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# Author: Mathieu Blondel
# License: BSD 3 clause
from ..utils.validation import _deprecate_positional_args
from ._stochastic_gradient import BaseSGDClassifier
class Perceptron(BaseSGDClassifier):
"""Perceptron
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.
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, 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 : double, 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, default=None
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
----------
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.
n_iter_ : int
The actual number of iterations to reach the stopping criterion.
For multiclass fits, it is the maximum over every binary fit.
classes_ : ndarray of shape (n_classes,)
The unique classes labels.
t_ : int
Number of weight updates performed during training.
Same as ``(n_iter_ * n_samples)``.
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)`.
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...
See also
--------
SGDClassifier
References
----------
https://en.wikipedia.org/wiki/Perceptron and references therein.
"""
@_deprecate_positional_args
def __init__(self, *, penalty=None, alpha=0.0001, 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=0,
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)
|
