Update app/forecast.py

app/forecast.py

@@ -3,7 +3,6 @@ import pandas as pd
 import numpy as np
 from . import storage
 
-# 可能なら Prophet / NeuralProphet を使用（無ければフォールバック）
 try:
     from prophet import Prophet
 except Exception:
@@ -20,17 +19,15 @@ class SeasonalityModel:
         self.campaign_id = campaign_id
         self.model = None
         self.model_type = "none"
-        self.global_mean = 0.05  # データが乏しいときの既定CTR
+        self.global_mean = 0.05
 
     def fit(self):
-        # イベントから時系列（1時間粒度のCTR）を作る
         with storage.get_conn() as con:
             df = pd.read_sql_query(
                 "SELECT ts, event_type FROM events WHERE campaign_id=?",
                 con,
                 params=(self.campaign_id,),
             )
-
         if df.empty:
             self.model_type = "none"
             return
@@ -38,28 +35,16 @@ class SeasonalityModel:
         df["ts"] = pd.to_datetime(df["ts"], errors="coerce")
         df = df.dropna(subset=["ts"])
         df["hour"] = df["ts"].dt.floor("h")
+        agg = df.pivot_table(index="hour", columns="event_type", values="ts", aggfunc="count").fillna(0)
+        if "impression" not in agg: agg["impression"] = 0
+        if "click" not in agg: agg["click"] = 0
 
-        agg = (
-            df.pivot_table(
-                index="hour", columns="event_type", values="ts", aggfunc="count"
-            )
-            .fillna(0)
-        )
-        if "impression" not in agg:
-            agg["impression"] = 0
-        if "click" not in agg:
-            agg["click"] = 0
-
-        ctr = np.where(
-            agg["impression"] > 0, agg["click"] / agg["impression"], np.nan
-        )
+        ctr = np.where(agg["impression"] > 0, agg["click"] / agg["impression"], np.nan)
         if np.all(np.isnan(ctr)):
             self.model_type = "none"
             return
-
         self.global_mean = float(np.nanmean(ctr))
 
-        # Prophet / NeuralProphet の学習データ
         ds = agg.index.to_series().reset_index(drop=True)
         train = pd.DataFrame({"ds": ds, "y": pd.Series(ctr).fillna(self.global_mean).values})
 
@@ -67,36 +52,27 @@ class SeasonalityModel:
             if Prophet is not None:
                 m = Prophet(weekly_seasonality=True, daily_seasonality=True)
                 m.fit(train)
-                self.model = m
-                self.model_type = "prophet"
+                self.model, self.model_type = m, "prophet"
             elif NeuralProphet is not None:
                 m = NeuralProphet(weekly_seasonality=True, daily_seasonality=True)
                 m.fit(train, freq="H")
-                self.model = m
-                self.model_type = "neuralprophet"
+                self.model, self.model_type = m, "neuralprophet"
             else:
                 self.model_type = "none"
         except Exception:
-            # 失敗時はフォールバック
             self.model_type = "none"
 
     def expected_ctr(self, context: dict) -> float:
         hour = int(context.get("hour", 12))
-
-        # モデルが無い場合は簡易ヒューリスティック
         if self.model_type in {None, "none"}:
             base = self.global_mean
-            if 11 <= hour <= 13:
-                return min(0.99, base * 1.1)
-            if 20 <= hour <= 23:
-                return min(0.99, base * 1.15)
+            if 11 <= hour <= 13: return min(0.99, base * 1.1)
+            if 20 <= hour <= 23: return min(0.99, base * 1.15)
             return max(0.01, base)
 
-        # モデルあり：当日・指定時間の1点予測
         now_ds = pd.Timestamp.utcnow().floor("D") + pd.Timedelta(hours=hour)
         if self.model_type == "prophet":
             yhat = float(self.model.predict(pd.DataFrame({"ds": [now_ds]}))["yhat"].iloc[0])
-        else:  # neuralprophet
+        else:
             yhat = float(self.model.predict(pd.DataFrame({"ds": [now_ds]}))["yhat1"].iloc[0])
-
         return max(0.01, min(0.99, yhat))