77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
|
"""
|
||
|
|
========================================
|
||
|
|
`__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.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 <kushansharma1@gmail.com>
|
||
|
|
#
|
||
|
|
# 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
|