166 lines
6.2 KiB
Python
166 lines
6.2 KiB
Python
# Author: Mathieu Blondel
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# License: BSD 3 clause
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from .stochastic_gradient import BaseSGDClassifier
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class Perceptron(BaseSGDClassifier):
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"""Perceptron
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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 : None, 'l2' or 'l1' or 'elasticnet'
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The penalty (aka regularization term) to be used. Defaults to None.
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alpha : float
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Constant that multiplies the regularization term if regularization is
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used. Defaults to 0.0001
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fit_intercept : bool
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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. Defaults to True.
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max_iter : int, optional
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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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`partial_fit`.
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Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.
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.. versionadded:: 0.19
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tol : float or None, optional
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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). Defaults to None.
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Defaults to 1e-3 from 0.21.
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.. versionadded:: 0.19
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shuffle : bool, optional, default True
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Whether or not the training data should be shuffled after each epoch.
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verbose : integer, optional
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The verbosity level
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eta0 : double
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Constant by which the updates are multiplied. Defaults to 1.
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n_jobs : int or None, optional (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, optional, default None
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The seed of the pseudo random number generator to use when shuffling
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the data. If int, random_state is the seed used by the random number
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generator; If RandomState instance, random_state is the random number
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generator; If None, the random number generator is the RandomState
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instance used by `np.random`.
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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 fraction of training data as validation and terminate training when
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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" or None, optional
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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, optional
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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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n_iter : int, optional
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The number of passes over the training data (aka epochs).
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Defaults to None. Deprecated, will be removed in 0.21.
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.. versionchanged:: 0.19
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Deprecated
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Attributes
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----------
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coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,\
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n_features]
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Weights assigned to the features.
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intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
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Constants in decision function.
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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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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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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) # doctest: +NORMALIZE_WHITESPACE
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Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,
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fit_intercept=True, max_iter=None, n_iter=None, n_iter_no_change=5,
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n_jobs=None, penalty=None, random_state=0, shuffle=True, tol=0.001,
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validation_fraction=0.1, verbose=0, warm_start=False)
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>>> clf.score(X, y) # doctest: +ELLIPSIS
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0.946...
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See also
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--------
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SGDClassifier
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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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"""
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def __init__(self, penalty=None, alpha=0.0001, fit_intercept=True,
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max_iter=None, tol=None, shuffle=True, verbose=0, eta0=1.0,
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n_jobs=None, random_state=0, early_stopping=False,
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validation_fraction=0.1, n_iter_no_change=5,
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class_weight=None, warm_start=False, n_iter=None):
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super().__init__(
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loss="perceptron", penalty=penalty, alpha=alpha, l1_ratio=0,
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fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
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shuffle=shuffle, verbose=verbose, random_state=random_state,
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learning_rate="constant", eta0=eta0, early_stopping=early_stopping,
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validation_fraction=validation_fraction,
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n_iter_no_change=n_iter_no_change, power_t=0.5,
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warm_start=warm_start, class_weight=class_weight, n_jobs=n_jobs,
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n_iter=n_iter)
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