# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#          Mathieu Blondel <mathieu@mblondel.org>
#          Olivier Grisel <olivier.grisel@ensta.org>
#          Andreas Mueller <amueller@ais.uni-bonn.de>
# License: BSD 3 clause

import numpy as np

from ..base import BaseEstimator, TransformerMixin

from ..utils.fixes import unique
from ..utils import deprecated, column_or_1d

from ..utils.multiclass import unique_labels
from ..utils.multiclass import type_of_target

from ..externals import six

zip = six.moves.zip
map = six.moves.map

__all__ = [
    'label_binarize',
    'LabelBinarizer',
    'LabelEncoder',
]


class LabelEncoder(BaseEstimator, TransformerMixin):
    """Encode labels with value between 0 and n_classes-1.

    Attributes
    ----------
    `classes_` : array of shape (n_class,)
        Holds the label for each class.

    Examples
    --------
    `LabelEncoder` can be used to normalize labels.

    >>> from sklearn import preprocessing
    >>> le = preprocessing.LabelEncoder()
    >>> le.fit([1, 2, 2, 6])
    LabelEncoder()
    >>> le.classes_
    array([1, 2, 6])
    >>> le.transform([1, 1, 2, 6]) #doctest: +ELLIPSIS
    array([0, 0, 1, 2]...)
    >>> le.inverse_transform([0, 0, 1, 2])
    array([1, 1, 2, 6])

    It can also be used to transform non-numerical labels (as long as they are
    hashable and comparable) to numerical labels.

    >>> le = preprocessing.LabelEncoder()
    >>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
    LabelEncoder()
    >>> list(le.classes_)
    ['amsterdam', 'paris', 'tokyo']
    >>> le.transform(["tokyo", "tokyo", "paris"]) #doctest: +ELLIPSIS
    array([2, 2, 1]...)
    >>> list(le.inverse_transform([2, 2, 1]))
    ['tokyo', 'tokyo', 'paris']

    """

    def _check_fitted(self):
        if not hasattr(self, "classes_"):
            raise ValueError("LabelEncoder was not fitted yet.")

    def fit(self, y):
        """Fit label encoder

        Parameters
        ----------
        y : array-like of shape (n_samples,)
            Target values.

        Returns
        -------
        self : returns an instance of self.
        """
        y = column_or_1d(y, warn=True)
        self.classes_ = np.unique(y)
        return self

    def fit_transform(self, y):
        """Fit label encoder and return encoded labels

        Parameters
        ----------
        y : array-like of shape [n_samples]
            Target values.

        Returns
        -------
        y : array-like of shape [n_samples]
        """
        y = column_or_1d(y, warn=True)
        self.classes_, y = unique(y, return_inverse=True)
        return y

    def transform(self, y):
        """Transform labels to normalized encoding.

        Parameters
        ----------
        y : array-like of shape [n_samples]
            Target values.

        Returns
        -------
        y : array-like of shape [n_samples]
        """
        self._check_fitted()

        classes = np.unique(y)
        if len(np.intersect1d(classes, self.classes_)) < len(classes):
            diff = np.setdiff1d(classes, self.classes_)
            raise ValueError("y contains new labels: %s" % str(diff))

        return np.searchsorted(self.classes_, y)

    def inverse_transform(self, y):
        """Transform labels back to original encoding.

        Parameters
        ----------
        y : numpy array of shape [n_samples]
            Target values.

        Returns
        -------
        y : numpy array of shape [n_samples]
        """
        self._check_fitted()

        y = np.asarray(y)
        return self.classes_[y]


class LabelBinarizer(BaseEstimator, TransformerMixin):
    """Binarize labels in a one-vs-all fashion

    Several regression and binary classification algorithms are
    available in the scikit. A simple way to extend these algorithms
    to the multi-class classification case is to use the so-called
    one-vs-all scheme.

    At learning time, this simply consists in learning one regressor
    or binary classifier per class. In doing so, one needs to convert
    multi-class labels to binary labels (belong or does not belong
    to the class). LabelBinarizer makes this process easy with the
    transform method.

