class_docstr: Test the LearningCurve visualizer
docstr: Test on a real dataset with NumPy arrays
docstr_tok: ["Test","on","a","real","dataset","with","NumPy","arrays"]
code: def test_numpy_integration(self):
      """
      Test on a real dataset with NumPy arrays
      """
      data = load_mushroom(return_dataset=True)
      X, y = data.to_numpy()
  
      X = OneHotEncoder().fit_transform(X).toarray()
  
      cv = StratifiedKFold(n_splits=4, shuffle=True, random_state=32)
      oz = LearningCurve(GaussianNB(), cv=cv, random_state=23)
      oz.fit(X, y)
      oz.finalize()
  
      self.assert_images_similar(oz)
  
imports: import sys
  import pytest
  import numpy
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from sklearn.svm import LinearSVC
  from sklearn.linear_model import Ridge
  from sklearn.naive_bayes import GaussianNB, MultinomialNB
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.preprocessing import OneHotEncoder, LabelEncoder
  from sklearn.impute import SimpleImputer
  from sklearn.model_selection import ShuffleSplit
  from sklearn.model_selection import StratifiedKFold
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from yellowbrick.datasets import load_mushroom, load_game
  from yellowbrick.model_selection.learning_curve import *
label_desc: Use image node_id 7 for calling the TestLearningCurve obj's underlying member method code with example usage: obj.test_numpy_integration() without return types