# 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").
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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
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from functools import partial
from typing import List, Optional
from mxnet.gluon import HybridBlock
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.mx.model.deepvar._estimator import (
get_lags_for_frequency,
time_features_from_frequency_str,
)
from gluonts.model.predictor import Predictor
from gluonts.mx.batchify import batchify
from gluonts.mx.distribution import DistributionOutput
from gluonts.mx.distribution.lowrank_gp import LowrankGPOutput
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
from gluonts.transform import (
AddObservedValuesIndicator,
AddTimeFeatures,
AsNumpyArray,
CDFtoGaussianTransform,
Chain,
ExpandDimArray,
ExpectedNumInstanceSampler,
InstanceSampler,
InstanceSplitter,
RenameFields,
SampleTargetDim,
SelectFields,
SetFieldIfNotPresent,
TargetDimIndicator,
TestSplitSampler,
Transformation,
ValidationSplitSampler,
VstackFeatures,
cdf_to_gaussian_forward_transform,
)
from ._network import GPVARPredictionNetwork, GPVARTrainingNetwork
[docs]class GPVAREstimator(GluonEstimator):
"""
Constructs a GPVAR estimator.
These models have been described as GP-Copula in this paper:
https://arxiv.org/abs/1910.03002
Note that this implementation will change over time and we further work on
this method. To replicate the results of the paper, please refer to our
(frozen) implementation here:
https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release
Parameters
----------
freq
Frequency of the data to train on and predict
prediction_length
Length of the prediction horizon
target_dim
Dimensionality of the input dataset
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')
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)
target_dim_sample
Number of dimensions to sample for the GP model
distr_output
Distribution to use to evaluate observations and sample predictions
(default: LowrankGPOutput with dim=target_dim and
rank=5). Note that target dim of the DistributionOutput and the
estimator constructor call need to match. Also note that the rank in
this constructor is meaningless if the DistributionOutput is
constructed outside of this class.
rank
Rank for the LowrankGPOutput. (default: 2)
scaling
Whether to automatically scale the target values (default: true)
pick_incomplete
Whether training examples can be sampled with only a part of
past_length time-units
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)
shuffle_target_dim
Shuffle the dimensions before sampling.
time_features
Time features to use as inputs of the RNN (default: None, in which
case these are automatically determined based on freq)
conditioning_length
Set maximum length for conditioning the marginal transformation
use_marginal_transformation
Whether marginal (CDFtoGaussianTransform) transformation is used by the
model
train_sampler
Controls the sampling of windows during training.
validation_sampler
Controls the sampling of windows during validation.
batch_size
The size of the batches to be used training and prediction.
"""
@validated()
def __init__(
self,
freq: str,
prediction_length: int,
target_dim: int,
trainer: Trainer = Trainer(),
# number of dimension to sample at training time
context_length: Optional[int] = None,
num_layers: int = 2,
num_cells: int = 40,
cell_type: str = "lstm",
num_parallel_samples: int = 100,
dropout_rate: float = 0.1,
target_dim_sample: Optional[int] = None,
distr_output: Optional[DistributionOutput] = None,
rank: Optional[int] = 2,
scaling: bool = True,
pick_incomplete: bool = False,
lags_seq: Optional[List[int]] = None,
shuffle_target_dim: bool = True,
time_features: Optional[List[TimeFeature]] = None,
conditioning_length: int = 100,
use_marginal_transformation: bool = False,
train_sampler: Optional[InstanceSampler] = None,
validation_sampler: Optional[InstanceSampler] = None,
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 (
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 (
num_parallel_samples > 0
), "The value of `num_eval_samples` should be > 0"
assert dropout_rate >= 0, "The value of `dropout_rate` should be >= 0"
if distr_output is not None:
self.distr_output = distr_output
else:
self.distr_output = LowrankGPOutput(rank=rank)
self.context_length = (
context_length if context_length is not None else prediction_length
)
self.prediction_length = prediction_length
self.target_dim = target_dim
self.target_dim_sample = (
target_dim
if target_dim_sample is None
else min(target_dim_sample, target_dim)
)
self.shuffle_target_dim = shuffle_target_dim
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.num_parallel_samples = num_parallel_samples
self.dropout_rate = dropout_rate
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(freq)
)
self.history_length = self.context_length + max(self.lags_seq)
self.pick_incomplete = pick_incomplete
self.scaling = scaling
self.conditioning_length = conditioning_length
self.use_marginal_transformation = use_marginal_transformation
self.output_transform = (
cdf_to_gaussian_forward_transform
if self.use_marginal_transformation
else None
)
self.train_sampler = (
train_sampler
if train_sampler is not None
else ExpectedNumInstanceSampler(
num_instances=1.0,
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
)
self.validation_sampler = (
validation_sampler
if validation_sampler is not None
else ValidationSplitSampler(
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
)
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,
],
)
+ (
CDFtoGaussianTransform(
target_field=FieldName.TARGET,
observed_values_field=FieldName.OBSERVED_VALUES,
max_context_length=self.conditioning_length,
target_dim=self.target_dim,
)
if self.use_marginal_transformation
else RenameFields(
{
f"past_{FieldName.TARGET}": (
f"past_{FieldName.TARGET}_cdf"
),
f"future_{FieldName.TARGET}": (
f"future_{FieldName.TARGET}_cdf"
),
}
)
)
+ SampleTargetDim(
field_name=FieldName.TARGET_DIM_INDICATOR,
target_field=FieldName.TARGET + "_cdf",
observed_values_field=FieldName.OBSERVED_VALUES,
num_samples=self.target_dim_sample,
shuffle=self.shuffle_target_dim,
)
)
[docs] def create_training_data_loader(
self,
data: Dataset,
**kwargs,
) -> DataLoader:
input_names = get_hybrid_forward_input_names(GPVARTrainingNetwork)
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(GPVARTrainingNetwork)
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) -> GPVARTrainingNetwork:
return GPVARTrainingNetwork(
target_dim=self.target_dim,
target_dim_sample=self.target_dim_sample,
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,
dropout_rate=self.dropout_rate,
lags_seq=self.lags_seq,
scaling=self.scaling,
distr_output=self.distr_output,
)
[docs] def create_predictor(
self, transformation: Transformation, trained_network: HybridBlock
) -> Predictor:
prediction_splitter = self._create_instance_splitter("test")
prediction_network = GPVARPredictionNetwork(
target_dim=self.target_dim,
target_dim_sample=self.target_dim,
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,
dropout_rate=self.dropout_rate,
lags_seq=self.lags_seq,
scaling=self.scaling,
distr_output=self.distr_output,
)
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,
output_transform=self.output_transform,
)