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32
serve.py
32
serve.py
@ -21,7 +21,7 @@ class Config:
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predict_day = 1
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input_size = len(feature_columns) - 1
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input_size = len(feature_columns)
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output_size = len(label_columns)
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hidden_size = 128
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@ -35,7 +35,7 @@ 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 = 0.95 # 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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@ -115,8 +115,8 @@ class Data:
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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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i + 1) * self.config.time_step] for i in range(time_step_size)]
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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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return np.array(test_x), label_data
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@ -128,9 +128,7 @@ class Data:
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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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print(feature_data[:, :1])
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test_x = [feature_data[:, :1]]
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print(feature_data)
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test_x = [feature_data]
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return np.array(test_x)
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@ -142,6 +140,7 @@ def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
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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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print("dsa")
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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 = 3
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@ -153,14 +152,16 @@ def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
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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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# print(label_norm_data[:, 1:2])
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label_data = label_norm_data[:, 1:2] * std_yqy[1:2] + mean_yqy[1:2]
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# print(label_data)
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print("2")
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# print(predict_norm_data)
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print(label_norm_data[:, 1:2])
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label_data = label_norm_data[:, 1:2] * std_yqy[1:2] + mean_yqy[1:2]
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print(label_data)
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print(predict_norm_data)
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predict_data = predict_norm_data * std_yqy[config.label_in_feature_columns] + mean_yqy[
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config.label_in_feature_columns]
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# print(predict_data)
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print(predict_data)
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print(label_data[:, -1])
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print(predict_data[:, -1])
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@ -178,13 +179,14 @@ def train_models():
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train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
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print(train_X, valid_X, train_Y, valid_Y)
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print(train_X.shape[0])
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if train_X.shape[0] < 500:
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config.batch_size = 32
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if train_X.shape[0] < 200:
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config.batch_size = 16
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print(train_X[:, :, :1], valid_X[:, :, :1], train_Y, valid_Y)
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train(config, train_X[:, :, :1], train_Y, valid_X[:, :, :1], valid_Y)
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train(config, train_X, train_Y, valid_X, valid_Y)
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lock.release()
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@ -328,7 +330,7 @@ if __name__ == '__main__':
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csvfile, delimiter=',',
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quotechar='|', quoting=csv.QUOTE_MINIMAL
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)
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# spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"])
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#spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"])
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spamwriter.writerow(["seq", "value"])
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# Wait forever for incoming http requests
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