autoprognosis.utils.data_encoder module

class ContinuousDataEncoder(n_components: int = 10, random_state: int = 0, weight_threshold: float = 0.005)

Bases: object

Continuous variables encoder

components() → int

fit(X: Series) → Any

fit_transform(X: Series) → Series

inverse_transform(X: DataFrame) → Series

transform(X: Series) → DataFrame

class DatetimeEncoder

Bases: object

Datetime encoder, with sklearn-style API

fit(X: Series) → Any

fit_transform(X: Series) → Series

inverse_transform(X: Series) → Series

transform(X: Series) → Series

class EncodersCallbacks(encoders: dict)

Bases: object

decode(df: DataFrame) → DataFrame

encode(df: DataFrame) → DataFrame

numeric_decode(df: DataFrame, strategy: str = 'max') → DataFrame

dataframe_encode(data: DataFrame) → Tuple[DataFrame, EncodersCallbacks]