module mlmodel.extended_features#
Short summary#
module mlinsights.mlmodel.extended_features
Implements new features such as polynomial features.
Classes#
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Generates extended features such as polynomial features. |
Properties#
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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. |
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Returns feature names for output features for the polynomial features. |
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Transforms data to polynomial features. |
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Transforms data to polynomial features. |
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Compute number of output features. |
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Returns feature names for output features. |
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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)#
Bases:
BaseEstimator,TransformerMixinGenerates extended features such as polynomial features.
- Parameters:
kind – string
'poly'for polynomial features,'poly-slow'for polynomial features in scikit-learn 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)#
- _fit_poly(X, y=None)#
Fitting method for the polynomial features.
- _get_feature_names_poly(input_features=None)#
Returns feature names for output features for the polynomial features.
- _transform_poly(X)#
Transforms data to polynomial features.
- _transform_poly_slow(X)#
Transforms data to polynomial features.
- fit(X, y=None)#
Compute number of output features.
- Parameters:
X – array-like, shape (n_samples, n_features) The data.
- Returns:
self : instance
- get_feature_names(input_features=None)#
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)#
Transforms data to extended features.
- Parameters:
X – array-like, 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.