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  • Metrics

    2020-11-23 03:21:17
    <div><p>Add Watchman metrics plugin that - must explicitly be enabled in node.conf to execute - has a configurable interval between executions - gathers gear cgroups and quota metrics - gathers ...
  • metrics

    2018-04-03 10:52:01
    1. sklearn.metrics.roc_curve(true_y. pred_proba_score, pos_labal)计算roc曲线,roc曲线有三个属性:fpr, tpr,和阈值,因此该函数返回这三个变量,l例如import numpy as np from sklearn.metrics import roc_curve...

    1. sklearn.metrics.roc_curve(true_y. pred_proba_score, pos_labal)

    计算roc曲线,roc曲线有三个属性:fpr, tpr,和阈值,因此该函数返回这三个变量,l例如

    import numpy as np
    from sklearn.metrics import roc_curve
    y = np.array([1,1,2,2])
    pred = np.array([0.1, 0.4, 0.35, 0.8])
    fpr, tpr, thresholds = roc_curve(y, pred, pos_label=2)
    fpr      # array([ 0. ,  0.5,  0.5,  1. ])
    tpr      # array([ 0.5,  0.5,  1. ,  1. ])
    thresholds      #array([ 0.8 ,  0.4 ,  0.35,  0.1 ])
    from sklearn.metrics import auc
    metrics.auc(fpr, tpr)
    0.75

    2. sklearn.metrics.auc(x, y, reorder=False):

    计算AUC值,其中x,y分别为数组形式,根据(xi, yi)在坐标上的点,生成的曲线,然后计算AUC值;

    3. sklearn.metrics.roc_auc_score(true_y, pred_proba_y)

    直接根据真实值(必须是二值)、预测值(可以是0/1, 也可以是proba值)计算出auc值,中间过程的roc计算省略

    4.accuracy_score

    分类准确率分数是指所有分类正确的百分比。分类准确率这一衡量分类器的标准比较容易理解,但是它不能告诉你响应值的潜在分布,并且它也不能告诉你分类器犯错的类型。

    • 形式

    sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

    normalize:默认值为True,返回正确分类的比例;如果为False,返回正确分类的样本数

    • 示例
    >>>import numpy as np
    >>>from sklearn.metrics import accuracy_score
    >>>y_pred = [0, 2, 1, 3]
    >>>y_true = [0, 1, 2, 3]
    >>>accuracy_score(y_true, y_pred)
    0.5
    >>>accuracy_score(y_true, y_pred, normalize=False)
    2



    展开全文
  • HIS Metrics

    2020-11-23 14:46:38
    QAC warning discription. HIS Metrics = Hersteller Initiative Software (HIS) [德国几大汽车OEM所倡议的软件] 按照一定的规则编写代码,例如MISRA-C++:2008 + HIS Metrics 软件静态测试)
  • This work will cover <code>appmetrics-dash</code> and <code>javametrics-dash</code> gaining code to poll a /metrics endpoint on localhost every 10 seconds and to display the data retrieved in as ...
  • Export metrics to /metrics

    2020-12-25 20:08:13
    <div><p>Since the Prometheus that I have will scrape in the path <code>/metrics, I would like to export prometheus metrics to <code>/metrics</code> instead of <code>/_prometheus.metrics</code>....
  • sklearn.metrics.roc_curve解析

    万次阅读 多人点赞 2018-05-28 21:52:20
    官方网址:http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics首先认识单词:metrics:['mɛtrɪks] : 度量‘指标 curve :[kɝv] : 曲线这个方法主要用来计算...sklearn.metrics.roc_cur...