    At prediction time, one assigns the class for which the corresponding
    model gave the greatest confidence. LabelBinarizer makes this easy
    with the inverse_transform method.

    Parameters
    ----------

    neg_label : int (default: 0)
        Value with which negative labels must be encoded.

    pos_label : int (default: 1)
        Value with which positive labels must be encoded.

    Attributes
    ----------
    `classes_` : array of shape [n_class]
        Holds the label for each class.

    `multilabel_` : boolean
        True if the transformer was fitted on a multilabel rather than a
        multiclass set of labels.

    Examples
    --------
    >>> from sklearn import preprocessing
    >>> lb = preprocessing.LabelBinarizer()
    >>> lb.fit([1, 2, 6, 4, 2])
    LabelBinarizer(neg_label=0, pos_label=1)
    >>> lb.classes_
    array([1, 2, 4, 6])
    >>> lb.multilabel_
    False
    >>> lb.transform([1, 6])
    array([[1, 0, 0, 0],
           [0, 0, 0, 1]])

    >>> lb.fit_transform([(1, 2), (3,)])
    array([[1, 1, 0],
           [0, 0, 1]])
    >>> lb.classes_
    array([1, 2, 3])
    >>> lb.multilabel_
    True

    See also
    --------
    label_binarize : function to perform the transform operation of
        LabelBinarizer with fixed classes.
    """

    def __init__(self, neg_label=0, pos_label=1):
        if neg_label >= pos_label:
            raise ValueError("neg_label must be strictly less than pos_label.")

        self.neg_label = neg_label
        self.pos_label = pos_label

    @property
    @deprecated("Attribute `multilabel` was renamed to `multilabel_` in "
                "0.14 and will be removed in 0.16")
    def multilabel(self):
        return self.multilabel_

    def _check_fitted(self):
        if not hasattr(self, "classes_"):
            raise ValueError("LabelBinarizer was not fitted yet.")

    def fit(self, y):
        """Fit label binarizer

        Parameters
        ----------
        y : numpy array of shape (n_samples,) or sequence of sequences
            Target values. In the multilabel case the nested sequences can
            have variable lengths.

        Returns
        -------
        self : returns an instance of self.
        """
        y_type = type_of_target(y)
        self.multilabel_ = y_type.startswith('multilabel')
        if self.multilabel_:
            self.indicator_matrix_ = y_type == 'multilabel-indicator'

        self.classes_ = unique_labels(y)

        return self

    def transform(self, y):
        """Transform multi-class labels to binary labels

        The output of transform is sometimes referred to by some authors as the
        1-of-K coding scheme.

        Parameters
        ----------
        y : numpy array of shape [n_samples] or sequence of sequences
            Target values. In the multilabel case the nested sequences can
            have variable lengths.

        Returns
        -------
        Y : numpy array of shape [n_samples, n_classes]
        """
        self._check_fitted()

        y_is_multilabel = type_of_target(y).startswith('multilabel')

        if y_is_multilabel and not self.multilabel_:
            raise ValueError("The object was not fitted with multilabel"
                             " input.")

        return label_binarize(y, self.classes_,
                              multilabel=self.multilabel_,
                              pos_label=self.pos_label,
                              neg_label=self.neg_label)

    def inverse_transform(self, Y, threshold=None):
        """Transform binary labels back to multi-class labels

        Parameters
        ----------
        Y : numpy array of shape [n_samples, n_classes]
            Target values.

        threshold : float or None
            Threshold used in the binary and multi-label cases.

            Use 0 when:
                - Y contains the output of decision_function (classifier)
            Use 0.5 when:
                - Y contains the output of predict_proba

            If None, the threshold is assumed to be half way between
            neg_label and pos_label.

        Returns
        -------
        y : numpy array of shape [n_samples] or sequence of sequences
            Target values. In the multilabel case the nested sequences can
            have variable lengths.

        Notes
        -----
        In the case when the binary labels are fractional
        (probabilistic), inverse_transform chooses the class with the
        greatest value. Typically, this allows to use the output of a
        linear model's decision_function method directly as the input
        of inverse_transform.
        """
        self._check_fitted()

        if threshold is None:
            half = (self.pos_label - self.neg_label) / 2.0