diff --git a/analysis.py b/analysis.py
index 470a567..720bf55 100644
--- a/analysis.py
+++ b/analysis.py
@@ -39,7 +39,8 @@
     "HandyTCC_Web",
 ]  # データセットの種類
 input_list = ["shape", "color", "texture", "all"]  # 入力の種類
-target_list = ["A", "B", "C", "Total"]  # ターゲットスコア
+target_list = ["A", "B", "C", "Total", "Raw", "Conv"]  # ターゲットスコア
+# target_list = ["Raw", "Conv"]  # ターゲットスコア追加解析
 model_list = [
     RandomForestClassifier(),
     AdaBoostClassifier(),
@@ -85,6 +86,7 @@
         # print("Using all features...")
         x = all_data.loc[:, "shape-width":"fiveClick-tip-b-kurtosis"]  # 全データ
     scores = all_data.loc[:, "A01":"C08"]  # スコア
+    scores["id"] = all_data["ID"]  # IDを追加
     invert_list = ["A01", "A02", "A03"]
     for invert in invert_list:
         scores[invert] = 5 - scores[invert]  # 逆転スコア
@@ -93,10 +95,43 @@
     scores["C"] = scores.loc[:, "C01":"C08"].sum(axis=1)  # C群
     scores["Total"] = scores.loc[:, "A":"C"].sum(axis=1)  # 総合スコア
     target = df.loc[df_index, "Target"]  # ターゲットスコア
-    # target = 'Total'
-    threshold = scores[target].median()  # 中央値を閾値とする
 
-    # print(f'Threshold for {target}: {threshold}')
+    # 高ストレス ラベル決定
+    scores["label"] = 0
+    if target == "Raw":
+        for idx, row in scores.iterrows():
+            if row["B"] >= 31 or (row["B"] >= 23 and (row["A"] + row["C"]) >= 39):
+                scores.loc[idx, "label"] = 1  # ラベル付け
+    elif target == "Conv":
+        for idx, row in scores.iterrows():
+            A1 = row["A01"] + row["A02"] +row["A03"]  # A群の1-3
+            A2 = 15 - (row["A08"] + row["A09"] +row["A10"])
+            B1 = row["B07"] + row["B08"] +row["B09"]  # B群の7-9
+            B2 = row["B10"] + row["B11"] +row["B12"]
+            B3 = row["B13"] + row["B14"] +row["B16"]
+            B4 = row["B27"]
+            B5 = row["B29"]
+            C1 = 15 - (row["C01"] + row["C04"] +row["C07"])
+            C2 = 15 - (row["C02"] + row["C05"] +row["C08"])
+            ConvA1 = 1 if A1 >= 12 else 2 if A1 >= 10 else 3 if A1 >= 8 else 4 if A1 >= 6 else 5
+            ConvA2 = 1 if A2 <= 4 else 2 if A2 <= 6 else 3 if A2 <= 8 else 4 if A2 <= 10 else 5
+            ConvB1 = 1 if B1 >= 11 else 2 if B1 >= 8 else 3 if B1 >= 5 else 4 if B1 >= 4 else 5
+            ConvB2 = 1 if B2 >= 10 else 2 if B2 >= 8 else 3 if B2 >= 5 else 4 if B2 >= 4 else 5
+            ConvB3 = 1 if B3 >= 10 else 2 if B3 >= 7 else 3 if B3 >= 5 else 4 if B3 >= 4 else 5
+            ConvB4 = 1 if B4 >= 4 else 2 if B4 >= 3 else 3 if B4 >= 2 else 5
+            ConvB5 = 1 if B5 >= 4 else 2 if B5 >= 3 else 3 if B5 >= 2 else 5
+            ConvC1 = 1 if C1 <= 4 else 2 if C1 <= 6 else 3 if C1 <= 8 else 4 if C1 <= 10 else 5
+            ConvC2 = 1 if C2 <= 5 else 2 if C2 <= 7 else 3 if C2 <= 9 else 4 if C2 <= 11 else 5
+            ConvAC = ConvA1 + ConvA2 + ConvC1 + ConvC2
+            ConvB = ConvB1 + ConvB2 + ConvB3 + ConvB4 + ConvB5
+            if ConvB <= 11 or (ConvB <= 16 and ConvAC <= 8):
+                scores.loc[idx, "label"] = 1
+    else:
+        threshold = scores[target].median()  # 中央値を閾値とする
+        # print(f'Threshold for {target}: {threshold}')
+        scores.loc[scores[target] >= threshold, "label"] = 1  # ラベル付け
+
+    # target = 'Total'
     # scores[target].plot.hist(bins=20, edgecolor='black')  # ヒストグラムを描画
     # import matplotlib.pyplot as plt
     # plt.title(f'{target} Score Distribution')
@@ -105,9 +140,10 @@
     # plt.show()  # ヒストグラムを表示
     # exit()
 
-    scores["label"] = 0
-    scores.loc[scores[target] >= threshold, "label"] = 1  # ラベル付け
-    # print(scores.head(3))
+    # print(scores[["id", "label"]])
+    # scores[["id", "label"]].to_csv("labels.csv", index=False)  # ラベルをCSVファイルに保存
+    # print("num positive labels:", scores["label"].sum())
+    # exit()
     return x, scores["label"]