0Ph@KzddlmZddlmZddlmZmZddlmZm Z m Z Gdde Z Gd d e Z d S) )Real) _fit_context)Interval StrOptions)DEFAULT_EPSILONBaseSGDClassifierBaseSGDRegressorceZdZUdZiejeddhgeedddgdZe e d <d d d d dddd ddddddddfd Z e d ddZ e d ddZxZS)PassiveAggressiveClassifieraePassive Aggressive Classifier. Read more in the :ref:`User Guide `. Parameters ---------- C : float, default=1.0 Maximum step size (regularization). Defaults to 1.0. 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:`~sklearn.linear_model.PassiveAggressiveClassifier.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 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 shuffle : bool, default=True Whether or not the training data should be shuffled after each epoch. verbose : int, default=0 The verbosity level. loss : str, default="hinge" The loss function to be used: hinge: equivalent to PA-I in the reference paper. squared_hinge: equivalent to PA-II in the reference paper. n_jobs : int or None, 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 ` 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 `. 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 `. Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled. class_weight : dict, {class_label: weight} or "balanced" or None, 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))``. .. versionadded:: 0.17 parameter *class_weight* to automatically weight samples. average : bool or int, default=False When set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples. .. versionadded:: 0.19 parameter *average* to use weights averaging in SGD. 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_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. 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 + 1)``. See Also -------- SGDClassifier : Incrementally trained logistic regression. Perceptron : Linear perceptron classifier. References ---------- Online Passive-Aggressive Algorithms K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006) Examples -------- >>> from sklearn.linear_model import PassiveAggressiveClassifier >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_features=4, random_state=0) >>> clf = PassiveAggressiveClassifier(max_iter=1000, random_state=0, ... tol=1e-3) >>> clf.fit(X, y) PassiveAggressiveClassifier(random_state=0) >>> print(clf.coef_) [[0.26642044 0.45070924 0.67251877 0.64185414]] >>> print(clf.intercept_) [1.84127814] >>> print(clf.predict([[0, 0, 0, 0]])) [1] hinge squared_hingerNrightclosed)lossC_parameter_constraints?TMbP?F皙?)r fit_interceptmax_itertolearly_stoppingvalidation_fractionn_iter_no_changeshuffleverbosern_jobs random_state warm_start class_weightaveragectd|||||||| | d| ||| ||_| |_dS)Nr)penaltyrrrrrr r!r"r$eta0r%r&r'r#super__init__rr)selfrrrrrrr r!r"rr#r$r%r&r' __class__s h/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/linear_model/_passive_aggressive.pyr-z$PassiveAggressiveClassifier.__init__sg& ') 3-%!%    $ prefer_skip_nested_validationc t|ds0|d|jdkrtd|jdkrdnd}|||d |jd|d |d d d S) a+Fit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Subset of the training data. y : array-like of shape (n_samples,) Subset of the target values. classes : ndarray of shape (n_classes,) 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`. Returns ------- self : object Fitted estimator. classes_Tfor_partial_fitbalanceda\class_weight 'balanced' is not supported for partial_fit. For 'balanced' weights, use `sklearn.utils.compute_class_weight` with `class_weight='balanced'`. In place of y you can use a large enough subset of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.rpa1pa2rrN) alpharr learning_raterclasses sample_weight coef_initintercept_init)hasattr_more_validate_paramsr& ValueErrorr _partial_fitr)r.Xyr=lrs r0 partial_fitz'PassiveAggressiveClassifier.partial_fits2tZ((   & &t & < < < J.. !   i7**UU  f!   r1c ||jdkrdnd}|||d|jd|||S)abFit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. coef_init : ndarray of shape (n_classes, n_features) The initial coefficients to warm-start the optimization. intercept_init : ndarray of shape (n_classes,) The initial intercept to warm-start the optimization. Returns ------- self : object Fitted estimator. rr9r:rr;rrr<r?r@rBr_fitrr.rErFr?r@rGs r0fitzPassiveAggressiveClassifier.fits`. ""$$$i7**UUyy f)   r1)NNN)__name__ __module__ __qualname____doc__r rrrrdict__annotations__r-rrHrN __classcell__r/s@r0r r s?