[2026-05-27] 포맷 후 복구 설치 스크립트 추가

app/ml/features.py

@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from typing import Iterable
+
+import pandas as pd
+
+
+LABEL_COLUMNS = {"label_win", "label_stop_loss"}
+
+EXCLUDED_COLUMNS = {
+    "id",
+    "trade_id",
+    "date",
+    "ticker",
+    "name",
+    "entry_time",
+    "exit_time",
+    "sample_time",
+    "created_at",
+    "exit_reason",
+    "strategy",
+    "reason",
+    "source_file",
+    "ai_win_score",
+    "ai_stop_loss_score",
+    "ai_model_version",
+}
+
+
+def select_feature_columns(df: pd.DataFrame, targets: Iterable[str] = LABEL_COLUMNS) -> list[str]:
+    excluded = EXCLUDED_COLUMNS | set(targets)
+    numeric_columns = [
+        column
+        for column in df.columns
+        if column not in excluded and pd.api.types.is_numeric_dtype(df[column])
+    ]
+    return sorted(numeric_columns)
+
+
+def build_feature_matrix(
+    df: pd.DataFrame,
+    feature_columns: list[str],
+    medians: dict[str, float] | None = None,
+) -> tuple[pd.DataFrame, dict[str, float]]:
+    features = df.reindex(columns=feature_columns)
+    features = features.apply(pd.to_numeric, errors="coerce")
+
+    if medians is None:
+        medians = {
+            column: float(value) if pd.notna(value) else 0.0
+            for column, value in features.median(numeric_only=True).items()
+        }
+
+    features = features.fillna(medians).fillna(0.0)
+    return features, medians