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feat: shorten package import path from pytorch_forecasting to ptf with legacy redirect
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Lines changed: 841 additions & 781 deletions

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‎ptf/__init__.py‎

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"""
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PyTorch Forecasting package for timeseries forecasting with PyTorch.
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"""
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__version__ = "1.8.0"
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from ptf.data import (
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EncoderNormalizer,
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GroupNormalizer,
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MultiNormalizer,
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NaNLabelEncoder,
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TimeSeriesDataSet,
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)
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from ptf.metrics import (
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MAE,
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MAPE,
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MASE,
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RMSE,
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SMAPE,
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BetaDistributionLoss,
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CrossEntropy,
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DistributionLoss,
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ImplicitQuantileNetworkDistributionLoss,
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LogNormalDistributionLoss,
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MQF2DistributionLoss,
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MultiHorizonMetric,
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MultiLoss,
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MultivariateNormalDistributionLoss,
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NegativeBinomialDistributionLoss,
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NormalDistributionLoss,
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PoissonLoss,
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QuantileLoss,
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)
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from ptf.models import (
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GRU,
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LSTM,
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AutoRegressiveBaseModel,
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AutoRegressiveBaseModelWithCovariates,
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Baseline,
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BaseModel,
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BaseModelWithCovariates,
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DecoderMLP,
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DeepAR,
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MultiEmbedding,
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NBeats,
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NBeatsKAN,
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NHiTS,
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RecurrentNetwork,
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TemporalFusionTransformer,
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TiDEModel,
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get_rnn,
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)
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from ptf.utils import (
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apply_to_list,
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autocorrelation,
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create_mask,
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detach,
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get_embedding_size,
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groupby_apply,
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integer_histogram,
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move_to_device,
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profile,
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to_list,
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unpack_sequence,
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)
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from ptf.utils._maint._show_versions import show_versions
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__all__ = [
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"TimeSeriesDataSet",
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"GroupNormalizer",
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"EncoderNormalizer",
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"NaNLabelEncoder",
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"MultiNormalizer",
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"TemporalFusionTransformer",
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"TiDEModel",
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"NBeats",
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"NBeatsKAN",
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"NHiTS",
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"Baseline",
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"DeepAR",
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"BaseModel",
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"BaseModelWithCovariates",
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"AutoRegressiveBaseModel",
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"AutoRegressiveBaseModelWithCovariates",
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"MultiHorizonMetric",
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"MultiLoss",
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"MAE",
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"MAPE",
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"MASE",
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"SMAPE",
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"DistributionLoss",
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"BetaDistributionLoss",
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"LogNormalDistributionLoss",
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"NegativeBinomialDistributionLoss",
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"NormalDistributionLoss",
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"ImplicitQuantileNetworkDistributionLoss",
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"MultivariateNormalDistributionLoss",
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"MQF2DistributionLoss",
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"CrossEntropy",
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"PoissonLoss",
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"QuantileLoss",
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"RMSE",
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"get_rnn",
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"LSTM",
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"GRU",
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"MultiEmbedding",
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"apply_to_list",
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"autocorrelation",
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"get_embedding_size",
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"create_mask",
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"to_list",
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"RecurrentNetwork",
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"DecoderMLP",
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"detach",
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"move_to_device",
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"integer_histogram",
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"groupby_apply",
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"profile",
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"show_versions",
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"unpack_sequence",
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]

pytorch_forecasting/_registry/__init__.py renamed to ptf/_registry/__init__.py

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"""PyTorch Forecasting registry."""
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from pytorch_forecasting._registry._lookup import all_objects
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from ptf._registry._lookup import all_objects
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__all__ = ["all_objects"]

pytorch_forecasting/_registry/_lookup.py renamed to ptf/_registry/_lookup.py

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from skbase.lookup import all_objects as _all_objects
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from pytorch_forecasting.base._base_object import _BaseObject
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from ptf.base._base_object import _BaseObject
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def all_objects(
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return_tags=None,
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suppress_import_stdout=True,
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):
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"""Get a list of all objects from pytorch_forecasting.
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"""Get a list of all objects from ptf.
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This function crawls the module and gets all classes that inherit
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from skbase compatible base classes.
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Examples
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--------
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>>> from pytorch_forecasting._registry import all_objects
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>>> from ptf._registry import all_objects
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>>> # return a complete list of objects as pd.Dataframe
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>>> all_objects(as_dataframe=True) # doctest: +SKIP
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as_dataframe=as_dataframe,
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return_tags=return_tags,
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suppress_import_stdout=suppress_import_stdout,
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package_name="pytorch_forecasting",
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package_name="ptf",
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path=ROOT,
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modules_to_ignore=MODULES_TO_IGNORE,
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)

pytorch_forecasting/adapters/__init__.py renamed to ptf/adapters/__init__.py

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"""Module for pytorch-forecasting adapters"""
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from pytorch_forecasting.adapters.scaler_adapters import ScalerAdapter
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from ptf.adapters.scaler_adapters import ScalerAdapter
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__all__ = ["ScalerAdapter"]

pytorch_forecasting/adapters/scaler_adapters.py renamed to ptf/adapters/scaler_adapters.py

