
-	)
+				environ={
-	#print(i, l)
+					'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"))
 
-testPredict = sess.run(Y_pred, feed_dict={X: testX})
+		else:
+			self.send_error(404, 'File Not Found: %s' % self.path)
 
-print(testY)
+
-print(testPredict)
+if __name__ == '__main__':
+	try:
+		# Create a web server and define the handler to manage the
+		# incoming request
+		server = HTTPServer(('', PORT_NUMBER), MyHandler)
+		print('Started http server on port ', PORT_NUMBER)
+
+		# Wait forever for incoming http requests
+		server.serve_forever()
+
+	except KeyboardInterrupt:
+		print('^C received, shutting down the web server')
+
+	server.socket.close()

test.py

@@ -1,111 +0,0 @@
-import pandas as pd
-import numpy as np
-import tensorflow as tf
-import tushare as ts
-
-rnn_unit = 10
-input_size = 7
-output_size = 1
-lr = 0.0006
-
-stock_data = ts.get_k_data('600000', start='2015-01-01', end='2017-12-01')
-data = stock_data.iloc[:, 2:10].values
-
-
-# ——————————获取训练集——————————
-def get_train_data(batch_size=60, time_step=20, train_begin=0, train_end=5800):
-	batch_index = []
-	data_train = data[train_begin:train_end]
-	normalized_train_data = (data_train - np.mean(data_train, axis=0)) / np.std(data_train, axis=0)  # 标准化
-	train_x, train_y = [], []  # 训练集x和y初定义
-	for i in range(len(normalized_train_data) - time_step):
-		if i % batch_size == 0:
-			batch_index.append(i)
-		x = normalized_train_data[i:i + time_step, :7]
-		y = normalized_train_data[i:i + time_step, 7, np.newaxis]
-		train_x.append(x.tolist())
-		train_y.append(y.tolist())
-	batch_index.append((len(normalized_train_data) - time_step))
-	return batch_index, train_x, train_y
-
-
-# ——————————获取测试集——————————
-def get_test_data(time_step=20, test_begin=5800):
-	data_test = data[test_begin:]
-	mean = np.mean(data_test, axis=0)
-	std = np.std(data_test, axis=0)
-	normalized_test_data = (data_test - mean) / std  # 标准化
-	size = (len(normalized_test_data) + time_step - 1) // time_step  # 有size个sample
-	test_x, test_y = [], []
-	for i in range(size - 1):
-		x = normalized_test_data[i * time_step:(i + 1) * time_step, :7]
-		y = normalized_test_data[i * time_step:(i + 1) * time_step, 7]
-		test_x.append(x.tolist())
-		test_y.extend(y)
-	test_x.append((normalized_test_data[(i + 1) * time_step:, :7]).tolist())
-	test_y.extend((normalized_test_data[(i + 1) * time_step:, 7]).tolist())
-	return mean, std, test_x, test_y
-
-
-# ——————————————————定义神经网络变量——————————————————
-def lstm(X):
-	batch_size = tf.shape(X)[0]
-	time_step = tf.shape(X)[1]
-	w_in = weights['in']
-	b_in = biases['in']
-	input = tf.reshape(X, [-1, input_size])  # 需要将tensor转成2维进行计算，计算后的结果作为隐藏层的输入
-	input_rnn = tf.matmul(input, w_in) + b_in
-	input_rnn = tf.reshape(input_rnn, [-1, time_step, rnn_unit])  # 将tensor转成3维，作为lstm cell的输入
-	cell = tf.nn.rnn_cell.BasicLSTMCell(rnn_unit)
-	init_state = cell.zero_state(batch_size, dtype=tf.float32)
-	output_rnn, final_states = tf.nn.dynamic_rnn(cell, input_rnn, initial_state=init_state,
-	                                             dtype=tf.float32)  # output_rnn是记录lstm每个输出节点的结果，final_states是最后一个cell的结果
-	output = tf.reshape(output_rnn, [-1, rnn_unit])  # 作为输出层的输入
-	w_out = weights['out']
-	b_out = biases['out']
-	pred = tf.matmul(output, w_out) + b_out
-	return pred, final_states
-
-
-# ——————————————————训练模型——————————————————
-def train_lstm(batch_size=80, time_step=15, train_begin=0, train_end=5800):
-	X = tf.placeholder(tf.float32, shape=[None, time_step, input_size])
-	Y = tf.placeholder(tf.float32, shape=[None, time_step, output_size])
-	batch_index, train_x, train_y = get_train_data(batch_size, time_step, train_begin, train_end)
-	pred, _ = lstm(X)
-	# 损失函数
-	loss = tf.reduce_mean(tf.square(tf.reshape(pred, [-1]) - tf.reshape(Y, [-1])))
-	train_op = tf.train.AdamOptimizer(lr).minimize(loss)
-	saver = tf.train.Saver(tf.global_variables(), max_to_keep=15)
-	module_file = tf.train.latest_checkpoint()
-	with tf.Session() as sess:
-		# sess.run(tf.global_variables_initializer())
-		saver.restore(sess, module_file)
-		# 重复训练2000次
-		for i in range(2000):
-			for step in range(len(batch_index) - 1):
-				_, loss_ = sess.run([train_op, loss], feed_dict={X: train_x[batch_index[step]:batch_index[step + 1]],
-				                                                 Y: train_y[batch_index[step]:batch_index[step + 1]]})
-			print(i, loss_)
-			if i % 200 == 0:
-				print("保存模型：", saver.save(sess, 'stock2.model', global_step=i))
-
-
-# ————————————————预测模型————————————————————
-def prediction(time_step=20):
-	X = tf.placeholder(tf.float32, shape=[None, time_step, input_size])
-	mean, std, test_x, test_y = get_test_data(time_step)
-	pred, _ = lstm(X)
-	saver = tf.train.Saver(tf.global_variables())
-	with tf.Session() as sess:
-		# 参数恢复
-		module_file = tf.train.latest_checkpoint()
-		saver.restore(sess, module_file)
-		test_predict = []
-		for step in range(len(test_x) - 1):
-			prob = sess.run(pred, feed_dict={X: [test_x[step]]})
-			predict = prob.reshape((-1))
-			test_predict.extend(predict)
-		test_y = np.array(test_y) * std[7] + mean[7]
-		test_predict = np.array(test_predict) * std[7] + mean[7]
-		acc = np.average(np.abs(test_predict - test_y[:len(test_predict)]) / test_y[:len(test_predict)])