models.columns

Module: models.columns

Inheritance diagram for ISLP.models.columns:

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Column

class ISLP.models.columns.Column(idx: Any, name: str, is_categorical: bool = False, is_ordinal: bool = False, columns: tuple = (), encoder: Any = None)

Bases: NamedTuple

A column extractor with a possible encoder (following sklearn fit/transform template).

Methods

count(value, /)	Return number of occurrences of value.
fit_encoder(X)	Fit self.encoder.
get_columns(X[, fit])	Extract associated column from X, encoding it with self.encoder if not None.
index(value[, start, stop])	Return first index of value.

__init__(*args, **kwargs)

columns: tuple

Alias for field number 4

count(value, /)

Return number of occurrences of value.

encoder: Any

Alias for field number 5

fit_encoder(X)

Fit self.encoder.

Parameters:

Xarray-like

X on which encoder will be fit.

Returns:

None

get_columns(X, fit=False)

Extract associated column from X, encoding it with self.encoder if not None.

Parameters:

Xarray-like

X on which model matrix will be evaluated. If a pd.DataFrame or pd.Series, variables that are of categorical dtype will be treated as categorical.

fitbool

If True, fit self.encoder on corresponding column.

Returns:

colsnp.ndarray

Evaluated columns – if an encoder is used, several columns may be produced.

names(str,)

Column names

idx: Any

Alias for field number 0

index(value, start=0, stop=sys.maxsize, /)

Return first index of value.

Raises ValueError if the value is not present.

is_categorical: bool

Alias for field number 2

is_ordinal: bool

Alias for field number 3

name: str

Alias for field number 1