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# 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.
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.itertools 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
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 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
@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],
}
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,
)
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,
)
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),
)
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,
)
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,
)