/ЦiRSSKJr SSKJr SSKJrJrJr SSKJ r SSK J r J r \ "S5"SS\55r \ "S 5"S S \55rg ) )Real) _fit_context)DEFAULT_EPSILONBaseSGDClassifierBaseSGDRegressor) deprecated)Interval StrOptionszthis is deprecated in version 1.8 and will be removed in 1.10. Use `SGDClassifier(loss='hinge', penalty=None, learning_rate='pa1', eta0=1.0)` instead.c^\rSrSr%Sr0\R E\"SS15/\"\ SSSS9/S .Er\ \ S '\RS 5 S S SSSSSS SSSSSSSS.U4Sjjr \"S S9SSj5r\"S S9SSj5rSrU=r$)PassiveAggressiveClassifieraPassive Aggressive Classifier. .. deprecated:: 1.8 The whole class `PassiveAggressiveClassifier` was deprecated in version 1.8 and will be removed in 1.10. Instead use: .. code-block:: python clf = SGDClassifier( loss="hinge", penalty=None, learning_rate="pa1", # or "pa2" eta0=1.0, # for parameter C ) Read more in the :ref:`User Guide `. Parameters ---------- C : float, default=1.0 Aggressiveness parameter for the passive-agressive algorithm, see [1]. For PA-I it is the maximum step size. For PA-II it regularizes the step size (the smaller `C` the more it regularizes). As a general rule-of-thumb, `C` should be small when the data is noisy. 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 ---------- .. [1] 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_constraintseta0?TMbP?F皙?)r fit_interceptmax_itertolearly_stoppingvalidation_fractionn_iter_no_changeshuffleverbosern_jobs random_state warm_start class_weightaveragecT>[TU]SUUUUUUUU U UU UUU S9 XlXlg)N)penaltyrrrrr r!r"r#r%rr&r'r(r$)super__init__rr)selfrrrrrr r!r"r#rr$r%r&r'r( __class__s g/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/sklearn/linear_model/_passive_aggressive.pyr,$PassiveAggressiveClassifier.__init__sP& ') 3-%!%  $ prefer_skip_nested_validationc [US5(d*URSS9 URS:Xa [S5eURS:XaSOSnUR UUS SUS US S S S 9 $) aFit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Subset of the training data. y : array-like of shape (n_samples,) Subset of the target values. classes : ndarray of shape (n_classes,) Classes across all calls to partial_fit. Can be obtained by via `np.unique(y_all)`, where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn't need to contain all labels in `classes`. Returns ------- self : object Fitted estimator. classes_Tfor_partial_fitbalanceda\class_weight 'balanced' is not supported for partial_fit. For 'balanced' weights, use `sklearn.utils.compute_class_weight` with `class_weight='balanced'`. In place of y you can use a large enough subset of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.rpa1pa2rN)alphar learning_raterclasses sample_weight coef_initintercept_init)hasattr_more_validate_paramsr' ValueErrorr _partial_fit)r-Xyr>lrs r/ partial_fit'PassiveAggressiveClassifier.partial_fits2tZ((  & &t & <  J. !  ii7*U  !  r1c tUR5 URS:XaSOSnURUUSSUUUS9$)aFit 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<rr=r@rArCr_fitr-rFrGr@rArHs r/fitPassiveAggressiveClassifier.fit1sM. ""$ii7*Uyy )  r1)rr)NNN)__name__ __module__ __qualname____firstlineno____doc__rrr r rdict__annotations__popr,rrIrP__static_attributes__ __classcell__r.s@r/r r s pd$  2 2$Wo678tQW5 6$D v&   #&&P56 66 p5! 6! r1r zthis is deprecated in version 1.8 and will be removed in 1.10. Use `SGDRegressor(loss='epsilon_insensitive', penalty=None, learning_rate='pa1', eta0 = 1.0)` instead.c ^\rSrSr%Sr0\R E\"SS15/\"\ SSSS9/\"\ SSS S9/S .Er\ \ S '\RS 5 S SSSSSSSSS\ SSSS.U4Sjjr\"SS9S5r\"SS9SSj5rSrU=r$)PassiveAggressiveRegressoriWaPassive Aggressive Regressor. .. deprecated:: 1.8 The whole class `PassiveAggressiveRegressor` was deprecated in version 1.8 and will be removed in 1.10. Instead use: .. code-block:: python reg = SGDRegressor( loss="epsilon_insensitive", penalty=None, learning_rate="pa1", # or "pa2" eta0=1.0, # for parameter C ) Read more in the :ref:`User Guide `. Parameters ---------- C : float, default=1.0 Aggressiveness parameter for the passive-agressive algorithm, see [1]. For PA-I it is the maximum step size. For PA-II it regularizes the step size (the smaller `C` the more it regularizes). As a general rule-of-thumb, `C` should be small when the data is noisy. 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)rrepsilonrrrTrrFrr)rrrrrr r!r"r#rrcr%r&r(cJ>[TU]U SSU UUUUUUUUU U U US9 Xlg)Nr)rr*l1_ratiorcrrrrrr r!r"r#r%r&r()r+r,r)r-rrrrrr r!r"r#rrcr%r&r(r.s r/r,#PassiveAggressiveRegressor.__init__sN$ ') 3-%!!  $r1r2c [US5(dURSS9 URS:XaSOSnURUUSSUSS S S S 9 $) a!Fit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Subset of training data. y : numpy array of shape [n_samples] Subset of target values. Returns ------- self : object Fitted estimator. coef_Tr6r`r9r:rr;N)r<rr=rr?r@rA)rBrCrrE)r-rFrGrHs r/rI&PassiveAggressiveRegressor.partial_fit+sh"tW%%  & &t & <ii#88Ue  &!  r1c tUR5 URS:XaSOSnURUUSSUUUS9$)aFit 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. r`r9r:rrLrMrOs r/rPPassiveAggressiveRegressor.fitLsN. ""$ii#88Ueyy &)  r1)rrR)rSrTrUrVrWrrr r rrXrYrZrr,rrIrPr[r\r]s@r/r_r_Ws ^@$  1 1$24QRSTtQW5 6T1d6:; $D v&   "!$$L5 6 @5! 6! r1r_N)numbersr sklearn.baser)sklearn.linear_model._stochastic_gradientrrr sklearn.utilsrsklearn.utils._param_validationr r r r_r1r/rrsr% %@  } "3}  } B   R !1R  R r1