from itertools import product

import numpy as np

from .. import confusion_matrix
from ...utils import check_matplotlib_support
from ...utils.multiclass import unique_labels
from ...utils.validation import _deprecate_positional_args
from ...base import is_classifier


class ConfusionMatrixDisplay:
    """Confusion Matrix visualization.

    It is recommend to use :func:`~sklearn.metrics.plot_confusion_matrix` to
    create a :class:`ConfusionMatrixDisplay`. All parameters are stored as
    attributes.

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

    Parameters
    ----------
    confusion_matrix : ndarray of shape (n_classes, n_classes)
        Confusion matrix.

    display_labels : ndarray of shape (n_classes,), default=None
        Display labels for plot. If None, display labels are set from 0 to
        `n_classes - 1`.

    Attributes
    ----------
    im_ : matplotlib AxesImage
        Image representing the confusion matrix.

    text_ : ndarray of shape (n_classes, n_classes), dtype=matplotlib Text, \
            or None
        Array of matplotlib axes. `None` if `include_values` is false.

    ax_ : matplotlib Axes
        Axes with confusion matrix.

    figure_ : matplotlib Figure
        Figure containing the confusion matrix.

    See Also
    --------
    confusion_matrix : Compute Confusion Matrix to evaluate the accuracy of a
        classification.
    ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix
        given an estimator, the data, and the label.
    ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix
        given the true and predicted labels.

    Examples
    --------
    >>> from sklearn.datasets import make_classification
    >>> from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
    >>> from sklearn.model_selection import train_test_split
    >>> from sklearn.svm import SVC
    >>> X, y = make_classification(random_state=0)
    >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
    ...                                                     random_state=0)
    >>> clf = SVC(random_state=0)
    >>> clf.fit(X_train, y_train)
    SVC(random_state=0)
    >>> predictions = clf.predict(X_test)
    >>> cm = confusion_matrix(y_test, predictions, labels=clf.classes_)
    >>> disp = ConfusionMatrixDisplay(confusion_matrix=cm,
    ...                               display_labels=clf.classes_)
    >>> disp.plot() # doctest: +SKIP
    """
    @_deprecate_positional_args
    def __init__(self, confusion_matrix, *, display_labels=None):
        self.confusion_matrix = confusion_matrix
        self.display_labels = display_labels

    @_deprecate_positional_args
    def plot(self, *, include_values=True, cmap='viridis',
             xticks_rotation='horizontal', values_format=None,
             ax=None, colorbar=True):
        """Plot visualization.

        Parameters
        ----------
        include_values : bool, default=True
            Includes values in confusion matrix.

        cmap : str or matplotlib Colormap, default='viridis'
            Colormap recognized by matplotlib.

        xticks_rotation : {'vertical', 'horizontal'} or float, \
                         default='horizontal'
            Rotation of xtick labels.

        values_format : str, default=None
            Format specification for values in confusion matrix. If `None`,
            the format specification is 'd' or '.2g' whichever is shorter.

        ax : matplotlib axes, default=None
            Axes object to plot on. If `None`, a new figure and axes is
            created.

        colorbar : bool, default=True
            Whether or not to add a colorbar to the plot.

        Returns
        -------
        display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
        """
        check_matplotlib_support("ConfusionMatrixDisplay.plot")
        import matplotlib.pyplot as plt

        if ax is None:
            fig, ax = plt.subplots()
        else:
            fig = ax.figure

        cm = self.confusion_matrix
        n_classes = cm.shape[0]
        self.im_ = ax.imshow(cm, interpolation='nearest', cmap=cmap)
        self.text_ = None
        cmap_min, cmap_max = self.im_.cmap(0), self.im_.cmap(256)

        if include_values:
            self.text_ = np.empty_like(cm, dtype=object)

