230 lines
7.5 KiB
Python
230 lines
7.5 KiB
Python
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# Author: Mathieu Blondel
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# License: BSD 3 clause
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from numbers import Real
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from ..utils._param_validation import Interval, StrOptions
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from ._stochastic_gradient import BaseSGDClassifier
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class Perceptron(BaseSGDClassifier):
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"""Linear perceptron classifier.
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The implementation is a wrapper around :class:`~sklearn.linear_model.SGDClassifier`
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by fixing the `loss` and `learning_rate` parameters as::
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SGDClassifier(loss="perceptron", learning_rate="constant")
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Other available parameters are described below and are forwarded to
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:class:`~sklearn.linear_model.SGDClassifier`.
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Read more in the :ref:`User Guide <perceptron>`.
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Parameters
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----------
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penalty : {'l2','l1','elasticnet'}, default=None
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The penalty (aka regularization term) to be used.
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alpha : float, default=0.0001
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Constant that multiplies the regularization term if regularization is
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used.
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l1_ratio : float, default=0.15
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The Elastic Net mixing parameter, with `0 <= l1_ratio <= 1`.
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`l1_ratio=0` corresponds to L2 penalty, `l1_ratio=1` to L1.
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Only used if `penalty='elasticnet'`.
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.. versionadded:: 0.24
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fit_intercept : bool, default=True
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Whether the intercept should be estimated or not. If False, the
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data is assumed to be already centered.
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max_iter : int, default=1000
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The maximum number of passes over the training data (aka epochs).
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It only impacts the behavior in the ``fit`` method, and not the
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:meth:`partial_fit` method.
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.. versionadded:: 0.19
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tol : float or None, default=1e-3
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The stopping criterion. If it is not None, the iterations will stop
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when (loss > previous_loss - tol).
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.. versionadded:: 0.19
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shuffle : bool, default=True
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Whether or not the training data should be shuffled after each epoch.
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verbose : int, default=0
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The verbosity level.
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eta0 : float, default=1
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Constant by which the updates are multiplied.
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n_jobs : int, default=None
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The number of CPUs to use to do the OVA (One Versus All, for
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multi-class problems) computation.
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``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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random_state : int, RandomState instance or None, default=0
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Used to shuffle the training data, when ``shuffle`` is set to
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``True``. Pass an int for reproducible output across multiple
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function calls.
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See :term:`Glossary <random_state>`.
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early_stopping : bool, default=False
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Whether to use early stopping to terminate training when validation
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score is not improving. If set to True, it will automatically set aside
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a stratified fraction of training data as validation and terminate
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training when validation score is not improving by at least `tol` for
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`n_iter_no_change` consecutive epochs.
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.. versionadded:: 0.20
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validation_fraction : float, default=0.1
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The proportion of training data to set aside as validation set for
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early stopping. Must be between 0 and 1.
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Only used if early_stopping is True.
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.. versionadded:: 0.20
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n_iter_no_change : int, default=5
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Number of iterations with no improvement to wait before early stopping.
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.. versionadded:: 0.20
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class_weight : dict, {class_label: weight} or "balanced", default=None
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Preset for the class_weight fit parameter.
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Weights associated with classes. If not given, all classes
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are supposed to have weight one.
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The "balanced" mode uses the values of y to automatically adjust
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weights inversely proportional to class frequencies in the input data
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as ``n_samples / (n_classes * np.bincount(y))``.
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warm_start : bool, default=False
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When set to True, reuse the solution of the previous call to fit as
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initialization, otherwise, just erase the previous solution. See
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:term:`the Glossary <warm_start>`.
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Attributes
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----------
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classes_ : ndarray of shape (n_classes,)
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The unique classes labels.
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coef_ : ndarray of shape (1, n_features) if n_classes == 2 else \
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(n_classes, n_features)
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Weights assigned to the features.
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intercept_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,)
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Constants in decision function.
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loss_function_ : concrete LossFunction
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The function that determines the loss, or difference between the
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output of the algorithm and the target values.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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n_iter_ : int
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The actual number of iterations to reach the stopping criterion.
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For multiclass fits, it is the maximum over every binary fit.
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t_ : int
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Number of weight updates performed during training.
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Same as ``(n_iter_ * n_samples + 1)``.
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See Also
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--------
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sklearn.linear_model.SGDClassifier : Linear classifiers
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(SVM, logistic regression, etc.) with SGD training.
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Notes
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-----
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``Perceptron`` is a classification algorithm which shares the same
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underlying implementation with ``SGDClassifier``. In fact,
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``Perceptron()`` is equivalent to `SGDClassifier(loss="perceptron",
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eta0=1, learning_rate="constant", penalty=None)`.
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References
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----------
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https://en.wikipedia.org/wiki/Perceptron and references therein.
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Examples
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--------
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>>> from sklearn.datasets import load_digits
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>>> from sklearn.linear_model import Perceptron
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>>> X, y = load_digits(return_X_y=True)
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>>> clf = Perceptron(tol=1e-3, random_state=0)
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>>> clf.fit(X, y)
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Perceptron()
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>>> clf.score(X, y)
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0.939...
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"""
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_parameter_constraints: dict = {**BaseSGDClassifier._parameter_constraints}
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_parameter_constraints.pop("loss")
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_parameter_constraints.pop("average")
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_parameter_constraints.update(
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{
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"penalty": [StrOptions({"l2", "l1", "elasticnet"}), None],
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"alpha": [Interval(Real, 0, None, closed="left")],
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"l1_ratio": [Interval(Real, 0, 1, closed="both")],
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"eta0": [Interval(Real, 0, None, closed="left")],
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}
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)
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def __init__(
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self,
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*,
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penalty=None,
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alpha=0.0001,
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l1_ratio=0.15,
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fit_intercept=True,
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max_iter=1000,
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tol=1e-3,
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shuffle=True,
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verbose=0,
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eta0=1.0,
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n_jobs=None,
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random_state=0,
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early_stopping=False,
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validation_fraction=0.1,
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n_iter_no_change=5,
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class_weight=None,
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warm_start=False,
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):
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super().__init__(
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loss="perceptron",
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penalty=penalty,
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alpha=alpha,
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l1_ratio=l1_ratio,
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fit_intercept=fit_intercept,
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max_iter=max_iter,
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tol=tol,
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shuffle=shuffle,
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verbose=verbose,
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random_state=random_state,
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learning_rate="constant",
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eta0=eta0,
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early_stopping=early_stopping,
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validation_fraction=validation_fraction,
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n_iter_no_change=n_iter_no_change,
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power_t=0.5,
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warm_start=warm_start,
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class_weight=class_weight,
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n_jobs=n_jobs,
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)
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