赛题介绍
在分布式系统中某个节点发生故障时，故障会沿着分布式系统的拓扑结构进行传播，造成自身节点及其邻接节点相关的KPI指标和发生大量日志异常。本次比赛提供分布式数据库的故障特征数据和标签数据，其中特征数据是系统发生故障时的KPI指标数据，KPI指标包括由feature0、feature1 …feature106共107个指标，标签数据为故障类别数据，共6个类别，用0、1、2、3、4、5分别表示6个故障，参赛人员可根据这些数据，借助机器学习、深度学习、web等技术搭建故障诊断系统，该系统支持用户上传训练集对模型进行训练和模型下载，同时支持用户上传单条或多条测试语句进行测试并可视化测试结果，支持测试结果下载。
baseline: DecisionTree
数据分析
读取数据
df = pd.read_csv('data/train/train.csv', index_col=None)
判断是否有缺失值
df.isnull().any() ''' output: True即为存在缺失值 sample_id False feature0 True feature1 True feature2 True feature3 True ... feature103 True feature104 True feature105 False feature106 True label False Length: 109, dtype: bool '''
数据标准化及缺失值填充
# 数据标准化 features = df.iloc[:, 1:-1] numeric_features = features.dtypes[features.dtypes != 'object'].index features[numeric_features] = features[numeric_features].apply( lambda x: (x - x.mean()) / (x.std()) ) # 在标准化数据之后，所有均值消失，因此我们可以将缺失值设置为0 features[numeric_features] = features[numeric_features].fillna(0) features_labels = pd.concat([features, df[['label']]], axis=1) train_features = pd.concat([df[['sample_id']], features], axis=1) train_label = df[['sample_id', 'label']] df = pd.concat([train_features, train_label[['label']]], axis=1)
观察数据基本信息
1. # 观察前五行数据 2. df.head()
# 数据大小 df.shape ''' output: (6296, 109) '''
df.dtypes ''' output: sample_id int64 feature0 float64 feature1 float64 feature2 float64 feature3 float64 ... feature103 float64 feature104 float64 feature105 float64 feature106 float64 label int64 Length: 109, dtype: object '''
# 类别分布 df['label'].value_counts().sort_index().plot(kind='bar') plt.show() df['label'].value_counts().sort_index().plot(kind='pie') plt.show()
features.describe()
# 分组的平均数据统计 label_Summary = features_labels.groupby('label') label_Summary.mean()
# 相关性矩阵 corr = features_labels.corr() sns.set_context({'figure.figsize':[100, 100]}) fig = sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values) heatmap = fig.get_figure() heatmap.savefig('work/heatmap.png', dpi=300) corr
# 各个特征的概率密度函数 feature_names = features.columns.values.tolist() for name in feature_names: fig = plt.figure(figsize=(15, 4), ) ax = sns.kdeplot(df.loc[(df['label'] == 0), name], color='b', shade=True, label='0') ax = sns.kdeplot(df.loc[(df['label'] == 1), name], color='r', shade=True, label='1') ax = sns.kdeplot(df.loc[(df['label'] == 2), name], color='g', shade=True, label='2') ax = sns.kdeplot(df.loc[(df['label'] == 3), name], color='y', shade=True, label='3') ax = sns.kdeplot(df.loc[(df['label'] == 4), name], color='m', shade=True, label='4') ax = sns.kdeplot(df.loc[(df['label'] == 5), name], color='c', shade=True, label='5') ax.set(xlabel=name, ylabel='频率') plt.title('{} Probabilitydensity function'.format(name)) plt.savefig('work/{}的概率密度函数图.png'.format(name))
划分数据集
from sklearn.model_selection import train_test_split target_name = 'label' x = df.drop(['sample_id', 'label'], axis=1) y = df[['label']] x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.15, random_state=123, stratify=y)
模型训练
from sklearn.tree import DecisionTreeClassifier # 实例化 dtree = tree.DecisionTreeClassifier( criterion='entropy', min_weight_fraction_leaf=0.01 ) # train dtree = dtree.fit(x_train, y_train)
评价指标计算
# 指标计算 参数：array def metrics_calculate(pred, y_test, txt_path): TP = [0, 0, 0, 0, 0, 0] FP = [0, 0, 0, 0, 0, 0] FN = [0, 0, 0, 0, 0, 0] for i in range(len(y_test)): if pred[i] == 0 and y_test[i] == 0: TP[0] += 1 if pred[i] != 0 and y_test[i] == 0: FN[0] += 1 if pred[i] == 0 and y_test[i] != 0: FP[0] += 1 if pred[i] == 1 and y_test[i] == 1: TP[1] += 1 if pred[i] != 1 and y_test[i] == 1: FN[1] += 1 if pred[i] == 1 and y_test[i] != 1: FP[1] += 1 if pred[i] == 2 and y_test[i] == 2: TP[2] += 1 if pred[i] != 2 and y_test[i] == 2: FN[2] += 1 if pred[i] == 2 and y_test[i] != 