            # print text with appropriate color depending on background
            thresh = (cm.max() + cm.min()) / 2.0

            for i, j in product(range(n_classes), range(n_classes)):
                color = cmap_max if cm[i, j] < thresh else cmap_min

                if values_format is None:
                    text_cm = format(cm[i, j], '.2g')
                    if cm.dtype.kind != 'f':
                        text_d = format(cm[i, j], 'd')
                        if len(text_d) < len(text_cm):
                            text_cm = text_d
                else:
                    text_cm = format(cm[i, j], values_format)

                self.text_[i, j] = ax.text(
                    j, i, text_cm,
                    ha="center", va="center",
                    color=color)

        if self.display_labels is None:
            display_labels = np.arange(n_classes)
        else:
            display_labels = self.display_labels
        if colorbar:
            fig.colorbar(self.im_, ax=ax)
        ax.set(xticks=np.arange(n_classes),
               yticks=np.arange(n_classes),
               xticklabels=display_labels,
               yticklabels=display_labels,
               ylabel="True label",
               xlabel="Predicted label")

        ax.set_ylim((n_classes - 0.5, -0.5))
        plt.setp(ax.get_xticklabels(), rotation=xticks_rotation)

        self.figure_ = fig
        self.ax_ = ax
        return self


@_deprecate_positional_args
def plot_confusion_matrix(estimator, X, y_true, *, labels=None,
                          sample_weight=None, normalize=None,
                          display_labels=None, include_values=True,
                          xticks_rotation='horizontal',
                          values_format=None,
                          cmap='viridis', ax=None, colorbar=True):
    """Plot Confusion Matrix.

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

    Parameters
    ----------
    estimator : estimator instance
        Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
        in which the last estimator is a classifier.

    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Input values.

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

    labels : array-like of shape (n_classes,), default=None
        List of labels to index the matrix. This may be used to reorder or
        select a subset of labels. If `None` is given, those that appear at
        least once in `y_true` or `y_pred` are used in sorted order.

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

    normalize : {'true', 'pred', 'all'}, default=None
        Normalizes confusion matrix over the true (rows), predicted (columns)
        conditions or all the population. If None, confusion matrix will not be
        normalized.

    display_labels : array-like of shape (n_classes,), default=None
        Target names used for plotting. By default, `labels` will be used if
        it is defined, otherwise the unique labels of `y_true` and `y_pred`
        will be used.

    include_values : bool, default=True
        Includes values in confusion matrix.

    xticks_rotation : {'vertical', 'horizontal'} or float, \
                        default='horizontal'
        Rotation of xtick labels.

    values_format : str, default=None
        Format specification for values in confusion matrix. If `None`,
        the format specification is 'd' or '.2g' whichever is shorter.

    cmap : str or matplotlib Colormap, default='viridis'
        Colormap recognized by matplotlib.

    ax : matplotlib Axes, default=None
        Axes object to plot on. If `None`, a new figure and axes is
        created.

    colorbar : bool, default=True
        Whether or not to add a colorbar to the plot.

        .. versionadded:: 0.24

    Returns
    -------
    display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`

    See Also
    --------
    confusion_matrix : Compute Confusion Matrix to evaluate the accuracy of a
        classification.
    ConfusionMatrixDisplay : Confusion Matrix visualization.

    Examples
    --------
    >>> import matplotlib.pyplot as plt  # doctest: +SKIP
    >>> from sklearn.datasets import make_classification
    >>> from sklearn.metrics import plot_confusion_matrix
    >>> from sklearn.model_selection import train_test_split
    >>> from sklearn.svm import SVC
    >>> X, y = make_classification(random_state=0)
    >>> X_train, X_test, y_train, y_test = train_test_split(
    ...         X, y, random_state=0)
    >>> clf = SVC(random_state=0)
    >>> clf.fit(X_train, y_train)
    SVC(random_state=0)
    >>> plot_confusion_matrix(clf, X_test, y_test)  # doctest: +SKIP
    >>> plt.show()  # doctest: +SKIP
    """
    check_matplotlib_support("plot_confusion_matrix")

    if not is_classifier(estimator):
        raise ValueError("plot_confusion_matrix only supports classifiers")

    y_pred = estimator.predict(X)
    cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight,
                          labels=labels, normalize=normalize)

    if display_labels is None:
        if labels is None:
            display_labels = unique_labels(y_true, y_pred)
        else:
            display_labels = labels

    disp = ConfusionMatrixDisplay(confusion_matrix=cm,
                                  display_labels=display_labels)
    return disp.plot(include_values=include_values,
                     cmap=cmap, ax=ax, xticks_rotation=xticks_rotation,
                     values_format=values_format, colorbar=colorbar)