77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
"""
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========================================
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`__sklearn_is_fitted__` as Developer API
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========================================
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The `__sklearn_is_fitted__` method is a convention used in scikit-learn for
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checking whether an estimator object has been fitted or not. This method is
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typically implemented in custom estimator classes that are built on top of
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scikit-learn's base classes like `BaseEstimator` or its subclasses.
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Developers should use :func:`~sklearn.utils.validation.check_is_fitted`
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at the beginning of all methods except `fit`. If they need to customize or
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speed-up the check, they can implement the `__sklearn_is_fitted__` method as
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shown below.
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In this example the custom estimator showcases the usage of the
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`__sklearn_is_fitted__` method and the `check_is_fitted` utility function
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as developer APIs. The `__sklearn_is_fitted__` method checks fitted status
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by verifying the presence of the `_is_fitted` attribute.
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"""
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# %%
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# An example custom estimator implementing a simple classifier
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# ------------------------------------------------------------
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# This code snippet defines a custom estimator class called `CustomEstimator`
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# that extends both the `BaseEstimator` and `ClassifierMixin` classes from
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# scikit-learn and showcases the usage of the `__sklearn_is_fitted__` method
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# and the `check_is_fitted` utility function.
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# Author: Kushan <kushansharma1@gmail.com>
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#
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# License: BSD 3 clause
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from sklearn.base import BaseEstimator, ClassifierMixin
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from sklearn.utils.validation import check_is_fitted
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class CustomEstimator(BaseEstimator, ClassifierMixin):
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def __init__(self, parameter=1):
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self.parameter = parameter
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def fit(self, X, y):
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"""
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Fit the estimator to the training data.
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"""
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self.classes_ = sorted(set(y))
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# Custom attribute to track if the estimator is fitted
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self._is_fitted = True
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return self
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def predict(self, X):
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"""
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Perform Predictions
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If the estimator is not fitted, then raise NotFittedError
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"""
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check_is_fitted(self)
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# Perform prediction logic
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predictions = [self.classes_[0]] * len(X)
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return predictions
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def score(self, X, y):
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"""
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Calculate Score
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If the estimator is not fitted, then raise NotFittedError
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"""
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check_is_fitted(self)
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# Perform scoring logic
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return 0.5
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def __sklearn_is_fitted__(self):
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"""
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Check fitted status and return a Boolean value.
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"""
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return hasattr(self, "_is_fitted") and self._is_fitted
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