How To Create A Subclass With Class Attributes Based On Constructor Function Arguments For Use In An Estimator For Gridsearchcv?

I want to subclass sklearn.svm.LinearSVC and use it as an estimator for sklearn.model_selection.GridSearchCV. I had some issues with subclassing earlier and I thought I fixed it ba

Solution 1:

1. Use __init__ constructor as a container to store the attributes.
2. Do all the corresponding logic in methods

from sklearn.datasets import make_classification
from sklearn.svm import LinearSVC
from sklearn.model_selection import GridSearchCV
from sklearn.kernel_approximation import RBFSampler
from sklearn.datasets import load_breast_cancer

RANDOM_STATE = 123


class LinearSVCSub(LinearSVC):
    
    def __init__(self, penalty='l2', loss='squared_hinge', sampler_gamma=None, sampler_n=None,
                 dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1,
                 class_weight=None, verbose=0, random_state=None, max_iter=1000, sampler=None):

        super(LinearSVCSub, self).__init__(penalty=penalty, loss=loss, dual=dual, tol=tol,
                                           C=C, multi_class=multi_class, fit_intercept=fit_intercept,
                                           intercept_scaling=intercept_scaling, class_weight=class_weight,
                                           verbose=verbose, random_state=random_state, max_iter=max_iter)

        self.sampler_gamma = sampler_gamma
        self.sampler_n = sampler_n
        self.sampler = sampler
       
    def fit(self, X, y, sample_weight=None):
        X = self.transform_this(X)
        super(LinearSVCSub, self).fit(X, y, sample_weight)
        return self

    def predict(self, X):
        X = self.transform_this(X)
        return super(LinearSVCSub, self).predict(X)

    def score(self, X, y, sample_weight=None):
        X = self.transform_this(X)
        return super(LinearSVCSub, self).score(X, y, sample_weight)

    def decision_function(self, X):
        X = self.transform_this(X)
        return super(LinearSVCSub, self).decision_function(X)

    def transform_this(self, X):
        if self.sampler:
            X = RBFSampler(gamma=self.sampler_gamma, n_components=self.sampler_n).fit_transform(X)
        return X



data = load_breast_cancer()
X, y = data.data, data.target

# Parameter tuning with custom LinearSVC
param_grid = {'C': [0.00001, 0.0005],
                  'dual': (True, False), 'random_state': [RANDOM_STATE],
                  'sampler_gamma': [0.90, 0.60, 0.30],
                  'sampler_n': [10, 200],
                  'sampler':[0,1]
             }

gs_model = GridSearchCV(estimator=LinearSVCSub(sampler=1), verbose=1, param_grid=param_grid,
                        scoring='roc_auc', n_jobs=-1, cv=2)
gs_model.fit(X, y)
gs_model.cv_results_