code: 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
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import itertools
  import matplotlib.pyplot
  import numpy
  from sklearn.metrics import confusion_matrix
  from sklearn.preprocessing import label_binarize
  from sklearn.preprocessing import LabelEncoder
  from sklearn.metrics import roc_curve
  from sklearn.metrics import auc
  from sklearn.metrics import precision_recall_curve
  from sklearn.metrics import average_precision_score
  from sklearn.utils.multiclass import unique_labels
  from sklearn.metrics import silhouette_score
  from sklearn.metrics import silhouette_samples
  from sklearn.calibration import calibration_curve
  from sklearn.utils import deprecated
  from scipy import interp
  from scikitplot.helpers import binary_ks_curve, validate_labels
  from scikitplot.helpers import cumulative_gain_curve
label_desc: Use image node_id 9 for calling a global function with example usage: plot_cumulative_gain(y_true, y_probas, title, ax, figsize, title_fontsize, text_fontsize) and returns: ax