mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-06-27 19:32:05 +08:00
Kashif Rasul
ea9b2b7df5
* Estimator needs an create_instance_splitter now * updated estimators and tests * fix test * validated
310 lines
8.0 KiB
Python
310 lines
8.0 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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# Third-party imports
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import numpy as np
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import pandas as pd
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import pytest
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from pandas.tseries.holiday import Holiday
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# First-party imports
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from gluonts.time_feature.holiday import (
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CHRISTMAS_DAY,
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CHRISTMAS_EVE,
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COLUMBUS_DAY,
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EASTER_MONDAY,
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EASTER_SUNDAY,
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GOOD_FRIDAY,
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INDEPENDENCE_DAY,
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LABOR_DAY,
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MARTIN_LUTHER_KING_DAY,
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MEMORIAL_DAY,
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MOTHERS_DAY,
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NEW_YEARS_DAY,
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NEW_YEARS_EVE,
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PRESIDENTS_DAY,
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SPECIAL_DATE_FEATURES,
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SUPERBOWL,
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THANKSGIVING,
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BLACK_FRIDAY,
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CYBER_MONDAY,
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SpecialDateFeatureSet,
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squared_exponential_kernel,
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exponential_kernel,
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)
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from pts.feature.holiday import CustomDateFeatureSet, CustomHolidayFeatureSet
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test_dates = {
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NEW_YEARS_DAY: [
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"2015-01-01",
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"2016-01-01",
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"2017-01-01",
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"2018-01-01",
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"2019-01-01",
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],
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MARTIN_LUTHER_KING_DAY: [
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"2012-01-16",
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"2014-01-20",
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"2015-01-19",
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"2018-01-15",
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"2019-01-21",
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],
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SUPERBOWL: ["2011-02-06", "2017-02-05", "2018-02-04", "2019-02-03"],
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PRESIDENTS_DAY: ["2011-02-21", "2017-02-20", "2018-02-19", "2019-02-18"],
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MEMORIAL_DAY: [
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"2015-05-25",
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"2016-05-30",
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"2017-05-29",
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"2018-05-28",
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"2019-05-27",
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],
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GOOD_FRIDAY: [
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"2014-04-18",
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"2015-04-03",
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"2017-04-14",
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"2018-03-30",
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"2019-04-19",
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],
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EASTER_SUNDAY: [
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"2014-04-20",
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"2015-04-05",
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"2017-04-16",
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"2018-04-01",
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"2019-04-21",
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],
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EASTER_MONDAY: [
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"2014-04-21",
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"2015-04-06",
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"2017-04-17",
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"2018-04-02",
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"2019-04-22",
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],
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MOTHERS_DAY: ["2016-05-08", "2017-05-14", "2018-05-13", "2019-05-12"],
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INDEPENDENCE_DAY: ["2016-07-04", "2017-07-04", "2018-07-04", "2019-07-04"],
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LABOR_DAY: ["2014-09-01", "2016-09-05", "2018-09-03", "2019-09-02"],
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COLUMBUS_DAY: ["2016-10-10", "2017-10-09", "2018-10-08", "2019-10-14"],
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THANKSGIVING: [
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"2015-11-26",
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"2016-11-24",
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"2017-11-23",
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"2018-11-22",
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"2019-11-28",
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],
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CHRISTMAS_EVE: ["2016-12-24", "2017-12-24", "2018-12-24", "2019-12-24"],
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CHRISTMAS_DAY: ["2016-12-25", "2017-12-25", "2018-12-25", "2019-12-25"],
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NEW_YEARS_EVE: ["2016-12-31", "2017-12-31", "2018-12-31", "2019-12-31"],
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BLACK_FRIDAY: [
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"2016-11-25",
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"2017-11-24",
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"2018-11-23",
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"2019-11-29",
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"2020-11-27",
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],
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CYBER_MONDAY: ["2016-11-28", "2017-11-27", "2018-11-26", "2019-12-2", "2020-11-30"],
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}
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@pytest.mark.parametrize("holiday", test_dates.keys())
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def test_holidays(holiday):
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for test_date in test_dates[holiday]:
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test_date = pd.to_datetime(test_date)
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distance_function = SPECIAL_DATE_FEATURES[holiday]
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assert (
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distance_function(test_date) == 0
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), "The supplied date should be {} but is not!".format(holiday)
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def test_special_date_feature_set_daily():
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date_indices = pd.date_range(start="2016-12-24", end="2016-12-31", freq="D")
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reference_features = np.array(
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[[1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1],]
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)
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sfs = SpecialDateFeatureSet([CHRISTMAS_EVE, CHRISTMAS_DAY, NEW_YEARS_EVE])
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computed_features = sfs(date_indices)
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assert (
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computed_features == reference_features
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).all(), "Computed features do not match reference features."
