8000 FIX DecisionBoundaryPlot should not raise spurious warning by glemaitre · Pull Request #23318 · scikit-learn/scikit-learn · GitHub
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FIX DecisionBoundaryPlot should not raise spurious warning #23318

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Merged
merged 1 commit into from
May 10, 2022
Merged

FIX DecisionBoundaryPlot should not raise spurious warning #23318

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9 changes: 8 additions & 1 deletion sklearn/inspection/_plot/decision_boundary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -294,9 +294,16 @@ def from_estimator(
np.linspace(x0_min, x0_max, grid_resolution),
np.linspace(x1_min, x1_max, grid_resolution),
)
if hasattr(X, "iloc"):
# we need to preserve the feature names and therefore get an empty dataframe
X_grid = X.iloc[[], :].copy()
X_grid.iloc[:, 0] = xx0.ravel()
X_grid.iloc[:, 1] = xx1.ravel()
else:
X_grid = np.c_[xx0.ravel(), xx1.ravel()]

pred_func = _check_boundary_response_method(estimator, response_method)
response = pred_func(np.c_[xx0.ravel(), xx1.ravel()])
response = pred_func(X_grid)

# convert classes predictions into integers
if pred_func.__name__ == "predict" and hasattr(estimator, "classes_"):
Expand Down
24 changes: 24 additions & 0 deletions sklearn/inspection/_plot/tests/test_boundary_decision_display.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
import warnings

import pytest
import numpy as np
from numpy.testing import assert_allclose
Expand Down Expand Up @@ -265,6 +267,11 @@ def test_multioutput_regressor_error(pyplot):
DecisionBoundaryDisplay.from_estimator(tree, X)


@pytest.mark.filterwarnings(
# We expect to raise the following warning because the classifier is fit on a
# NumPy array
"ignore:X has feature names, but LogisticRegression was fitted without"
)
def test_dataframe_labels_used(pyplot, fitted_clf):
"""Check that column names are used for pandas."""
pd = pytest.importorskip("pandas")
Expand Down Expand Up @@ -319,3 +326,20 @@ def test_string_target(pyplot):
grid_resolution=5,
response_method="predict",
)


def test_dataframe_support():
"""Check that passing a dataframe at fit and to the Display does not
raise warnings.

Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/23311
"""
pd = pytest.importorskip("pandas")
df = pd.DataFrame(X, columns=["col_x", "col_y"])
estimator = LogisticRegression().fit(df, y)

with warnings.catch_warnings():
# no warnings linked to feature names validation should be raised
warnings.simplefilter("error", UserWarning)
DecisionBoundaryDisplay.from_estimator(estimator, df, response_method="predict")
0