Create prediction_baseline.py

prediction_baseline.py

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+import pandas as pd
+
+df = pd.read_csv("data/tester.csv")
+
+# naive baseline:
+# predict unstable if boundary is very small or drift is strongly positive
+df["prediction"] = (
+    (df["boundary_distance"] <= 0.08) |
+    (df["drift_gradient"] >= 0.07)
+).astype(int)
+
+# optional intervention direction baseline
+def predict_direction(row):
+    if pd.isna(row["boundary_distance_before"]) or pd.isna(row["boundary_distance_after"]):
+        return None
+    if row["boundary_distance_after"] > row["boundary_distance_before"]:
+        return 1
+    if row["boundary_distance_after"] < row["boundary_distance_before"]:
+        return -1
+    return 0
+
+df["intervention_effect_direction"] = df.apply(predict_direction, axis=1)
+
+out = df[["scenario_id", "prediction", "intervention_effect_direction"]]
+out.to_csv("predictions.csv", index=False)
+
+print("Baseline predictions written to predictions.csv")
+print(f"rows: {len(out)}")