package sklearn
Module Linear_model.PassiveAggressiveClassifierSource
Source
val create :
?c:float ->
?fit_intercept:bool ->
?max_iter:int ->
?tol:[ `F of float | `None ] ->
?early_stopping:bool ->
?validation_fraction:float ->
?n_iter_no_change:int ->
?shuffle:bool ->
?verbose:int ->
?loss:string ->
?n_jobs:int ->
?random_state:int ->
?warm_start:bool ->
?class_weight:Py.Object.t ->
?average:[ `I of int | `Bool of bool ] ->
unit ->
tPassive Aggressive Classifier
Read more in the :ref:`User Guide <passive_aggressive>`.
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 (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:`partial_fit` method.
.. versionadded:: 0.19
tol : float or None, optional (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 : 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 <n_jobs>` 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 <warm_start>`.
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
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.
classes_ : array of shape (n_classes,) The unique classes labels.
t_ : int Number of weight updates performed during training. Same as ``(n_iter_ * n_samples)``.
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
See also --------
SGDClassifier Perceptron
References ---------- 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)
Source
val decision_function :
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tPredict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
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.
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 Fitted estimator.
Source
val fit :
?coef_init:[> `ArrayLike ] Np.Obj.t ->
?intercept_init:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tFit 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_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.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Source
val partial_fit :
?classes:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tFit linear model with Passive Aggressive algorithm.
Parameters ---------- X : array-like, sparse matrix of 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.
Predict class labels for samples in X.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns ------- C : array, shape n_samples Predicted class label per sample.
Source
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatReturn 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 of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
Set and validate the parameters of estimator.
Parameters ---------- **kwargs : dict Estimator parameters.
Returns ------- self : object Estimator instance.
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 Fitted estimator.
Notes ----- 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.
Attribute coef_: get value or raise Not_found if None.
Attribute intercept_: get value or raise Not_found if None.
Attribute intercept_: get value as an option.
Attribute classes_: get value or raise Not_found if None.
Attribute classes_: get value as an option.
Pretty-print the object to a formatter.