ddl.local.RandomFeaturePairs

class ddl.local.RandomFeaturePairs(random_state=None)[source]

Bases: sklearn.base.BaseEstimator

Random feature pairs estimator for use with FeatureGroupsDestructor.

Randomly groups features into pairs.

Parameters:
random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by numpy.random.
Attributes:
groups_ : array-like, shape (n_groups, 2)

Feature indices for each group. Note that there should be no duplicate indices so that each group can be transformed independently.

See also

FeatureGroupsDestructor

Methods

fit(self, X[, y]) Fit estimator to X.
get_params(self[, deep]) Get parameters for this estimator.
set_params(self, \*\*params) Set the parameters of this estimator.
__init__(self, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)[source]

Fit estimator to X.

Parameters:
X : array-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used in the fitting process but kept for compatibility.
Returns:
self : estimator

Returns the instance itself.
get_params(self, deep=True)

Get parameters for this estimator.

Parameters:
deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
params : mapping of string to any

Parameter names mapped to their values.
set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:
self