    官方网址:http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics

    首先认识单词:metrics:  ['mɛtrɪks] : 度量‘指标

                            curve : [kɝv]  :  曲线

    这个方法主要用来计算ROC曲线面积的;

    sklearn.metrics.roc_curve(y_truey_scorepos_label=Nonesample_weight=Nonedrop_intermediate=True)

    Parameters

    y_true : 数组,shape = [样本数]           

    在范围{0,1}或{-1,1}中真正的二进制标签。如果标签不是二进制的,则应该显式地给出pos_label

    y_score : 数组, shape = [样本数]            

    目标得分,可以是积极类的概率估计,信心值,或者是决定的非阈值度量(在某些分类器上由“decision_function”返回)。

    pos_label:int or str, 标签被认为是积极的,其他的被认为是消极的。

    sample_weight: 顾名思义,样本的权重,可选择的

    drop_intermediate:  boolean, optional (default=True)                

     是否放弃一些不出现在绘制的ROC曲线上的次优阈值。这有助于创建更轻的ROC曲线

    Returns : 

    fpr : array, shape = [>2]                增加假阳性率,例如,i是预测的假阳性率,得分>=临界值[i]

    tpr : array, shape = [>2]                增加真阳性率,例如,i是预测的真阳性率,得分>=临界值[i]。  

    thresholds : array, shape = [n_thresholds]            

    减少了用于计算fpr和tpr的决策函数的阈值。阈值[0]表示没有被预测的实例,并且被任意设置为max(y_score) + 1

    要弄明白ROC的概念可以参考 :https://www.deeplearn.me/1522.html

     

    介绍ROC曲线的两个重要指标:

    真阳性率 = true positive rate = TPR = TP/ (TP + FN)

    可以这样理解:真阳性率就是在标准的阳性(标准的阳性就等于真阳性加假阴性=TP + FN)中,同时被检测为阳性的概率,有点绕,自行理解。

    假阳性率 = false positive rate = FPR = FP / (FP+TN)

    可以这样理解:假阳性就是在标准的阴性(标准的阴性就等于假阳性真阴性=FP + TN)中,被检测为阳性的概率。很好理解的,本来是阴性,检测成了阳性的概率就是假阳性率呗。

     

    ROC曲线就由这两个值绘制而成。接下来进入sklearn.metrics.roc_curve实战,找遍了网络也没找到像我一样解释这么清楚的。

    import numpy as np
    from sklearn import metrics
    y = np.array([1, 1, 2, 2])
    scores = np.array([0.1, 0.4, 0.35, 0.8])
    fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)

    y 就是标准值,scores 是每个预测值对应的阳性概率,比如0.1就是指第一个数预测为阳性的概率为0.1,很显然,y 和 socres应该有相同多的元素,都等于样本数。pos_label=2 是指在y中标签为2的是标准阳性标签,其余值是阴性。

    所以在标准值y中,阳性有2个,后两个;阴性有2个,前两个。

    接下来选取一个阈值计算TPR/FPR,阈值的选取规则是在scores值中从大到小的以此选取,于是第一个选取的阈值是0.8

    scores中大于阈值的就是预测为阳性,小于的预测为阴性。所以预测的值设为y_=(0,0,0,1),0代表预测为阴性,1代表预测为阳性。可以看出,真阴性都被预测为阴性,真阳性有一个预测为假阴性了。

    FPR = FP / (FP+TN) = 0 / 0 + 2 = 0

    TPR = TP/ (TP + FN) = 1 / 1 + 1 = 0.5

    thresholds = 0.8

    我们验证一下结果

    print(fpr[0],tpr[0],thresholds[0])

     

    同代码结果一致,其余的就不演示了,剩下的阈值一次等于 0.4  0.35  0.1  自行验证。

     

    最后结果等于

    print(fpr,'\n',tpr,'\n',thresholds)

     

    全部代码

     

    import numpy as np
    from sklearn import metrics
    y = np.array([1, 1, 2, 2])
    scores = np.array([0.1, 0.4, 0.35, 0.8])
    fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)
    print(fpr,'\n',tpr,'\n',thresholds)

