Update causal.py

causal.py

@@ -5,14 +5,6 @@ import pymc as pm
 import pytensor.tensor as at
 from typing import Dict, Any, Optional
 
-"""
-階層ロジスティック回帰で uplift（コントロールとの差）を推定。
-- 目的変数: click (0/1) 推奨。集計単位が日次のときは二項モデルに切替可能。
-- 階層: 媒体ランダム効果。
-- 処置: creative（control=1 の行を基準）
-- 共変量: 任意の数値列（正規化して利用）
-"""
-
 def _zscore(df: pd.DataFrame, cols):
     x = df[cols].astype(float)
     return (x - x.mean()) / (x.std(ddof=0) + 1e-9)
@@ -28,11 +20,8 @@ def fit_uplift_binary(
     target_accept: float = 0.9,
     random_seed: int = 42,
 ) -> Dict[str, Any]:
-    # 前処理
     d = df.copy().reset_index(drop=True)
     if outcome_col not in d.columns:
-        # 集計データ (impr, clicks) から擬似サンプル化
-        # 二項尤度に切替
         d["n"] = d["impressions"].astype(int)
         d["y"] = d["clicks"].astype(int)
         binomial = True
@@ -41,7 +30,6 @@ def fit_uplift_binary(
         d["n"] = 1
         binomial = False
 
-    # creative の one-hot と control 基準
     creatives = d[creative_col].astype(str).unique().tolist()
     control_creatives = d.loc[d[control_flag_col] == 1, creative_col].astype(str).unique().tolist()
     control_ref = control_creatives[0] if len(control_creatives) else creatives[0]
@@ -49,7 +37,6 @@ def fit_uplift_binary(
     d["creative_idx"] = d[creative_col].astype(str).apply(lambda x: creatives.index(x)).astype(int)
     d["medium_idx"] = d[medium_col].astype(str).astype('category').cat.codes.values
 
-    # 特徴量
     X = None
     if feature_cols:
         X = _zscore(d, feature_cols).values
@@ -58,27 +45,24 @@ def fit_uplift_binary(
         p = 0
 
     with pm.Model() as model:
-        # ランダム効果: 媒体
         n_medium = int(d["medium_idx"].max()) + 1
         mu_re = pm.Normal("mu_re", 0.0, 1.0)
         sd_re = pm.HalfNormal("sd_re", 1.0)
         z_re = pm.Normal("z_re", 0.0, 1.0, shape=n_medium)
         b_medium = pm.Deterministic("b_medium", mu_re + z_re * sd_re)
 
-        # creative 固定効果（baseline = control_ref）
         n_creative = len(creatives)
         b0 = pm.Normal("intercept", 0.0, 1.5)
         b_cre = pm.Normal("b_cre_raw", 0.0, 1.0, shape=n_creative)
-        # 基準調整
+
         ref_idx = creatives.index(control_ref)
         b_cre_adj = at.set_subtensor(b_cre[ref_idx], 0.0)
 
-        # 連続特徴量
         if p > 0:
             b_x = pm.Normal("b_x", 0.0, 1.0, shape=p)
-            lin = b0 + b_cre_adj[d["creative_idx"].values] + b_medium[d["medium_idx"]].values + at.dot(X, b_x)
+            lin = b0 + b_cre_adj[d["creative_idx"].values] + b_medium[d["medium_idx"].values] + at.dot(X, b_x)
         else:
-            lin = b0 + b_cre_adj[d["creative_idx"].values] + b_medium[d["medium_idx"]].values
+            lin = b0 + b_cre_adj[d["creative_idx"].values] + b_medium[d["medium_idx"].values]
 
         p_click = pm.Deterministic("p_click", pm.math.sigmoid(lin))
 
@@ -87,18 +71,16 @@ def fit_uplift_binary(
         else:
             pm.Bernoulli("y_obs", p=p_click, observed=d["y"].values)
 
-        idata = pm.sample(draws=draws, tune=draws, chains=2, target_accept=target_accept, random_seed=random_seed, progressbar=False)
+        idata = pm.sample(draws=draws, tune=draws, chains=2,
+                          target_accept=target_accept, random_seed=random_seed,
+                          progressbar=False)
 
-    # uplift: 各 creative の p_click を control_ref と比較
     post = idata.posterior
-    # サンプル次元: chain, draw
     b0_s = post["intercept"].stack(sample=("chain", "draw"))
     b_cre_s = post["b_cre_raw"].stack(sample=("chain", "draw"))
-    # uplift は "平均的ユーザー" と "平均的媒体効果" を前提に比較
     mu_re = post["mu_re"].stack(sample=("chain", "draw"))
 
-    def sigmoid(x):
-        return 1 / (1 + np.exp(-x))
+    def sigmoid(x): return 1 / (1 + np.exp(-x))
 
     results = []
     for cr in creatives:
@@ -113,8 +95,4 @@ def fit_uplift_binary(
             "control_ref": control_ref,
         })
 
-    return {
-        "control_ref": control_ref,
-        "creatives": creatives,
-        "results": results,
-    }
+    return {"control_ref": control_ref, "creatives": creatives, "results": results}