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def test_special_date_feature_set_hourly():
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date_indices = pd.date_range(start="2016-12-24", end="2016-12-25", freq="H")
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reference_features = np.array(
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[
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[
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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0,
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],
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[
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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1,
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],
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[
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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],
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]
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)
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sfs = SpecialDateFeatureSet([CHRISTMAS_EVE, CHRISTMAS_DAY, NEW_YEARS_EVE])
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computed_features = sfs(date_indices)
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assert (
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computed_features == reference_features
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).all(), "Computed features do not match reference features."
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def test_special_date_feature_set_daily_squared_exponential():
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date_indices = pd.date_range(start="2016-12-24", end="2016-12-29", freq="D")
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reference_features = np.array(
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[
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[
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1.00000e00,
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3.67879e-01,
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1.83156e-02,
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1.23410e-04,
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1.12535e-07,
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0.00000e00,
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],
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[
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3.67879e-01,
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1.00000e00,
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3.67879e-01,
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1.83156e-02,
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1.23410e-04,
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1.12535e-07,
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],
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],
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dtype=float,
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)
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squared_exp_kernel = squared_exponential_kernel(alpha=1.0)
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sfs = SpecialDateFeatureSet([CHRISTMAS_EVE, CHRISTMAS_DAY], squared_exp_kernel)
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computed_features = sfs(date_indices)
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np.testing.assert_almost_equal(computed_features, reference_features, decimal=6)
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def test_custom_date_feature_set():
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ref_dates = [
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pd.to_datetime("20191129", format="%Y%m%d"),
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pd.to_datetime("20200101", format="%Y%m%d"),
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]
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kernel = exponential_kernel(alpha=1.0)
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cfs = CustomDateFeatureSet(ref_dates, kernel)
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sfs = SpecialDateFeatureSet([BLACK_FRIDAY, NEW_YEARS_DAY], kernel)
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date_indices = pd.date_range(
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start=pd.to_datetime("20191101", format="%Y%m%d"),
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end=pd.to_datetime("20200131", format="%Y%m%d"),
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freq="D",
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)
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assert (
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np.sum(cfs(date_indices) - sfs(date_indices).sum(0, keepdims=True)) == 0
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), "Features don't match"
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def test_custom_holiday_feature_set():
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custom_holidays = [
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Holiday("New Years Day", month=1, day=1),
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Holiday("Christmas Day", month=12, day=25),
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]
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kernel = exponential_kernel(alpha=1.0)
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cfs = CustomHolidayFeatureSet(custom_holidays, kernel)
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sfs = SpecialDateFeatureSet([NEW_YEARS_DAY, CHRISTMAS_DAY], kernel)
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date_indices = pd.date_range(
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start=pd.to_datetime("20191101", format="%Y%m%d"),
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end=pd.to_datetime("20200131", format="%Y%m%d"),
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freq="D",
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)
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assert np.sum(cfs(date_indices) - sfs(date_indices)) == 0, "Features don't match"
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