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98 lines
3.9 KiB
Python
98 lines
3.9 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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Tencent is pleased to support the open source community by making Metis available.
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Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
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Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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https://opensource.org/licenses/BSD-3-Clause
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
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"""
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import numpy as np
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DEFAULT_WINDOW = 180
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def is_standard_time_series(time_series, window=DEFAULT_WINDOW):
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"""
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Check the length of time_series. If window = 180, then the length of time_series should be 903.
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The mean value of last window should be larger than 0.
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:param time_series: the time series to check, like [data_c, data_b, data_a]
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:type time_series: pandas.Series
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:param window: the length of window
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:return: True or False
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:return type: boolean
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"""
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return bool(len(time_series) == 5 * window + 3 and np.mean(time_series[(4 * window + 2):]) > 0)
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def split_time_series(time_series, window=DEFAULT_WINDOW):
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"""
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Spilt the time_series into five parts. Each has a length of window + 1
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:param time_series: [data_c, data_b, data_a]
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:param window: the length of window
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:return: spilt list [[data_c_left], [data_c_right], [data_b_left], [data_b_right], [data_a]]
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"""
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data_c_left = time_series[0:(window + 1)]
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data_c_right = time_series[window:(2 * window + 1)]
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data_b_left = time_series[(2 * window + 1):(3 * window + 2)]
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data_b_right = time_series[(3 * window + 1):(4 * window + 2)]
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data_a = time_series[(4 * window + 2):]
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split_time_series = [
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data_c_left,
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data_c_right,
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data_b_left,
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data_b_right,
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data_a
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]
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return split_time_series
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def normalize_time_series(split_time_series):
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"""
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Normalize the split_time_series.
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:param split_time_series: [[data_c_left], [data_c_right], [data_b_left], [data_b_right], [data_a]]
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:return: all list / mean(split_time_series)
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"""
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value = np.mean(split_time_series[4])
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if value > 1:
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normalized_data_c_left = list(split_time_series[0] / value)
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normalized_data_c_right = list(split_time_series[1] / value)
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normalized_data_b_left = list(split_time_series[2] / value)
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normalized_data_b_right = list(split_time_series[3] / value)
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normalized_data_a = list(split_time_series[4] / value)
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else:
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normalized_data_c_left = split_time_series[0]
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normalized_data_c_right = split_time_series[1]
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normalized_data_b_left = split_time_series[2]
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normalized_data_b_right = split_time_series[3]
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normalized_data_a = split_time_series[4]
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normalized_split_time_series = [
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normalized_data_c_left,
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normalized_data_c_right,
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normalized_data_b_left,
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normalized_data_b_right,
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normalized_data_a
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]
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return normalized_split_time_series
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def normalize_time_series_by_max_min(split_time_series):
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"""
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Normalize the split_time_series by max_min_normalization.
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:param split_time_series: [[data_c_left], [data_c_right], [data_b_left], [data_b_right], [data_a]]
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:return: max_min_normalized time_series
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"""
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time_series = split_time_series[0] + split_time_series[1][1:] + split_time_series[2] + split_time_series[3][1:] + split_time_series[4]
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max_value = np.max(time_series)
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min_value = np.min(time_series)
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normalized_time_series = [0.0]*len(time_series)
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if max_value - min_value > 0:
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normalized_time_series = list((np.array(time_series) - min_value) / float(max_value - min_value))
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return normalized_time_series
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