_perceptron.py

# Author: Mathieu Blondel
# License: BSD 3 clause
from numbers import Real

from ..utils._param_validation import Interval, StrOptions
from ._stochastic_gradient import BaseSGDClassifier


class Perceptron(BaseSGDClassifier):
    """Linear perceptron classifier.

    The implementation is a wrapper around :class:`~sklearn.linear_model.SGDClassifier`
    by fixing the `loss` and `learning_rate` parameters as::

        SGDClassifier(loss="perceptron", learning_rate="constant")

    Other available parameters are described below and are forwarded to
    :class:`~sklearn.linear_model.SGDClassifier`.

    Read more in the :ref:`User Guide <perceptron>`.

    Parameters
    ----------

    penalty : {'l2','l1','elasticnet'}, default=None
        The penalty (aka regularization term) to be used.

    alpha : float, default=0.0001
        Constant that multiplies the regularization term if regularization is
        used.

    l1_ratio : float, default=0.15
        The Elastic Net mixing parameter, with `0 <= l1_ratio <= 1`.
        `l1_ratio=0` corresponds to L2 penalty, `l1_ratio=1` to L1.
        Only used if `penalty='elasticnet'`.

        .. versionadded:: 0.24

    fit_intercept : bool, default=True
        Whether the intercept should be estimated or not. If False, the
        data is assumed to be already centered.

    max_iter : int, default=1000
        The maximum number of passes over the training data (aka epochs).
        It only impacts the behavior in the ``fit`` method, and not the
        :meth:`partial_fit` method.

        .. versionadded:: 0.19

    tol : float or None, default=1e-3
        The stopping criterion. If it is not None, the iterations will stop
        when (loss > previous_loss - tol).

        .. versionadded:: 0.19

    shuffle : bool, default=True
        Whether or not the training data should be shuffled after each epoch.

    verbose : int, default=0
        The verbosity level.

    eta0 : float, default=1
        Constant by which the updates are multiplied.

    n_jobs : int, default=None
        The number of CPUs to use to do the OVA (One Versus All, for
        multi-class problems) computation.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    random_state : int, RandomState instance or None, default=0
        Used to shuffle the training data, when ``shuffle`` is set to
        ``True``. Pass an int for reproducible output across multiple
        function calls.
        See :term:`Glossary <random_state>`.

    early_stopping : bool, default=False
        Whether to use early stopping to terminate training when validation
        score is not improving. If set to True, it will automatically set aside
        a stratified fraction of training data as validation and terminate
        training when validation score is not improving by at least `tol` for
        `n_iter_no_change` consecutive epochs.

        .. versionadded:: 0.20

    validation_fraction : float, default=0.1
        The proportion of training data to set aside as validation set for
        early stopping. Must be between 0 and 1.
        Only used if early_stopping is True.

        .. versionadded:: 0.20

    n_iter_no_change : int, default=5
        Number of iterations with no improvement to wait before early stopping.

        .. versionadded:: 0.20

    class_weight : dict, {class_label: weight} or "balanced", default=None
        Preset for the class_weight fit parameter.

        Weights associated with classes. If not given, all classes
        are supposed to have weight one.

        The "balanced" mode uses the values of y to automatically adjust
        weights inversely proportional to class frequencies in the input data
        as ``n_samples / (n_classes * np.bincount(y))``.

    warm_start : bool, default=False
        When set to True, reuse the solution of the previous call to fit as
        initialization, otherwise, just erase the previous solution. See
        :term:`the Glossary <warm_start>`.

    Attributes
    ----------
    classes_ : ndarray of shape (n_classes,)
        The unique classes labels.

    coef_ : ndarray of shape (1, n_features) if n_classes == 2 else \
            (n_classes, n_features)
        Weights assigned to the features.

    intercept_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,)
        Constants in decision function.

    loss_function_ : concrete LossFunction
        The function that determines the loss, or difference between the
        output of the algorithm and the target values.

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    n_iter_ : int
        The actual number of iterations to reach the stopping criterion.
        For multiclass fits, it is the maximum over every binary fit.

    t_ : int
        Number of weight updates performed during training.
        Same as ``(n_iter_ * n_samples + 1)``.

    See Also
    --------
    sklearn.linear_model.SGDClassifier : Linear classifiers
        (SVM, logistic regression, etc.) with SGD training.

    Notes
    -----
    ``Perceptron`` is a classification algorithm which shares the same
    underlying implementation with ``SGDClassifier``. In fact,
    ``Perceptron()`` is equivalent to `SGDClassifier(loss="perceptron",
    eta0=1, learning_rate="constant", penalty=None)`.

    References
    ----------
    https://en.wikipedia.org/wiki/Perceptron and references therein.

    Examples
    --------
    >>> from sklearn.datasets import load_digits
    >>> from sklearn.linear_model import Perceptron
    >>> X, y = load_digits(return_X_y=True)
    >>> clf = Perceptron(tol=1e-3, random_state=0)
    >>> clf.fit(X, y)
    Perceptron()
    >>> clf.score(X, y)
    0.939...
    """

    _parameter_constraints: dict = {**BaseSGDClassifier._parameter_constraints}
    _parameter_constraints.pop("loss")
    _parameter_constraints.pop("average")
    _parameter_constraints.update(
        {
            "penalty": [StrOptions({"l2", "l1", "elasticnet"}), None],
            "alpha": [Interval(Real, 0, None, closed="left")],
            "l1_ratio": [Interval(Real, 0, 1, closed="both")],
            "eta0": [Interval(Real, 0, None, closed="left")],
        }
    )

    def __init__(
        self,
        *,
        penalty=None,
        alpha=0.0001,
        l1_ratio=0.15,
        fit_intercept=True,
        max_iter=1000,
        tol=1e-3,
        shuffle=True,
        verbose=0,
        eta0=1.0,
        n_jobs=None,
        random_state=0,
        early_stopping=False,
        validation_fraction=0.1,
        n_iter_no_change=5,
        class_weight=None,
        warm_start=False,
    ):
        super().__init__(
            loss="perceptron",
            penalty=penalty,
            alpha=alpha,
            l1_ratio=l1_ratio,
            fit_intercept=fit_intercept,
            max_iter=max_iter,
            tol=tol,
            shuffle=shuffle,
            verbose=verbose,
            random_state=random_state,
            learning_rate="constant",
            eta0=eta0,
            early_stopping=early_stopping,
            validation_fraction=validation_fraction,
            n_iter_no_change=n_iter_no_change,
            power_t=0.5,
            warm_start=warm_start,
            class_weight=class_weight,
            n_jobs=n_jobs,
        )