mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-06-27 18:06:19 +08:00
Kashif Rasul
ea9b2b7df5
* Estimator needs an create_instance_splitter now * updated estimators and tests * fix test * validated
304 lines
11 KiB
Python
304 lines
11 KiB
Python
from typing import List, Optional, Callable
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import numpy as np
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import torch
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from gluonts.core.component import validated
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from gluonts.dataset.field_names import FieldName
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from gluonts.time_feature import TimeFeature
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from gluonts.torch.modules.distribution_output import DistributionOutput
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from gluonts.torch.support.util import copy_parameters
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from gluonts.torch.model.predictor import PyTorchPredictor
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from gluonts.model.predictor import Predictor
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from gluonts.transform import (
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AddObservedValuesIndicator,
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AddTimeFeatures,
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AsNumpyArray,
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CDFtoGaussianTransform,
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Chain,
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ExpandDimArray,
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ExpectedNumInstanceSampler,
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InstanceSplitter,
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ValidationSplitSampler,
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TestSplitSampler,
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RenameFields,
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SetField,
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TargetDimIndicator,
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Transformation,
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VstackFeatures,
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RemoveFields,
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AddAgeFeature,
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cdf_to_gaussian_forward_transform,
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)
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from pts import Trainer
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from pts.model.utils import get_module_forward_input_names
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from pts.feature import (
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fourier_time_features_from_frequency,
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lags_for_fourier_time_features_from_frequency,
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)
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from pts.model import PyTorchEstimator
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from pts.modules import LowRankMultivariateNormalOutput
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from .deepvar_network import DeepVARTrainingNetwork, DeepVARPredictionNetwork
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class DeepVAREstimator(PyTorchEstimator):
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@validated()
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def __init__(
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self,
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input_size: int,
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freq: str,
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prediction_length: int,
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target_dim: int,
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trainer: Trainer = Trainer(),
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context_length: Optional[int] = None,
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num_layers: int = 2,
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num_cells: int = 40,
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cell_type: str = "LSTM",
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num_parallel_samples: int = 100,
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dropout_rate: float = 0.1,
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use_feat_dynamic_real: bool = False,
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use_feat_static_cat: bool = False,
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use_feat_static_real: bool = False,
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cardinality: Optional[List[int]] = None,
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embedding_dimension: Optional[List[int]] = None,
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distr_output: Optional[DistributionOutput] = None,
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rank: Optional[int] = 5,
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scaling: bool = True,
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pick_incomplete: bool = False,
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lags_seq: Optional[List[int]] = None,
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time_features: Optional[List[TimeFeature]] = None,
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conditioning_length: int = 200,
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use_marginal_transformation=False,
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**kwargs,
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) -> None:
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super().__init__(trainer=trainer, **kwargs)
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self.freq = freq
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self.context_length = (
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context_length if context_length is not None else prediction_length
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)
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if distr_output is not None:
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self.distr_output = distr_output
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else:
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self.distr_output = LowRankMultivariateNormalOutput(
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dim=target_dim, rank=rank
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)
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self.input_size = input_size
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self.prediction_length = prediction_length
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self.target_dim = target_dim
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self.num_layers = num_layers
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self.num_cells = num_cells
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self.cell_type = cell_type
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self.num_parallel_samples = num_parallel_samples
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self.dropout_rate = dropout_rate
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self.use_feat_dynamic_real = use_feat_dynamic_real
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self.use_feat_static_cat = use_feat_static_cat
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self.use_feat_static_real = use_feat_static_real
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self.cardinality = cardinality if cardinality and use_feat_static_cat else [1]
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self.embedding_dimension = (
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embedding_dimension
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if embedding_dimension is not None
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else [min(50, (cat + 1) // 2) for cat in self.cardinality]
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)
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self.conditioning_length = conditioning_length
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self.use_marginal_transformation = use_marginal_transformation
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self.lags_seq = (
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lags_seq
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if lags_seq is not None
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else lags_for_fourier_time_features_from_frequency(freq_str=freq)
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)
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self.time_features = (
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time_features
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if time_features is not None
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else fourier_time_features_from_frequency(self.freq)
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)
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self.history_length = self.context_length + max(self.lags_seq)
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self.pick_incomplete = pick_incomplete
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self.scaling = scaling
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if self.use_marginal_transformation:
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self.output_transform: Optional[
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Callable
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] = cdf_to_gaussian_forward_transform
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else:
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self.output_transform = None
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self.train_sampler = ExpectedNumInstanceSampler(
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num_instances=1.0,
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min_past=0 if pick_incomplete else self.history_length,
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min_future=prediction_length,
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)
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self.validation_sampler = ValidationSplitSampler(
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min_past=0 if pick_incomplete else self.history_length,
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min_future=prediction_length,
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)
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def create_transformation(self) -> Transformation:
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remove_field_names = [FieldName.FEAT_DYNAMIC_CAT]
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if not self.use_feat_dynamic_real:
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remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
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if not self.use_feat_static_real:
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remove_field_names.append(FieldName.FEAT_STATIC_REAL)
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return Chain(
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[RemoveFields(field_names=remove_field_names)]
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+ (
