class sklearn.linear_model.PassiveAggressiveClassifier(C=1.0, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, loss='hinge', n_jobs=1, random_state=None, warm_start=False, class_weight=None)
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Passive Aggressive Classifier
Read more in the 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. n_iter : int, optional The number of passes over the training data (aka epochs). Defaults to 5. shuffle : bool, default=True Whether or not the training data should be shuffled after each epoch. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. verbose : integer, optional The verbosity level 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. 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. 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 New in version 0.17: parameter class_weight to automatically weight samples. |
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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. |
See also
Online Passive-Aggressive Algorithms <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf> K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)
decision_function (X) | Predict confidence scores for samples. |
densify () | Convert coefficient matrix to dense array format. |
fit (X, y[, coef_init, intercept_init]) | Fit linear model with Passive Aggressive algorithm. |
get_params ([deep]) | Get parameters for this estimator. |
partial_fit (X, y[, classes]) | Fit linear model with Passive Aggressive algorithm. |
predict (X) | Predict class labels for samples in X. |
score (X, y[, sample_weight]) | Returns the mean accuracy on the given test data and labels. |
set_params (*args, **kwargs) | |
sparsify () | Convert coefficient matrix to sparse format. |
__init__(C=1.0, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, loss='hinge', n_jobs=1, random_state=None, warm_start=False, class_weight=None)
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decision_function(X)
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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, sparse matrix}, shape = (n_samples, n_features) Samples. |
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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. |
densify()
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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: estimator : |
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fit(X, y, coef_init=None, intercept_init=None)
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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. |
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Returns: |
self : returns an instance of self. |
get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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Returns: |
params : mapping of string to any Parameter names mapped to their values. |
partial_fit(X, y, classes=None)
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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 |
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Returns: |
self : returns an instance of self. |
predict(X)
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Predict class labels for samples in X.
Parameters: |
X : {array-like, sparse matrix}, shape = [n_samples, n_features] Samples. |
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Returns: |
C : array, shape = [n_samples] Predicted class label per sample. |
score(X, y, sample_weight=None)
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Returns 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, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. |
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Returns: |
score : float Mean accuracy of self.predict(X) wrt. y. |
sparsify()
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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: estimator : |
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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.
sklearn.linear_model.PassiveAggressiveClassifier
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html