package sklearn

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sklearn
- Sklearn

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Class type

type tag = [

| `PassiveAggressiveClassifier

]

type t =
  [ `BaseEstimator
  | `BaseSGD
  | `BaseSGDClassifier
  | `ClassifierMixin
  | `LinearClassifierMixin
  | `Object
  | `PassiveAggressiveClassifier
  | `SparseCoefMixin ]
    Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_sgd : t -> [ `BaseSGD ] Obj.t

val as_classifier : t -> [ `ClassifierMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val as_sparse_coef : t -> [ `SparseCoefMixin ] Obj.t

val as_linear_classifier : t -> [ `LinearClassifierMixin ] Obj.t

val as_sgd_classifier : t -> [ `BaseSGDClassifier ] Obj.t

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:[ `Bool of bool | `I of int ] ->
  unit ->
  t

Passive 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, 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 <random_state>`.

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)``.

loss_function_ : callable Loss function used by the algorithm.

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)

val decision_function : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

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 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.

val densify : [> tag ] Obj.t -> t

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.

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 ->
  t

Fit 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.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

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.

val partial_fit : 
  ?classes:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

Fit 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.

val predict : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

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.

val score : 
  ?sample_weight:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  float

Return 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.

val set_params : ?kwargs:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

Set and validate the parameters of estimator.

Parameters ---------- **kwargs : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val sparsify : [> tag ] Obj.t -> t

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.

val coef_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute coef_: get value or raise Not_found if None.

val coef_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute coef_: get value as an option.

val intercept_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute intercept_: get value or raise Not_found if None.

val intercept_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute intercept_: get value as an option.

val n_iter_ : t -> int

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> int option

Attribute n_iter_: get value as an option.

val classes_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute classes_: get value or raise Not_found if None.

val classes_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute classes_: get value as an option.

val t_ : t -> int

Attribute t_: get value or raise Not_found if None.

val t_opt : t -> int option

Attribute t_: get value as an option.

val loss_function_ : t -> Py.Object.t

Attribute loss_function_: get value or raise Not_found if None.

val loss_function_opt : t -> Py.Object.t option

Attribute loss_function_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.