import numpy as np

from ._base import _fit_liblinear, BaseSVC, BaseLibSVM
from ..base import BaseEstimator, RegressorMixin, OutlierMixin
from ..linear_model._base import LinearClassifierMixin, SparseCoefMixin, \
    LinearModel
from ..utils.validation import _num_samples
from ..utils.validation import _deprecate_positional_args
from ..utils.multiclass import check_classification_targets
from ..utils.deprecation import deprecated


class LinearSVC(BaseEstimator, LinearClassifierMixin,
                SparseCoefMixin):
    """Linear Support Vector Classification.

    Similar to SVC with parameter kernel='linear', but implemented in terms of
    liblinear rather than libsvm, so it has more flexibility in the choice of
    penalties and loss functions and should scale better to large numbers of
    samples.

    This class supports both dense and sparse input and the multiclass support
    is handled according to a one-vs-the-rest scheme.

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

    Parameters
    ----------
    penalty : {'l1', 'l2'}, default='l2'
        Specifies the norm used in the penalization. The 'l2'
        penalty is the standard used in SVC. The 'l1' leads to ``coef_``
        vectors that are sparse.

    loss : {'hinge', 'squared_hinge'}, default='squared_hinge'
        Specifies the loss function. 'hinge' is the standard SVM loss
        (used e.g. by the SVC class) while 'squared_hinge' is the
        square of the hinge loss.

    dual : bool, default=True
        Select the algorithm to either solve the dual or primal
        optimization problem. Prefer dual=False when n_samples > n_features.

    tol : float, default=1e-4
        Tolerance for stopping criteria.

    C : float, default=1.0
        Regularization parameter. The strength of the regularization is
        inversely proportional to C. Must be strictly positive.

    multi_class : {'ovr', 'crammer_singer'}, default='ovr'
        Determines the multi-class strategy if `y` contains more than
        two classes.
        ``"ovr"`` trains n_classes one-vs-rest classifiers, while
        ``"crammer_singer"`` optimizes a joint objective over all classes.
        While `crammer_singer` is interesting from a theoretical perspective
        as it is consistent, it is seldom used in practice as it rarely leads
        to better accuracy and is more expensive to compute.
        If ``"crammer_singer"`` is chosen, the options loss, penalty and dual
        will be ignored.

    fit_intercept : bool, default=True
        Whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (i.e. data is expected to be already centered).

    intercept_scaling : float, default=1
        When self.fit_intercept is True, instance vector x becomes
        ``[x, self.intercept_scaling]``,
        i.e. a "synthetic" feature with constant value equals to
        intercept_scaling is appended to the instance vector.
        The intercept becomes intercept_scaling * synthetic feature weight
        Note! the synthetic feature weight is subject to l1/l2 regularization
        as all other features.
        To lessen the effect of regularization on synthetic feature weight
        (and therefore on the intercept) intercept_scaling has to be increased.

    class_weight : dict or 'balanced', default=None
        Set the parameter C of class i to ``class_weight[i]*C`` for
        SVC. 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))``.

    verbose : int, default=0
        Enable verbose output. Note that this setting takes advantage of a
        per-process runtime setting in liblinear that, if enabled, may not work
        properly in a multithreaded context.

    random_state : int or RandomState instance, default=None
        Controls the pseudo random number generation for shuffling the data for
        the dual coordinate descent (if ``dual=True``). When ``dual=False`` the
        underlying implementation of :class:`LinearSVC` is not random and
        ``random_state`` has no effect on the results.
        Pass an int for reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.

    max_iter : int, default=1000
        The maximum number of iterations to be run.

    Attributes
    ----------
    coef_ : ndarray of shape (1, n_features) if n_classes == 2 \
            else (n_classes, n_features)
        Weights assigned to the features (coefficients in the primal
        problem). This is only available in the case of a linear kernel.

        ``coef_`` is a readonly property derived from ``raw_coef_`` that
        follows the internal memory layout of liblinear.

    intercept_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,)
        Constants in decision function.

    classes_ : ndarray of shape (n_classes,)
        The unique classes labels.

    n_iter_ : int
        Maximum number of iterations run across all classes.

