perf_events

perf_events是一个Linux内核提供的性能分析工具，它可以用于监测和分析系统的性能指标。它通过使用硬件性能计数器来收集各种事件的数据，如CPU周期、缓存命中率、指令执行数等。perf_events可以帮助开发人员和系统管理员深入了解系统的性能瓶颈，并进行性能优化。

perf_events具有以下特点：
1. 灵活性：perf_events支持多种事件类型，包括硬件事件、软件事件和tracepoint事件，可以根据需求选择合适的事件进行监测。
2. 低开销：perf_events利用硬件性能计数器进行数据采集，具有较低的开销，对系统性能影响较小。
3. 多维度分析：perf_events可以提供丰富的性能数据，可以通过不同的工具和选项进行数据分析和可视化，帮助用户深入了解系统的性能状况。
4. 扩展性：perf_events提供了一套API，可以方便地与其他工具和框架集成，实现更复杂的性能分析需求。

相关问题

纠正代码：trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv') testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv') y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited'] X_train = trainsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited'] X_test = testsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42) rfc.fit(X_train, y_train_forced_turnover_nolimited) y_pred = rfc.predict_proba(X_test) # 计算AUC值 auc = roc_auc_score(y_test_forced_turnover_nolimited, y_pred) # 输出AUC值 print('测试集AUC值为：', auc)

trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv')
testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv')
y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited']
X_train = trainsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',
'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',
'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',
'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',
'm3_forced_turnover_5year','m3_forced_turnover_10year','CEOid','CEO_turnover_N','year',
'Firmid','appo_year'], axis=1)
y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited']
X_test = testsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',
'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',
'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',
'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',
'm3_forced_turnover_5year','m3_forced_turnover_10year','CEOid','CEO_turnover_N','year',
'Firmid','appo_year'], axis=1)

from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
rfc.fit(X_train, y_train_forced_turnover_nolimited)
y_pred = rfc.predict_proba(X_test)[:, 1] # 计算AUC值时需要使用预测结果的概率值而不是预测结果本身

from sklearn.metrics import roc_auc_score

auc = roc_auc_score(y_test_forced_turnover_nolimited, y_pred) # 计算AUC值
print('测试集AUC值为：', auc) # 输出AUC值

select COLLECT_TIME, PERF_VALUE, t1.unit, RULEA, RULEB, RULEC from ( select COLLECT_TIME, PERF_VALUE, UNIT as unit, EQP_OBJ_ID, OBJECT_TYPE, INDEX_TYPE, DEVICE_ID, INDEX_NAME from t_perf_sensor_history tpsh, t_perf_defined tpd where PERF_OBJ_ID = tpd.OBJ_ID ) as t1 left join t_alarm_rule tar on IF(tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = OBJECT_TYPE) where 1=1 and COLLECT_TIME BETWEEN DATE_SUB(NOW(), INTERVAL 53 DAY) AND NOW() union select COLLECT_TIME, PERF_VALUE, t1.unit, RULEA, RULEB, RULEC from ( select COLLECT_TIME, PERF_VALUE, UNIT as unit, EQP_OBJ_ID, OBJECT_TYPE, INDEX_TYPE, DEVICE_ID, INDEX_NAME from t_perf_sensor_run tpsr, t_perf_defined tpd where PERF_OBJ_ID = tpd.OBJ_ID ) as t1 left join t_alarm_rule tar on IF(tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID, tar.ALARM_OBJECT_TYPE = OBJECT_TYPE) where 1=1 and COLLECT_TIME BETWEEN DATE_SUB(NOW(), INTERVAL 53 DAY) AND NOW() order by COLLECT_TIME desc怎么优化速度

首先，可以优化查询语句中的子查询，将其转换为 JOIN。例如：

SELECT COLLECT_TIME, PERF_VALUE, tpd.UNIT AS unit, t1.EQP_OBJ_ID, t1.OBJECT_TYPE, t1.INDEX_TYPE, t1.DEVICE_ID, t1.INDEX_NAME, tar.RULEA, tar.RULEB, tar.RULEC 
FROM ( 
    SELECT tpsh.COLLECT_TIME, tpsh.PERF_VALUE, tpsh.EQP_OBJ_ID, tpsh.OBJECT_TYPE, tpsh.INDEX_TYPE, tpsh.DEVICE_ID, tpsh.INDEX_NAME 
    FROM t_perf_sensor_history tpsh 
    JOIN t_perf_defined tpd ON tpsh.PERF_OBJ_ID = tpd.OBJ_ID 
    WHERE tpsh.COLLECT_TIME BETWEEN DATE_SUB(NOW(), INTERVAL 53 DAY) AND NOW() 
    
    UNION ALL 
    
    SELECT tpsr.COLLECT_TIME, tpsr.PERF_VALUE, tpsr.EQP_OBJ_ID, tpsr.OBJECT_TYPE, tpsr.INDEX_TYPE, tpsr.DEVICE_ID, tpsr.INDEX_NAME 
    FROM t_perf_sensor_run tpsr 
    JOIN t_perf_defined tpd ON tpsr.PERF_OBJ_ID = tpd.OBJ_ID 
    WHERE tpsr.COLLECT_TIME BETWEEN DATE_SUB(NOW(), INTERVAL 53 DAY) AND NOW() 
) t1 
LEFT JOIN t_alarm_rule tar ON (tar.ALARM_OBJECT_TYPE = t1.DEVICE_ID OR tar.ALARM_OBJECT_TYPE = t1.OBJECT_TYPE) 
ORDER BY COLLECT_TIME DESC

这样可以避免重复子查询的问题，提高查询效率。

其次，可以考虑对表进行索引优化。在这个查询中，可以为 t_perf_sensor_history 表的 COLLECT_TIME 字段和 PERF_OBJ_ID 字段，以及 t_perf_sensor_run 表的 COLLECT_TIME 字段和 PERF_OBJ_ID 字段建立联合索引，以优化子查询的效率。

最后，可以考虑对 WHERE 子句中的日期范围限制进行优化。例如，可以根据业务需求调整日期范围的大小，避免查询过多无用数据。同时，可以考虑将日期范围限制转换为日期时间戳的形式，以避免对日期字符串的转换和比较，提高查询效率。