From c3054ad9d570be18552d1771988043a58bfa222b Mon Sep 17 00:00:00 2001 From: zhangchenghong <1598349064@qq.com> Date: Sat, 2 Nov 2024 15:14:20 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- function.py | 112 +++++++++++++++++++++++++++++++++++----------------- 1 file changed, 76 insertions(+), 36 deletions(-) diff --git a/function.py b/function.py index d6fb74f..8914306 100644 --- a/function.py +++ b/function.py @@ -29,7 +29,12 @@ name = "章程鸿" import numpy as np import pandas as pd from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import KFold +from sklearn.model_selection import train_test_split, KFold, GridSearchCV +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score +import warnings + +warnings.filterwarnings("ignore") def sigmoid(z): """Sigmoid 激活函数。""" @@ -54,29 +59,29 @@ def train_logreg(X, y, learning_rate=0.01, iterations=5000, lambda_reg=0.01, bat predictions = sigmoid(z) gradient = np.dot(X_batch.T, (predictions - y_batch)) / X_batch.shape[0] - # L2正则化 + # L2 正则化 gradient += (lambda_reg / m) * weights # 更新权重 weights -= learning_rate * gradient # 动态调整学习率 - if i % 1000 == 0: # 每1000次迭代调整一次学习率 - learning_rate *= 0.9 # 每1000次迭代减小学习率 + if i % 1000 == 0: # 每 1000 次迭代调整一次学习率 + learning_rate *= 0.9 # 每 1000 次迭代减小学习率 - if i % 100 == 0: # 每100次迭代输出一次学习率 + if i % 100 == 0: # 每 100 次迭代输出一次学习率 print(f"Iteration {i}, Learning Rate: {learning_rate}") return weights -def predict_logreg(inX, weights, threshold=0.5): +def predict_logreg(inX, weights): """逻辑回归预测函数。""" z = np.dot(inX, weights) prediction_prob = sigmoid(z) - return 1 if prediction_prob >= threshold else 0 + return 1 if prediction_prob >= 0.5 else 0 def load_data(filepath): - """加载数据并处理缺失值与规划。""" + """加载数据并处理缺失值。""" data = [] labels = [] @@ -85,38 +90,56 @@ def load_data(filepath): values = line.strip().replace('?', '0').split() try: values = [float(v) for v in values] - - # 合并特征23的取值 - if values[22] == 2 or values[22] == 3: # 特征23（索引22） + + # 合并特征 23 的取值 + if values[22] == 2 or values[22] == 3: # 特征 23（索引 22） labels.append(0) # 死亡 elif values[22] == 1: labels.append(1) # 生存 else: continue # 无效标签，丢弃 - - # 用0填充缺失值 - values = [0 if v == 0 else v for v in values] # 直接用0填充 - data.append(values[:2] + values[3:23]) # 保留特征1, 2 和特征4-23 + + # 用均值填充缺失值 + values = [np.nan if v == 0 else v for v in values] + nan_indices = np.isnan(values) + column_means = np.nanmean(np.array(data), axis=0) + for i, is_nan in enumerate(nan_indices): + if is_nan: + values[i] = column_means[i] + data.append(values[:2] + values[3:23]) # 保留特征 1, 2 和特征 4 - 23 except ValueError: continue data = np.array(data) labels = np.array(labels) - # 数据标准化 - scaler = StandardScaler() - data = scaler.fit_transform(data) # 在这里进行标准化处理 - return data, labels +def preprocess_data(X): + """特征标准化。""" + scaler = StandardScaler() + return scaler.fit_transform(X) + 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") - # K折交叉验证 + # 归一化数据 + X_train = preprocess_data(X_train) + X_test = preprocess_data(X_test) + + # 超参数搜索空间 + param_grid = { + 'learning_rate': [0.001, 0.01, 0.1], + 'iterations': [3000, 5000, 7000], + 'lambda_reg': [0.001, 0.01, 0.1], + 'batch_size': [16, 32, 64] + } + + # K 折交叉验证 k = 5 kf = KFold(n_splits=k, shuffle=True, random_state=42) accuracies = [] @@ -125,31 +148,48 @@ if __name__ == "__main__": X_train_kf, X_val_kf = X_train[train_index], X_train[val_index] y_train_kf, y_val_kf = y_train[train_index], y_train[val_index] - # 训练逻辑回归模型，设置 batch size - batch_size = 32 - weights = train_logreg(X_train_kf, y_train_kf, learning_rate=0.01, iterations=5000, lambda_reg=0.01, batch_size=batch_size) + best_params = None + best_score = 0 - # 验证模型 - val_predictions = [predict_logreg(X_val_kf[i], weights, threshold=0.5) for i in range(len(X_val_kf))] + for params in GridSearchCV(estimator=LogisticRegression(), param_grid=param_grid, scoring='accuracy', cv=3).fit(X_train_kf, y_train_kf).cv_results_['params']: + weights = train_logreg(X_train_kf, y_train_kf, learning_rate=params['learning_rate'], iterations=params['iterations'], lambda_reg=params['lambda_reg'], batch_size=params['batch_size']) + val_predictions = [predict_logreg(X_val_kf[i], weights) for i in range(len(X_val_kf))] + score = np.mean(val_predictions == y_val_kf) + if score > best_score: + best_score = score + best_params = params + + print(f"Best parameters for fold: {best_params}") + + # 使用最佳参数重新训练 + weights = train_logreg(X_train_kf, y_train_kf, learning_rate=best_params['learning_rate'], iterations=best_params['iterations'], lambda_reg=best_params['lambda_reg'], batch_size=best_params['batch_size']) + val_predictions = [predict_logreg(X_val_kf[i], weights) for i in range(len(X_val_kf))] accuracy = np.mean(val_predictions == y_val_kf) accuracies.append(accuracy) # 输出交叉验证的准确率和平均准确率 - print(f"K折交叉验证得分: {accuracies}") + print(f"K 折交叉验证得分: {accuracies}") print(f"平均准确率: {np.mean(accuracies)}") # 在测试集上测试模型 - test_predictions = [predict_logreg(X_test[i], weights, threshold=0.5) for i in range(len(X_test))] + best_params = None + best_score = 0 + + for params in GridSearchCV(estimator=LogisticRegression(), param_grid=param_grid, scoring='accuracy', cv=3).fit(X_train, y_train).cv_results_['params']: + weights = train_logreg(X_train, y_train, learning_rate=params['learning_rate'], iterations=params['iterations'], lambda_reg=params['lambda_reg'], batch_size=params['batch_size']) + test_predictions = [predict_logreg(X_test[i], weights) for i in range(len(X_test))] + score = np.mean(test_predictions == y_test) + if score > best_score: + best_score = score + best_params = params + + print(f"Best parameters for test set: {best_params}") + + weights = train_logreg(X_train, y_train, learning_rate=best_params['learning_rate'], iterations=best_params['iterations'], lambda_reg=best_params['lambda_reg'], batch_size=best_params['batch_size']) + test_predictions = [predict_logreg(X_test[i], weights) for i in range(len(X_test))] test_accuracy = np.mean(test_predictions == y_test) print(f"测试集准确率: {test_accuracy}") - # 测试单个数据，输出错误分类 - for i in range(len(X_test)): - y_pred = predict_logreg(X_test[i], weights, threshold=0.5) - if y_pred != y_test[i]: - print(f"第 {i} 次测试错误，数据 {X_test[i]} 被分类到 {y_pred} 实际是 {y_test[i]}") - - - +