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function.py

@@ -1,54 +1,80 @@
-import numpy as np
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import PolynomialFeatures, StandardScaler
-
-id = "202210423104"
-name = "张钦诚"
-
-def load_data(filepath):
-    data = []
-    with open(filepath, 'r') as file:
-        for line in file:
-            line_data = line.strip().split()
-            if line_data:
-                try:
-                    data.append([float(x) if x != '?' else np.nan for x in line_data])
-                except ValueError as e:
-                    print(f"Error processing line: {line}. Error: {e}")
-    data = np.array(data)
-    data = np.nan_to_num(data, nan=np.nanmean(data, axis=0))
-
-    X = data[:, :-1]  # 特征
-    y = data[:, -1]   # 标签
-    return X, y
-
-def preprocess_data(X, scaler=None, poly=None):
-    if scaler is None:
-        scaler = StandardScaler()
-        X_standardized = scaler.fit_transform(X)
-    else:
-        X_standardized = scaler.transform(X)
-        
-    if poly is None:
-        poly = PolynomialFeatures(degree=2, include_bias=False)
-        X_poly = poly.fit_transform(X_standardized)
-    else:
-        X_poly = poly.transform(X_standardized)
-        
-    return X_poly, scaler, poly
-
-def train_logreg(X, y):
-    X_processed, scaler, poly = preprocess_data(X)  # 预处理训练数据
-    model = RandomForestClassifier(n_estimators=113, max_depth=10, random_state=42)
-    model.fit(X_processed, y)
-    return model, scaler, poly  # 返回训练好的模型和预处理对象
-
-def predict_logreg(X, model, scaler, poly):
-    # 确保输入数据是一个 numpy 数组
-    if len(X.shape) == 1:
-        X = X.reshape(1, -1)  # 保持输入数据的形状为 (1, n)
-    X_processed, _, _ = preprocess_data(X, scaler, poly)  # 预处理测试数据
-    return model.predict(X_processed)[0]  # 返回预测结果
-
-if __name__ == "__main__":
-    X, y = load_data("./data/horse-colic.data")
+import numpy as np
+
+"""
+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。
+姓名请直接**填写中文**
+例如: name = "张三"
+"""
+id = "202210423xxx"
+name = "test_name"
+
+
+# 训练对率回归模型
+def train_logreg(X, y, learning_rate=0.01, iterations=1000):
+    weights = np.zeros(X.shape[1])
+
+    for _ in range(iterations):
+        predictions = 1 / (1 + np.exp(-np.dot(X, weights)))
+        errors = y - predictions
+        gradient = np.dot(X.T, errors)
+        weights += learning_rate * gradient
+
+    return weights
+
+
+# 预测函数
+def predict_logreg(inX, weights):
+    prob = 1 / (1 + np.exp(-np.dot(inX, weights)))
+    return 1 if prob >= 0.5 else 0
+
+
+# 数据加载函数
+def load_data(filepath):
+    data = np.genfromtxt(filepath, delimiter=' ', dtype=float, filling_values=np.nan)
+
+    # 去掉包含NaN的行
+    data = data[~np.isnan(data).any(axis=1)]
+
+    X = data[:, :-1]  # 特征
+    y = data[:, -1]  # 标签
+    return X, y
+
+
+if __name__ == "__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")
+
+    # 输出数据形状以便调试
+    print(f"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}")
+    print(f"X_test shape: {X_test.shape}, y_test shape: {y_test.shape}")
+
+    # 确保测试集有足够的数据进行预测
+    if X_test.shape[0] > 0:
+        # 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，确保索引有效
+        index_to_test = 0 if X_test.shape[0] == 1 else 11
+        y_pred = predict_logreg(X_test[index_to_test], weights)
+        print(f"预测结果: {y_pred}, 实际结果: {y_test[index_to_test]}")
+    else:
+        print("测试集没有数据，无法进行预测。")