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[SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0])]
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if not self.use_feat_static_cat
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else []
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)
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+ (
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[SetField(output_field=FieldName.FEAT_STATIC_REAL, value=[0.0])]
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if not self.use_feat_static_real
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else []
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)
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+ [
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AsNumpyArray(
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field=FieldName.TARGET,
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expected_ndim=1 + len(self.distr_output.event_shape),
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),
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# maps the target to (1, T)
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# if the target data is uni dimensional
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ExpandDimArray(
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field=FieldName.TARGET,
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axis=0 if self.distr_output.event_shape[0] == 1 else None,
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),
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AddObservedValuesIndicator(
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target_field=FieldName.TARGET,
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output_field=FieldName.OBSERVED_VALUES,
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),
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AddTimeFeatures(
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start_field=FieldName.START,
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target_field=FieldName.TARGET,
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output_field=FieldName.FEAT_TIME,
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time_features=self.time_features,
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pred_length=self.prediction_length,
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),
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AddAgeFeature(
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target_field=FieldName.TARGET,
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output_field=FieldName.FEAT_AGE,
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pred_length=self.prediction_length,
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log_scale=True,
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),
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VstackFeatures(
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output_field=FieldName.FEAT_TIME,
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input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
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+ (
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[FieldName.FEAT_DYNAMIC_REAL]
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if self.use_feat_dynamic_real
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else []
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),
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),
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TargetDimIndicator(
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field_name="target_dimension_indicator",
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target_field=FieldName.TARGET,
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),
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AsNumpyArray(
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field=FieldName.FEAT_STATIC_CAT, expected_ndim=1, dtype=np.long
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),
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AsNumpyArray(field=FieldName.FEAT_STATIC_REAL, expected_ndim=1),
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]
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)
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def create_instance_splitter(self, mode: str):
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assert mode in ["training", "validation", "test"]
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instance_sampler = {
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"training": self.train_sampler,
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"validation": self.validation_sampler,
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"test": TestSplitSampler(),
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}[mode]
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return InstanceSplitter(
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target_field=FieldName.TARGET,
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is_pad_field=FieldName.IS_PAD,
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start_field=FieldName.START,
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forecast_start_field=FieldName.FORECAST_START,
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instance_sampler=instance_sampler,
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past_length=self.history_length,
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future_length=self.prediction_length,
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time_series_fields=[
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FieldName.FEAT_TIME,
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FieldName.OBSERVED_VALUES,
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],
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) + (
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CDFtoGaussianTransform(
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target_field=FieldName.TARGET,
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observed_values_field=FieldName.OBSERVED_VALUES,
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max_context_length=self.conditioning_length,
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target_dim=self.target_dim,
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)
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if self.use_marginal_transformation
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else RenameFields(
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{
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f"past_{FieldName.TARGET}": f"past_{FieldName.TARGET}_cdf",
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f"future_{FieldName.TARGET}": f"future_{FieldName.TARGET}_cdf",
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}
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)
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)
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def create_training_network(self, device: torch.device) -> DeepVARTrainingNetwork:
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return DeepVARTrainingNetwork(
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input_size=self.input_size,
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target_dim=self.target_dim,
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num_layers=self.num_layers,
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num_cells=self.num_cells,
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cell_type=self.cell_type,
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history_length=self.history_length,
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context_length=self.context_length,
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prediction_length=self.prediction_length,
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distr_output=self.distr_output,
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dropout_rate=self.dropout_rate,
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cardinality=self.cardinality,
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embedding_dimension=self.embedding_dimension,
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lags_seq=self.lags_seq,
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scaling=self.scaling,
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).to(device)
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def create_predictor(
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self,
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transformation: Transformation,
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trained_network: DeepVARTrainingNetwork,
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device: torch.device,
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) -> Predictor:
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prediction_network = DeepVARPredictionNetwork(
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input_size=self.input_size,
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target_dim=self.target_dim,
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num_parallel_samples=self.num_parallel_samples,
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num_layers=self.num_layers,
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num_cells=self.num_cells,
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cell_type=self.cell_type,
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history_length=self.history_length,
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context_length=self.context_length,
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prediction_length=self.prediction_length,
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distr_output=self.distr_output,
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dropout_rate=self.dropout_rate,
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cardinality=self.cardinality,
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embedding_dimension=self.embedding_dimension,
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lags_seq=self.lags_seq,
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scaling=self.scaling,
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).to(device)
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copy_parameters(trained_network, prediction_network)
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input_names = get_module_forward_input_names(prediction_network)
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prediction_splitter = self.create_instance_splitter("test")
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return PyTorchPredictor(
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input_transform=transformation + prediction_splitter,
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input_names=input_names,
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prediction_net=prediction_network,
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batch_size=self.trainer.batch_size,
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freq=self.freq,
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prediction_length=self.prediction_length,
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device=device,
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output_transform=self.output_transform,
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)
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