module mlmodel.extended_features
¶
Short summary¶
module mlinsights.mlmodel.extended_features
Implements new features such as polynomial features.
Classes¶
class 
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Generates extended features such as polynomial features. 
Properties¶
property 
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HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should … 
Methods¶
method 
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Fitting method for the polynomial features. 

Returns feature names for output features for the polynomial features. 

Transforms data to polynomial features. 

Transforms data to polynomial features. 

Compute number of output features. 

Returns feature names for output features. 

Transforms data to extended features. 
Documentation¶
Implements new features such as polynomial features.

class
mlinsights.mlmodel.extended_features.
ExtendedFeatures
(kind='poly', poly_degree=2, poly_interaction_only=False, poly_include_bias=True)[source]¶ Bases:
sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
Generates extended features such as polynomial features.
 Parameters
kind – string
'poly'
for polynomial features,'polyslow'
for polynomial features in scikitlearn 0.20.2poly_degree – integer The degree of the polynomial features. Default = 2.
poly_interaction_only – boolean If true, only interaction features are produced: features that are products of at most degree distinct input features (so not
x[1] ** 2, x[0] * x[2] ** 3
, etc.).poly_include_bias – boolean If True (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones  acts as an intercept term in a linear model).
Fitted attributes:
 n_input_features_: int
The total number of input features.
 n_output_features_: int
The total number of polynomial output features. The number of output features is computed by iterating over all suitably sized combinations of input features.

__init__
(kind='poly', poly_degree=2, poly_interaction_only=False, poly_include_bias=True)[source]¶ Initialize self. See help(type(self)) for accurate signature.

_get_feature_names_poly
(input_features=None)[source]¶ Returns feature names for output features for the polynomial features.

fit
(X, y=None)[source]¶ Compute number of output features.
 Parameters
X – arraylike, shape (n_samples, n_features) The data.
 Returns
self : instance

get_feature_names
(input_features=None)[source]¶ Returns feature names for output features.
 Parameters
input_features – list of string, length n_features, optional String names for input features if available. By default, “x0”, “x1”, … “xn_features” is used.
 Returns
output_feature_names : list of string, length n_output_features

transform
(X)[source]¶ Transforms data to extended features.
 Parameters
X – arraylike, shape [n_samples, n_features] The data to transform, row by row. rns
XP – numpy.ndarray, shape [n_samples, NP] The matrix of features, where NP is the number of polynomial features generated from the combination of inputs.