更新 function.py

function.py

@@ -1,7 +1,6 @@
 import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import PolynomialFeatures, StandardScaler
-from sklearn.metrics import accuracy_score, f1_score
 
 id = "202210423104"
 name = "张钦诚"
@@ -29,7 +28,7 @@ def preprocess_data(X, scaler=None, poly=None):
         X_standardized = scaler.fit_transform(X)
     else:
         X_standardized = scaler.transform(X)
-    
+        
     if poly is None:
         poly = PolynomialFeatures(degree=2, include_bias=False)
         X_poly = poly.fit_transform(X_standardized)
@@ -42,18 +41,14 @@ def train_logreg(X, y):
     X_processed, scaler, poly = preprocess_data(X)  # 预处理训练数据
     model = RandomForestClassifier(n_estimators=113, max_depth=10, random_state=42)
     model.fit(X_processed, y)
-    return model, scaler, poly  # 返回训练好的模型及预处理器
+    return model, scaler, poly  # 返回训练好的模型和预处理对象
 
-def predict_logreg(model, X, scaler, poly):
+def predict_logreg(X, model, scaler, poly):
     # 确保输入数据是一个 numpy 数组
     if len(X.shape) == 1:
-        X = X.reshape(1, -1)  # 保持输入数据的形状为 (1, n)，符合模型的输入要求
+        X = X.reshape(1, -1)  # 保持输入数据的形状为 (1, n)
     X_processed, _, _ = preprocess_data(X, scaler, poly)  # 预处理测试数据
-    return model.predict(X_processed)[0]  # 确保 model 是训练好的模型
+    return model.predict(X_processed)[0]  # 返回预测结果
 
 if __name__ == "__main__":
     X, y = load_data("./data/horse-colic.data")
-    model, scaler, poly = train_logreg(X, y)  # 训练模型
-    y_pred = [predict_logreg(model, sample, scaler, poly) for sample in X]  # 预测
-    print(f"Accuracy: {accuracy_score(y, y_pred):.2f}")
-    print(f"F1 Score: {f1_score(y, y_pred, average='weighted'):.2f}")