data: fix PartialDependence crash on datasets with fewer than 10 rows

python/interpret-core/interpret/blackbox/_partialdependence.py

@@ -51,6 +51,11 @@ def _gen_pdp(
     mean = np.mean(ice_lines, axis=0)
     std = np.std(ice_lines, axis=0)
 
+    # Sample without replacement, so we cannot draw more background lines than
+    # there are rows in the data. Datasets with fewer than num_ice_samples rows
+    # used to raise "Cannot take a larger sample than population when
+    # 'replace=False'", so clamp the count to the available population.
+    num_ice_samples = min(num_ice_samples, ice_lines.shape[0])
     ice_lines = ice_lines[
         np.random.choice(ice_lines.shape[0], num_ice_samples, replace=False), :
     ]

python/interpret-core/tests/blackbox/test_partialdependence.py

@@ -0,0 +1,74 @@
+# Copyright (c) 2023 The InterpretML Contributors
+# Distributed under the MIT software license
+
+import numpy as np
+import pytest
+from interpret.blackbox._partialdependence import PartialDependence, _gen_pdp
+from sklearn.linear_model import LinearRegression
+
+
+def _make_regression_data(n_samples):
+    rng = np.random.default_rng(42)
+    X = rng.normal(size=(n_samples, 2))
+    y = X[:, 0] * 2.0 - X[:, 1] + 0.5
+    return X, y
+
+
+@pytest.mark.parametrize("n_samples", [1, 2, 5, 9])
+def test_gen_pdp_fewer_rows_than_ice_samples(n_samples):
+    """Datasets with fewer rows than the default num_ice_samples must not crash.
+
+    Before the fix, sampling num_ice_samples (10) rows without replacement
+    raised "ValueError: Cannot take a larger sample than population when
+    'replace=False'" for any dataset with fewer than 10 rows.
+    """
+    X, y = _make_regression_data(n_samples)
+    model = LinearRegression().fit(X, y)
+
+    pdp = _gen_pdp(
+        X,
+        model.predict,
+        col_idx=0,
+        feature_type="continuous",
+        num_points=10,
+        std_coef=1.0,
+    )
+
+    # One background line per available row, no more.
+    assert pdp["background_scores"].shape[0] == n_samples
+    assert pdp["background_scores"].shape[1] == len(pdp["names"])
+
+
+def test_partial_dependence_small_dataset_does_not_crash():
+    """PartialDependence must work end-to-end on a dataset with < 10 rows."""
+    X, y = _make_regression_data(3)
+    model = LinearRegression().fit(X, y)
+
+    pdp = PartialDependence(
+        model, X, feature_names=["a", "b"], feature_types=["continuous", "continuous"]
+    )
+
+    assert len(pdp.pdps_) == 2
+    for pdp_item in pdp.pdps_:
+        assert pdp_item["background_scores"].shape[0] == 3
+
+    explanation = pdp.explain_global()
+    assert explanation.feature_names == ["a", "b"]
+
+
+def test_gen_pdp_enough_rows_still_caps_at_num_ice_samples():
+    """With enough rows, exactly num_ice_samples background lines are kept."""
+    X, y = _make_regression_data(25)
+    model = LinearRegression().fit(X, y)
+
+    pdp = _gen_pdp(
+        X,
+        model.predict,
+        col_idx=0,
+        feature_type="continuous",
+        num_points=10,
+        std_coef=1.0,
+        num_ice_samples=10,
+    )
+
+    assert pdp["background_scores"].shape[0] == 10