    See Also
    --------
    SVC
        Implementation of Support Vector Machine classifier using libsvm:
        the kernel can be non-linear but its SMO algorithm does not
        scale to large number of samples as LinearSVC does.

        Furthermore SVC multi-class mode is implemented using one
        vs one scheme while LinearSVC uses one vs the rest. It is
        possible to implement one vs the rest with SVC by using the
        :class:`sklearn.multiclass.OneVsRestClassifier` wrapper.

        Finally SVC can fit dense data without memory copy if the input
        is C-contiguous. Sparse data will still incur memory copy though.

    sklearn.linear_model.SGDClassifier
        SGDClassifier can optimize the same cost function as LinearSVC
        by adjusting the penalty and loss parameters. In addition it requires
        less memory, allows incremental (online) learning, and implements
        various loss functions and regularization regimes.

    Notes
    -----
    The underlying C implementation uses a random number generator to
    select features when fitting the model. It is thus not uncommon
    to have slightly different results for the same input data. If
    that happens, try with a smaller ``tol`` parameter.

    The underlying implementation, liblinear, uses a sparse internal
    representation for the data that will incur a memory copy.

    Predict output may not match that of standalone liblinear in certain
    cases. See :ref:`differences from liblinear <liblinear_differences>`
    in the narrative documentation.

    References
    ----------
    `LIBLINEAR: A Library for Large Linear Classification
    <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>`__

    Examples
    --------
    >>> from sklearn.svm import LinearSVC
    >>> from sklearn.pipeline import make_pipeline
    >>> from sklearn.preprocessing import StandardScaler
    >>> from sklearn.datasets import make_classification
    >>> X, y = make_classification(n_features=4, random_state=0)
    >>> clf = make_pipeline(StandardScaler(),
    ...                     LinearSVC(random_state=0, tol=1e-5))
    >>> clf.fit(X, y)
    Pipeline(steps=[('standardscaler', StandardScaler()),
                    ('linearsvc', LinearSVC(random_state=0, tol=1e-05))])

    >>> print(clf.named_steps['linearsvc'].coef_)
    [[0.141...   0.526... 0.679... 0.493...]]

    >>> print(clf.named_steps['linearsvc'].intercept_)
    [0.1693...]
    >>> print(clf.predict([[0, 0, 0, 0]]))
    [1]
    """
    @_deprecate_positional_args
    def __init__(self, penalty='l2', loss='squared_hinge', *, dual=True,
                 tol=1e-4, C=1.0, multi_class='ovr', fit_intercept=True,
                 intercept_scaling=1, class_weight=None, verbose=0,
                 random_state=None, max_iter=1000):
        self.dual = dual
        self.tol = tol
        self.C = C
        self.multi_class = multi_class
        self.fit_intercept = fit_intercept
        self.intercept_scaling = intercept_scaling
        self.class_weight = class_weight
        self.verbose = verbose
        self.random_state = random_state
        self.max_iter = max_iter
        self.penalty = penalty
        self.loss = loss

    def fit(self, X, y, sample_weight=None):
        """Fit the model according to the given training data.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Training vector, where n_samples in the number of samples and
            n_features is the number of features.

        y : array-like of shape (n_samples,)
            Target vector relative to X.

        sample_weight : array-like of shape (n_samples,), default=None
            Array of weights that are assigned to individual
            samples. If not provided,
            then each sample is given unit weight.

