Add tests for explore module (correlations + class means)

4 tests verify:
- Strongest feature ranks first in feature_target_correlations
- Empty results when n < 30 or target missing
- Class-mean differences detect separating features

Total tests: 19/19 passing.

tests/test_explore.py

@@ -0,0 +1,62 @@
+"""Tests for the feature/target exploration helpers."""
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from microbe_model.explore import class_mean_features, feature_target_correlations
+
+
+def _synthetic_with_signal(n: int = 200, seed: int = 0) -> pd.DataFrame:
+    rng = np.random.default_rng(seed)
+    df = pd.DataFrame({
+        "f_strong": rng.normal(size=n),
+        "f_weak": rng.normal(size=n),
+        "f_noise": rng.normal(size=n),
+    })
+    # Strong signal: target = 30 + 5 * f_strong + noise
+    df["optimal_temperature_c"] = 30 + 5 * df["f_strong"] + rng.normal(scale=1, size=n)
+    # Make some NaN
+    df.loc[rng.random(n) > 0.95, "optimal_temperature_c"] = np.nan
+    return df
+
+
+def test_correlations_rank_strong_feature_first() -> None:
+    df = _synthetic_with_signal()
+    feats = ["f_strong", "f_weak", "f_noise"]
+    corrs = feature_target_correlations(df, feats, "optimal_temperature_c", top_n=3)
+    assert len(corrs) == 3
+    # Strongest feature should be ranked first
+    assert corrs[0]["feature"] == "f_strong"
+    assert abs(corrs[0]["spearman_rho"]) > 0.5
+
+
+def test_correlations_skip_low_n() -> None:
+    df = pd.DataFrame({
+        "f0": np.arange(10),
+        "y": [1.0] * 5 + [np.nan] * 5,
+    })
+    assert feature_target_correlations(df, ["f0"], "y", top_n=5) == []
+
+
+def test_correlations_skip_missing_target() -> None:
+    df = pd.DataFrame({"f0": [1.0, 2.0]})
+    assert feature_target_correlations(df, ["f0"], "missing_target", top_n=5) == []
+
+
+def test_class_means_orders_most_separating_first() -> None:
+    rng = np.random.default_rng(0)
+    n = 200
+    cls = rng.choice(["a", "b", "c"], size=n)
+    df = pd.DataFrame({
+        "f_separating": np.where(cls == "a", 0, np.where(cls == "b", 5, 10)) + rng.normal(scale=0.5, size=n),
+        "f_useless": rng.normal(size=n),
+        "target": cls,
+    })
+    out = class_mean_features(df, ["f_separating", "f_useless"], "target", top_features=2)
+    assert len(out) == 2
+    assert out[0]["feature"] == "f_separating"
+    assert out[1]["feature"] == "f_useless"
+    # Class means should differ substantially for f_separating
+    means = out[0]["means"]
+    assert max(means.values()) - min(means.values()) > 5