def plot_cumulative_gain(y_true, y_probas, title='Cumulative Gains Curve',
                         ax=None, figsize=None, title_fontsize="large",
                         text_fontsize="medium"):
    """Generates the Cumulative Gains Plot from labels and scores/probabilities

    The cumulative gains chart is used to determine the effectiveness of a
    binary classifier. A detailed explanation can be found at
    http://mlwiki.org/index.php/Cumulative_Gain_Chart. The implementation
    here works only for binary classification.

    Args:
        y_true (array-like, shape (n_samples)):
            Ground truth (correct) target values.

        y_probas (array-like, shape (n_samples, n_classes)):
            Prediction probabilities for each class returned by a classifier.

        title (string, optional): Title of the generated plot. Defaults to
            "Cumulative Gains Curve".

        ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to
            plot the learning curve. If None, the plot is drawn on a new set of
            axes.

        figsize (2-tuple, optional): Tuple denoting figure size of the plot
            e.g. (6, 6). Defaults to ``None``.

        title_fontsize (string or int, optional): Matplotlib-style fontsizes.
            Use e.g. "small", "medium", "large" or integer-values. Defaults to
            "large".

        text_fontsize (string or int, optional): Matplotlib-style fontsizes.
            Use e.g. "small", "medium", "large" or integer-values. Defaults to
            "medium".

    Returns:
        ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was
            drawn.

    Example:
        >>> import scikitplot as skplt
        >>> lr = LogisticRegression()
        >>> lr = lr.fit(X_train, y_train)
        >>> y_probas = lr.predict_proba(X_test)
        >>> skplt.metrics.plot_cumulative_gain(y_test, y_probas)
        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
        >>> plt.show()

        .. image:: _static/examples/plot_cumulative_gain.png
           :align: center
           :alt: Cumulative Gains Plot
    """
    y_true = np.array(y_true)
    y_probas = np.array(y_probas)

    classes = np.unique(y_true)
    if len(classes) != 2:
        raise ValueError('Cannot calculate Cumulative Gains for data with '
                         '{} category/ies'.format(len(classes)))

    # Compute Cumulative Gain Curves
    percentages, gains1 = cumulative_gain_curve(y_true, y_probas[:, 0],
                                                classes[0])
    percentages, gains2 = cumulative_gain_curve(y_true, y_probas[:, 1],
                                                classes[1])

    if ax is None:
        fig, ax = plt.subplots(1, 1, figsize=figsize)

    ax.set_title(title, fontsize=title_fontsize)

    ax.plot(percentages, gains1, lw=3, label='Class {}'.format(classes[0]))
    ax.plot(percentages, gains2, lw=3, label='Class {}'.format(classes[1]))

    ax.set_xlim([0.0, 1.0])
    ax.set_ylim([0.0, 1.0])

    ax.plot([0, 1], [0, 1], 'k--', lw=2, label='Baseline')

    ax.set_xlabel('Percentage of sample', fontsize=text_fontsize)
    ax.set_ylabel('Gain', fontsize=text_fontsize)
    ax.tick_params(labelsize=text_fontsize)
    ax.grid('on')
    ax.legend(loc='lower right', fontsize=text_fontsize)

    return ax
Generates the Cumulative Gains Plot from labels and scores/probabilities

    The cumulative gains chart is used to determine the effectiveness of a
    binary classifier. A detailed explanation can be found at
    http://mlwiki.org/index.php/Cumulative_Gain_Chart. The implementation
    here works only for binary classification.

    Args:
        y_true (array-like, shape (n_samples)):
            Ground truth (correct) target values.

        y_probas (array-like, shape (n_samples, n_classes)):
            Prediction probabilities for each class returned by a classifier.

        title (string, optional): Title of the generated plot. Defaults to
            "Cumulative Gains Curve".

        ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to
            plot the learning curve. If None, the plot is drawn on a new set of
            axes.

        figsize (2-tuple, optional): Tuple denoting figure size of the plot
            e.g. (6, 6). Defaults to ``None``.

        title_fontsize (string or int, optional): Matplotlib-style fontsizes.
            Use e.g. "small", "medium", "large" or integer-values. Defaults to
            "large".

        text_fontsize (string or int, optional): Matplotlib-style fontsizes.
            Use e.g. "small", "medium", "large" or integer-values. Defaults to
            "medium".

    Returns:
        ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was
            drawn.

    Example:
        >>> import scikitplot as skplt
        >>> lr = LogisticRegression()
        >>> lr = lr.fit(X_train, y_train)
        >>> y_probas = lr.predict_proba(X_test)
        >>> skplt.metrics.plot_cumulative_gain(y_test, y_probas)
        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
        >>> plt.show()

        .. image:: _static/examples/plot_cumulative_gain.png
           :align: center
           :alt: Cumulative Gains Plot