many_class_classifier ¶

ManyClassClassifier ¶

Bases: OutputCodeClassifier

Output-Code multiclass strategy with deciary codebook.

This class extends the original OutputCodeClassifier to support n-ary codebooks (with n=alphabet_size), allowing for handling more classes.

Parameters:

Name	Type	Description	Default
`estimator`		estimator object An estimator object implementing :term:`fit` and one of :term:`decision_function` or :term:`predict_proba`. The base classifier should be able to handle up to `alphabet_size` classes.	required
`random_state`		int, RandomState instance, default=None The generator used to initialize the codebook. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.	`None`

Attributes:

Name	Type	Description
`estimators_`		list of `int(n_classes * code_size)` estimators Estimators used for predictions.
`classes_`		ndarray of shape (n_classes,) Array containing labels.
`code_book_`		ndarray of shape (n_classes, `len(estimators_)`) Deciary array containing the code of each class.

Example

>>> from sklearn.datasets import load_iris
>>> from tabpfn.scripts.estimator import ManyClassClassifier, TabPFNClassifier
>>> from sklearn.model_selection import train_test_split
>>> x, y = load_iris(return_X_y=True)
>>> x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)
>>> clf = TabPFNClassifier()
>>> clf = ManyClassClassifier(clf, alphabet_size=clf.max_num_classes_)
>>> clf.fit(x_train, y_train)
>>> clf.predict(x_test)

fit ¶

fit(X, y, **fit_params)

Fit underlying estimators.

Parameters:

Name	Description	Default
`X`	{array-like, sparse matrix} of shape (n_samples, n_features) Data.	required
`y`	array-like of shape (n_samples,) Multi-class targets.	required
`**fit_params`	dict Parameters passed to the `estimator.fit` method of each sub-estimator.	`{}`

Returns:

Name	Type	Description
`self`		object Returns a fitted instance of self.

predict_proba ¶

predict_proba(X)

Predict probabilities using the underlying estimators.

Parameters:

Name	Type	Description	Default
`X`		{array-like, sparse matrix} of shape (n_samples, n_features) Data.	required

Returns:

Name	Type	Description
`p`		ndarray of shape (n_samples, n_classes) Returns the probability of the samples for each class in the model, where classes are ordered as they are in `self.classes_`.