generated from LufsX/Experiment-LogReg
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
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, GridSearchCV
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from sklearn.linear_model import LogisticRegression
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def load_data(filepath):
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data = []
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labels = []
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with open(filepath, 'r') as f:
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for line in f:
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values = line.strip().replace('?', '0').split()
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try:
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values = [float(v) for v in values]
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# 合并特征23的取值
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if values[22] == 2 or values[22] == 3: # 特征23（索引22）
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labels.append(0) # 死亡
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elif values[22] == 1:
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labels.append(1) # 生存
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else:
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continue # 无效标签，丢弃
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# 用0填充缺失值
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values = [0 if np.isnan(v) else v for v in values]
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# 去掉特征3（索引2）和特征24~28（索引23-27）
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data.append(values[:2] + values[3:23]) # 保留特征1, 2 和特征4-23
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except ValueError:
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continue
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return np.array(data), np.array(labels)
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def preprocess_data(X):
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# 特征标准化
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scaler = StandardScaler()
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return scaler.fit_transform(X)
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def train_logreg(X_train, y_train):
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# 定义要调优的超参数范围
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param_grid = {
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'C': [0.001, 0.01, 0.1, 1, 10, 100], # 正则化参数
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'solver': ['liblinear', 'lbfgs'], # 求解器
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'max_iter': [100, 500, 1000] # 最大迭代次数
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}
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# 创建逻辑回归模型
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log_reg = LogisticRegression()
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# 创建网格搜索对象
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grid_search = GridSearchCV(estimator=log_reg, param_grid=param_grid, cv=5, scoring='accuracy')
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# 进行网格搜索
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grid_search.fit(X_train, y_train)
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return grid_search.best_estimator_
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def predict_logreg(inX, weights):
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"""
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逻辑回归预测函数。
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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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z = np.dot(inX, weights)
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prediction_prob = 1 / (1 + np.exp(-z))
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return 1 if prediction_prob >= 0.5 else 0
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if __name__ == "__main__":
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# 加载数据集
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X, y = load_data("./data/horse-colic.data")
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X = preprocess_data(X)
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# 使用 train_test_split 打乱并随机抽取数据
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 训练逻辑回归模型
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best_model = train_logreg(X_train, y_train)
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# 测试单个数据
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y_pred = best_model.predict(X_test[11].reshape(1, -1)) # 进行预测时需要调整输入形状
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print(f"预测结果: {y_pred[0]}, 实际结果: {y_test[11]}")
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