mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-06-27 18:06:19 +08:00
Kashif Rasul
ea9b2b7df5
* Estimator needs an create_instance_splitter now * updated estimators and tests * fix test * validated
115 lines
3.7 KiB
Python
115 lines
3.7 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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import torch
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from torch.distributions import Uniform
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# First-party imports
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from gluonts.model.forecast import SampleForecast
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from gluonts.torch.model.forecast import DistributionForecast
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QUANTILES = np.arange(1, 100) / 100
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SAMPLES = np.arange(101).reshape(101, 1) / 100
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START_DATE = pd.Timestamp(2017, 1, 1, 12)
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FREQ = "1D"
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FORECASTS = {
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"SampleForecast": SampleForecast(samples=SAMPLES, start_date=START_DATE, freq=FREQ),
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"DistributionForecast": DistributionForecast(
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distribution=Uniform(low=torch.zeros(1), high=torch.ones(1)),
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start_date=START_DATE,
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freq=FREQ,
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),
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}
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@pytest.mark.parametrize("name", FORECASTS.keys())
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def test_Forecast(name):
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forecast = FORECASTS[name]
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def percentile(value):
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return f"p{int(round(value * 100)):02d}"
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num_samples, pred_length = SAMPLES.shape
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for quantile in QUANTILES:
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test_cases = [quantile, str(quantile), percentile(quantile)]
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for quant_pred in map(forecast.quantile, test_cases):
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assert np.isclose(
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quant_pred[0], quantile
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), f"Expected {percentile(quantile)} quantile {quantile}. Obtained {quant_pred}."
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assert forecast.prediction_length == 1
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assert len(forecast.index) == pred_length
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assert forecast.index[0] == pd.Timestamp(START_DATE)
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def test_DistributionForecast():
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forecast = DistributionForecast(
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distribution=Uniform(
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low=torch.tensor([0.0, 0.0]), high=torch.tensor([1.0, 2.0])
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),
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start_date=START_DATE,
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freq=FREQ,
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)
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def percentile(value):
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return f"p{int(round(value * 100)):02d}"
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for quantile in QUANTILES:
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test_cases = [quantile, str(quantile), percentile(quantile)]
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for quant_pred in map(forecast.quantile, test_cases):
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expected = quantile * np.array([1.0, 2.0])
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assert np.allclose(
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quant_pred, expected
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), f"Expected {percentile(quantile)} quantile {quantile}. Obtained {quant_pred}."
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pred_length = 2
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assert forecast.prediction_length == pred_length
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assert len(forecast.index) == pred_length
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assert forecast.index[0] == pd.Timestamp(START_DATE)
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@pytest.mark.parametrize(
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"forecast, exp_index",
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[
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(
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SampleForecast(
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samples=np.random.normal(size=(100, 7, 3)),
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start_date=pd.Timestamp("2020-01-01 00:00:00"),
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freq="1D",
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),
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pd.date_range(
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start=pd.Timestamp("2020-01-01 00:00:00"), freq="1D", periods=7,
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),
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),
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(
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DistributionForecast(
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Uniform(low=torch.zeros(size=(5, 2)), high=torch.ones(size=(5, 2)),),
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start_date=pd.Timestamp("2020-01-01 00:00:00"),
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freq="W",
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),
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pd.date_range(
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start=pd.Timestamp("2020-01-01 00:00:00"), freq="W", periods=5,
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),
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),
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],
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)
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def test_forecast_multivariate(forecast, exp_index):
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assert forecast.prediction_length == len(exp_index)
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assert np.all(forecast.index == exp_index)
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