222 lines
7.2 KiB
Python
222 lines
7.2 KiB
Python
# Author: Mathieu Blondel
|
||
# License: BSD 3 clause
|
||
from numbers import Real
|
||
|
||
from ._stochastic_gradient import BaseSGDClassifier
|
||
from ..utils._param_validation import StrOptions, Interval
|
||
|
||
|
||
class Perceptron(BaseSGDClassifier):
|
||
"""Linear perceptron classifier.
|
||
|
||
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,
|
||
)
|