# PolynomialFeatures¶

class ibex.sklearn.preprocessing.PolynomialFeatures(degree=2, interaction_only=False, include_bias=True)

Bases: sklearn.preprocessing.data.PolynomialFeatures, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Generate polynomial and interaction features.

Generate a new feature matrix consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, if an input sample is two dimensional and of the form [a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].

degree : integer
The degree of the polynomial features. Default = 2.
interaction_only : boolean, default = False
If true, only interaction features are produced: features that are products of at most degree distinct input features (so not x ** 2, x * x ** 3, etc.).
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).
>>> X = np.arange(6).reshape(3, 2)
>>> X
array([[0, 1],
[2, 3],
[4, 5]])
>>> poly = PolynomialFeatures(2)
>>> poly.fit_transform(X)
array([[  1.,   0.,   1.,   0.,   0.,   1.],
[  1.,   2.,   3.,   4.,   6.,   9.],
[  1.,   4.,   5.,  16.,  20.,  25.]])
>>> poly = PolynomialFeatures(interaction_only=True)
>>> poly.fit_transform(X)
array([[  1.,   0.,   1.,   0.],
[  1.,   2.,   3.,   6.],
[  1.,   4.,   5.,  20.]])

powers_ : array, shape (n_output_features, n_input_features)
powers_[i, j] is the exponent of the jth input in the ith output.
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.

Be aware that the number of features in the output array scales polynomially in the number of features of the input array, and exponentially in the degree. High degrees can cause overfitting.

fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Compute number of output features.

X : array-like, shape (n_samples, n_features)
The data.

self : instance

fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
transform(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Transform data to polynomial features

X : array-like, shape [n_samples, n_features]
The data to transform, row by row.
XP : np.ndarray shape [n_samples, NP]
The matrix of features, where NP is the number of polynomial features generated from the combination of inputs.