``D$  2$Wo6778htQW555 6$$$D   #&&&&&&&P\5555 5 5 655 n\555" " " 65" " " " " r1r ceZdZUdZiejeddhgeedddgeedddgd Ze e d <d d d ddddd dde ddddfd Z e d dZe d ddZxZS)PassiveAggressiveRegressora6Passive Aggressive Regressor. Read more in the :ref:`User Guide `. Parameters ---------- C : float, default=1.0 Maximum step size (regularization). Defaults to 1.0. fit_intercept : bool, default=True Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True. 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:`~sklearn.linear_model.PassiveAggressiveRegressor.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 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 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 shuffle : bool, default=True Whether or not the training data should be shuffled after each epoch. verbose : int, default=0 The verbosity level. loss : str, default="epsilon_insensitive" The loss function to be used: epsilon_insensitive: equivalent to PA-I in the reference paper. squared_epsilon_insensitive: equivalent to PA-II in the reference paper. epsilon : float, default=0.1 If the difference between the current prediction and the correct label is below this threshold, the model is not updated. 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 `. 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 `. Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled. average : bool or int, default=False When set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples. .. versionadded:: 0.19 parameter *average* to use weights averaging in SGD. Attributes ---------- coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes, n_features] Weights assigned to the features. intercept_ : array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function. 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. t_ : int Number of weight updates performed during training. Same as ``(n_iter_ * n_samples + 1)``. See Also -------- SGDRegressor : Linear model fitted by minimizing a regularized empirical loss with SGD. References ---------- Online Passive-Aggressive Algorithms K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006). Examples -------- >>> from sklearn.linear_model import PassiveAggressiveRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_features=4, random_state=0) >>> regr = PassiveAggressiveRegressor(max_iter=100, random_state=0, ... tol=1e-3) >>> regr.fit(X, y) PassiveAggressiveRegressor(max_iter=100, random_state=0) >>> print(regr.coef_) [20.48736655 34.18818427 67.59122734 87.94731329] >>> print(regr.intercept_) [-0.02306214] >>> print(regr.predict([[0, 0, 0, 0]])) [-0.02306214] epsilon_insensitivesquared_epsilon_insensitiverNrrleft)rrepsilonrrTrrFrr)rrrrrrr r!r"rr]r$r%r'ctdd| d|||||||| | | |||_| |_dS)Nrr)r)l1_ratior]r*rrrrrr r!r"r$r%r'r+)r.rrrrrrr r!r"rr]r$r%r'r/s r0r-z#PassiveAggressiveRegressor.__init__sg$ ') 3-%!    " r1r2c t|ds|d|jdkrdnd}|||d|jd|dd d d S) ayFit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Subset of training data. y : numpy array of shape [n_samples] Subset of target values. Returns ------- self : object Fitted estimator. coef_Tr6rZr9r:rrN)r;rrr<rr>r?r@)rArBrrDr)r.rErFrGs r0rHz&PassiveAggressiveRegressor.partial_fits"tW%% =  & &t & < < <i#888UUe  f&!   r1c ||jdkrdnd}|||d|jd|||S)aJFit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : numpy array of shape [n_samples] Target values. coef_init : array, shape = [n_features] The initial coefficients to warm-start the optimization. intercept_init : array, shape = [1] The initial intercept to warm-start the optimization. Returns ------- self : object Fitted estimator. rZr9r:rrJrKrMs r0rNzPassiveAggressiveRegressor.fitsa. ""$$$i#888UUeyy f&)   r1rO)rPrQrRrSr rrrrrTrUr r-rrHrNrVrWs@r0rYrY:sRNN`$  1$24QRSSThtQW555 6HT1d6:::; $$$D   "!$$$$$$$L\555  65 B\555" " " 65" " " " " r1rYN) numbersrbaserutils._param_validationrr_stochastic_gradientr r r r rYr1r0rhs::::::::VVVVVVVVVVl l l l l "3l l l ^ C C C C C !1C C C C C r1