更新 function.py

function.py

@@ -1,69 +1,140 @@
+# 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 = "202210423224"
+id = "202210423221"
-name = "吴杨洋"
+name = "廖子豪"
+def load_data(filepath):
+    file = open(filepath)
+    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_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)
+    X_train = np.insert(X, 0, 1, axis=1)  # 添加偏置项
+    return X_train, y_train
 
-
+def train_logreg(X, y, numIter=1000, learning_rate=0.06, batch_size=128, lambda_reg=0.01):
-def sigmoid(z):
-    return 1 / (1 + np.exp(-z))
-
-
-def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     m, n = X.shape
     weights = np.zeros(n)
-    for i in range(iterations):
+    for j in range(numIter):
-        linear_model = np.dot(X, weights)
+        dataIndex = np.random.permutation(m)
-        y_predicted = sigmoid(linear_model)
+        for i in range(0, m, batch_size):
-        gradient = np.dot(X.T, (y_predicted - y)) / m
+            end = min(i + batch_size, m)
-        weights -= learning_rate * gradient
+            batch_X = X[dataIndex[i:end]]
+            batch_y = y[dataIndex[i:end]]
+            z = np.dot(batch_X, weights)
+            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)
+            weights += learning_rate * gradient
     return weights
 
-
 def predict_logreg(inX, weights):
-    linear_model = np.dot(inX, weights)
+    z = np.dot(inX, weights)
-    y_predicted = sigmoid(linear_model)
+    prob = 1 / (1 + np.exp(-z))
-    return 1 if y_predicted > 0.5 else 0
+    return 1 if prob >= 0.5 else 0
-
-
-def load_data(filepath):
-    data = np.genfromtxt(filepath, delimiter=' ', dtype=float)
-    X = data[:, :-1]
-    y = data[:, -1]
-    return X, y
-
-
-def standardize(X):
-    mean = X.mean(axis=0)
-    std = X.std(axis=0)
-    return (X - mean) / std, mean, std
-
-
-def main():
-    # 加载训练集
-    X_train, y_train = load_data("./data/horse-colic.data")
-    # 加载测试集
-    X_test, y_test = load_data("./data/horse-colic.test")
-
-    # 标准化特征数据
-    X_train, mean, std = standardize(X_train)
-    X_test = (X_test - mean) / std
-
-    # 训练对率回归模型
-    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
-
-    # 测试单个数据
-    correct_predictions = 0
-    for i in range(X_test.shape[0]):
-        y_pred = predict_logreg(X_test[i], weights)
-        if y_pred == y_test[i]:
-            correct_predictions += 1
-
-    accuracy = correct_predictions / X_test.shape[0]
-    print(f"Accuracy: {accuracy:.2f}")
-
 
 if __name__ == "__main__":
-    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_)
+
+    # 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
+    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))
+
+    # 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)