package sklearn

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type tag = [
  1. | `PassiveAggressiveRegressor
]
type t = [ `BaseEstimator | `BaseSGD | `BaseSGDRegressor | `Object | `PassiveAggressiveRegressor | `RegressorMixin | `SparseCoefMixin ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_regressor : t -> [ `RegressorMixin ] Obj.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val as_sgd_regressor : t -> [ `BaseSGDRegressor ] Obj.t
val as_sparse_coef : t -> [ `SparseCoefMixin ] Obj.t
val as_sgd : t -> [ `BaseSGD ] 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 -> ?epsilon:float -> ?random_state:int -> ?warm_start:bool -> ?average:[ `I of int | `Bool of bool ] -> unit -> t

Passive Aggressive Regressor

Read more in the :ref:`User Guide <passive_aggressive>`.

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 (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 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 <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.

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.

t_ : int Number of weight updates performed during training. Same as ``(n_iter_ * n_samples)``.

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(max_iter=100, random_state=0) >>> 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 <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf> K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)

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_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.

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 : 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 training data

y : numpy array of shape n_samples Subset of target values

Returns ------- self : returns an instance of self.

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

Predict using the linear model

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features)

Returns ------- ndarray of shape (n_samples,) Predicted target values per element in X.

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

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float R^2 of self.predict(X) wrt. y.

Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).

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 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 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.

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