code: def roc_auc(
      estimator,
      X_train,
      y_train,
      X_test=None,
      y_test=None,
      ax=None,
      micro=True,
      macro=True,
      per_class=True,
      binary=False,
      classes=None,
      encoder=None,
      is_fitted="auto",
      force_model=False,
      show=True,
      **kwargs,
  ):
      """ROCAUC
  
      Receiver Operating Characteristic (ROC) curves are a measure of a
      classifier's predictive quality that compares and visualizes the tradeoff
      between the models' sensitivity and specificity. The ROC curve displays
      the true positive rate on the Y axis and the false positive rate on the
      X axis on both a global average and per-class basis. The ideal point is
      therefore the top-left corner of the plot: false positives are zero and
      true positives are one.
  
      This leads to another metric, area under the curve  (AUC), a computation
      of the relationship between false positives and true positives. The higher
      the AUC, the better the model generally is. However, it is also important
      to inspect the "steepness" of the curve, as this describes the
      maximization of the true positive rate while minimizing the false positive
      rate. Generalizing "steepness" usually leads to discussions about
      convexity, which we do not get into here.
  
      Parameters
      ----------
      estimator : estimator
          A scikit-learn estimator that should be a classifier. If the model is
          not a classifier, an exception is raised. If the internal model is not
          fitted, it is fit when the visualizer is fitted, unless otherwise specified
          by ``is_fitted``.
  
      X_train : array-like, 2D
          The table of instance data or independent variables that describe the outcome of
          the dependent variable, y. Used to fit the visualizer and also to score the
          visualizer if test splits are not specified.
  
      y_train : array-like, 2D
          The vector of target data or the dependent variable predicted by X. Used to fit
          the visualizer and also to score the visualizer if test splits not specified.
  
      X_test: array-like, 2D, default: None
          The table of instance data or independent variables that describe the outcome of
          the dependent variable, y. Used to score the visualizer if specified.
  
      y_test: array-like, 1D, default: None
          The vector of target data or the dependent variable predicted by X.
          Used to score the visualizer if specified.
  
      ax : matplotlib Axes, default: None
          The axes to plot the figure on. If not specified the current axes will be
          used (or generated if required).
  
      test_size : float, default=0.2
          The percentage of the data to reserve as test data.
  
      random_state : int or None, default=None
          The value to seed the random number generator for shuffling data.
  
      micro : bool, default: True
          Plot the micro-averages ROC curve, computed from the sum of all true
          positives and false positives across all classes. Micro is not defined
          for binary classification problems with estimators with only a
          decision_function method.
  
      macro : bool, default: True
          Plot the macro-averages ROC curve, which simply takes the average of
          curves across all classes. Macro is not defined for binary
          classification problems with estimators with only a decision_function
          method.
  
      per_class : bool, default: True
          Plot the ROC curves for each individual class. This should be set
          to false if only the macro or micro average curves are required. For true
          binary classifiers, setting per_class=False will plot the positive class
          ROC curve, and per_class=True will use ``1-P(1)`` to compute the curve of
          the negative class if only a decision_function method exists on the estimator.
  
      binary : bool, default: False
          This argument quickly resets the visualizer for true binary classification
          by updating the micro, macro, and per_class arguments to False (do not use
          in conjunction with those other arguments). Note that this is not a true
          hyperparameter to the visualizer, it just collects other parameters into
          a single, simpler argument.
  
      classes : list of str, defult: None
          The class labels to use for the legend ordered by the index of the sorted
          classes discovered in the ``fit()`` method. Specifying classes in this
          manner is used to change the class names to a more specific format or
          to label encoded integer classes. Some visualizers may also use this
          field to filter the visualization for specific classes. For more advanced
          usage specify an encoder rather than class labels.
  
      encoder : dict or LabelEncoder, default: None
          A mapping of classes to human readable labels. Often there is a mismatch
          between desired class labels and those contained in the target variable
          passed to ``fit()`` or ``score()``. The encoder disambiguates this mismatch
          ensuring that classes are labeled correctly in the visualization.
  
      is_fitted : bool or str, default="auto"
          Specify if the wrapped estimator is already fitted. If False, the estimator
          will be fit when the visualizer is fit, otherwise, the estimator will not be
          modified. If "auto" (default), a helper method will check if the estimator
          is fitted before fitting it again.
  
      force_model : bool, default: False
          Do not check to ensure that the underlying estimator is a classifier. This
          will prevent an exception when the visualizer is initialized but may result
          in unexpected or unintended behavior.
  
      show: bool, default: True
          If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot
          call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply
          calls ``finalize()``
  
      kwargs : dict
          Keyword arguments passed to the visualizer base classes.
  
      Notes
      -----
      ROC curves are typically used in binary classification, and in fact the
      Scikit-Learn ``roc_curve`` metric is only able to perform metrics for
      binary classifiers. As a result it is necessary to binarize the output or
      to use one-vs-rest or one-vs-all strategies of classification. The
      visualizer does its best to handle multiple situations, but exceptions can
      arise from unexpected models or outputs.
  
      Another important point is the relationship of class labels specified on
      initialization to those drawn on the curves. The classes are not used to
      constrain ordering or filter curves; the ROC computation happens on the
      unique values specified in the target vector to the ``score`` method. To
      ensure the best quality visualization, do not use a LabelEncoder for this
      and do not pass in class labels.
  
      .. seealso:: https://bit.ly/2IORWO2
      .. todo:: Allow the class list to filter the curves on the visualization.
  
      Examples
      --------
      >>> from yellowbrick.classifier import ROCAUC
      >>> from sklearn.linear_model import LogisticRegression
      >>> data = load_data("occupancy")
      >>> features = ["temp", "relative humidity", "light", "C02", "humidity"]
      >>> X = data[features].values
      >>> y = data.occupancy.values
      >>> roc_auc(LogisticRegression(), X, y)
  
      Returns
      -------
      viz : ROCAUC
          Returns the fitted, finalized visualizer object
      """
      # Instantiate the visualizer
      visualizer = ROCAUC(
          estimator=estimator,
          ax=ax,
          micro=micro,
          macro=macro,
          per_class=per_class,
          binary=binary,
          classes=classes,
          encoder=encoder,
          is_fitted=is_fitted,
          force_model=force_model,
          **kwargs,
      )
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X_train, y_train, **kwargs)
  
      # Scores the visualizer with X_test and y_test if provided,
      # X_train, y_train if not provided
      if X_test is not None and y_test is not None:
          visualizer.score(X_test, y_test)
      else:
          visualizer.score(X_train, y_train)
  
      if show:
          visualizer.show()
      else:
          visualizer.finalize()
  
      # Return the visualizer
      return visualizer
  
file_path: yellowbrick/yellowbrick/classifier/rocauc.py
imports: import numpy
  from sklearn.metrics import auc, roc_curve
  from sklearn.preprocessing import label_binarize
  from sklearn.utils.multiclass import type_of_target
  from yellowbrick.exceptions import ModelError
  from yellowbrick.style.palettes import LINE_COLOR
  from yellowbrick.exceptions import YellowbrickValueError
  from yellowbrick.classifier.base import ClassificationScoreVisualizer
label_desc: Use image node_id 1 for calling a global function with example usage: roc_auc(estimator, X_train, y_train, X_test, y_test, ax, micro, macro, per_class, binary, classes, encoder, is_fitted, force_model, show) and returns: visualizer