更新 function.py

function.py

@@ -1,102 +1,101 @@
 import numpy as np
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split, KFold
+from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
+from imblearn.over_sampling import SMOTE  # 导入SMOTE
 
 id = "202210423130"
 name = "董秋"
 
+
 def sigmoid(z):
     return 1 / (1 + np.exp(-z))
 
+
 def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01):
     m, n = X.shape
     weights = np.random.randn(n) * 0.01
+
     for i in range(iterations):
         indices = np.random.permutation(m)
         X = X[indices]
         y = y[indices]
+
         z = np.dot(X, weights)
         predictions = sigmoid(z)
         gradient = np.dot(X.T, (predictions - y)) / m
+
         gradient += (lambda_reg / m) * weights
         weights -= learning_rate * gradient
         learning_rate *= 0.99
+
     return weights
 
+
 def predict_logreg(inX, weights):
     z = np.dot(inX, weights)
     prediction_prob = sigmoid(z)
     return 1 if prediction_prob >= 0.5 else 0
 
+
 def handle_outliers(df, threshold=3):
     z_scores = np.abs((df - df.mean()) / df.std())
     df[z_scores > threshold] = np.nan
     df.fillna(df.mean(), inplace=True)
     return df
 
+
 def load_data(filepath):
     data = []
     labels = []
+
     with open(filepath, 'r') as f:
         for line in f:
             values = line.strip().replace('?', '0').split()
             try:
                 values = [float(v) for v in values]
                 if values[22] == 2 or values[22] == 3:
-                    labels.append(0)
+                    labels.append(0)  # 死亡
                 elif values[22] == 1:
-                    labels.append(1)
+                    labels.append(1)  # 生存
                 else:
                     continue
+
                 values = [np.nan if v == 0 else v for v in values]
                 data.append(values[:2] + values[3:23])
             except ValueError:
                 continue
+
     data = np.array(data)
     labels = np.array(labels)
+
     df = pd.DataFrame(data)
     df.fillna(df.mean(), inplace=True)
     df = handle_outliers(df)
+
     return df.to_numpy(), labels
 
+
 def preprocess_data(X):
     scaler = StandardScaler()
     return scaler.fit_transform(X)
 
-def cross_validate_learning_rate(X, y, learning_rates, k=5, iterations=1000, lambda_reg=0.01):
-    best_lr = None
-    best_score = 0
-    kf = KFold(n_splits=k, shuffle=True, random_state=42)
-
-    for lr in learning_rates:
-        cv_scores = []
-        for train_index, val_index in kf.split(X):
-            X_train, X_val = X[train_index], X[val_index]
-            y_train, y_val = y[train_index], y[val_index]
-            weights = train_logreg(X_train, y_train, learning_rate=lr, iterations=iterations, lambda_reg=lambda_reg)
-            y_pred = [predict_logreg(x, weights) for x in X_val]
-            cv_scores.append(accuracy_score(y_val, y_pred))
-
-        avg_score = np.mean(cv_scores)
-        print(f"Learning Rate: {lr}, Cross-Validation Accuracy: {avg_score:.4f}")
-
-        if avg_score > best_score:
-            best_score = avg_score
-            best_lr = lr
-
-    print(f"Best Learning Rate: {best_lr}, Best CV Accuracy: {best_score:.4f}")
-    return best_lr
 
 if __name__ == "__main__":
+    # 加载数据集
     X, y = load_data("./data/horse-colic.data")
     X = preprocess_data(X)
 
-    learning_rates = [0.001, 0.01, 0.05, 0.1, 0.2]
+    # 使用 train_test_split 打乱并随机抽取数据
-    best_learning_rate = cross_validate_learning_rate(X, y, learning_rates)
-
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-    weights = train_logreg(X_train, y_train, learning_rate=best_learning_rate, iterations=1000, lambda_reg=0.01)
+
+    # 使用 SMOTE 进行数据集平衡
+    smote = SMOTE(random_state=42)
+    X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)
+
+    # 训练逻辑回归模型
+    weights = train_logreg(X_train_balanced, y_train_balanced, learning_rate=0.1, iterations=1000, lambda_reg=0.01)
+
+    # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")