# Authors: Rob Zinkov, Mathieu Blondel # License: BSD 3 clause from .stochastic_gradient import BaseSGDClassifier from .stochastic_gradient import BaseSGDRegressor from .stochastic_gradient import DEFAULT_EPSILON class PassiveAggressiveClassifier(BaseSGDClassifier): """Passive 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 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 : integer, optional The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. -1 means 'all CPUs'. Defaults to 1. 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. 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(random_state=0) >>> clf.fit(X, y) PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None, fit_intercept=True, loss='hinge', max_iter=None, n_iter=None, n_jobs=1, random_state=0, shuffle=True, tol=None, verbose=0, warm_start=False) >>> print(clf.coef_) [[ 0.49324685 1.0552176 1.49519589 1.33798314]] >>> print(clf.intercept_) [ 2.18438388] >>> 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) """ def __init__(self, C=1.0, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, loss="hinge", n_jobs=1, random_state=None, warm_start=False, class_weight=None, average=False, n_iter=None): super(PassiveAggressiveClassifier, self).__init__( penalty=None, fit_intercept=fit_intercept, max_iter=max_iter, tol=tol, shuffle=shuffle, verbose=verbose, random_state=random_state, eta0=1.0, warm_start=warm_start, class_weight=class_weight, average=average, n_jobs=n_jobs, n_iter=n_iter) self.C = C self.loss = loss def partial_fit(self, X, y, classes=None): """Fit 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. """ if self.class_weight == 'balanced': raise ValueError("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.") lr = "pa1" if self.loss == "hinge" else "pa2" return self._partial_fit(X, y, alpha=1.0, C=self.C, loss="hinge", learning_rate=lr, max_iter=1, classes=classes, sample_weight=None, coef_init=None, intercept_init=None) def fit(self, X, y, coef_init=None, intercept_init=None): """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_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. """ lr = "pa1" if self.loss == "hinge" else "pa2" return self._fit(X, y, alpha=1.0, C=self.C, loss="hinge", learning_rate=lr, coef_init=coef_init, intercept_init=intercept_init) class PassiveAggressiveRegressor(BaseSGDRegressor): """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 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. 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(random_state=0) >>> regr.fit(X, y) PassiveAggressiveRegressor(C=1.0, average=False, epsilon=0.1, fit_intercept=True, loss='epsilon_insensitive', max_iter=None, n_iter=None, random_state=0, shuffle=True, tol=None, 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) """ def __init__(self, C=1.0, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, loss="epsilon_insensitive", epsilon=DEFAULT_EPSILON, random_state=None, warm_start=False, average=False, n_iter=None): super(PassiveAggressiveRegressor, self).__init__( penalty=None, l1_ratio=0, epsilon=epsilon, eta0=1.0, fit_intercept=fit_intercept, max_iter=max_iter, tol=tol, shuffle=shuffle, verbose=verbose, random_state=random_state, warm_start=warm_start, average=average, n_iter=n_iter) self.C = C self.loss = loss def partial_fit(self, X, y): """Fit 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. """ self._validate_params() lr = "pa1" if self.loss == "epsilon_insensitive" else "pa2" return self._partial_fit(X, y, alpha=1.0, C=self.C, loss="epsilon_insensitive", learning_rate=lr, max_iter=1, sample_weight=None, coef_init=None, intercept_init=None) def fit(self, X, y, coef_init=None, intercept_init=None): """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. """ lr = "pa1" if self.loss == "epsilon_insensitive" else "pa2" return self._fit(X, y, alpha=1.0, C=self.C, loss="epsilon_insensitive", learning_rate=lr, coef_init=coef_init, intercept_init=intercept_init)