Module minder_utils.models.feature_selectors.supervised.wrapper

Classes

class Wrapper_Selector (estimator, model_name='rfe', num_features=10)

This class provide a set of supervised feature selection methods. Particularly, it contains a set of filter methods, which will perform SEPARATELY with the classifier.

Currently, it contains: - REF: Recursive feature elimination

Example

from minder_utils.models.feature_selectors.supervised.wrapper import Supervised_wrapper
from sklearn.svm import SVC

selector = Supervised_wrapper(SVC(kernel='linear'), model_name='rfe')
# show the available methods:
selector.get_info(verbose=True)

# train the selector
selector.fit(X, y)

# do the selection
X = selector.transform(X)

Parameters

estimator : sklearn estimator

 

model_name : 'rfe' or 'refcv'

 

num_features : int / float, number / percentage of features to be selected

 

Expand source code

class Wrapper_Selector(Feature_selector):
    '''
    This class provide a set of supervised feature selection methods.
    Particularly, it contains a set of filter methods, which will perform SEPARATELY with the classifier.

    Currently, it contains:
        - REF: Recursive feature elimination

    ```Example```
    ```
    from minder_utils.models.feature_selectors.supervised.wrapper import Supervised_wrapper
    from sklearn.svm import SVC

    selector = Supervised_wrapper(SVC(kernel='linear'), model_name='rfe')
    # show the available methods:
    selector.get_info(verbose=True)

    # train the selector
    selector.fit(X, y)

    # do the selection
    X = selector.transform(X)
    ```
    '''

    def __init__(self, estimator, model_name='rfe', num_features=10):
        '''

        Parameters
        ----------
        estimator: sklearn estimator
        model_name: 'rfe' or 'refcv'
        num_features: int / float, number / percentage of features to be selected
        '''
        self.estimator = estimator
        self.num_features = num_features
        super().__init__(model_name)

    def reset_model(self, model_name, num_features=None):
        self.num_features = self.num_features if num_features is None else num_features
        self.name = self.methods[model_name]
        self.model = getattr(self, model_name)()

    @property
    def methods(self):
        return {
            'rfe': 'Recursive feature elimination',
            'rfecv': 'Recursive feature elimination with cross-validation ',
        }

    def rfe(self):
        return RFE(self.estimator, n_features_to_select=self.num_features)

    def rfecv(self):
        return RFECV(self.estimator, min_features_to_select=self.num_features, cv=5)

    def fit(self, X, y):
        if y.ndim > 1:
            y = np.argmax(y, axis=1)
        return self.model.fit(X, y)

    def transform(self, X):
        return self.model.transform(X)

    def mask_of_features(self):
        return self.model.support_

    def __name__(self):
        return 'Supervised Filter', self.name

Ancestors

• Feature_selector
• abc.ABC

Instance variables

var methods

Expand source code

@property
def methods(self):
    return {
        'rfe': 'Recursive feature elimination',
        'rfecv': 'Recursive feature elimination with cross-validation ',
    }

Methods

def fit(self, X, y)

Expand source code

def fit(self, X, y):
    if y.ndim > 1:
        y = np.argmax(y, axis=1)
    return self.model.fit(X, y)

def mask_of_features(self)

Expand source code

def mask_of_features(self):
    return self.model.support_

def reset_model(self, model_name, num_features=None)

Expand source code

def reset_model(self, model_name, num_features=None):
    self.num_features = self.num_features if num_features is None else num_features
    self.name = self.methods[model_name]
    self.model = getattr(self, model_name)()

def rfe(self)

Expand source code

def rfe(self):
    return RFE(self.estimator, n_features_to_select=self.num_features)

def rfecv(self)

Expand source code

def rfecv(self):
    return RFECV(self.estimator, min_features_to_select=self.num_features, cv=5)

def transform(self, X)

Expand source code

def transform(self, X):
    return self.model.transform(X)