generated from LufsX/Experiment-LogReg
164 lines
4.7 KiB
Python
164 lines
4.7 KiB
Python
# function.py
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split, KFold
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"""
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实验说明:
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- 自行编写实现数据加载与预测病马死亡的对率回归算法。
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注意事项:
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- 在 function.py 中实现对率回归算法。
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- 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。
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- 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。
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测评环境:
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- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
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- OS: Ubuntu 22.04.4 LTS
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- Python: 3.10.12
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- numpy: 2.1.1
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"""
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"""
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请在这里填写学号和姓名，不填写即成绩无效。
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"""
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id = "202210423209"
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name = "万子玉"
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# 这里写自己的代码实现
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def sigmoid(z):
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return 1 / (1 + np.exp(-z))
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def train_logreg(X, y, learning_rate=0.01, iterations=2000, lambda_reg=0.01, batch_size=32):
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X = np.insert(X, 0, 1, axis=1) # 添加偏置项
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m, n = X.shape
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weights = np.random.randn(n) * 0.01 # 随机初始化权重
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for i in range(iterations):
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indices = np.arange(m)
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np.random.shuffle(indices)
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X_shuffled = X[indices]
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y_shuffled = y[indices]
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for j in range(0, m, batch_size):
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X_batch = X_shuffled[j:j + batch_size]
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y_batch = y_shuffled[j:j + batch_size]
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z = np.dot(X_batch, weights)
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predictions = sigmoid(z)
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gradient = np.dot(X_batch.T, (predictions - y_batch)) / X_batch.shape[0]
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# L2正则化
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gradient += (lambda_reg / m) * weights
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# 更新权重
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weights -= learning_rate * gradient
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return weights
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"""
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对率回归训练函数。
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Args:
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- X (numpy array): 特征数据。
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- y (numpy array): 标签数据。
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- other_params (str): 其它参数，可选设置。若需设置其它参数，必须设置其默认值，否则将**无法通过**自动评测，**成绩为零**。
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Returns:
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- numpy array: 训练得到的权重。
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"""
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def predict_logreg(inX, weights):
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inX = np.insert(inX, 0, 1) # 添加偏置项
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z = np.dot(inX, weights)
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prediction_prob = sigmoid(z)
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return 1 if prediction_prob >= 0.5 else 0
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"""
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对率回归预测函数。
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Args:
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- inX (numpy array): 输入待分类的样本。
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- weights (numpy array): 训练得到的权重。
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Returns:
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- int: 预测的分类标签。
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"""
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def evaluate_model(X_val, y_val, weights):
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predictions = np.array([predict_logreg(x, weights) for x in X_val])
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accuracy = np.mean(predictions == y_val)
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return accuracy
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def split_train_val(X, y, val_size=0.2):
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shuffled_indices = list(range(X.shape[0]))
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random.shuffle(shuffled_indices)
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X_shuffled = X[shuffled_indices]
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y_shuffled = y[shuffled_indices]
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val_count = int(X.shape[0] * val_size)
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X_val = X_shuffled[:val_count]
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y_val = y_shuffled[:val_count]
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X_train = X_shuffled[val_count:]
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y_train = y_shuffled[val_count:]
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return X_train, X_val, y_train, y_val
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# 这里也写自己的代码实现
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def load_data(filepath):
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with open(filepath, 'r') as file:
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lines = file.readlines()
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data = [line.strip().split() for line in lines if line.strip().split()[-1].isdigit()]
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data = [list(map(lambda x: float(x) if x != '?' else np.nan, row)) for row in data]
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# 移除类别标签缺失的样本
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data = [row for row in data if not np.isnan(row[-1])]
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# 使用“0”填充缺失值
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for row in data:
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for i in range(len(row)):
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if np.isnan(row[i]):
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row[i] = 0.0
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# 合并特征23的值2和3为0
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for row in data:
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if row[22] == 2 or row[22] == 3: # 假设特征23是第23个元素
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row[22] = 0
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# 移除不需要的特征
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data = [row[:2] + row[3:23] + row[28:] for row in data]
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# 分离特征和标签
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X = [row[:-1] for row in data]
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y = [row[-1] for row in data]
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scaler = StandardScaler()
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X = scaler.fit_transform(X)
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return np.array(X), np.array(y)
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"""
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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**。
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Args:
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- directory (str): 数据文件路径。
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Returns:
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- np.array: 特征数据。
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- np.array: 标签数据。
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"""
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if __name__ == "__main__":
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X_train, y_train = load_data("./data/horse-colic.data")
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X_test, y_test = load_data("./data/horse-colic.test")
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weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=2000)
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y_pred = predict_logreg(X_test[11], weights)
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print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}") |