delete

admin_site/readme.md

@@ -13,7 +13,6 @@ celery -A admin_site beat
 ```
 
 ### 3、启动异步任务队列
-#celery -A admin_site worker -l info
 ```bash
 celery -A admin_site worker -l info --max-memory-per-child=524288000
 ```

admin_site/test/main.py

@@ -1,34 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import train_test_split
-
-# 加载数据
-url = "https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data"
-names = ['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',
-         'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean',
-         'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se',
-         'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se',
-         'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst',
-         'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst',
-         'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']
-data = pd.read_csv(url, names=names)
-
-# 前10个特征作为基因和基因变异
-X = data.iloc[:, 2:12]
-# 诊断结果作为标签
-y = data['diagnosis'].map({'M': 1, 'B': 0})
-
-# 将数据集分为训练集和测试集
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
-
-# 训练逻辑回归模型
-lr = LogisticRegression()
-lr.fit(X_train, y_train)
-
-# 预测测试集结果
-y_pred = lr.predict(X_test)
-
-# 计算准确率
-accuracy = np.mean(y_pred == y_test)
-print("Accuracy: ", accuracy)

admin_site/test/nri.py

@@ -1,45 +0,0 @@
-import pandas as pd
-from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import classification_report
-from sklearn.metrics.regression import net_reclassification_index
-import matplotlib.pyplot as plt
-
-# 加载数据
-snp_data = pd.read_csv('snp_data.csv')  # 假设SNP数据已保存在CSV文件中
-# other_data = pd.read_csv('other_data.csv')  # 假设其他特征数据已保存在CSV文件中
-target = pd.read_csv('target.csv')  # 假设目标数据已保存在CSV文件中
-
-# 合并数据
-data = pd.concat([snp_data, target], axis=1)
-
-# 划分训练集和测试集
-train_data = data.sample(frac=0.8, random_state=1)
-test_data = data.drop(train_data.index)
-
-# 训练基线模型和改进模型
-X_train = train_data.drop('target', axis=1)
-y_train = train_data['target']
-X_test = test_data.drop('target', axis=1)
-y_test = test_data['target']
-
-base_model = LogisticRegression(random_state=1)
-base_model.fit(X_train, y_train)
-improved_model = LogisticRegression(random_state=1, solver='liblinear', penalty='l1')
-improved_model.fit(X_train, y_train)
-
-# 使用模型进行分类并计算NRI指标
-base_proba = base_model.predict_proba(X_test)
-improved_proba = improved_model.predict_proba(X_test)
-nri = net_reclassification_index(y_test, base_proba[:, 1], improved_proba[:, 1])
-
-# 输出NRI指标
-print('NRI:', nri)
-
-# 可视化NRI指标
-nri_df = pd.DataFrame({'Model': ['Baseline', 'Improved'], 'NRI': [0, nri]})
-plt.bar(nri_df['Model'], nri_df['NRI'], color=['#1f77b4', '#ff7f0e'])
-plt.ylim([0, 1])
-plt.xlabel('Model')
-plt.ylabel('NRI')
-plt.title('Net Reclassification Improvement')
-plt.show()