Source code for gluonts.model.deepar._estimator

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from functools import partial
from typing import List, Optional

import numpy as np
from mxnet.gluon import HybridBlock

from gluonts.core.component import DType, validated
from gluonts.dataset.common import Dataset
from gluonts.dataset.field_names import FieldName
from gluonts.dataset.loader import (
    DataLoader,
    TrainDataLoader,
    ValidationDataLoader,
)
from gluonts.dataset.stat import calculate_dataset_statistics
from gluonts.env import env
from gluonts.model.predictor import Predictor
from gluonts.mx.batchify import as_in_context, batchify
from gluonts.mx.distribution import DistributionOutput, StudentTOutput
from gluonts.mx.model.estimator import GluonEstimator
from gluonts.mx.model.predictor import RepresentableBlockPredictor
from gluonts.mx.trainer import Trainer
from gluonts.mx.util import copy_parameters, get_hybrid_forward_input_names
from gluonts.support.util import maybe_len
from gluonts.time_feature import (
    TimeFeature,
    get_lags_for_frequency,
    time_features_from_frequency_str,
)
from gluonts.transform import (
    AddAgeFeature,
    AddObservedValuesIndicator,
    AddTimeFeatures,
    AsNumpyArray,
    Chain,
    ExpectedNumInstanceSampler,
    InstanceSampler,
    InstanceSplitter,
    RemoveFields,
    SelectFields,
    SetField,
    TestSplitSampler,
    Transformation,
    ValidationSplitSampler,
    VstackFeatures,
)
from gluonts.transform.feature import (
    DummyValueImputation,
    MissingValueImputation,
)

