Refactor score method using base class implementation

stree/Strees.py

@@ -16,7 +16,6 @@ import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.svm import SVC, LinearSVC
 from sklearn.preprocessing import StandardScaler
-from sklearn.utils import check_consistent_length
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.utils.validation import (
@@ -25,7 +24,6 @@ from sklearn.utils.validation import (
     check_is_fitted,
     _check_sample_weight,
 )
-from sklearn.metrics._classification import _weighted_sum, _check_targets
 
 
 class Snode:
@@ -832,36 +830,6 @@ class Stree(BaseEstimator, ClassifierMixin):
         )
         return self.classes_[result]
 
-    def score(
-        self, X: np.array, y: np.array, sample_weight: np.array = None
-    ) -> float:
-        """Compute accuracy of the prediction
-
-        Parameters
-        ----------
-        X : np.array
-            dataset of samples to make predictions
-        y : np.array
-            samples labels
-        sample_weight : np.array, optional
-            weights of the samples. Rescale C per sample, by default None
-
-        Returns
-        -------
-        float
-            accuracy of the prediction
-        """
-        # sklearn check
-        check_is_fitted(self)
-        check_classification_targets(y)
-        X, y = check_X_y(X, y)
-        y_pred = self.predict(X).reshape(y.shape)
-        # Compute accuracy for each possible representation
-        _, y_true, y_pred = _check_targets(y, y_pred)
-        check_consistent_length(y_true, y_pred, sample_weight)
-        score = y_true == y_pred
-        return _weighted_sum(score, sample_weight, normalize=True)
-
     def nodes_leaves(self) -> tuple:
         """Compute the number of nodes and leaves in the built tree