modified: __pycache__/evaluation_studio.cpython-314.pyc

modified:   evaluation_studio.py
	modified:   scripts/dataset.py
	new file:   scripts/inference_utils.py
	modified:   scripts/predict_single.py
	modified:   scripts_2/__pycache__/task3_identify.cpython-314.pyc
	modified:   scripts_2/task3_identify.py

scripts/inference_utils.py

@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+def _task1_curve_candidates(project_root: Path) -> tuple[Path, ...]:
+    base_dir = project_root / "evaluation_outputs" / "task1_final"
+    return (
+        base_dir / "task1_final_dense_curve.csv",
+        base_dir / "evaluation_dense_curve.csv",
+    )
+
+
+@lru_cache(maxsize=8)
+def load_task1_dense_curve(project_root_str: str) -> tuple[np.ndarray, np.ndarray, np.ndarray] | None:
+    project_root = Path(project_root_str)
+    for path in _task1_curve_candidates(project_root):
+        if not path.exists():
+            continue
+        try:
+            curve_df = pd.read_csv(path)
+        except Exception:
+            continue
+        required = {"frequency_hz", "pred_tr", "x_rms_interp"}
+        if not required.issubset(set(curve_df.columns)):
+            continue
+        cleaned = curve_df[["frequency_hz", "pred_tr", "x_rms_interp"]].copy()
+        cleaned["frequency_hz"] = pd.to_numeric(cleaned["frequency_hz"], errors="coerce")
+        cleaned["pred_tr"] = pd.to_numeric(cleaned["pred_tr"], errors="coerce")
+        cleaned["x_rms_interp"] = pd.to_numeric(cleaned["x_rms_interp"], errors="coerce")
+        cleaned = cleaned.dropna()
+        if cleaned.empty:
+            continue
+        cleaned = cleaned.sort_values("frequency_hz").drop_duplicates(subset="frequency_hz", keep="first")
+        return (
+            cleaned["frequency_hz"].to_numpy(dtype=np.float64),
+            cleaned["pred_tr"].to_numpy(dtype=np.float64),
+            cleaned["x_rms_interp"].to_numpy(dtype=np.float64),
+        )
+    return None
+
+
+def predict_task1_tr(
+    model,
+    *,
+    feature_vector: np.ndarray,
+    frequency_hz: float,
+    normalization_eps: float,
+    project_root: Path,
+    prefer_curve: bool = True,
+) -> tuple[float, str]:
+    features = np.asarray(feature_vector, dtype=np.float64).reshape(1, -1)
+    tr_value = max(float(model.predict(features)[0]), normalization_eps)
+    source = "model_direct"
+
+    if prefer_curve:
+        curve = load_task1_dense_curve(str(project_root.resolve()))
+        if curve is not None and features.shape[1] >= 3:
+            freq_grid, _tr_grid, x_ref_grid = curve
+            query_freq = float(np.clip(frequency_hz, freq_grid.min(), freq_grid.max()))
+            x_ref = float(np.interp(query_freq, freq_grid, x_ref_grid))
+            x_obs = float(features[0, 2])
+            ratio = x_obs / max(x_ref, normalization_eps)
+            if ratio > 1.15 or ratio < 0.85:
+                tr_value = max(tr_value / ratio, normalization_eps)
+                source = "model_amp_adapt"
+
+    return tr_value, source