# 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 pandas.tseries.frequencies import to_offset
from gluonts.core.component import validated
from gluonts.dataset.common import Dataset
from gluonts.dataset.field_names import FieldName
from gluonts.dataset.loader import (
DataLoader,
TrainDataLoader,
ValidationDataLoader,
)
from gluonts.model.deepstate.issm import ISSM, CompositeISSM
from gluonts.model.predictor import Predictor
from gluonts.mx.batchify import batchify
from gluonts.mx.distribution.lds import ParameterBounds
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.time_feature import (
TimeFeature,
norm_freq_str,
time_features_from_frequency_str,
)
from gluonts.transform import (
AddAgeFeature,
AddObservedValuesIndicator,
AddTimeFeatures,
AsNumpyArray,
CanonicalInstanceSplitter,
Chain,
ExpandDimArray,
RemoveFields,
SelectFields,
SetField,
TestSplitSampler,
Transformation,
VstackFeatures,
)
from ._network import DeepStatePredictionNetwork, DeepStateTrainingNetwork
SEASON_INDICATORS_FIELD = "seasonal_indicators"
# A dictionary mapping granularity to the period length of the longest season
# one can expect given the granularity of the time series. This is similar to
# the frequency value in the R forecast package:
# https://stats.stackexchange.com/questions/120806/frequency-value-for-seconds-minutes-intervals-data-in-r
# This is useful for setting default values for past/context length for models
# that do not do data augmentation and uses a single training example per time
# series in the dataset.
FREQ_LONGEST_PERIOD_DICT = {
"M": 12, # yearly seasonality
"W": 52, # yearly seasonality
"D": 31, # monthly seasonality
"B": 22, # monthly seasonality
"H": 168, # weekly seasonality
"T": 1440, # daily seasonality
}
def longest_period_from_frequency_str(freq_str: str) -> int:
offset = to_offset(freq_str)
return FREQ_LONGEST_PERIOD_DICT[norm_freq_str(offset.name)] // offset.n
[docs]class DeepStateEstimator(GluonEstimator):
"""
Construct a DeepState estimator.
This implements the deep state space model described in
[RSG+18]_.
Parameters
----------
freq
Frequency of the data to train on and predict
prediction_length
Length of the prediction horizon
cardinality
Number of values of each categorical feature.
This must be set by default unless ``use_feat_static_cat``
is set to `False` explicitly (which is NOT recommended).
add_trend
Flag to indicate whether to include trend component in the
state space model
past_length
This is the length of the training time series;
i.e., number of steps to unroll the RNN for before computing
predictions.
Set this to (at most) the length of the shortest time series in the
dataset.
(default: None, in which case the training length is set such that
at least
`num_seasons_to_train` seasons are included in the training.
See `num_seasons_to_train`)
num_periods_to_train
(Used only when `past_length` is not set)
Number of periods to include in the training time series. (default: 4)
Here period corresponds to the longest cycle one can expect given
the granularity of the time series.
See: https://stats.stackexchange.com/questions/120806/frequency
-value-for-seconds-minutes-intervals-data-in-r
trainer
Trainer object to be used (default: Trainer())
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')
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).
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: True)
embedding_dimension
Dimension of the embeddings for categorical features
(default: [min(50, (cat+1)//2) for cat in cardinality])
scaling
Whether to automatically scale the target values (default: true)
time_features
Time features to use as inputs of the RNN (default: None, in which
case these are automatically determined based on freq)
noise_std_bounds
Lower and upper bounds for the standard deviation of the observation
noise
prior_cov_bounds
Lower and upper bounds for the diagonal of the prior covariance matrix
innovation_bounds
Lower and upper bounds for the standard deviation of the observation
noise
batch_size
The size of the batches to be used training and prediction.
