sklearn_is_fitted as Developer API (original) (raw)

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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_fittedat 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 CustomEstimatorthat 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](../../modules/generated/sklearn.utils.validation.check%5Fis%5Ffitted.html#sklearn.utils.validation.check%5Fis%5Ffitted "sklearn.utils.validation.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](../../modules/generated/sklearn.utils.validation.check%5Fis%5Ffitted.html#sklearn.utils.validation.check%5Fis%5Ffitted "sklearn.utils.validation.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

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