type t =
[ `BaseEstimator
| `BaseSGD
| `BaseSGDRegressor
| `Object
| `PassiveAggressiveRegressor
| `RegressorMixin
| `SparseCoefMixin ]
Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval 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:[ `Bool of bool | `I of int ] ->
unit ->
tPassive 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, 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.
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)
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 ->
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_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.
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.
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.
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 ->
floatReturn 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 uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).
val set_params : ?kwargs:(string * Py.Object.t) list -> [> tag ] Obj.t -> tSet 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 intercept_: get value or raise Not_found if None.
Attribute intercept_: get value as an option.
val n_iter_ : t -> intAttribute n_iter_: get value or raise Not_found if None.
val n_iter_opt : t -> int optionAttribute n_iter_: get value as an option.
val t_ : t -> intAttribute t_: get value or raise Not_found if None.
val t_opt : t -> int optionAttribute t_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.