Table Of Contents
Table Of Contents

Source code for gluonts.model.deepar._estimator

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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
# permissions and limitations under the License.

# Standard library imports
from typing import List, Optional

# Third-party imports
from mxnet.gluon import HybridBlock

# First-party imports
from gluonts.core.component import validated
from gluonts.distribution import DistributionOutput, StudentTOutput
from gluonts.model.estimator import GluonEstimator
from gluonts.model.predictor import Predictor, RepresentableBlockPredictor
from gluonts.support.util import copy_parameters
from gluonts.time_feature.lag import (
    TimeFeature,
    get_lags_for_frequency,
    time_features_from_frequency_str,
)
from gluonts.trainer import Trainer
from gluonts.transform import (
    AddAgeFeature,
    AddObservedValuesIndicator,
    AddTimeFeatures,
    AsNumpyArray,
    Chain,
    ExpectedNumInstanceSampler,
    FieldName,
    InstanceSplitter,
    RemoveFields,
    SetField,
    Transformation,
    VstackFeatures,
)

# Relative imports
from ._network import DeepARPredictionNetwork, DeepARTrainingNetwork


[docs]class DeepAREstimator(GluonEstimator): """ Construct a DeepAR estimator. This implements an RNN-based model, close to the one described in [SFG17]_. *Note:* the code of this model is unrelated to the implementation behind `SageMaker's DeepAR Forecasting Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html>`_. Parameters ---------- freq Frequency of the data to train on and predict prediction_length Length of the prediction horizon trainer Trainer object to be used (default: Trainer()) context_length Number of steps to unroll the RNN for before computing predictions (default: None, in which case context_length = prediction_length) num_layers Number of RNN layers (default: 2) num_cells Number of RNN cells for each layer (default: 40) cell_type Type of recurrent cells to use (available: 'lstm' or 'gru'; default: 'lstm') dropout_rate Dropout regularization parameter (default: 0.1) use_feat_dynamic_real Whether to use the ``feat_dynamic_real`` field from the data (default: False) use_feat_static_cat Whether to use the ``feat_static_cat`` field from the data (default: False) cardinality Number of values of each categorical feature. This must be set if ``use_feat_static_cat == True`` (default: None) embedding_dimension Dimension of the embeddings for categorical features (the same dimension is used for all embeddings, default: 5) distr_output Distribution to use to evaluate observations and sample predictions (default: StudentTOutput()) scaling Whether to automatically scale the target values (default: true) lags_seq Indices of the lagged target values to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq) time_features Time features to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq) num_parallel_samples Number of evaluation samples per time series to increase parallelism during inference. This is a model optimization that does not affect the accuracy (default: 100) """ @validated() def __init__( self, freq: str, prediction_length: int, trainer: Trainer = Trainer(), context_length: Optional[int] = None, num_layers: int = 2, num_cells: int = 40, cell_type: str = "lstm", dropout_rate: float = 0.1, use_feat_dynamic_real: bool = False, use_feat_static_cat: bool = False, cardinality: Optional[List[int]] = None, embedding_dimension: int = 20, distr_output: DistributionOutput = StudentTOutput(), scaling: bool = True, lags_seq: Optional[List[int]] = None, time_features: Optional[List[TimeFeature]] = None, num_parallel_samples: int = 100, ) -> None: super().