generated from LufsX/Experiment-LogReg
139 lines
4.0 KiB
Python
139 lines
4.0 KiB
Python
import numpy as np
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from sklearn.impute import SimpleImputer
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from sklearn.feature_selection import SelectKBest, f_classif
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import warnings
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warnings.filterwarnings("ignore", message="numpy.dtype size changed")
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# 填写学号和姓名
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id = "202210423206"
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name = "詹宇豪"
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def load_data(filepath, k_features=20):
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"""
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从文件中加载数据并进行预处理，包括缺失值填充和特征选择。
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Args:
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- filepath (str): 数据文件路径。
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- k_features (int): 选择的特征数量。
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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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data = []
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labels = []
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with open(filepath, 'r') as file:
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for line in file:
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line = line.strip().split()
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if line:
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features = []
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for feature in line[:-1]: # 最后一个元素是标签
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try:
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features.append(float(feature))
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except ValueError:
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features.append(np.nan) # 用 NaN 表示缺失值
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label = int(line[-1])
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labels.append(label)
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data.append(features)
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# 转换为 numpy 数组
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data = np.array(data)
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labels = np.array(labels)
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# 填充缺失值
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imputer = SimpleImputer(strategy='mean')
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data = imputer.fit_transform(data)
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# 特征标准化
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mean = data.mean(axis=0)
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std_dev = data.std(axis=0)
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std_dev[std_dev == 0] = 1 # 避免除以零
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data = (data - mean) / std_dev
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# 特征选择
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selector = SelectKBest(f_classif, k=k_features)
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data = selector.fit_transform(data, labels)
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return data, labels
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def sigmoid(z):
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"""
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Sigmoid激活函数，用于对率回归模型。
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Args:
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- z (numpy array): 输入数据。
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Returns:
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- numpy array: Sigmoid函数输出值。
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"""
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return 1 / (1 + np.exp(-z))
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def train_logreg(X, y, initial_lr=0.005, iterations=5000, regularization=0.01, decay=0.0001):
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"""
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对率回归训练函数，包含学习率衰减策略。
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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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- initial_lr (float): 初始学习率。
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- iterations (int): 迭代次数。
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- regularization (float): 正则化强度。
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- decay (float): 学习率衰减系数。
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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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m, n = X.shape
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weights = np.zeros(n)
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learning_rate = initial_lr
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for i in range(iterations):
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z = np.dot(X, weights)
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predictions = sigmoid(z)
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errors = y - predictions
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gradient = np.dot(X.T, errors) / m - regularization * weights / m
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weights += learning_rate * gradient
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# 学习率衰减
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learning_rate = initial_lr / (1 + decay * i)
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return weights
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def predict_logreg(inX, weights, threshold=0.5):
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"""
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对率回归预测函数，包含可调阈值。
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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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- threshold (float): 判定阈值，默认为0.5。
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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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probability = sigmoid(np.dot(inX, weights))
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return 1 if probability >= threshold else 2
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# 测试代码
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if __name__ == "__main__":
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# 加载数据
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X_train, y_train = load_data("./data/horse-colic.data", k_features=20)
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X_test, y_test = load_data("./data/horse-colic.test", k_features=20)
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# 将标签转换为二分类的0和1
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y_train = np.where(y_train == 2, 0, 1)
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y_test = np.where(y_test == 2, 0, 1)
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# 训练模型
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weights = train_logreg(X_train, y_train, initial_lr=0.005, iterations=5000, regularization=0.01, decay=0.0001)
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# 模型评估
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correct_predictions = 0
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for i in range(len(X_test)):
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y_pred = predict_logreg(X_test[i], weights, threshold=0.5)
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if y_pred == y_test[i]:
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correct_predictions += 1
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print(f"预测结果: {y_pred}, 实际结果: {y_test[i]}")
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accuracy = correct_predictions / len(X_test)
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print(f"测试集准确率: {accuracy:.2f}") |