package sklearn

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.

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.

Returns ------- array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_1 where >0 means this class would be predicted.

Convert coefficient matrix to dense array format.

Converts the ``coef_`` member (back) to a numpy.ndarray. This is the default format of ``coef_`` and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

Returns ------- self Fitted estimator.

Fit linear model with Stochastic Gradient Descent.

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) Training data.

y : ndarray of shape (n_samples,) Target values.

coef_init : ndarray of shape (n_classes, n_features), default=None The initial coefficients to warm-start the optimization.

intercept_init : ndarray of shape (n_classes,), default=None The initial intercept to warm-start the optimization.

sample_weight : array-like, shape (n_samples,), default=None Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.

Returns ------- self : Returns an instance of self.

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

Perform one epoch of stochastic gradient descent on given samples.

Internally, this method uses ``max_iter = 1``. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) Subset of the training data.

y : ndarray of shape (n_samples,) Subset of the target values.

classes : ndarray of shape (n_classes,), default=None Classes across all calls to partial_fit. Can be obtained by via `np.unique(y_all)`, where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn't need to contain all labels in `classes`.

sample_weight : array-like, shape (n_samples,), default=None Weights applied to individual samples. If not provided, uniform weights are assumed.

Returns ------- self : Returns an instance of self.

Predict class labels for samples in X.

Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.

Returns ------- C : array, shape n_samples Predicted class label per sample.

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.

Set and validate the parameters of estimator.

Parameters ---------- **kwargs : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Convert coefficient matrix to sparse format.

Converts the ``coef_`` member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

The ``intercept_`` member is not converted.

Returns ------- self Fitted estimator.

Notes ----- For non-sparse models, i.e. when there are not many zeros in ``coef_``, this may actually *increase* memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with ``(coef_ == 0).sum()``, must be more than 50% for this to provide significant benefits.

After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.

Attribute coef_: get value or raise Not_found if None.

Attribute coef_: get value as an option.

Attribute intercept_: get value or raise Not_found if None.

Attribute intercept_: get value as an option.

Attribute n_iter_: get value or raise Not_found if None.

Attribute n_iter_: get value as an option.

Attribute classes_: get value or raise Not_found if None.

Attribute classes_: get value as an option.

Attribute t_: get value or raise Not_found if None.

Attribute t_: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.