PytorchForecastingTransform

class PytorchForecastingTransform(max_encoder_length: int = 30, min_encoder_length: Optional[int] = None, min_prediction_idx: Optional[int] = None, min_prediction_length: Optional[int] = None, max_prediction_length: int = 1, static_categoricals: Optional[List[str]] = None, static_reals: Optional[List[str]] = None, time_varying_known_categoricals: Optional[List[str]] = None, time_varying_known_reals: Optional[List[str]] = None, time_varying_unknown_categoricals: Optional[List[str]] = None, time_varying_unknown_reals: Optional[List[str]] = None, variable_groups: Optional[Dict[str, List[int]]] = None, constant_fill_strategy: Optional[Dict[str, Union[str, float, int, bool]]] = None, allow_missing_timesteps: bool = True, lags: Optional[Dict[str, List[int]]] = None, add_relative_time_idx: bool = True, add_target_scales: bool = True, add_encoder_length: Union[bool, str] = True, target_normalizer: Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer, str, List[Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer]], Tuple[Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer]]] = 'auto', categorical_encoders: Optional[Dict[str, pytorch_forecasting.data.encoders.NaNLabelEncoder]] = None, scalers: Optional[Dict[str, Union[sklearn.preprocessing._data.StandardScaler, sklearn.preprocessing._data.RobustScaler, pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.EncoderNormalizer]]] = None)

Bases: etna.transforms.base.Transform

Transform for models from PytorchForecasting library.

Notes

This transform should be added at the very end of transforms parameter.

Init transform.

Parameters here is used for initialization of pytorch_forecasting.data.timeseries.TimeSeriesDataSet object.

Inherited-members

Parameters

• max_encoder_length (int) –

• min_encoder_length (Optional[int]) –

• min_prediction_idx (Optional[int]) –

• min_prediction_length (Optional[int]) –

• max_prediction_length (int) –

• static_categoricals (Optional[List[str]]) –

• static_reals (Optional[List[str]]) –

• time_varying_known_categoricals (Optional[List[str]]) –

• time_varying_known_reals (Optional[List[str]]) –

• time_varying_unknown_categoricals (Optional[List[str]]) –

• time_varying_unknown_reals (Optional[List[str]]) –

• variable_groups (Optional[Dict[str, List[int]]]) –

• constant_fill_strategy (Optional[Dict[str, Union[str, float, int, bool]]]) –

• allow_missing_timesteps (bool) –

• lags (Optional[Dict[str, List[int]]]) –

• add_relative_time_idx (bool) –

• add_target_scales (bool) –

• add_encoder_length (Union[bool, str]) –

• target_normalizer (Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer, str, List[Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer]], Tuple[Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer]]]) –

• categorical_encoders (Optional[Dict[str, pytorch_forecasting.data.encoders.NaNLabelEncoder]]) –

• scalers (Optional[Dict[str, Union[sklearn.preprocessing._data.StandardScaler, sklearn.preprocessing._data.RobustScaler, pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.EncoderNormalizer]]]) –

Methods

fit(df)	Fit TimeSeriesDataSet.
fit_transform(df)	May be reimplemented.
inverse_transform(df)	Inverse transforms dataframe.
transform(df)	Transform raw df to TimeSeriesDataSet.

fit(df: pandas.core.frame.DataFrame) → etna.transforms.nn.pytorch_forecasting.PytorchForecastingTransform

Fit TimeSeriesDataSet.

Parameters

df (pandas.core.frame.DataFrame) – data to be fitted.

Return type

PytorchForecastingTransform

transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Transform raw df to TimeSeriesDataSet.

Parameters

df (pandas.core.frame.DataFrame) – data to be transformed.

Return type

DataFrame

Notes

We save TimeSeriesDataSet in instance to use it in the model. It`s not right pattern of using Transforms and TSDataset.