Refactor _continuous_index method and add distance consistency test for explainers

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2025 Yoemi
+Copyright (c) 2025 Vidal, and the OCEAN contributors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -2,6 +2,7 @@
 
 [![Maintained](https://img.shields.io/badge/Maintained-YES-14b8a6?style=for-the-badge&logo=github)](https://github.com/vidalt/OCEAN/graphs/commit-activity)
 [![License](https://img.shields.io/github/license/vidalt/OCEAN?style=for-the-badge&color=0ea5e9&logo=unlicense&logoColor=white)](https://github.com/vidalt/OCEAN/blob/main/LICENSE)
+[![Documentation](https://img.shields.io/badge/Documentation-YES-06b6d4?style=for-the-badge&logo=readthedocs&logoColor=white)](https://ocean-py.readthedocs.io/en/latest/)
 [![Contributors](https://img.shields.io/github/contributors/vidalt/OCEAN?style=for-the-badge&color=38bdf8&logo=github)](https://github.com/vidalt/OCEAN/graphs/contributors)
 [![Stars](https://img.shields.io/github/stars/vidalt/OCEAN?style=for-the-badge&color=0284c7&logo=github)](https://github.com/vidalt/OCEAN/stargazers)
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docs/conf.py

@@ -16,6 +16,7 @@
 
 project = "OCEAN"
 author = "OCEAN contributors"
+copyright = "2026, Awa Khouna and the OCEAN contributors"
 
 try:
     release = version("oceanpy")

ocean/maxsat/_explainer.py

@@ -156,6 +156,10 @@ def explain(
             raise
         except TimeoutError as exc:
             warnings.warn(str(exc), category=UserWarning, stacklevel=2)
+            signal.alarm(0)
+            if clean_up:
+                self.cleanup()
+            return None
         finally:
             signal.alarm(0)
         # Store the query in the explanation

ocean/mip/_explanation.py

@@ -87,13 +87,11 @@ def format_discrete_value(
             return float(val)
         return float(query_arr[f])
 
-    def _continuous_index(self, feature: FeatureVar) -> int:
-        idx = 0
-        for mu_idx in range(len(feature.levels) - 1):
-            value = feature.mget(mu_idx).X
-            if not np.isclose(value, 0.0, rtol=0.0, atol=self._atol):
-                idx = mu_idx
-        return idx
+    @staticmethod
+    def _continuous_index(feature: FeatureVar) -> int:
+        x = float(feature.xget().X)
+        idx = int(np.searchsorted(feature.levels, x, side="left")) - 1
+        return max(0, min(idx, len(feature.levels) - 2))
 
     @property
     def value(self) -> Mapping[Key, Key | Number]:

tests/test_explainer.py

@@ -1,13 +1,18 @@
+import sys
+
 import gurobipy as gp
 import numpy as np
+import pandas as pd
 import pytest
 from sklearn.ensemble import RandomForestClassifier
 from xgboost import XGBClassifier
 
 from ocean import (
     ConstraintProgrammingExplainer,
+    MaxSATExplainer,
     MixedIntegerProgramExplainer,
 )
+from ocean.feature import parse_features
 
 from .utils import ENV, generate_data
 
@@ -197,3 +202,61 @@ def test_cp_explain_xgb(
 
         except gp.GurobiError as e:
             pytest.skip(f"Skipping test due to {e}")
+
+
+@pytest.mark.skipif(
+    sys.platform == "win32", reason="tests for non-windows platforms"
+)
+def test_explainers_return_same_distance_on_discrete_data() -> None:
+    raw = pd.DataFrame({
+        "age_bucket": [0, 0, 1, 1, 2, 2, 3, 3, 1, 2, 0, 3],
+        "owns_home": [0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1],
+        "job_type": [
+            "office",
+            "office",
+            "manual",
+            "manual",
+            "service",
+            "service",
+            "office",
+            "manual",
+            "service",
+            "office",
+            "manual",
+            "service",
+        ],
+    })
+    y = np.array([0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1], dtype=np.int64)
+    data, mapper = parse_features(
+        raw,
+        discretes=("age_bucket",),
+        encoded=("job_type",),
+        scale=False,
+    )
+    clf = RandomForestClassifier(
+        random_state=7,
+        n_estimators=5,
+        max_depth=3,
+    )
+    clf.fit(data, y)
+
+    x = data.iloc[0, :].to_numpy(dtype=float).flatten()
+    prediction = np.asarray(clf.predict([x]), dtype=np.int64)
+    target = int(1 - prediction[0])
+
+    mip = MixedIntegerProgramExplainer(clf, mapper=mapper, env=ENV)
+    cp = ConstraintProgrammingExplainer(clf, mapper=mapper)
+    maxsat = MaxSATExplainer(clf, mapper=mapper)
+
+    exp_mip = mip.explain(x, y=target, norm=1, random_seed=7)
+    exp_cp = cp.explain(x, y=target, norm=1, random_seed=7)
+    exp_maxsat = maxsat.explain(x, y=target, norm=1, random_seed=7)
+
+    assert exp_mip is not None
+    assert exp_cp is not None
+    assert exp_maxsat is not None
+    assert clf.predict([exp_mip.to_numpy()])[0] == target
+    assert clf.predict([exp_cp.to_numpy()])[0] == target
+    assert clf.predict([exp_maxsat.to_numpy()])[0] == target
+    assert mip.get_distance() == pytest.approx(cp.get_distance())
+    assert mip.get_distance() == pytest.approx(maxsat.get_distance())