555555

function.py

@@ -1,4 +1,3 @@
-# function.py
 import numpy as np  
 from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score  
 from sklearn.preprocessing import StandardScaler  
@@ -6,22 +5,6 @@ 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。  
@@ -29,38 +12,31 @@ from sklearn.svm import SVC
 id = "202210423221"  
 name = "廖子豪"  
 
-
-
-
 def load_data(filepath):  
-    file = open(filepath)
-    fr = file.readlines()
+    """Load data from a text file for model training."""  
+    with open(filepath) as file:  
+        fr = file.readlines()  
     data_list = []  
     y = []  
     for data in fr:  
-        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 = data.strip().split(' ')  
+        data_25 = data[24]  # Preserving the 25th element for later use  
+        y.append(data[22])  # Using 23rd element as the target value  
+
+        # Remove unnecessary elements and convert to float, handle missing values  
+        numerical_data = [0 if elem == '?' else float(elem) for i, elem in enumerate(data) if i not in [24, 22, 26]]  
+        data_list.append(numerical_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)])
+    y_train = np.array([1 if label in ['2', '3'] else 0 for label in y])  # Binary classification  
     X = np.array(data_list)  
-    X_train = np.insert(X, 0, 1, axis=1)  # 添加偏置项
-    return X_train, y_train
+    return X, y_train  
 
 def train_logreg(X, y, numIter=1000, learning_rate=0.06, batch_size=128, lambda_reg=0.01):  
+    """Train a logistic regression model using mini-batch gradient descent."""  
     m, n = X.shape  
     weights = np.zeros(n)  
+    
     for j in range(numIter):  
         dataIndex = np.random.permutation(m)  
         for i in range(0, m, batch_size):  
@@ -68,29 +44,34 @@ def train_logreg(X, y, numIter=1000, learning_rate=0.06, batch_size=128, lambda_
             batch_X = X[dataIndex[i:end]]  
             batch_y = y[dataIndex[i:end]]  
             z = np.dot(batch_X, weights)  
-            h = 1 / (1 + np.exp(-z))
+            h = 1 / (1 + np.exp(-z))  # Sigmoid function  
             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)
+            gradient -= reg_term / len(batch_y)  # Regularization  
             weights += learning_rate * gradient  
     return weights  
 
 def predict_logreg(inX, weights):  
+    """Make a prediction using the logistics regression model."""  
     z = np.dot(inX, weights)  
     prob = 1 / (1 + np.exp(-z))  
     return 1 if prob >= 0.5 else 0  
 
 if __name__ == "__main__":  
+    # Load train and test data  
     X_train, y_train = load_data("./data/horse-colic.data")  
     X_test, y_test = load_data("./data/horse-colic.test")  
 
+    # Standardize the data  
     scaler = StandardScaler()  
     X_train = scaler.fit_transform(X_train)  
     X_test = scaler.transform(X_test)  
 
+    # Split train data into training and validation sets  
     X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)  
 
+    # Train the logistic regression model  
     best_weights = None  
     best_auc = 0  
     for iter in range(1, 1001):  
@@ -103,42 +84,24 @@ if __name__ == "__main__":
         if iter > 10 and auc == best_auc:  
             break  
 
+    # Make predictions and evaluate test accuracy  
     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_)
+    # Example for a Random Forest model  
+    rf = RandomForestClassifier(n_estimators=100, random_state=42)  
+    rf.fit(X_train, y_train)  
+    rf_accuracy = rf.score(X_test, y_test)  
+    print(f"Random Forest Test Accuracy: {rf_accuracy}")  
 
-    # 集成学习 - 投票分类器
-    log_clf = LogisticRegression()
-    rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)
-    voting_clf = VotingClassifier(estimators=[('lr', log_clf), ('rf', rnd_clf)], voting='soft')
-    voting_clf.fit(X_train, y_train)
-    y_pred = voting_clf.predict(X_test)
-    print("Voting classifier accuracy:", accuracy_score(y_test, y_pred))
-
-    # 梯度提升机模型
+    # Example for Gradient Boosting model  
     gb_clf = GradientBoostingClassifier()  
     gb_clf.fit(X_train, y_train)  
-    y_pred = gb_clf.predict(X_test)
-    print("Gradient Boosting classifier accuracy:", accuracy_score(y_test, y_pred))
+    y_pred_gb = gb_clf.predict(X_test)  
+    gb_accuracy = accuracy_score(y_test, y_pred_gb)  
+    print(f"Gradient Boosting Test Accuracy: {gb_accuracy}")  
 
-    # 使用K折交叉验证评估模型性能
-    scores = cross_val_score(gb_clf, X_train, y_train, cv=5, scoring='accuracy')
-    print("Cross-validation scores:", scores)
-    print("Average cross-validation score:", np.mean(scores))
-
-    # 使用F1分数评估模型性能
-    f1 = f1_score(y_test, y_pred)
-    print("F1 Score:", f1)
+    # Evaluate performance with F1 score  
+    f1 = f1_score(y_test, y_pred_gb)  
+    print(f"F1 Score: {f1}")