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import pandas as pd
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import torch
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from pytorch_forecasting._registry import all_objects
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from pytorch_forecasting.adapters.scaler_strategy import (
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from ptf._registry import all_objects
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from ptf.adapters.scaler_strategy import (
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ScalerStrategy,
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)
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from pytorch_forecasting.adapters.utils import (
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from ptf.adapters.utils import (
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ArrayLike,
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_to_numpy,
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_to_tensor,
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)
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from pytorch_forecasting.data.encoders import (
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from ptf.data.encoders import (
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MultiNormalizer,
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)
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Implements ``.fit(X)` and ``.transform(X)``. Expects 2D
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numpy arrays of shape ``(n_samples, 1)``. Outputs numpy arrays.
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* ``TorchNormalizer`` (from ``pytorch_forecasting.data.encoders``):
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* ``TorchNormalizer`` (from ``ptf.data.encoders``):
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Implements `.fit(data)` and `.transform(data)`. Expects 1D
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tensors or numpy arrays. Output can be tensor or array.
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*``EncoderNormalizer`` (from ``pytorch_forecasting.data.encoders``):
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*``EncoderNormalizer`` (from ``ptf.data.encoders``):
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Implements `.fit(data)` and `.transform(data)`. Expects 1D
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tensors or numpy arrays. Output can be tensor or array.
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`EncoderNormalizer` signals that it must be fit per-sequence.
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* ``NaNLabelEncoder`` (from `pytorch_forecasting.data.encoders`):
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* ``NaNLabelEncoder`` (from `ptf.data.encoders`):
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Implements ``.fit(data)`` and ``.transform(data)``. Expects a
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1D ``pd.Series`` (or 1D array). Used for categorical encoding.
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* ``GroupNormalizer`` (from ``pytorch_forecasting.data.encoders``):
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* ``GroupNormalizer`` (from ``ptf.data.encoders``):
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Implements ``.fit(data, X)`` and ``.transform(data, X)``. Expects
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`data` as a 1D ``pd.Series`` and `X` as a ``pd.DataFrame`` containing
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required group columns to compute grouped statistics.
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* ``MultiNormalizer`` (from ``pytorch_forecasting.data.encoders``):
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* ``MultiNormalizer`` (from ``ptf.data.encoders``):
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Implements ``.fit(data, X)`` and ``.transform(data.T, X)``.
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Expects 2D array-like inputs of shape ``(n_samples, n_targets)``.
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Must expose a ``.normalizers`` attribute (iterable) containing the

pytorch_forecasting/adapters/scaler_strategy.py renamed to ptf/adapters/scaler_strategy.py

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import pandas as pd
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import torch
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from pytorch_forecasting import EncoderNormalizer, GroupNormalizer, NaNLabelEncoder
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from pytorch_forecasting.adapters.utils import (
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from ptf import EncoderNormalizer, GroupNormalizer, NaNLabelEncoder
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from ptf.adapters.utils import (
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ArrayLike,
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_is_sklearn_transformer,
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_series_from,
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_to_numpy,
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_to_tensor,
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_was_2d_singleton,
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)
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from pytorch_forecasting.base._base_object import _BaseObject
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from ptf.base._base_object import _BaseObject
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class ScalerStrategy(_BaseObject):

pytorch_forecasting/adapters/utils.py renamed to ptf/adapters/utils.py

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from sklearn.base import TransformerMixin
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import torch
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from pytorch_forecasting.data.encoders import TransformMixIn
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from ptf.data.encoders import TransformMixIn
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ArrayLike = torch.Tensor | np.ndarray | pd.Series
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pytorch_forecasting/base/__init__.py renamed to ptf/base/__init__.py

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"""Base classes for pytorch-forecasting."""
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from pytorch_forecasting.base._base_object import _BaseObject
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from ptf.base._base_object import _BaseObject
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__all__ = ["_BaseObject"]

pytorch_forecasting/base/_base_object.py renamed to ptf/base/_base_object.py

File renamed without changes.

pytorch_forecasting/base/_base_pkg.py renamed to ptf/base/_base_pkg.py

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from torch.utils.data import DataLoader
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import yaml
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from pytorch_forecasting.data import TimeSeries
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from pytorch_forecasting.models.base._base_object import _BasePtForecasterV2
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from ptf.data import TimeSeries
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from ptf.models.base._base_object import _BasePtForecasterV2
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class Base_pkg(_BasePtForecasterV2):
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"""
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Creates and returns D1 TimeSeries dataSet objects for testing.
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"""
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from pytorch_forecasting.tests._data_scenarios import (
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from ptf.tests._data_scenarios import (
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data_with_covariates_v2,
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make_datasets_v2,
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)

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