__init__(trainer=trainer) assert ( prediction_length > 0 ), "The value of `prediction_length` should be > 0" assert ( context_length is None or context_length > 0 ), "The value of `context_length` should be > 0" assert num_layers > 0, "The value of `num_layers` should be > 0" assert num_cells > 0, "The value of `num_cells` should be > 0" assert dropout_rate >= 0, "The value of `dropout_rate` should be >= 0" assert ( cardinality is not None or not use_feat_static_cat ), "You must set `cardinality` if `use_feat_static_cat=True`" assert cardinality is None or [ c > 0 for c in cardinality ], "Elements of `cardinality` should be > 0" assert ( embedding_dimension > 0 ), "The value of `embedding_dimension` should be > 0" assert ( num_parallel_samples > 0 ), "The value of `num_parallel_samples` should be > 0" self.freq = freq self.context_length = ( context_length if context_length is not None else prediction_length ) self.prediction_length = prediction_length self.distr_output = distr_output self.num_layers = num_layers self.num_cells = num_cells self.cell_type = cell_type self.dropout_rate = dropout_rate self.use_feat_dynamic_real = use_feat_dynamic_real self.use_feat_static_cat = use_feat_static_cat self.cardinality = cardinality if use_feat_static_cat else [1] self.embedding_dimension = embedding_dimension self.scaling = scaling self.lags_seq = ( lags_seq if lags_seq is not None else get_lags_for_frequency(freq_str=freq) ) self.time_features = ( time_features if time_features is not None else time_features_from_frequency_str(self.freq) ) self.history_length = self.context_length + max(self.lags_seq) self.num_parallel_samples = num_parallel_samples
[docs] def create_transformation(self) -> Transformation: remove_field_names = [ FieldName.FEAT_DYNAMIC_CAT, FieldName.FEAT_STATIC_REAL, ] if not self.use_feat_dynamic_real: remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL) return Chain( [RemoveFields(field_names=remove_field_names)] + ( [SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0.0])] if not self.use_feat_static_cat else [] ) + [ AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1), AsNumpyArray( field=FieldName.TARGET, # in the following line, we add 1 for the time dimension expected_ndim=1 + len(self.distr_output.event_shape), ), AddObservedValuesIndicator( target_field=FieldName.TARGET, output_field=FieldName.OBSERVED_VALUES, ), AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field=FieldName.FEAT_TIME, time_features=self.time_features, pred_length=self.prediction_length, ), AddAgeFeature( target_field=FieldName.TARGET, output_field=FieldName.FEAT_AGE, pred_length=self.prediction_length, log_scale=True, ), VstackFeatures( output_field=FieldName.FEAT_TIME, input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE] + ( [FieldName.FEAT_DYNAMIC_REAL] if self.use_feat_dynamic_real else [] ), ), InstanceSplitter( target_field=FieldName.TARGET, is_pad_field=FieldName.IS_PAD, start_field=FieldName.START, forecast_start_field=FieldName.FORECAST_START, train_sampler=ExpectedNumInstanceSampler(num_instances=1), past_length=self.history_length, future_length=self.prediction_length, time_series_fields=[ FieldName.FEAT_TIME, FieldName.OBSERVED_VALUES, ], ), ] )
[docs] def create_training_network(self) -> DeepARTrainingNetwork: return DeepARTrainingNetwork( num_layers=self.num_layers, num_cells=self.num_cells, cell_type=self.cell_type, history_length=self.history_length, context_length=self.context_length, prediction_length=self.prediction_length, distr_output=self.distr_output, dropout_rate=self.dropout_rate, cardinality=self.cardinality, embedding_dimension=self.embedding_dimension, lags_seq=self.lags_seq, scaling=self.scaling, )
[docs] def create_predictor( self, transformation: Transformation, trained_network: HybridBlock ) -> Predictor: prediction_network = DeepARPredictionNetwork( num_parallel_samples=self.num_parallel_samples, num_layers=self.num_layers, num_cells=self.num_cells, cell_type=self.cell_type, history_length=self.history_length, context_length=self.context_length, prediction_length=self.prediction_length, distr_output=self.distr_output, dropout_rate=self.dropout_rate, cardinality=self.cardinality, embedding_dimension=self.embedding_dimension, lags_seq=self.lags_seq, scaling=self.scaling, ) copy_parameters(trained_network, prediction_network) return RepresentableBlockPredictor( input_transform=transformation, prediction_net=prediction_network, batch_size=self.trainer.batch_size, freq=self.freq, prediction_length=self.prediction_length, ctx=self.trainer.ctx, )