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 ...base import is_classifier


class ConfusionMatrixDisplay:
    """Confusion Matrix visualization.

    It is recommend to use
    :func:`~sklearn.metrics.ConfusionMatrixDisplay.from_estimator` or
    :func:`~sklearn.metrics.ConfusionMatrixDisplay.from_predictions` 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
    --------
    >>> import matplotlib.pyplot as plt
    >>> 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()
    <...>
    >>> plt.show()
    """

    def __init__(self, confusion_matrix, *, display_labels=None):
        self.confusion_matrix = confusion_matrix
        self.display_labels = display_labels

    def plot(
        self,
        *,
        include_values=True,
        cmap="viridis",
        xticks_rotation="horizontal",
        values_format=None,
        ax=None,
        colorbar=True,
        im_kw=None,
        text_kw=None,
    ):
        """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.

        im_kw : dict, default=None
            Dict with keywords passed to `matplotlib.pyplot.imshow` call.

        text_kw : dict, default=None
            Dict with keywords passed to `matplotlib.pyplot.text` call.

            .. versionadded:: 1.2

        Returns
        -------
        display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
            Returns a :class:`~sklearn.metrics.ConfusionMatrixDisplay` instance
            that contains all the information to plot the confusion matrix.
        """
        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]

        default_im_kw = dict(interpolation="nearest", cmap=cmap)
        im_kw = im_kw or {}
        im_kw = {**default_im_kw, **im_kw}
        text_kw = text_kw or {}

        self.im_ = ax.imshow(cm, **im_kw)
        self.text_ = None
        cmap_min, cmap_max = self.im_.cmap(0), self.im_.cmap(1.0)

        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)

                default_text_kwargs = dict(ha="center", va="center", color=color)
                text_kwargs = {**default_text_kwargs, **text_kw}

                self.text_[i, j] = ax.text(j, i, text_cm, **text_kwargs)

        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

    @classmethod
    def from_estimator(
        cls,
        estimator,
        X,
        y,
        *,
        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,
        im_kw=None,
        text_kw=None,
    ):
        """Plot Confusion Matrix given an estimator and some data.

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

        .. versionadded:: 1.0

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

        labels : array-like of shape (n_classes,), default=None
            List of labels to index the confusion 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
            Either to normalize the counts display in the matrix:

            - if `'true'`, the confusion matrix is normalized over the true
              conditions (e.g. rows);
            - if `'pred'`, the confusion matrix is normalized over the
              predicted conditions (e.g. columns);
            - if `'all'`, the confusion matrix is normalized by the total
              number of samples;
            - if `None` (default), the 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.

        im_kw : dict, default=None
            Dict with keywords passed to `matplotlib.pyplot.imshow` call.

        text_kw : dict, default=None
            Dict with keywords passed to `matplotlib.pyplot.text` call.

            .. versionadded:: 1.2

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

        See Also
        --------
        ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix
            given the true and predicted labels.

        Examples
        --------
        >>> import matplotlib.pyplot as plt
        >>> from sklearn.datasets import make_classification
        >>> from sklearn.metrics import 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)
        >>> ConfusionMatrixDisplay.from_estimator(
        ...     clf, X_test, y_test)
        <...>
        >>> plt.show()
        """
        method_name = f"{cls.__name__}.from_estimator"
        check_matplotlib_support(method_name)
        if not is_classifier(estimator):
            raise ValueError(f"{method_name} only supports classifiers")
        y_pred = estimator.predict(X)

        return cls.from_predictions(
            y,
            y_pred,
            sample_weight=sample_weight,
            labels=labels,
            normalize=normalize,
            display_labels=display_labels,
            include_values=include_values,
            cmap=cmap,
            ax=ax,
            xticks_rotation=xticks_rotation,
            values_format=values_format,
            colorbar=colorbar,
            im_kw=im_kw,
            text_kw=text_kw,
        )

    @classmethod
    def from_predictions(
        cls,
        y_true,
        y_pred,
        *,
        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,
        im_kw=None,
        text_kw=None,
    ):
        """Plot Confusion Matrix given true and predicted labels.

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

        .. versionadded:: 1.0

        Parameters
        ----------
        y_true : array-like of shape (n_samples,)
            True labels.

        y_pred : array-like of shape (n_samples,)
            The predicted labels given by the method `predict` of an
            classifier.

        labels : array-like of shape (n_classes,), default=None
            List of labels to index the confusion 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
            Either to normalize the counts display in the matrix:

            - if `'true'`, the confusion matrix is normalized over the true
              conditions (e.g. rows);
            - if `'pred'`, the confusion matrix is normalized over the
              predicted conditions (e.g. columns);
            - if `'all'`, the confusion matrix is normalized by the total
              number of samples;
            - if `None` (default), the 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.

        im_kw : dict, default=None
            Dict with keywords passed to `matplotlib.pyplot.imshow` call.

        text_kw : dict, default=None
            Dict with keywords passed to `matplotlib.pyplot.text` call.

            .. versionadded:: 1.2

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

        See Also
        --------
        ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix
            given an estimator, the data, and the label.

        Examples
        --------
        >>> import matplotlib.pyplot as plt
        >>> from sklearn.datasets import make_classification
        >>> from sklearn.metrics import 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)
        >>> y_pred = clf.predict(X_test)
        >>> ConfusionMatrixDisplay.from_predictions(
        ...    y_test, y_pred)
        <...>
        >>> plt.show()
        """
        check_matplotlib_support(f"{cls.__name__}.from_predictions")

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

        cm = confusion_matrix(
            y_true,
            y_pred,
            sample_weight=sample_weight,
            labels=labels,
            normalize=normalize,
        )

        disp = cls(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,
            im_kw=im_kw,
            text_kw=text_kw,
        )
