tempor.models.clairvoyance2.data package¶

Subpackages¶

Submodules¶

Module contents¶

class tempor.models.clairvoyance2.data.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]¶

class tempor.models.clairvoyance2.data.EventSamples(data: DataFrame, missing_indicator: float = nan)[source]¶

Bases: HasFeaturesMixin, HasMissingMixin, Copyable, SupportsNewLike, BaseContainer[int, Union[int, str]]

static from_df(data: DataFrame, column_sample_index: int | str, column_time_index: int | str)[source]¶

property n_samples : int¶

validate()[source]¶

property sample_index : RangeIndex | Index¶

property sample_indices : Sequence[int]¶

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

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

class tempor.models.clairvoyance2.data.Feature(name: str, series: Series)[source]¶

Bases: object

property inferred_dtype : type¶

property numeric_compatible : bool¶

property categorical_compatible : bool¶

property binary_compatible : bool¶

property categories : Sequence[int | float | str]¶

class tempor.models.clairvoyance2.data.StaticSamples(data: DataFrame | ndarray, sample_indices: Sequence[int] | RangeIndex | Index | None = None, missing_indicator: float = nan)[source]¶

Bases: HasFeaturesMixin, HasMissingMixin, ToTensorLikeMixin, Copyable, SupportsNewLike, BaseContainer[int, Union[int, str]]

property n_samples : int¶

validate()[source]¶

property sample_index : RangeIndex | Index¶

property sample_indices : Sequence[int]¶

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

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

class tempor.models.clairvoyance2.data.TimeSeries(data: DataFrame | ndarray, missing_indicator: float = nan)[source]¶

Bases: UpdateFromArrayExtension, HasFeaturesMixin, HasMissingMixin, ToTensorLikeMixin, Copyable, SupportsNewLike, BaseContainer[Union[int, float, datetime64], Union[int, str]]

apply_time_indexing(key, inplace: bool = False) → TimeSeries | None[source]¶

property time_index¶

is_regular() → tuple[bool, float | int | Timedelta | None][source]¶

property n_timesteps : int¶

validate()[source]¶

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

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

class tempor.models.clairvoyance2.data.TimeSeriesSamples(data: Sequence[TimeSeries | DataFrame | ndarray], sample_indices: Sequence[int] | RangeIndex | Index | None = None, missing_indicator: float = nan)[source]¶

Bases: UpdateFromSequenceOfArraysExtension, HasFeaturesMixin, HasMissingMixin, ToTensorLikeMixin, Copyable, SupportsNewLike, BaseContainer[int, Union[int, str]]

property has_missing : bool¶

property df_repr¶

property df_repr_html¶

apply_time_indexing(key, inplace: bool = False) → TimeSeriesSamples | None[source]¶

plot(n: int | None = None, **kwargs) → Any[source]¶

property empty : bool¶

property df : DataFrame¶

property n_samples : int¶

property n_timesteps_per_sample : Sequence[int]¶

is_regular() → tuple[bool, float | int | Timedelta | None][source]¶

property all_samples_same_n_timesteps : bool¶

property all_samples_aligned : bool¶

validate()[source]¶

to_multi_index_dataframe() → DataFrame[source]¶

property sample_index : RangeIndex | Index¶

property sample_indices : Sequence[int]¶

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

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