Pushing the docs to dev/ for branch: main, commit 150ff34e85f91a3a19a29dc4e19d50131fbb0251

Author: sklearn-ci

dev/.buildinfo

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dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# `__sklearn_is_fitted__` as Developer API\n\nThe `__sklearn_is_fitted__` method is a convention used in scikit-learn for\nchecking whether an estimator object has been fitted or not. This method is\ntypically implemented in custom estimator classes that are built on top of\nscikit-learn's base classes like `BaseEstimator` or its subclasses.\n\nDevelopers should use :func:`~sklearn.sklearn.utils.validation.check_is_fitted`\nat the beginning of all methods except `fit`. If they need to customize or\nspeed-up the check, they can implement the `__sklearn_is_fitted__` method as\nshown below.\n\nIn this example the custom estimator showcases the usage of the\n`__sklearn_is_fitted__` method and the `check_is_fitted` utility function\nas developer APIs. The `__sklearn_is_fitted__` method checks fitted status\nby verifying the presence of the `_is_fitted` attribute.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## An example custom estimator implementing a simple classifier\nThis code snippet defines a custom estimator class called `CustomEstimator`\nthat extends both the `BaseEstimator` and `ClassifierMixin` classes from\nscikit-learn and showcases the usage of the `__sklearn_is_fitted__` method\nand the `check_is_fitted` utility function.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Kushan <[email protected]>\n#\n# License: BSD 3 clause\n\nfrom sklearn.base import BaseEstimator, ClassifierMixin\nfrom sklearn.utils.validation import check_is_fitted\n\n\nclass CustomEstimator(BaseEstimator, ClassifierMixin):\n    def __init__(self, parameter=1):\n        self.parameter = parameter\n\n    def fit(self, X, y):\n        \"\"\"\n        Fit the estimator to the training data.\n        \"\"\"\n        self.classes_ = sorted(set(y))\n        # Custom attribute to track if the estimator is fitted\n        self._is_fitted = True\n        return self\n\n    def predict(self, X):\n        \"\"\"\n        Perform Predictions\n\n        If the estimator is not fitted, then raise NotFittedError\n        \"\"\"\n        check_is_fitted(self)\n        # Perform prediction logic\n        predictions = [self.classes_[0]] * len(X)\n        return predictions\n\n    def score(self, X, y):\n        \"\"\"\n        Calculate Score\n\n        If the estimator is not fitted, then raise NotFittedError\n        \"\"\"\n        check_is_fitted(self)\n        # Perform scoring logic\n        return 0.5\n\n    def __sklearn_is_fitted__(self):\n        \"\"\"\n        Check fitted status and return a Boolean value.\n        \"\"\"\n        return hasattr(self, \"_is_fitted\") and self._is_fitted"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/1ba22806e611e4b95c469e74c4b23796/sklearn_is_fitted.ipynb

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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 :func:`~sklearn.sklearn.utils.validation.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.
+
+# Author: Kushan <[email protected]>
+#
+# License: 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