2: FP[2] += 1 if pred[i] == 3 and y_test[i] == 3: TP[3] += 1 if pred[i] != 3 and y_test[i] == 3: FN[3] += 1 if pred[i] == 3 and y_test[i] != 3: FP[3] += 1 if pred[i] == 4 and y_test[i] == 4: TP[4] += 1 if pred[i] != 4 and y_test[i] == 4: FN[4] += 1 if pred[i] == 4 and y_test[i] != 4: FP[4] += 1 if pred[i] == 5 and y_test[i] == 5: TP[5] += 1 if pred[i] != 5 and y_test[i] == 5: FN[5] += 1 if pred[i] == 5 and y_test[i] != 5: FP[5] += 1 Precision = [0, 0, 0, 0, 0, 0] Recall = [0, 0, 0, 0, 0, 0] F1 = [0, 0, 0, 0, 0, 0] Precision[0] = TP[0] / (TP[0] + FP[0]) Precision[1] = TP[1] / (TP[1] + FP[1]) Precision[2] = TP[2] / (TP[2] + FP[2]) Precision[3] = TP[3] / (TP[3] + FP[3]) Precision[4] = TP[4] / (TP[4] + FP[4]) Precision[5] = TP[5] / (TP[5] + FP[5]) for i in range(6): print('Precision: {}\n'.format(Precision[i])) Recall[0] = TP[0] / (TP[0] + FN[0]) Recall[1] = TP[1] / (TP[1] + FN[1]) Recall[2] = TP[2] / (TP[2] + FN[2]) Recall[3] = TP[3] / (TP[3] + FN[3]) Recall[4] = TP[4] / (TP[4] + FN[4]) Recall[5] = TP[5] / (TP[5] + FN[5]) for i in range(6): print('Recall: {}\n'.format(Recall[i])) F1[0] = (2 * Precision[0] * Recall[0]) / (Precision[0] + Recall[0]) F1[1] = (2 * Precision[1] * Recall[1]) / (Precision[1] + Recall[1]) F1[2] = (2 * Precision[2] * Recall[2]) / (Precision[2] + Recall[2]) F1[3] = (2 * Precision[3] * Recall[3]) / (Precision[3] + Recall[3]) F1[4] = (2 * Precision[4] * Recall[4]) / (Precision[4] + Recall[4]) F1[5] = (2 * Precision[5] * Recall[5]) / (Precision[5] + Recall[5]) for i in range(6): print('F1: {}\n'.format(F1[i])) Macro_Precision = sum([Precision[0], Precision[1], Precision[2], Precision[3], Precision[4], Precision[5]]) / 6 Macro_Recall = sum([Recall[0], Recall[1], Recall[2], Recall[3], Recall[4], Recall[5]]) / 6 Macro_F1 = sum([F1[0], F1[1], F1[2], F1[3], F1[4], F1[5]]) / 6 l_sum = sum([TP[0], TP[1], TP[2], TP[3], TP[4], TP[5]]) m_sum = l_sum + sum([FP[0], FP[1], FP[2], FP[3], FP[4], FP[5]]) n_sum = l_sum + sum([FN[0], FN[1], FN[2], FN[3], FN[4], FN[5]]) Micro_Precision = l_sum / m_sum print('Micro_Precision: {}\n'.format(Micro_Precision)) Micro_Recall = l_sum / n_sum print('Micro_Recall: {}\n'.format(Micro_Recall)) Micro_F1 = (2 * Micro_Precision * Micro_Recall) / (Micro_Precision + Micro_Recall) print('Micro_F1: {}\n'.format(Micro_F1)) f = open(txt_path, 'a', encoding='utf-8') for i in range(6): f.write('类别{}: '.format(i)) f.write('\n') f.write('Precision: {:.2f}%'.format(Precision[i] * 100)) f.write('\n') f.write('Recall: {:.2f}%'.format(Recall[i] * 100)) f.write('\n') f.write('F1: {:.2f}'.format(F1[i])) f.write('\n') f.write('Macro_Precision: {:.2f}%'.format(Macro_Precision * 100)) f.write('\n') f.write('Macro_Recall: {:.2f}%'.format(Macro_Recall * 100)) f.write('\n') f.write('Macro_F1: {:.2f}'.format(Macro_F1)) f.write('\n') f.write('Micro_Precision: {:.2f}%'.format(Micro_Precision * 100)) f.write('\n') f.write('Micro_Recall: {:.2f}%'.format(Micro_Recall * 100)) f.write('\n') f.write('Micro_F1: {:.2f}'.format(Micro_F1)) f.write('\n') f.close()
验证模型
# 验证 pred = dtree.predict(x_test) y_test = y_test.reshape((-1, )) txt_path = 'work/result_RandomForest.txt' metrics_calculate(pred, y_test, txt_path)
''' Precision: 0.8382066276803118 Precision: 0.6823529411764706 Precision: 0.7553956834532374 Precision: 0.7368421052631579 Precision: 0.972972972972973 Precision: 0.8157894736842105 Recall: 0.8829568788501027 Recall: 0.5858585858585859 Recall: 0.6907894736842105 Recall: 0.8433734939759037 Recall: 0.6792452830188679 Recall: 0.8732394366197183 F1: 0.86 F1: 0.6304347826086957 F1: 0.7216494845360826 F1: 0.7865168539325843 F1: 0.7999999999999999 F1: 0.8435374149659864 Micro_Precision: 0.8052910052910053 Micro_Recall: 0.8052910052910053 Micro_F1: 0.8052910052910053 '''
