ó ‡ˆ\c@s`ddlmZddlmZddlmZdefd„ƒYZdefd„ƒYZdS( i(tBaseSGDClassifier(tBaseSGDRegressor(tDEFAULT_EPSILONtPassiveAggressiveClassifiercBsbeZdZded d eddeddd d ed ed d„Zd d„Zd d d„ZRS( s¼Passive Aggressive Classifier Read more in the :ref:`User Guide `. Parameters ---------- C : float Maximum step size (regularization). Defaults to 1.0. fit_intercept : bool, default=False Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. max_iter : int, optional The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the ``fit`` method, and not the `partial_fit`. Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None. .. versionadded:: 0.19 tol : float or None, optional The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol). Defaults to None. Defaults to 1e-3 from 0.21. .. 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 : integer, optional The verbosity level loss : string, optional 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, optional (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 or None, optional, default=None The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. warm_start : bool, optional 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, optional 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, optional 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 n_iter : int, optional The number of passes over the training data (aka epochs). Defaults to None. Deprecated, will be removed in 0.21. .. versionchanged:: 0.19 Deprecated 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_iter_ : int The actual number of iterations to reach the stopping criterion. For multiclass fits, it is the maximum over every binary fit. 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(C=1.0, average=False, class_weight=None, early_stopping=False, fit_intercept=True, loss='hinge', max_iter=1000, n_iter=None, n_iter_no_change=5, n_jobs=None, random_state=0, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) >>> print(clf.coef_) [[-0.6543424 1.54603022 1.35361642 0.22199435]] >>> print(clf.intercept_) [0.63310933] >>> print(clf.predict([[0, 0, 0, 0]])) [1] See also -------- SGDClassifier Perceptron References ---------- Online Passive-Aggressive Algorithms K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006) gð?gš™™™™™¹?iithingec!Cs‰tt|ƒjddd|d|d|d|d|d|d|d | d | d d d | d|d|d| d|ƒ||_| |_dS(Ntpenaltyt fit_intercepttmax_iterttoltearly_stoppingtvalidation_fractiontn_iter_no_changetshuffletverboset random_stateteta0gð?t warm_startt class_weighttaveragetn_jobstn_iter(tsuperRt__init__tNonetCtloss(tselfRRRRR R R R R RRRRRRR((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pyRªs& cCs’|jdtƒ|jdkr.tdƒ‚n|jdkrCdnd}|j||ddd |jd dd |d d d|ddddddƒ S(sFit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Subset of the training data y : numpy array of shape [n_samples] Subset of the target values classes : array, 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 : returns an instance of self. tfor_partial_fittbalanceds\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.Rtpa1tpa2talphagð?RRt learning_rateRitclassest sample_weightt coef_inittintercept_initN(t_validate_paramstTrueRt ValueErrorRt _partial_fitRR(RtXtyR!tlr((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pyt partial_fitÄs  cCs\|jƒ|jdkrdnd}|j||ddd|jddd|d |d |ƒS( sRFit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training data y : numpy array of shape [n_samples] Target values coef_init : array, shape = [n_classes,n_features] The initial coefficients to warm-start the optimization. intercept_init : array, shape = [n_classes] The initial intercept to warm-start the optimization. Returns ------- self : returns an instance of self. RRRRgð?RRR R#R$(R%Rt_fitR(RR)R*R#R$R+((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pytfitìs   N( t__name__t __module__t__doc__R&RtFalseRR,R.(((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pyR s     (tPassiveAggressiveRegressorcBs\eZdZded d eddedded eed d„Zd„Zd d d„Z RS( s¯Passive Aggressive Regressor Read more in the :ref:`User Guide `. Parameters ---------- C : float Maximum step size (regularization). Defaults to 1.0. fit_intercept : bool Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True. max_iter : int, optional The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the ``fit`` method, and not the `partial_fit`. Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None. .. versionadded:: 0.19 tol : float or None, optional The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol). Defaults to None. Defaults to 1e-3 from 0.21. .. 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 : integer, optional The verbosity level loss : string, optional 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 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 or None, optional, default=None The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. warm_start : bool, optional 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, optional 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 n_iter : int, optional The number of passes over the training data (aka epochs). Defaults to None. Deprecated, will be removed in 0.21. .. versionchanged:: 0.19 Deprecated 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_iter_ : int The actual number of iterations to reach the stopping criterion. 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(C=1.0, average=False, early_stopping=False, epsilon=0.1, fit_intercept=True, loss='epsilon_insensitive', max_iter=100, n_iter=None, n_iter_no_change=5, random_state=0, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) >>> 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] See also -------- SGDRegressor References ---------- Online Passive-Aggressive Algorithms K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006) gð?gš™™™™™¹?iitepsilon_insensitivec!Cs‰tt|ƒjddddd| ddd|d|d |d |d |d |d |d| d| d| d|d|ƒ||_| |_dS(NRtl1_ratioitepsilonRgð?RRRR R R R R RRRR(RR3RRRR(RRRRRR R R R R RR6RRRR((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pyR˜s& cCsn|jdtƒ|jdkr%dnd}|j||ddd|jddd |d d d dd dddƒS(soFit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Subset of training data y : numpy array of shape [n_samples] Subset of target values Returns ------- self : returns an instance of self. RR4RRRgð?RRR RiR"R#R$N(R%R&RR(RR(RR)R*R+((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pyR,²s cCs\|jƒ|jdkrdnd}|j||ddd|jddd|d |d |ƒS( s@Fit linear model with Passive Aggressive algorithm. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training data y : numpy array of shape [n_samples] Target values coef_init : array, shape = [n_features] The initial coefficients to warm-start the optimization. intercept_init : array, shape = [1] The initial intercept to warm-start the optimization. Returns ------- self : returns an instance of self. R4RRRgð?RRR R#R$(R%RR-R(RR)R*R#R$R+((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pyR.És N( R/R0R1R&RR2RRR,R.(((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pyR3s   N(tstochastic_gradientRRRRR3(((sFlib/python2.7/site-packages/sklearn/linear_model/passive_aggressive.pytsÿ