metrics

Mean Percentile Ranking (MPR) explanation

由 99封情书 提交于 2020-01-23 03:01:06
问题 I am trying to use MPR as a metric to evaluate my recommendation system based on implicit feedback. Can somebody please explain MPR? I have gone through this paper However, I can't seem to get an intuitive understanding. Any help would be appreciated. EDIT : I went through Microsoft's research on metrics for recommendation engine metrics It is recommended that MPR is recommended when we're looking for one 'positive' result. Can somebody also explain why that is the case? EDIT 2 : 来源： https:/

Hystrix属性配置策略

由 荒凉一梦 提交于 2020-01-23 00:36:48
Hystrix属性配置 Command可配参数 设置隔离策略 execution.isolation.strategy = THREAD 设置超时时间 execution.isolation.thread.timeoutInMilliseconds = 1000 信号量隔离策略设置最大并发请求数（仅在信号量隔离策略下生效） execution.isolation.semaphore.maxConcurrentRequests = 10 设置最大Fallback数量 fallback.isolation.semaphore.maxConcurrentRequests = 10 设置熔断器滑动窗口最小任务 circuitBreaker.requestVolumeThreshold = 20 设置熔断器持续时间 circuitBreaker.sleepWindowInMilliseconds = 5000 设置触发熔断器的失败任务阈值（百分比） circuitBreaker.errorThresholdPercentage = 50 设置Metrics监视器的范围时间（过去多少ms内） metrics.rollingStats.timeInMilliseconds = 10000 设置监视器内桶的数量（将监视器范围划分为若干块） metrics.rollingStats

sklearn metrics.log_loss is positive vs. scoring 'neg_log_loss' is negative

由 社会主义新天地 提交于 2020-01-22 11:18:39
问题 Making sure I am getting this right: If we use sklearn.metrics.log_loss standalone, i.e. log_loss(y_true,y_pred), it generates a positive score -- the smaller the score, the better the performance. However, if we use 'neg_log_loss' as a scoring scheme as in 'cross_val_score", the score is negative -- the bigger the score, the better the performance. And this is due to the scoring scheme is built to be consistent with other scoring schemes. Since generally, the higher the better, we negate

sklearn metrics.log_loss is positive vs. scoring 'neg_log_loss' is negative

由 安稳与你 提交于 2020-01-22 11:17:13
问题 Making sure I am getting this right: If we use sklearn.metrics.log_loss standalone, i.e. log_loss(y_true,y_pred), it generates a positive score -- the smaller the score, the better the performance. However, if we use 'neg_log_loss' as a scoring scheme as in 'cross_val_score", the score is negative -- the bigger the score, the better the performance. And this is due to the scoring scheme is built to be consistent with other scoring schemes. Since generally, the higher the better, we negate

sklearn metrics.log_loss is positive vs. scoring 'neg_log_loss' is negative

由 我怕爱的太早我们不能终老 提交于 2020-01-22 11:16:50
问题 Making sure I am getting this right: If we use sklearn.metrics.log_loss standalone, i.e. log_loss(y_true,y_pred), it generates a positive score -- the smaller the score, the better the performance. However, if we use 'neg_log_loss' as a scoring scheme as in 'cross_val_score", the score is negative -- the bigger the score, the better the performance. And this is due to the scoring scheme is built to be consistent with other scoring schemes. Since generally, the higher the better, we negate

OpenTSDB介绍

由 爱⌒轻易说出口 提交于 2020-01-19 21:43:34
OpenTSDB 2.0, the scalable, distributed time series database可扩展、分布式时间序列数据库 1、背景 一些老的监控系统，它常常会出现这样的问题： 1）中心化数据存储进而导致单点故障。 2）有限的存储空间。 3）数据会因为时间问题而变得不准确。 4）不易于定制图形。 5）不能扩展采集数据点到100亿级别。 6）不能扩展metrics到K级别。 7）不支持秒级别的数据。 OpenTSDB解决上面的问题： 1、它用 hbase存储所有的时序 （无须采样）来构建一个分布式、可伸缩的时间序列数据库。 2、它支持 秒级数据采集所有metrics ，支持永久存储，可以做容量规划，并很容易的接入到现有的报警系统里。 3、OpenTSDB可以从 大规模的集群（ 包括集群中的网络设备、操作系统、应用程序）中 获取相应的metrics并进行存储、索引以及服务 从而使得这些数据更容易让人理解，如web化，图形化等。 对于运维工程师而言，OpenTSDB可以获取基础设施和服务的实时状态信息，展示集群的各种软硬件错误，性能变化以及性能瓶颈。 对于管理者而言，OpenTSDB可以衡量系统的SLA，理解复杂系统间的相互作用，展示资源消耗情况。集群的整体作业情况，可以用以辅助预算和集群资源协调。 对于开发者而言，OpenTSDB可以展示集群的主要性能瓶颈

API for metrics per Azure Site instance

由 自闭症网瘾萝莉.ら 提交于 2020-01-17 08:38:08
问题 In Azure's Portal you can view instance specific metrics per site if you go to a resource , select Metrics per instance (Apps) , select the tab Site Metrics and then click an individual instance (starting with RD00... in the screenshot below): I'd like to get this data (per instance, including the instance name RD00... ) using some REST API call. I've looked at Azure's Resource Manager and their Metrics API, but couldn't find a way to get this data. Is this possible, and, if so, how/where can

How to show Dropwizard active requests in logs

由 江枫思渺然 提交于 2020-01-16 13:19:08
问题 This question is related to the one I ask here. I'm trying to log the number of active requests being made to my Dropwizard application every 10 minutes. The idea is to track usage of the app so it can get the proper support. In doing this, I'm trying to expose the dropwizard metrics to the logs. For testing purposes, I've made the following code: @GET @Path("/metrics") public MetricRegistry provideMetrics() { MetricRegistry metrics = new MetricRegistry(); metrics.register("io.dropwizard

How to show Dropwizard active requests in logs

由 偶尔善良 提交于 2020-01-16 13:19:01
问题 This question is related to the one I ask here. I'm trying to log the number of active requests being made to my Dropwizard application every 10 minutes. The idea is to track usage of the app so it can get the proper support. In doing this, I'm trying to expose the dropwizard metrics to the logs. For testing purposes, I've made the following code: @GET @Path("/metrics") public MetricRegistry provideMetrics() { MetricRegistry metrics = new MetricRegistry(); metrics.register("io.dropwizard
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