pytorch-transformer-ts/fedformer/estimator.py

from typing import Any, Dict, Iterable, List, Optional

import torch
from gluonts.core.component import validated
from gluonts.dataset.common import Dataset
from gluonts.dataset.field_names import FieldName
from gluonts.itertools import Cyclic, IterableSlice, PseudoShuffled
from gluonts.time_feature import TimeFeature, time_features_from_frequency_str
from gluonts.torch.model.estimator import PyTorchLightningEstimator
from gluonts.torch.model.predictor import PyTorchPredictor
from gluonts.torch.distributions import DistributionOutput, StudentTOutput
from gluonts.torch.modules.loss import DistributionLoss, NegativeLogLikelihood
from gluonts.torch.util import IterableDataset
from gluonts.transform import (
    AddAgeFeature,
    AddObservedValuesIndicator,
    AddTimeFeatures,
    AsNumpyArray,
    Chain,
    ExpectedNumInstanceSampler,
    InstanceSplitter,
    RemoveFields,
    SelectFields,
    SetField,
    TestSplitSampler,
    Transformation,
    ValidationSplitSampler,
    VstackFeatures,
)
from gluonts.transform.sampler import InstanceSampler
from torch.utils.data import DataLoader

from lightning_module import FEDformerLightningModule
from module import FEDformerModel
import random
PREDICTION_INPUT_NAMES = [
    "feat_static_cat",
    "feat_static_real",
    "past_time_feat",
    "past_target",
    "past_observed_values",
    "future_time_feat",
]

TRAINING_INPUT_NAMES = PREDICTION_INPUT_NAMES + [
    "future_target",
    "future_observed_values",
]


class FEDformerEstimator(PyTorchLightningEstimator):
    @validated()
    def __init__(
        self,
        freq: str,
        prediction_length: int,
        nhead: int,
        num_encoder_layers: int, 
        num_decoder_layers: int, 
        dim_feedforward: int = 2048, #dimension of fcn
        version:str = 'Fourier',#Fourier, Wavelets
        features: str = 'M',#options:[M, S, MS]; M:multivariate predict multivariate, 'S':univariate predict univariate, MS:multivariate predict univariate' 
        target: str = '0T',#'target feature in S or MS task'
        checkpoints:str = './checkpoints/', #location of model checkpoints
        modes:int = 64,
        mode_select:str = 'random',#options: [random, low]
        base:str = 'legendre',
        cross_activation:str = 'tanh',
        L: int = 3,
        #forecasting task
        context_length: Optional[int] = None,#seq_len : input sequence length
        label_length: Optional[int] = 48,#start token length
        # model argument
        input_size: int = 1, #encoder input size
        dec_in: int = 7, #decoder input size
        c_out: int = 7, #output size
        moving_avg: List[int] = [24],
        factor:int = 1,
        activation: str = "gelu",
        dropout: float = 0.05,
        embed: str = "timeF",#options:[timeF, fixed, learned]
        output_attention: bool = True,#whether to output attention in encoder
        distil: bool = True,
        num_feat_dynamic_real: int = 0,
        num_feat_static_cat: int = 0,
        num_feat_static_real: int = 0,
        cardinality: Optional[List[int]] = None,
        embedding_dimension: Optional[List[int]] = None,
        distr_output: DistributionOutput = StudentTOutput(),
        loss: DistributionLoss = NegativeLogLikelihood(),
        scaling: bool = True,
        lags_seq: Optional[List[int]] = None,
        time_features: Optional[List[TimeFeature]] = None,
        num_parallel_samples: int = 100,
        batch_size: int = 32,
        num_batches_per_epoch: int = 512,
        trainer_kwargs: Optional[Dict[str, Any]] = dict(),
        train_sampler: Optional[InstanceSampler] = None,
        validation_sampler: Optional[InstanceSampler] = None,
    ) -> None:
        trainer_kwargs = {
            "max_epochs": 100,
            **trainer_kwargs,
        }
        super().__init__(trainer_kwargs=trainer_kwargs)
        self.version = version
        self.modes = modes
        self.mode_select = mode_select
        self.base = base
        self.cross_activation = cross_activation
        self.L = L
        self.freq = freq
        self.context_length = (
            context_length if context_length is not None else prediction_length
        )
        self.label_length = label_length
        self.prediction_length = prediction_length
        self.distr_output = distr_output
        self.loss = loss
        
        self.input_size = input_size
        # self.dec_in = dec_in
        self.c_out = c_out
        self.nhead = nhead
        self.num_encoder_layers = num_encoder_layers
        self.num_decoder_layers = num_decoder_layers
        self.activation = activation
        self.dim_feedforward = dim_feedforward
        self.dropout = dropout
        self.output_attention = output_attention
        # self.distil = distil
        self.factor = factor
        self.moving_avg = moving_avg
        self.embed = embed
        
        self.num_feat_dynamic_real = num_feat_dynamic_real
        self.num_feat_static_cat = num_feat_static_cat
        self.num_feat_static_real = num_feat_static_real
        self.cardinality = (
            cardinality if cardinality and num_feat_static_cat > 0 else [1]
        )
        self.embedding_dimension = embedding_dimension
        self.scaling = scaling
        self.lags_seq = lags_seq
        self.time_features = (
            time_features
            if time_features is not None
            else time_features_from_frequency_str(self.freq)
        )

