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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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 notx[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).
>>> 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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Compute number of output features.
- X : array-like, shape (n_samples, n_features)
- The data.
self : instance
- A parameter
-
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
transform
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
- A parameter