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, sparse])	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, sparse=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 (if it is not already fit). The encoder must be fit to evaluate the columns: a Column used in the terms of a ModelSpec is not itself fit when the ModelSpec is, as a copy of it is.

sparsebool

If True, the columns may be returned as a scipy.sparse matrix, e.g. for a categorical encoding.

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