Note

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`__sklearn_is_fitted__` as Developer API#

The __sklearn_is_fitted__ method is a convention used in scikit-learn for checking whether an estimator object has been fitted or not. This method is typically implemented in custom estimator classes that are built on top of scikit-learn’s base classes like BaseEstimator or its subclasses.

Developers should use check_is_fitted at the beginning of all methods except fit. If they need to customize or speed-up the check, they can implement the __sklearn_is_fitted__ method as shown below.

In this example the custom estimator showcases the usage of the __sklearn_is_fitted__ method and the check_is_fitted utility function as developer APIs. The __sklearn_is_fitted__ method checks fitted status by verifying the presence of the _is_fitted attribute.

An example custom estimator implementing a simple classifier#

This code snippet defines a custom estimator class called CustomEstimator that extends both the BaseEstimator and ClassifierMixin classes from scikit-learn and showcases the usage of the __sklearn_is_fitted__ method and the check_is_fitted utility function.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_is_fitted


class CustomEstimator(BaseEstimator, ClassifierMixin):
    def __init__(self, parameter=1):
        self.parameter = parameter

    def fit(self, X, y):
        """
        Fit the estimator to the training data.
        """
        self.classes_ = sorted(set(y))
        # Custom attribute to track if the estimator is fitted
        self._is_fitted = True
        return self

    def predict(self, X):
        """
        Perform Predictions

        If the estimator is not fitted, then raise NotFittedError
        """
        check_is_fitted(self)
        # Perform prediction logic
        predictions = [self.classes_[0]] * len(X)
        return predictions

    def score(self, X, y):
        """
        Calculate Score

        If the estimator is not fitted, then raise NotFittedError
        """
        check_is_fitted(self)
        # Perform scoring logic
        return 0.5

    def __sklearn_is_fitted__(self):
        """
        Check fitted status and return a Boolean value.
        """
        return hasattr(self, "_is_fitted") and self._is_fitted