ITMO_FS.wrappers.deterministic.RecursiveElimination

class ITMO_FS.wrappers.deterministic.RecursiveElimination(estimator, n_features, measure, weight_func, cv=3)

Recursive feature elimination algorithm.

Parameters:
  • estimator (object) – A supervised learning estimator that should have a fit(X, y) method, a predict(X) method and a field corresponding to feature weights.
  • n_features (int) – Number of features to leave.
  • measure (string or callable) – A standard estimator metric (e.g. ‘f1’ or ‘roc_auc’) or a callable with signature measure(estimator, X, y) which should return only a single value.
  • weight_func (callable) – A function to extract weights from the model.
  • cv (int) – Number of folds in cross-validation.

See also

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., “Gene selection for cancer classification using support vector machines”, Mach. Learn., 46(1-3), 389–422, 2002. https://link.springer.com/article/10.1023/A:1012487302797

Examples

>>> from sklearn.datasets import make_classification
>>> from ITMO_FS.wrappers import RecursiveElimination
>>> from sklearn.svm import SVC
>>> import numpy as np
>>> dataset = make_classification(n_samples=100, n_features=20,
... n_informative=4, n_redundant=0, shuffle=False, random_state=42)
>>> x, y = np.array(dataset[0]), np.array(dataset[1])
>>> model = SVC(kernel='linear')
>>> rfe = RecursiveElimination(model, 5, measure='f1_macro',
... weight_func=lambda model: np.square(model.coef_).sum(axis=0)).fit(x, y)
>>> rfe.selected_features_
array([ 0,  1,  2, 11, 19], dtype=int64)
__init__(estimator, n_features, measure, weight_func, cv=3)

Initialize self. See help(type(self)) for accurate signature.

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

Fit the algorithm.

Parameters:
  • X (array-like, shape (n_samples, n_features)) – The training input samples.
  • y (array-like, shape (n_samples,), optional) – The class labels.
  • fit_params (dict, optional) – Additional parameters to pass to underlying _fit function.
Returns:
Return type:

Self, i.e. the transformer object.
fit_transform(X, y=None, **fit_params)

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 ({array-like, sparse matrix, dataframe} of shape (n_samples, n_features)) –
  • y (ndarray of shape (n_samples,), default=None) – Target values.
  • **fit_params (dict) – Additional fit parameters.
Returns:

X_new – Transformed array.
Return type:

ndarray array of shape (n_samples, n_features_new)
get_params(deep=True)

Get parameters for this estimator.

Parameters:deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:params – Parameter names mapped to their values.
Return type:mapping of string to any
predict(X)

Predict class labels for the input data.

Parameters:X (array-like, shape (n_samples, n_features)) – The input samples.
Returns:array-like, shape (n_samples,)
Return type:class labels
set_params(**params)

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.

Parameters:**params (dict) – Estimator parameters.
Returns:self – Estimator instance.
Return type:object
transform(X)

Transform given data by slicing it with selected features.

Parameters:X (array-like, shape (n_samples, n_features)) – The training input samples.
Returns:
Return type:Transformed 2D numpy array