Python: module sega_learn.utils.polynomialTransform

sega_learn.utils.polynomialTransform

# Importing the required libraries

Modules

Classes

PolynomialTransform

class PolynomialTransform(builtins.object)

PolynomialTransform(degree=2)

Implements Polynomial Feature Transformation.

Polynomial feature transformation creates new features by raising existing features to a power or creating interaction terms.

Args:
    degree (int): The degree of the polynomial features (default is 2).

Attributes:
    n_samples (int): The number of samples in the input data.
    n_features (int): The number of features in the input data.
    n_output_features (int): The number of output features after transformation.
    combinations (list of tuples): The combinations of features for polynomial terms.

Methods defined here:

__init__(self, degree=2): Initialize the PolynomialTransform object.

fit(self, X): Fit the model to the data.

Uses itertools.combinations_with_replacement to generate all possible combinations of features(X) of degree n.

fit_transform(self, X): Fit to data, then transform it.

transform(self, X): Transform the data into polynomial features by computing the product of the features for each combination of features.

Data descriptors defined here:

__dict__: dictionary for instance variables

__weakref__: list of weak references to the object