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Test PermutationImportance with XGBClassifier and pd.DataFrame issue#256 #261

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Test PermutationImportance with XGBClassifier and pd.DataFrame issue#256 #261

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14 changes: 14 additions & 0 deletions tests/test_sklearn_permutation_importance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,8 @@
from sklearn.pipeline import make_pipeline
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression
import pandas as pd
from xgboost import XGBClassifier

import eli5
from eli5.sklearn import PermutationImportance
Expand Down Expand Up @@ -164,3 +166,15 @@ def test_explain_weights(iris_train):
res = format_as_all(expl, perm.wrapped_estimator_)
for _expl in res:
assert "petal width (cm)" in _expl


def test_dataframe_input_to_xgbclassifier():
# 30 items of data, pairs of a useless feature and a predictive feature
X_np = np.array([[0,1]]*15 + [[0,2]]*15)
y_np = np.array([0]*15 + [1]*15)
X = pd.DataFrame(X_np)
y = pd.Series(y_np)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
est = XGBClassifier()
est.fit(X_train, y_train)
perm = PermutationImportance(est).fit(X_test, y_test)