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@ -2,6 +2,7 @@
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<list default="true" id="0aedafd8-e57e-462a-beda-65af0b91f3df" name="Default Changelist" comment="">
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24
serve.py
24
serve.py
@ -72,15 +72,10 @@ class Data:
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self.data_num = self.data.shape[0]
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self.train_num = int(self.data_num * self.config.train_data_rate)
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print(self.data)
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self.mean = np.mean(self.data, axis=0)
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print(1)
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self.std = np.std(self.data, axis=0)
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print(self.std)
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print(self.mean)
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self.norm_data = (self.data - self.mean) / self.std
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print(2)
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self.start_num_in_test = 0
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@ -116,15 +111,26 @@ class Data:
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return train_x, valid_x, train_y, valid_y
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def get_test_data(self, return_label_data=False):
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feature_data = self.norm_data[self.train_num:]
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init_data = pd.read_csv(
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self.config.train_data_path,
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usecols=self.config.feature_and_label_columns
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)
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data, data_column_name = init_data.values, init_data.columns.tolist()
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train_num = 0
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mean = np.mean(data, axis=0)
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std = np.std(data, axis=0)
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norm_data = (data - mean) / std
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start_num_in_test = 0
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feature_data = norm_data[0:]
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self.start_num_in_test = feature_data.shape[0] % self.config.time_step
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time_step_size = feature_data.shape[0] // self.config.time_step
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test_x = [feature_data[self.start_num_in_test + i * self.config.time_step: self.start_num_in_test + (
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test_x = [feature_data[start_num_in_test + i * self.config.time_step: start_num_in_test + (
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i + 1) * self.config.time_step]
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for i in range(time_step_size)]
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if return_label_data:
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label_data = self.norm_data[self.train_num + self.start_num_in_test:, self.config.label_in_feature_columns]
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label_data = norm_data[train_num + start_num_in_test:, self.config.label_in_feature_columns]
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return np.array(test_x), label_data
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return np.array(test_x)
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@ -191,7 +197,7 @@ class MyHandler(BaseHTTPRequestHandler):
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gpu_model = query.get('gpu_model')[0]
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time = query.get('time')[0]
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data_gainer = Data(config)
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test_X, test_Y = np.array([[job, gpu_model, time]]), np.array([[1,1,1]]) #data_gainer.get_test_data(return_label_data=True)
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test_X, test_Y = data_gainer.get_test_data(return_label_data=True)
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print(test_X, test_Y)
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pred_result = predict(config, test_X)
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print(pred_result)
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