"""
@validated()
def __init__(
self,
freq: str,
prediction_length: int,
cardinality: List[int],
add_trend: bool = False,
past_length: Optional[int] = None,
num_periods_to_train: int = 4,
trainer: Trainer = Trainer(
epochs=100, num_batches_per_epoch=50, hybridize=False
),
num_layers: int = 2,
num_cells: int = 40,
cell_type: str = "lstm",
num_parallel_samples: int = 100,
dropout_rate: float = 0.1,
use_feat_dynamic_real: bool = False,
use_feat_static_cat: bool = True,
embedding_dimension: Optional[List[int]] = None,
issm: Optional[ISSM] = None,
scaling: bool = True,
time_features: Optional[List[TimeFeature]] = None,
noise_std_bounds: ParameterBounds = ParameterBounds(1e-6, 1.0),
prior_cov_bounds: ParameterBounds = ParameterBounds(1e-6, 1.0),
innovation_bounds: ParameterBounds = ParameterBounds(1e-6, 0.01),
batch_size: int = 32,
) -> None:
super().__init__(trainer=trainer, batch_size=batch_size)
assert (
prediction_length > 0
), "The value of `prediction_length` should be > 0"
assert (
past_length is None or past_length > 0
), "The value of `past_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 (
num_parallel_samples > 0
), "The value of `num_parallel_samples` should be > 0"
assert dropout_rate >= 0, "The value of `dropout_rate` should be >= 0"
assert not use_feat_static_cat or any(c > 1 for c in cardinality), (
"Cardinality of at least one static categorical feature must be"
" larger than 1 if `use_feat_static_cat=True`. But cardinality"
f" provided is: {cardinality}"
)
assert embedding_dimension is None or all(
e > 0 for e in embedding_dimension
), "Elements of `embedding_dimension` should be > 0"
assert all(
np.isfinite(p.lower) and np.isfinite(p.upper) and p.lower > 0
for p in [noise_std_bounds, prior_cov_bounds, innovation_bounds]
), (
"All parameter bounds should be finite, and lower bounds should be"
" positive"
)
self.past_length = (
past_length
if past_length is not None
else num_periods_to_train * longest_period_from_frequency_str(freq)
)
self.prediction_length = prediction_length
self.add_trend = add_trend
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.num_parallel_samples = num_parallel_samples
self.scaling = scaling
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 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.issm = (
issm
if issm is not None
else CompositeISSM.get_from_freq(freq, add_trend)
)
self.time_features = (
time_features
if time_features is not None
else time_features_from_frequency_str(freq)
)
self.noise_std_bounds = noise_std_bounds
self.prior_cov_bounds = prior_cov_bounds
self.innovation_bounds = innovation_bounds
def _create_instance_splitter(self, mode: str):
assert mode in ["training", "validation", "test"]
return CanonicalInstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=TestSplitSampler(),
time_series_fields=[
FieldName.FEAT_TIME,
SEASON_INDICATORS_FIELD,
FieldName.OBSERVED_VALUES,
],
allow_target_padding=True,
instance_length=self.past_length,
use_prediction_features=(mode != "training"),
prediction_length=self.prediction_length,
)
[docs] def create_training_data_loader(
self,
data: Dataset,
**kwargs,
) -> DataLoader:
input_names = get_hybrid_forward_input_names(DeepStateTrainingNetwork)
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),
**kwargs,
)
[docs] def create_validation_data_loader(
self,
data: Dataset,
**kwargs,
) -> DataLoader:
input_names = get_hybrid_forward_input_names(DeepStateTrainingNetwork)
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) -> DeepStateTrainingNetwork:
return DeepStateTrainingNetwork(
num_layers=self.num_layers,
num_cells=self.num_cells,
cell_type=self.cell_type,
past_length=self.past_length,
prediction_length=self.prediction_length,
issm=self.issm,
dropout_rate=self.dropout_rate,
cardinality=self.cardinality,
embedding_dimension=self.embedding_dimension,
scaling=self.scaling,
noise_std_bounds=self.noise_std_bounds,
prior_cov_bounds=self.prior_cov_bounds,
innovation_bounds=self.innovation_bounds,
)
[docs] def create_predictor(
self, transformation: Transformation, trained_network: HybridBlock
) -> Predictor:
prediction_splitter = self._create_instance_splitter("test")
prediction_network = DeepStatePredictionNetwork(
num_layers=self.num_layers,
num_cells=self.num_cells,
cell_type=self.cell_type,
past_length=self.past_length,
prediction_length=self.prediction_length,
issm=self.issm,
dropout_rate=self.dropout_rate,
cardinality=self.cardinality,
embedding_dimension=self.embedding_dimension,
scaling=self.scaling,
num_parallel_samples=self.num_parallel_samples,
noise_std_bounds=self.noise_std_bounds,
prior_cov_bounds=self.prior_cov_bounds,
innovation_bounds=self.innovation_bounds,
params=trained_network.collect_params(),
)
copy_parameters(trained_network, prediction_network)
return RepresentableBlockPredictor(
input_transform=transformation + prediction_splitter,
prediction_net=prediction_network,
batch_size=self.batch_size,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
)