[2026-05-27] 포맷 후 복구 설치 스크립트 추가

app/ml/predictor.py

@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import joblib
+import pandas as pd
+
+from app.ml.features import build_feature_matrix
+
+
+DEFAULT_MODEL_PATH = Path("models/scalping_model.joblib")
+
+
+class ScalpingModel:
+    def __init__(self, model_path: str | Path = DEFAULT_MODEL_PATH):
+        self.model_path = Path(model_path)
+        self.bundle: dict[str, Any] | None = None
+
+    @property
+    def available(self) -> bool:
+        return self.model_path.exists()
+
+    def load(self) -> bool:
+        if not self.available:
+            return False
+        self.bundle = joblib.load(self.model_path)
+        return True
+
+    @property
+    def version(self) -> str:
+        if self.bundle is None and not self.load():
+            return ""
+        assert self.bundle is not None
+        return str(self.bundle.get("created_at", ""))
+
+    def score(self, row: dict[str, Any]) -> dict[str, float]:
+        if self.bundle is None and not self.load():
+            return {}
+
+        assert self.bundle is not None
+        frame = pd.DataFrame([row])
+        features, _ = build_feature_matrix(
+            frame,
+            self.bundle["feature_columns"],
+            self.bundle.get("medians"),
+        )
+
+        scores: dict[str, float] = {}
+        for target, model in self.bundle.get("models", {}).items():
+            if hasattr(model, "predict_proba"):
+                scores[target] = float(model.predict_proba(features)[0][1])
+            else:
+                scores[target] = float(model.predict(features)[0])
+        return scores