    坚持已实践为主,手把手带你做项目,打比赛,写论文。凡原创文章皆提供理论讲解,实验代码,实验数据。只有实践才能成长的更快,关注我们,一起成长进步~

    展开全文
  • <div><p>Some <code>INNODB_METRICS</code> would better be aggregated. (which are disabled by default and can be enabled with <code>innodb_monitor_enable</code> variable) <p>Example: <code>mysql_info_...
  • Prometheus metrics

    2020-12-08 19:00:40
    <ul><li>Refactor Metrics package to be extensible by different backend formats.</li><li>Adds Prometheus format metrics backend.</li><li>Refactor metrics handler to be generic for metrics backends....
  • gRPC Metrics

    2021-01-06 11:59:05
    <div><p>This PR adds Prometheus metrics export (using <code>--listen-metrics) as well as gRPC interceptors to automatically collect gRPC metrics. <p>Eventually we'll instrument code where it makes...
  • <ul><li>Add mirage layer and influxdb reporter (mirage/metrics#28, )</li><li>Gnuplot: namespacing improvements (mirage/metrics#34, )</li><li>Gnuplot: optional graph generation (mirage/metrics#35, )...
  • Metrics Aggregator

    2020-12-02 19:41:25
    <div><p>In some cases, it would be nice to be able to have a single endpoint which was able to aggregate multiple different providers of the custom metrics API into a single implementation of the ...
  • Controller metrics

    2021-01-09 04:53:12
    <p>None of our controllers expose metrics regarding their behavior and performance. Metrics such as reconciliation failures, latency & count will be helpful metrics for debugging issues. Metrics ...
  • Metrics improvements

    2020-11-30 06:27:48
    <div><ul><li>Metrics deployment is currently disabled by default. Enable it by setting <code>openshift_hosted_metrics_deploy=true</code></li><li>Metrics currently only supports one single ...
  • Districtm metrics

    2020-12-01 18:27:36
    <div><p>We use Influxdb for metrics. In our own tracking we see districtm, but not in the metrics. The meters are there but have no data. Since districtm uses the appnexus adapter, i could assume the ...
  • Metrics check

    2020-12-09 04:12:04
    <div><p>Allow to verify mongoose'...<p>Example usage is <a href="https://github.com/esl/ejabberd_tests/blob/more-metrics-2/tests/metrics_roster_SUITE.erl#L64-L71">metrics_roster_SUITE here</a> and ...
  • Renaming Metrics

    2020-11-27 20:55:04
    The naming of the metrics are a little disorganized. Also, we want to come up with a better way to name metrics so its easier to filter the metrics. <p><strong>Problem location Metrics <p><strong>...
  • Push Metrics

    2021-01-07 00:25:25
    <div><p>This introduces an initial implementation of push metrics (as proposed in #1129). It supports both the Prometheus format with both text and protobuf format. It works as such: <p>All metrics, ...
  • L7 metrics

    2021-01-12 00:49:30
    <div><p>Scope would be much more useful if it was able to show L7 metrics, e.g. HTTP request rate, latency, error rate. <p>These metrics can be associated with <em>nodes</em> - containers, services, ...
  • Metrics Review

    2020-12-05 18:14:23
    <p>When a team member is working async during the week, they need access to the metric document links for the team so that they can review existing metrics or add/edit/delete links to existing metrics...
  • Monitoring metrics

    2021-01-10 23:57:01
    <p>Now I would like to <a href="http://docs.keymetrics.io/docs/pages/custom-metrics/">monitor some "key metrics"</a> by getting data from Primus internals. I already put custom metrics to ...
  • <p><strong>What this PR does / why we need it</strong>: Enable restclient metrics in metrics output <p><strong>Which issue(s) this PR fixes</strong>: Fixes #636 <p>/kind feature</p><p>该提问来源于开源...
  • Metrics updates

    2021-01-06 12:12:17
    <p>This PR takes a first pass at documenting what each metrics does. <p>While documenting metrics * I removed the ones that didn't seem to add value * I renamed some for consistency <p>From what I...

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