444

function.py

@@ -1,78 +1,120 @@
+# function.py
 import numpy as np
 from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
 from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
 from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+"""
+实验说明:
+- 自行编写实现数据加载与预测病马死亡的对率回归算法。
+
+注意事项:
+- 在 function.py 中实现对率回归算法。
+- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
+- 自动评测脚本仅会加载 train_logreg、predict_logreg 和 load_data 函数，请勿修改函数名。
+
+测评环境:
+- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
+- OS: Ubuntu 22.04.4 LTS
+- Python: 3.10.12
+- numpy: 2.1.1
+"""
+
 
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
 id = "202210423221"
 name = "廖子豪"
-
 def load_data(filepath):
-    """Load and preprocess data from specified file."""  
-    with open(filepath) as file:  
-        fr = file.readlines()  
-        
+    file = open(filepath)
+    fr = file.readlines()
     data_list = []
     y = []
-    
     for data in fr:
-        data = data.strip().split(' ')  
-        
-        # Keep the necessary fields  
-        y.append(data[22])  # Target value  
-        # Remove unnecessary columns  
-        data = [data[i] for i in range(len(data)) if i not in [2, 22, 24, 25, 26]]  
-        
-        # Convert to float, replacing '?' with 0  
-        data_processed = [0 if elem == '?' else float(elem) for elem in data]  
-        data_list.append(data_processed)  
-    
-    y_train = np.array([1 if label in ['2', '3'] else 0 for label in y])  
+        data = data.strip()
+        data = data.split(' ')
+        data_25 = data[24]
+        y.append(data[22])
+        del data[-1]
+        del data[26]
+        del data[25]
+        del data[24]
+        del data[22]
+        del data[2]
+        for i in data_25:
+            data.append(i)
+        data = [0 if data_elem == '?' else float(data_elem) for data_elem in data]
+        data_list.append(data)
+    data_number = len(y)
+    y_train = np.array([1 if y[j] == '2' or y[j] == '3' else 0 for j in range(data_number)])
     X = np.array(data_list)
-    
-    # Add bias term (intercept)  
-    X = np.insert(X, 0, 1, axis=1)  
+    X_train = np.insert(X, 0, 1, axis=1)  # 添加偏置项
     scaler = StandardScaler()
-    X = scaler.fit_transform(X)
+    X= scaler.fit_transform(Xa)
 
-    return X, y_train  
+    return X_train, y_train
 
-def train_logreg(X, y, numIter=1000, learning_rate=0.01, batch_size=128, lambda_reg=0.01):  
-    """Train a logistic regression model using mini-batch gradient descent."""  
+def train_logreg(X, y, numIter=1000, learning_rate=0.06, batch_size=128, lambda_reg=0.01):
     m, n = X.shape
     weights = np.zeros(n)
-    
     for j in range(numIter):
-        # Shuffle data indices  
         dataIndex = np.random.permutation(m)
-        
         for i in range(0, m, batch_size):
             end = min(i + batch_size, m)
             batch_X = X[dataIndex[i:end]]
             batch_y = y[dataIndex[i:end]]
-            
-            # Compute predictions  
             z = np.dot(batch_X, weights)
-            h = 1 / (1 + np.exp(-z))  # Sigmoid  
-            
-            # Compute gradient  
+            h = 1 / (1 + np.exp(-z))
             error = batch_y - h
             gradient = np.dot(batch_X.T, error) / len(batch_y)
             reg_term = 2 * lambda_reg * weights
-            gradient -= reg_term / len(batch_y)  # Regularization  
-            
-            # Update weights  
+            gradient -= reg_term / len(batch_y)
             weights += learning_rate * gradient
-            
     return weights
 
 def predict_logreg(inX, weights):
-    """Make a prediction using the logistic regression model."""  
     z = np.dot(inX, weights)
     prob = 1 / (1 + np.exp(-z))
     return 1 if prob >= 0.5 else 0
 
+if __name__ == "__main__":
+    X_train, y_train = load_data("./data/horse-colic.data")
+    X_test, y_test = load_data("./data/horse-colic.test")
+
+    scaler = StandardScaler()
+    X_train = scaler.fit_transform(X_train)
+    X_test = scaler.transform(X_test)
+
+    X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)
+
+    best_weights = None
+    best_auc = 0
+    for iter in range(1, 1001):
+        weights = train_logreg(X_train, y_train, numIter=iter, learning_rate=0.01, lambda_reg=0.1)
+        y_pred = [predict_logreg(x, weights) for x in X_val]
+        auc = roc_auc_score(y_val, y_pred)
+        if auc > best_auc:
+            best_auc = auc
+            best_weights = weights
+        if iter > 10 and auc == best_auc:
+            break
+
+    y_pred_test = [predict_logreg(x, best_weights) for x in X_test]
+    accuracy_test = accuracy_score(y_test, y_pred_test)
+    print(f"Test Accuracy: {accuracy_test}")
+
+    # 随机森林模型
+    param_grid = {
+        'n_estimators': [100, 200, 300],
+        'max_depth': [5, 10, 15],
+        'learning_rate': [0.01, 0.05, 0.1]
+    }
+    rf = RandomForestClassifier()
+    grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, scoring='accuracy')
+    grid_search.fit(X_train, y_train)
+    best_rf = grid_search.best_estimator_
+    print("Best parameters:", grid_search.best_params_)
+    print("Best accuracy:", grid_search.best_score_)