tempor.models.clairvoyance2.data.dataset module¶

class tempor.models.clairvoyance2.data.dataset.Dataset(temporal_covariates: TimeSeriesSamples | Sequence[TimeSeries | DataFrame | ndarray], static_covariates: StaticSamples | DataFrame | ndarray | None = None, event_covariates: EventSamples | None = None, temporal_targets: TimeSeriesSamples | Sequence[TimeSeries | DataFrame | ndarray] | None = None, temporal_treatments: TimeSeriesSamples | Sequence[TimeSeries | DataFrame | ndarray] | None = None, event_targets: EventSamples | None = None, event_treatments: EventSamples | None = None, sample_indices: Sequence[int] | RangeIndex | Index | None = None, missing_indicator: float = nan)[source]¶

Bases: Copyable, SupportsNewLike, Sequence

temporal_covariates : TimeSeriesSamples¶

static_covariates : StaticSamples | None = None¶

event_covariates : EventSamples | None = None¶

temporal_targets : TimeSeriesSamples | None = None¶

temporal_treatments : TimeSeriesSamples | None = None¶

event_targets : EventSamples | None = None¶

event_treatments : EventSamples | None = None¶

property n_samples : int¶

property sample_index : RangeIndex | Index¶

property sample_indices : Sequence[int]¶

property static_data_containers : dict[str, StaticSamples]¶

property temporal_data_containers : dict[str, TimeSeriesSamples]¶

property event_data_containers : dict[str, EventSamples]¶

property all_data_containers : dict[str, StaticSamples | TimeSeriesSamples | EventSamples]¶

validate() → None[source]¶

check_temporal_containers_have_same_time_index() → tuple[bool, tuple[str, str] | None][source]¶

static new_like(like: Dataset, **kwargs) → Dataset[source]¶

static new_empty_like(like: Dataset, **kwargs) → Dataset[source]¶

index(value[, start[, stop]]) → integer -- return first index of value.[source]¶

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

count(value) → integer -- return number of occurrences of value[source]¶