Source code for tempor.datasources.prediction.temporal.plugin_dummy_prediction

"""Module with the dummy data source for temporal prediction."""

from typing import Any, Optional, cast

from tempor.core import plugins
from tempor.data import dataset
from tempor.data.clv2conv import clairvoyance2_dataset_to_tempor_dataset
from tempor.datasources import datasource
from tempor.models.clairvoyance2.datasets.dummy import dummy_dataset


[docs]@plugins.register_plugin(name="dummy_prediction", category="prediction.temporal", plugin_type="datasource")
class DummyTemporalPredictionDataSource(datasource.TemporalPredictionDataSource):
    def __init__(
        self,
        n_samples: int = 100,
        temporal_covariates_n_features: int = 5,
        temporal_covariates_max_len: int = 20,
        temporal_covariates_missing_prob: float = 0.1,
        static_covariates_n_features: int = 3,
        static_covariates_missing_prob: float = 0.0,
        temporal_targets_n_features: int = 2,
        temporal_targets_n_categories: Optional[int] = None,
        random_state: int = 12345,
        **kwargs: Any,
    ) -> None:
        """Dummy data source for temporal prediction tasks; generates a dataset with random data.

        Args:
            n_samples (int, optional):
                Number of samples. Defaults to ``100``.
            temporal_covariates_n_features (int, optional):
                Number of time series covariates features. Defaults to ``5``.
            temporal_covariates_max_len (int, optional):
                Maximum number of time steps in time series covariates. Defaults to ``20``.
            temporal_covariates_missing_prob (float, optional):
                The missingness probability of time series covariates. Defaults to ``0.1``.
            static_covariates_n_features (int, optional):
                Number of static covariates features. Defaults to ``3``.
            static_covariates_missing_prob (float, optional):
                The missingness probability of static covariates. Defaults to ``0.0``.
            temporal_targets_n_features (int, optional):
                Number of time series target features. Defaults to 2.
            temporal_targets_n_categories (Optional[int], optional):
                Number of categories in time series targets features. If `None`, the target features will be real \
                rather than categorical. Defaults to `None`.
            random_state (int, optional):
                Random state to use. Defaults to ``12345``.
            **kwargs (Any):
                Any additional keyword arguments will be passed to `~tempor.datasources.DataSource`.
        """
        super().__init__(**kwargs)

        self.n_samples = n_samples
        self.temporal_covariates_n_features = temporal_covariates_n_features
        self.temporal_covariates_max_len = temporal_covariates_max_len
        self.temporal_covariates_missing_prob = temporal_covariates_missing_prob
        self.static_covariates_n_features = static_covariates_n_features
        self.static_covariates_missing_prob = static_covariates_missing_prob
        self.temporal_targets_n_features = temporal_targets_n_features
        self.temporal_targets_n_categories = temporal_targets_n_categories
        self.random_state = random_state

[docs]    @staticmethod
    def url() -> None:  # noqa: D102
        return None

[docs]    @staticmethod
    def dataset_dir() -> None:  # noqa: D102
        return None

[docs]    def load(self, **kwargs: Any) -> dataset.TemporalPredictionDataset:  # noqa: D102
        clv_dataset = dummy_dataset(
            n_samples=self.n_samples,
            temporal_covariates_n_features=self.temporal_covariates_n_features,
            temporal_covariates_max_len=self.temporal_covariates_max_len,
            temporal_covariates_missing_prob=self.temporal_covariates_missing_prob,
            static_covariates_n_features=self.static_covariates_n_features,
            temporal_targets_n_features=self.temporal_targets_n_features,
            temporal_targets_n_categories=self.temporal_targets_n_categories,
            random_seed=self.random_state,
            # No temporal treatments features in this case:
            temporal_treatments_n_features=0,
            temporal_treatments_n_categories=None,
        )
        data = clairvoyance2_dataset_to_tempor_dataset(clv_dataset)
        data = cast(dataset.TemporalPredictionDataset, data)
        return data