code: def test_p_percent_pandas_multiclass(
      sensitive_multiclass_classification_dataset,
  ):
      X, y = sensitive_multiclass_classification_dataset
      mod_unfair = LogisticRegression(multi_class="ovr").fit(X, y)
      assert p_percent_score("x2")(mod_unfair, X) == 0
      assert (
          p_percent_score("x2", positive_target=2)(mod_unfair, X) == 0
      )
  
      mod_fair = make_pipeline(
          ColumnSelector("x1"), LogisticRegression()
      ).fit(X, y)
      assert p_percent_score("x2")(mod_fair, X) == pytest.approx(
          0.9333333
      )
      assert p_percent_score("x2", positive_target=2)(mod_fair, X) == 0
  
file_path: scikit-lego/tests/test_metrics/test_p_percent.py
imports: import warnings
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklearn.pipeline import make_pipeline
  from sklego.metrics import p_percent_score
  from sklego.preprocessing import ColumnSelector
label_desc: Use image node_id 2 for calling a global function with example usage: test_p_percent_pandas_multiclass(sensitive_multiclass_classification_dataset) without return types