sklearn.preprocessing.PolynomialFeatures

class sklearn.preprocessing.PolynomialFeatures(degree=2, interaction_only=False, include_bias=True, order=’C’)[source]

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].

Parameters:
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[1] ** 2, x[0] * x[2] ** 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).

order : str in {‘C’, ‘F’}, default ‘C’

Order of output array in the dense case. ‘F’ order is faster to compute, but may slow down subsequent estimators.

New in version 0.21.
Attributes:
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.

Notes

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.

See examples/linear_model/plot_polynomial_interpolation.py

Examples

>>> import numpy as np
>>> from sklearn.preprocessing import PolynomialFeatures
>>> 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.]])

Methods

fit(self, X[, y]) Compute number of output features.
fit_transform(self, X[, y]) Fit to data, then transform it.
get_feature_names(self[, input_features]) Return feature names for output features
get_params(self[, deep]) Get parameters for this estimator.
set_params(self, \*\*params) Set the parameters of this estimator.
transform(self, X) Transform data to polynomial features
__init__(self, degree=2, interaction_only=False, include_bias=True, order=’C’)[source]
fit(self, X, y=None)[source]

Compute number of output features.

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

The data.
Returns:
self : instance
fit_transform(self, X, y=None, **fit_params)[source]

Fit to data, then transform it.

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

Parameters:
X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.
Returns:
X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.
get_feature_names(self, input_features=None)[source]

Return 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
get_params(self, deep=True)[source]

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)[source]

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
transform(self, X)[source]

Transform data to polynomial features

Parameters:
X : array-like or CSR/CSC sparse matrix, shape [n_samples, n_features]

The data to transform, row by row.

Prefer CSR over CSC for sparse input (for speed), but CSC is required if the degree is 4 or higher. If the degree is less than 4 and the input format is CSC, it will be converted to CSR, have its polynomial features generated, then converted back to CSC.

If the degree is 2 or 3, the method described in “Leveraging Sparsity to Speed Up Polynomial Feature Expansions of CSR Matrices Using K-Simplex Numbers” by Andrew Nystrom and John Hughes is used, which is much faster than the method used on CSC input. For this reason, a CSC input will be converted to CSR, and the output will be converted back to CSC prior to being returned, hence the preference of CSR.
Returns:
XP : np.ndarray or CSR/CSC sparse matrix, shape [n_samples, NP]

The matrix of features, where NP is the number of polynomial features generated from the combination of inputs.

Examples using sklearn.preprocessing.PolynomialFeatures