更新 function.py

2024-11-02 21:05:22 +08:00
parent 8a362fbfac
commit 6c364dc100
1 changed files with 75 additions and 100 deletions
@@ -1,34 +1,65 @@
-# 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"
+id = "202210423224"
+name = "吴杨洋"
+
+
+def sigmoid(z):
+    """
+    Sigmoid 激活函数。
+    """
+    return 1 / (1 + np.exp(-z))
+
+
+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):
+        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]]
+            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):
+    """
+    对率回归预测函数。
+
+    Args:
+    - inX (numpy array): 输入待分类的样本。
+    - weights (numpy array): 训练得到的权重。
+
+    Returns:
+    - int: 预测的分类标签。
+    """
+    z = np.dot(inX, weights)
+    prob = 1 / (1 + np.exp(-z))
+    return 1 if prob >= 0.5 else 0
+
+
 def load_data(filepath):
+    """
+    从文件中加载数据，若不自行正确实现，则无法通过评测，不记录成绩。
+
+    Args:
+    - filepath (str): 数据文件路径。
+
+    Returns:
+    - X (np.array): 特征数据。
+    - y (np.array): 标签数据。
+    """
    file = open(filepath)
    fr = file.readlines()
    data_list = []
@@ -54,87 +85,31 @@ def load_data(filepath):
    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):
-    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):
-            end = min(i + batch_size, m)
-            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):
-    z = np.dot(inX, weights)
-    prob = 1 / (1 + np.exp(-z))
-    return 1 if prob >= 0.5 else 0
+def standardize(X):
+    mean = X.mean(axis=0)
+    std = X.std(axis=0)
+    return (X - mean) / std, mean, std

-if __name__ == "__main__":
+
+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")

-    scaler = StandardScaler()
-    X_train = scaler.fit_transform(X_train)
-    X_test = scaler.transform(X_test)
+    # 标准化特征数据
+    X_train, mean, std = standardize(X_train)
+    X_test = (X_test - mean) / std

-    X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)
+    # 训练对率回归模型
+    weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=0.01, lambda_reg=0.1)

-    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
+    # 测试单个数据
+    y_pred_test = [predict_logreg(x, weights) for x in X_test]
+    accuracy_test = np.mean(y_pred_test == y_test)
+    print(f"Test Accuracy: {accuracy_test:.2f}")

-    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)
+if __name__ == "__main__":
+    main()