class_docstr: Test FeatureImportances visualizer
docstr: Test stack plot with only the three least important features by sum of
  each feature's importance across all classes
docstr_tok: ["Test","stack","plot","with","only","the","three","least","important","features","by","sum","of","each","feature","'s","importance","across","all","classes"]
code: def test_topn_negative_stacked(self):
      """
      Test stack plot with only the three least important features by sum of
      each feature's importance across all classes
      """
      X, y = load_iris(return_X_y=True)
  
      viz = FeatureImportances(
          LogisticRegression(solver="liblinear", random_state=222),
          stack=True,
          topn=-3,
      )
      viz.fit(X, y)
      viz.finalize()
  
      npt.assert_equal(viz.feature_importances_.shape, (3, 3))
      # Appveyor and Linux conda non-text-based differences
      self.assert_images_similar(viz, tol=17.5)
  
imports: import pytest
  import numpy
  import numpy.testing
  import matplotlib.pyplot
  from yellowbrick.exceptions import NotFitted
  from yellowbrick.model_selection.importances import *
  from yellowbrick.datasets import load_occupancy, load_concrete
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.linear_model import LogisticRegression, Lasso
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from unittest import mock
  from tests.base import VisualTestCase
label_desc: Use image node_id 24 for calling the TestFeatureImportancesVisualizer obj's underlying member method code with example usage: obj.test_topn_negative_stacked() without return types