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') dropoutcell_type Type of dropout cells to use (available: 'ZoneoutCell', 'RNNZoneoutCell', 'VariationalDropoutCell' or 'VariationalZoneoutCell'; default: 'ZoneoutCell') 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) use_feat_static_real Whether to use the ``feat_static_real`` 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 (default: [min(50, (cat+1)//2) for cat in cardinality]) 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) imputation_method One of the methods from ImputationStrategy train_sampler Controls the sampling of windows during training. validation_sampler Controls the sampling of windows during validation. alpha The scaling coefficient of the activation regularization beta The scaling coefficient of the temporal activation regularization batch_size The size of the batches to be used training and prediction. minimum_scale The minimum scale that is returned by the MeanScaler default_scale Default scale that is applied if the context length window is completely unobserved. If not set, the scale in this case will be the mean scale in the batch. impute_missing_values Whether to impute the missing values during training by using the current model parameters. Recommended if the dataset contains many missing values. However, this is a lot slower than the default mode. num_imputation_samples How many samples to use to impute values when impute_missing_values=True """ @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", dropoutcell_type: str = "ZoneoutCell", dropout_rate: float = 0.1, use_feat_dynamic_real: bool = False, use_feat_static_cat: bool = False, use_feat_static_real: bool = False, cardinality: Optional[List[int]] = None, embedding_dimension: Optional[List[int]] = None, distr_output: DistributionOutput = StudentTOutput(), scaling: bool = True, lags_seq: Optional[List[int]] = None, time_features: Optional[List[TimeFeature]] = None, num_parallel_samples: int = 100, imputation_method: Optional[MissingValueImputation] = None, train_sampler: Optional[InstanceSampler] = None, validation_sampler: Optional[InstanceSampler] = None, dtype: DType = np.float32, alpha: float = 0.0, beta: float = 0.0, batch_size: int = 32, default_scale: Optional[float] = None, minimum_scale: float = 1e-10, impute_missing_values: bool = False, num_imputation_samples: int = 1, ) -> None: super().__init__(trainer=trainer, batch_size=batch_size, dtype=dtype) 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" supported_dropoutcell_types = [ "ZoneoutCell", "RNNZoneoutCell", "VariationalDropoutCell", "VariationalZoneoutCell", ] assert ( dropoutcell_type in supported_dropoutcell_types ), f"`dropoutcell_type` should be one of {supported_dropoutcell_types}" assert dropout_rate >= 0, "The value of `dropout_rate` should be >= 0" assert (cardinality and use_feat_static_cat) or ( not (cardinality or use_feat_static_cat) ), "You should set `cardinality` if and only if `use_feat_static_cat=True`" assert cardinality is None or all( [c > 0 for c in cardinality] ), "Elements of `cardinality` should be > 0" assert embedding_dimension is None or all( [e > 0 for e in embedding_dimension] ), "Elements of `embedding_dimension` should be > 0" assert ( num_parallel_samples > 0 ), "The value of `num_parallel_samples` should be > 0" assert alpha >= 0, "The value of `alpha` should be >= 0" assert beta >= 0, "The value of `beta` 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.distr_output.dtype = dtype self.num_layers = num_layers self.num_cells = num_cells self.cell_type = cell_type self.dropoutcell_type = dropoutcell_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.use_feat_static_real = use_feat_static_real self.cardinality = ( cardinality if cardinality and use_feat_static_cat else [1] ) self.embedding_dimension = ( embedding_dimension if embedding_dimension is not None else [min(50, (cat + 1) // 2) for cat in self.cardinality] ) 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 self.imputation_method = ( imputation_method if imputation_method is not None else DummyValueImputation(self.distr_output.value_in_support) ) self.train_sampler = ( train_sampler if train_sampler is not None else ExpectedNumInstanceSampler( num_instances=1.0, min_future=prediction_length ) ) self.validation_sampler = ( validation_sampler if validation_sampler is not None else ValidationSplitSampler(min_future=prediction_length) ) self.alpha = alpha self.beta = beta self.num_imputation_samples = num_imputation_samples self.default_scale = default_scale self.minimum_scale = minimum_scale self.impute_missing_values = impute_missing_values
[docs] @classmethod def derive_auto_fields(cls, train_iter): stats = calculate_dataset_statistics(train_iter) return { "use_feat_dynamic_real": stats.num_feat_dynamic_real > 0, "use_feat_static_cat": bool(stats.feat_static_cat), "cardinality": [len(cats) for cats in stats.feat_static_cat], }
[docs] def create_transformation(self) -> Transformation: remove_field_names = [FieldName.FEAT_DYNAMIC_CAT] if not self.use_feat_static_real: remove_field_names.append(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 [] ) + ( [ SetField( output_field=FieldName.FEAT_STATIC_REAL, value=[0.0] ) ] if not self.use_feat_static_real else [] ) + [ AsNumpyArray( field=FieldName.FEAT_STATIC_CAT, expected_ndim=1, dtype=self.dtype, ), AsNumpyArray( field=FieldName.FEAT_STATIC_REAL, expected_ndim=1, dtype=self.dtype, ), AsNumpyArray( field=FieldName.TARGET, # in the following line, we add 1 for the time dimension expected_ndim=1 + len(self.distr_output.event_shape), dtype=self.dtype, ), AddObservedValuesIndicator( target_field=FieldName.TARGET, output_field=FieldName.OBSERVED_VALUES, dtype=self.dtype, imputation_method=self.imputation_method, ), 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, dtype=self.dtype, ), 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 [] ), ), ] )
def _create_instance_splitter(self, mode: str): assert mode in ["training", "validation", "test"] instance_sampler = { "training": self.train_sampler, "validation": self.validation_sampler, "test": TestSplitSampler(), }[mode] return InstanceSplitter( target_field=FieldName.TARGET, is_pad_field=FieldName.IS_PAD, start_field=FieldName.START, forecast_start_field=FieldName.FORECAST_START, instance_sampler=instance_sampler, past_length=self.history_length, future_length=self.prediction_length, time_series_fields=[ FieldName.FEAT_TIME, FieldName.OBSERVED_VALUES, ], dummy_value=self.distr_output.value_in_support, )
[docs] def create_training_data_loader( self, data: Dataset, **kwargs, ) -> DataLoader: input_names = get_hybrid_forward_input_names(DeepARTrainingNetwork) with env._let(max_idle_transforms=maybe_len(data) or 0): instance_splitter = self._create_instance_splitter("training") return TrainDataLoader( dataset=data, transform=instance_splitter + SelectFields(input_names), batch_size=self.batch_size, stack_fn=partial(batchify, ctx=self.trainer.ctx, dtype=self.dtype), decode_fn=partial(as_in_context, ctx=self.trainer.ctx), **kwargs, )
[docs] def create_validation_data_loader( self, data: Dataset, **kwargs, ) -> DataLoader: input_names = get_hybrid_forward_input_names(DeepARTrainingNetwork) with env._let(max_idle_transforms=maybe_len(data) or 0): instance_splitter = self._create_instance_splitter("validation") return ValidationDataLoader( dataset=data, transform=instance_splitter + SelectFields(input_names), batch_size=self.batch_size, stack_fn=partial(batchify, ctx=self.trainer.ctx, dtype=self.dtype), )
[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, dropoutcell_type=self.dropoutcell_type, dropout_rate=self.dropout_rate, cardinality=self.cardinality, embedding_dimension=self.embedding_dimension, lags_seq=self.lags_seq, scaling=self.scaling, dtype=self.dtype, alpha=self.alpha, beta=self.beta, num_imputation_samples=self.num_imputation_samples, default_scale=self.default_scale, minimum_scale=self.minimum_scale, impute_missing_values=self.impute_missing_values, )
[docs] def create_predictor( self, transformation: Transformation, trained_network: HybridBlock ) -> Predictor: prediction_splitter = self._create_instance_splitter("test") 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, dropoutcell_type=self.dropoutcell_type, dropout_rate=self.dropout_rate, cardinality=self.cardinality, embedding_dimension=self.embedding_dimension, lags_seq=self.lags_seq, scaling=self.scaling, dtype=self.dtype, num_imputation_samples=self.num_imputation_samples, default_scale=self.default_scale, minimum_scale=self.minimum_scale, impute_missing_values=self.impute_missing_values, ) copy_parameters(trained_network, prediction_network) return RepresentableBlockPredictor( input_transform=transformation + prediction_splitter, prediction_net=prediction_network, batch_size=self.batch_size, freq=self.freq, prediction_length=self.prediction_length, ctx=self.trainer.ctx, dtype=self.dtype, )