code: def roc_multi_classification_raw_data_set2():
      from sklearn.datasets import load_iris
  
      iris = load_iris()
      X, y = iris.data, iris.target
      y = iris.target_names[y]
  
      random_state = np.random.RandomState(0)
      n_samples, n_features = X.shape
  
      X = np.concatenate(
          [X, random_state.randn(n_samples, 200 * n_features)], axis=1
      )
      (
          X_train,
          X_test,
          y_train,
          y_test,
      ) = train_test_split(
          X, y, test_size=0.5, stratify=y, random_state=0
      )
  
      classifier = LogisticRegression()
      y_score = classifier.fit(X_train, y_train).predict_proba(X_test)
  
      return y_test, y_score
  
imports: from copy import copy
  import sys
  import os
  from pathlib import Path
  import numpy
  from sklearn.linear_model import LogisticRegression
  import pytest
  from matplotlib.testing.conftest import mpl_test_settings, mpl_image_comparison_parameters
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import GridSearchCV
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 13 for calling a global function with example usage: roc_multi_classification_raw_data_set2() and returns: y_test,  y_score