        self.num_parallel_samples = num_parallel_samples
        self.batch_size = batch_size
        self.num_batches_per_epoch = num_batches_per_epoch

        self.train_sampler = train_sampler or ExpectedNumInstanceSampler(
            num_instances=1.0, min_future=prediction_length
        )
        self.validation_sampler = validation_sampler or ValidationSplitSampler(
            min_future=prediction_length
        )

    def create_transformation(self) -> Transformation:
        remove_field_names = []
        if self.num_feat_static_real == 0:
            remove_field_names.append(FieldName.FEAT_STATIC_REAL)
        if self.num_feat_dynamic_real == 0:
            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])]
                if not self.num_feat_static_cat > 0
                else []
            )
            + (
                [SetField(output_field=FieldName.FEAT_STATIC_REAL, value=[0.0])]
                if not self.num_feat_static_real > 0
                else []
            )
            + [
                AsNumpyArray(
                    field=FieldName.FEAT_STATIC_CAT,
                    expected_ndim=1,
                    dtype=int,
                ),
                AsNumpyArray(
                    field=FieldName.FEAT_STATIC_REAL,
                    expected_ndim=1,
                ),
                AsNumpyArray(
                    field=FieldName.TARGET,
                    # in the following line, we add 1 for the time dimension
                    expected_ndim=1 + len(self.distr_output.event_shape),
                ),
                AddObservedValuesIndicator(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.OBSERVED_VALUES,
                ),
                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,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                    + (
                        [FieldName.FEAT_DYNAMIC_REAL]
                        if self.num_feat_dynamic_real > 0
                        else []
                    ),
                ),
            ]
        )

    def _create_instance_splitter(self, module: FEDformerLightningModule, 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=module.model._past_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,
        module: FEDformerLightningModule,
        shuffle_buffer_length: Optional[int] = None,
        **kwargs,
    ) -> Iterable:
        transformation = self._create_instance_splitter(
            module, "training"
        ) + SelectFields(TRAINING_INPUT_NAMES)

        training_instances = transformation.apply(
            Cyclic(data)
            if shuffle_buffer_length is None
            else PseudoShuffled(
                Cyclic(data), shuffle_buffer_length=shuffle_buffer_length
            )
        )

        return IterableSlice(
            iter(
                DataLoader(
                    IterableDataset(training_instances),
                    batch_size=self.batch_size,
                    **kwargs,
                )
            ),
            self.num_batches_per_epoch,
        )

    def create_validation_data_loader(
        self,
        data: Dataset,
        module: FEDformerLightningModule,
        **kwargs,
    ) -> Iterable:
        transformation = self._create_instance_splitter(
            module, "validation"
        ) + SelectFields(TRAINING_INPUT_NAMES)

        validation_instances = transformation.apply(data)

        return DataLoader(
            IterableDataset(validation_instances),
            batch_size=self.batch_size,
            **kwargs,
        )

    def create_predictor(
        self,
        transformation: Transformation,
        module: FEDformerLightningModule,
    ) -> PyTorchPredictor:
        prediction_splitter = self._create_instance_splitter(module, "test")

        return PyTorchPredictor(
            input_transform=transformation + prediction_splitter,
            input_names=PREDICTION_INPUT_NAMES,
            prediction_net=module.model,
            batch_size=self.batch_size,
            prediction_length=self.prediction_length,
            device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
        )

    def create_lightning_module(self) -> FEDformerLightningModule:
        model = FEDformerModel(
        version = self.version,#Fourier, Wavelets
        freq = self.freq,
        modes = self.modes,
        mode_select = self.mode_select,
        base = self.base,
        cross_activation = self.cross_activation,
        L = self.L,
        
        #forecasting task
        prediction_length = self.prediction_length,
        context_length = self.context_length,#seq_len : input sequence length
        label_length = self.label_length,#start token length
        
        # model arguments
        nhead = self.nhead,
        num_encoder_layers = self.num_encoder_layers, 
        num_decoder_layers = self.num_decoder_layers, 
        dim_feedforward = self.dim_feedforward, #dimension of fcn
        input_size = self.input_size, #encoder input size
        # dec_in = self.dec_in, #decoder input size
        c_out =self.c_out, #output size
        moving_avg = self.moving_avg,
        factor = self.factor,
        scaling = self.scaling,
        activation = self.activation,
        dropout = self.dropout,
        embed = self.embed,#options:[timeF, fixed, learned]
        output_attention = self.output_attention,#whether to output attention in encoder
        num_feat_dynamic_real=1
        + self.num_feat_dynamic_real
        + len(self.time_features),
        num_feat_static_real=max(1, self.num_feat_static_real),
        num_feat_static_cat=max(1, self.num_feat_static_cat),
        cardinality=self.cardinality,
        embedding_dimension=self.embedding_dimension,
        # univariate input
        distr_output=self.distr_output,
        lags_seq=self.lags_seq,
        num_parallel_samples=self.num_parallel_samples,
        )

        return FEDformerLightningModule(model=model, loss=self.loss)