            .. versionadded:: 0.18

        Returns
        -------
        self : object
            An instance of the estimator.
        """
        if self.C < 0:
            raise ValueError("Penalty term must be positive; got (C=%r)"
                             % self.C)

        X, y = self._validate_data(X, y, accept_sparse='csr',
                                   dtype=np.float64, order="C",
                                   accept_large_sparse=False)
        check_classification_targets(y)
        self.classes_ = np.unique(y)

        self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
            X, y, self.C, self.fit_intercept, self.intercept_scaling,
            self.class_weight, self.penalty, self.dual, self.verbose,
            self.max_iter, self.tol, self.random_state, self.multi_class,
            self.loss, sample_weight=sample_weight)

        if self.multi_class == "crammer_singer" and len(self.classes_) == 2:
            self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)
            if self.fit_intercept:
                intercept = self.intercept_[1] - self.intercept_[0]
                self.intercept_ = np.array([intercept])

        return self


class LinearSVR(RegressorMixin, LinearModel):
    """Linear Support Vector Regression.

    Similar to SVR with parameter kernel='linear', but implemented in terms of
    liblinear rather than libsvm, so it has more flexibility in the choice of
    penalties and loss functions and should scale better to large numbers of
    samples.

    This class supports both dense and sparse input.

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

    .. versionadded:: 0.16

    Parameters
    ----------
    epsilon : float, default=0.0
        Epsilon parameter in the epsilon-insensitive loss function. Note
        that the value of this parameter depends on the scale of the target
        variable y. If unsure, set ``epsilon=0``.

    tol : float, default=1e-4
        Tolerance for stopping criteria.

    C : float, default=1.0
        Regularization parameter. The strength of the regularization is
        inversely proportional to C. Must be strictly positive.

    loss : {'epsilon_insensitive', 'squared_epsilon_insensitive'}, \
            default='epsilon_insensitive'
        Specifies the loss function. The epsilon-insensitive loss
        (standard SVR) is the L1 loss, while the squared epsilon-insensitive
        loss ('squared_epsilon_insensitive') is the L2 loss.

    fit_intercept : bool, default=True
        Whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (i.e. data is expected to be already centered).

    intercept_scaling : float, default=1.
        When self.fit_intercept is True, instance vector x becomes
        [x, self.intercept_scaling],
        i.e. a "synthetic" feature with constant value equals to
        intercept_scaling is appended to the instance vector.
        The intercept becomes intercept_scaling * synthetic feature weight
        Note! the synthetic feature weight is subject to l1/l2 regularization
        as all other features.
        To lessen the effect of regularization on synthetic feature weight
        (and therefore on the intercept) intercept_scaling has to be increased.

    dual : bool, default=True
        Select the algorithm to either solve the dual or primal
        optimization problem. Prefer dual=False when n_samples > n_features.

    verbose : int, default=0
        Enable verbose output. Note that this setting takes advantage of a
        per-process runtime setting in liblinear that, if enabled, may not work
        properly in a multithreaded context.

    random_state : int or RandomState instance, default=None
        Controls the pseudo random number generation for shuffling the data.
        Pass an int for reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.

    max_iter : int, default=1000
        The maximum number of iterations to be run.

    Attributes
    ----------
    coef_ : ndarray of shape (n_features) if n_classes == 2 \
            else (n_classes, n_features)
        Weights assigned to the features (coefficients in the primal
        problem). This is only available in the case of a linear kernel.

        `coef_` is a readonly property derived from `raw_coef_` that
        follows the internal memory layout of liblinear.

    intercept_ : ndarray of shape (1) if n_classes == 2 else (n_classes)
        Constants in decision function.

    n_iter_ : int
        Maximum number of iterations run across all classes.

    Examples
    --------
    >>> from sklearn.svm import LinearSVR
    >>> from sklearn.pipeline import make_pipeline
    >>> from sklearn.preprocessing import StandardScaler
    >>> from sklearn.datasets import make_regression
    >>> X, y = make_regression(n_features=4, random_state=0)
    >>> regr = make_pipeline(StandardScaler(),
    ...                      LinearSVR(random_state=0, tol=1e-5))
    >>> regr.fit(X, y)
    Pipeline(steps=[('standardscaler', StandardScaler()),
                    ('linearsvr', LinearSVR(random_state=0, tol=1e-05))])

    >>> print(regr.named_steps['linearsvr'].coef_)
    [18.582... 27.023... 44.357... 64.522...]