Source code for gluonts.model.trivial.constant

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# or in the "license" file accompanying this file. This file 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
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# Standard library imports
from functools import partial
from typing import Iterator

# Third-party imports
import numpy as np

# First-party imports
from gluonts.core.component import validated
from gluonts.dataset.common import DataEntry
from gluonts.dataset.field_names import FieldName
from gluonts.model.forecast import SampleForecast
from gluonts.model.predictor import RepresentablePredictor, FallbackPredictor
from gluonts.support.pandas import forecast_start


[docs]class ConstantPredictor(RepresentablePredictor): """ A `Predictor` that always produces the same forecast. Parameters ---------- samples Samples to use to construct SampleForecast objects for every prediction. freq Frequency of the predicted data. """ @validated() def __init__(self, samples: np.ndarray, freq: str) -> None: super().__init__(samples.shape[1], freq) self.samples = samples
[docs] def predict_item(self, item: DataEntry) -> SampleForecast: return SampleForecast( samples=self.samples, start_date=item["start"], freq=self.freq, item_id=item.get(FieldName.ITEM_ID), )
[docs]class ConstantValuePredictor(RepresentablePredictor, FallbackPredictor): """ A `Predictor` that always produces the same value as forecast. Parameters ---------- value The value to use as forecast. prediction_length Prediction horizon. freq Frequency of the predicted data. """ @validated() def __init__( self, prediction_length: int, freq: str, value: float = 0.0, # since we are emitting a constant values, we just predict a single # line on default num_samples: int = 1, ) -> None: super().__init__(freq=freq, prediction_length=prediction_length) self.value = value self.num_samples = num_samples
[docs] def predict_item(self, item: DataEntry) -> SampleForecast: samples_shape = self.num_samples, self.prediction_length samples = np.full(samples_shape, self.value) return SampleForecast( samples=samples, start_date=forecast_start(item), freq=self.freq, item_id=item.get("id"), )