444

function.py

@@ -25,7 +25,7 @@ from sklearn.model_selection import train_test_split, GridSearchCV
 id = "202210423221"
 name = "廖子豪"
 
-def load_data(filepath, fit_scaler=False):
+def load_data(filepath):
     data = np.genfromtxt(filepath, delimiter=' ', missing_values='?', filling_values=0)
     labels = data[:, -1].astype(int)
     features = data[:, :-1]
@@ -35,28 +35,12 @@ def load_data(filepath, fit_scaler=False):
     
     # 数据标准化
     scaler = StandardScaler()
-    if fit_scaler:
+    features = scaler.fit_transform(features)
-        features = scaler.fit_transform(features)
-    else:
-        features = scaler.transform(features)
     
-    return features, labels, scaler
+    return features, labels
 
-# 示例使用
+def train_logreg(X, y, random_state=42):
-if __name__ == "__main__":
+    # 使用 GridSearchCV 进行参数调优
-    # 加载训练数据和测试数据
-    X_train, y_train, scaler = load_data("./data/horse-colic.data", fit_scaler=True)
-    X_test, y_test, _ = load_data("./data/horse-colic.test", fit_scaler=False)  # 使用相同的scaler
-
-    # 训练模型
-    model = train_logreg(X_train, y_train, random_state=42)
-
-    # 进行预测
-    y_pred = [predict_logreg(x, model) for x in X_test]
-
-    # 计算准确率
-    accuracy = np.mean(y_pred == y_test)
-    print(f"Test Accuracy: {accuracy:.2f}")
     param_grid = {'C': [0.01, 0.1, 1, 10, 100], 'penalty': ['l1', 'l2']}
     model = LogisticRegression(solver='liblinear', random_state=random_state)
     grid_search = GridSearchCV(model, param_grid, cv=5)
@@ -69,8 +53,8 @@ def predict_logreg(inX, model):
 # 示例使用
 if __name__ == "__main__":
     # 加载训练数据和测试数据
-    X_train, y_train, scaler = load_data("./data/horse-colic.data")
+    X_train, y_train = load_data("./data/horse-colic.data")
-    X_test, y_test, _ = load_data("./data/horse-colic.test")  # 使用相同的scaler
+    X_test, y_test = load_data("./data/horse-colic.test")
 
     # 训练模型
     model = train_logreg(X_train, y_train, random_state=42)