Source code for tempor.methods.prediction.temporal.classification

"""Temporal classification estimators."""

import abc
from typing import Any

import pydantic
from typing_extensions import Self

import tempor.methods.core as methods_core
from tempor.core import plugins, pydantic_utils
from tempor.data import dataset, samples


def check_data_class(data: Any) -> None:
    """Check that the passed data is a temporal prediction dataset.

    Args:
        data (Any): The data to check.

    Raises:
        TypeError: If the data is not a temporal prediction dataset.
    """
    if not isinstance(data, dataset.TemporalPredictionDataset):
        raise TypeError(
            "Expected `data` passed to a temporal classification estimator to be "
            f"`{dataset.TemporalPredictionDataset.__name__}` but was {type(data)}"
        )


[docs]class BaseTemporalClassifier(methods_core.BasePredictor):
    def __init__(self, **params: Any) -> None:  # pylint: disable=useless-super-delegation
        """Base class for temporal classification estimators.

        Args:
            **params (Any):
                Parameters and defaults as defined in :class:`BasePredictorParams`.
        """
        super().__init__(**params)

[docs]    def fit(self, data: dataset.BaseDataset, *args: Any, **kwargs: Any) -> Self:  # noqa: D102
        check_data_class(data)
        super().fit(data, *args, **kwargs)
        return self

[docs]    @pydantic_utils.validate_arguments(config=pydantic.ConfigDict(arbitrary_types_allowed=True))
    def predict(  # pylint: disable=arguments-differ
        self,
        data: dataset.PredictiveDataset,
        n_future_steps: int,
        *args: Any,
        time_delta: int = 1,
        **kwargs: Any,
    ) -> samples.TimeSeriesSamplesBase:  # noqa: D102
        check_data_class(data)
        return super().predict(data, n_future_steps, *args, time_delta=time_delta, **kwargs)

[docs]    @pydantic_utils.validate_arguments(config=pydantic.ConfigDict(arbitrary_types_allowed=True))
    def predict_proba(  # pylint: disable=arguments-differ
        self,
        data: dataset.PredictiveDataset,
        n_future_steps: int,
        *args: Any,
        time_delta: int = 1,
        **kwargs: Any,
    ) -> samples.TimeSeriesSamplesBase:  # noqa: D102
        check_data_class(data)
        return super().predict_proba(data, n_future_steps, *args, time_delta=time_delta, **kwargs)

    @abc.abstractmethod
    def _predict(  # pylint: disable=arguments-differ
        self, data: dataset.PredictiveDataset, n_future_steps: int, *args: Any, time_delta: int = 1, **kwargs: Any
    ) -> samples.TimeSeriesSamplesBase:  # pragma: no cover
        ...

    @abc.abstractmethod
    def _predict_proba(  # pylint: disable=arguments-differ
        self, data: dataset.PredictiveDataset, n_future_steps: int, *args: Any, time_delta: int = 1, **kwargs: Any
    ) -> samples.TimeSeriesSamplesBase:  # pragma: no cover
        ...


plugins.register_plugin_category("prediction.temporal.classification", BaseTemporalClassifier)

plugins.importing.import_plugins(__file__)

__all__ = [  # pyright: ignore
    *plugins.importing.gather_modules_names(__file__),
    "BaseTemporalClassifier",
]