mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-06-27 18:06:19 +08:00
Kashif Rasul
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License").
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# You may not use this file except in compliance with the License.
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# A copy of the License is located at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# or in the "license" file accompanying this file. This file is distributed
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# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
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# express or implied. See the License for the specific language governing
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# permissions and limitations under the License.
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from typing import List
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import numpy as np
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import pandas as pd
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from pandas.tseries.frequencies import to_offset
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from gluonts.core.component import validated
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from gluonts.time_feature import TimeFeature, norm_freq_str
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class FourierDateFeatures(TimeFeature):
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@validated()
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def __init__(self, freq: str) -> None:
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super().__init__()
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# reocurring freq
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freqs = [
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"month",
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"day",
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"hour",
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"minute",
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"weekofyear",
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"weekday",
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"dayofweek",
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"dayofyear",
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"daysinmonth",
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]
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assert freq in freqs
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self.freq = freq
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def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
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values = getattr(index, self.freq)
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num_values = max(values) + 1
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steps = [x * 2.0 * np.pi / num_values for x in values]
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return np.vstack([np.cos(steps), np.sin(steps)])
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def fourier_time_features_from_frequency(freq_str: str) -> List[TimeFeature]:
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offset = to_offset(freq_str)
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granularity = norm_freq_str(offset.name)
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features = {
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"M": ["weekofyear"],
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"W": ["daysinmonth", "weekofyear"],
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"D": ["dayofweek"],
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"B": ["dayofweek", "dayofyear"],
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"H": ["hour", "dayofweek"],
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"min": ["minute", "hour", "dayofweek"],
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"T": ["minute", "hour", "dayofweek"],
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}
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assert granularity in features, f"freq {granularity} not supported"
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feature_classes: List[TimeFeature] = [
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FourierDateFeatures(freq=freq) for freq in features[granularity]
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]
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return feature_classes
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