Alink漫谈(八) : 二分类评估 AUC、K-S、PRC、Precision、Recall、LiftChart 如何实现
目录
0x00 摘要
Alink 是阿里巴巴基于实时计算引擎 Flink 研发的新一代机器学习算法平台，是业界首个同时支持批式算法、流式算法的机器学习平台。二分类评估是对二分类算法的预测结果进行效果评估。本文将剖析Alink中对应代码实现。
0x01 相关概念
如果对本文某些概念有疑惑，可以参见之前文章 [白话解析] 通过实例来梳理概念 ：准确率 (Accuracy)、精准率(Precision)、召回率(Recall) 和 F值(F-Measure)
0x02 示例代码
public class EvalBinaryClassExample {
AlgoOperator getData(boolean isBatch) {
Row[] rows = new Row[]{
Row.of("prefix1", "{"prefix1": 0.9, "prefix0": 0.1}"),
Row.of("prefix1", "{"prefix1": 0.8, "prefix0": 0.2}"),
Row.of("prefix1", "{"prefix1": 0.7, "prefix0": 0.3}"),
Row.of("prefix0", "{"prefix1": 0.75, "prefix0": 0.25}"),
Row.of("prefix0", "{"prefix1": 0.6, "prefix0": 0.4}")
};
String[] schema = new String[]{"label", "detailInput"};
if (isBatch) {
return new MemSourceBatchOp(rows, schema);
} else {
return new MemSourceStreamOp(rows, schema);
}
}
public static void main(String[] args) throws Exception {
EvalBinaryClassExample test = new EvalBinaryClassExample();
BatchOperator batchData = (BatchOperator) test.getData(true);
BinaryClassMetrics metrics = new EvalBinaryClassBatchOp()
.setLabelCol("label")
.setPredictionDetailCol("detailInput")
.linkFrom(batchData)
.collectMetrics();
System.out.println("RocCurve:" + metrics.getRocCurve());
System.out.println("AUC:" + metrics.getAuc());
System.out.println("KS:" + metrics.getKs());
System.out.println("PRC:" + metrics.getPrc());
System.out.println("Accuracy:" + metrics.getAccuracy());
System.out.println("Macro Precision:" + metrics.getMacroPrecision());
System.out.println("Micro Recall:" + metrics.getMicroRecall());
System.out.println("Weighted Sensitivity:" + metrics.getWeightedSensitivity());
}
}
程序输出
RocCurve:([0.0, 0.0, 0.0, 0.5, 0.5, 1.0, 1.0],[0.0, 0.3333333333333333, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 1.0])
AUC:0.8333333333333333
KS:0.6666666666666666
PRC:0.9027777777777777
Accuracy:0.6
Macro Precision:0.3
Micro Recall:0.6
Weighted Sensitivity:0.6
在 Alink 中，二分类评估有批处理，流处理两种实现，下面一一为大家介绍（ Alink 复杂之一在于大量精细的数据结构，所以下文会大量打印程序中变量以便大家理解）。
2.1 主要思路
-
把 [0,1] 分成假设 100000个桶(bin)。所以得到positiveBin / negativeBin 两个100000的数组。
-
根据输入给positiveBin / negativeBin赋值。positiveBin就是 TP + FP，negativeBin就是 TN + FN。这些是后续计算的基础。
-
遍历bins中每一个有意义的点，计算出totalTrue和totalFalse，并且在每一个点上计算该点的混淆矩阵，tpr，以及rocCurve，recallPrecisionCurve，liftChart在该点对应的数据；
-
依据曲线内容计算并且存储 AUC/PRC/KS
具体后续还有详细调用关系综述。
0x03 批处理
3.1 EvalBinaryClassBatchOp
EvalBinaryClassBatchOp是二分类评估的实现，功能是计算二分类的评估指标(evaluation metrics)。
输入有两种：
- label column and predResult column
- label column and predDetail column。如果有predDetail，则predResult被忽略
我们例子中 "prefix1" 就是 label，"{"prefix1": 0.9, "prefix0": 0.1}" 就是 predDetail
Row.of("prefix1", "{"prefix1": 0.9, "prefix0": 0.1}")
具体类摘录如下：
public class EvalBinaryClassBatchOp extends BaseEvalClassBatchOp<EvalBinaryClassBatchOp> implements BinaryEvaluationParams <EvalBinaryClassBatchOp>, EvaluationMetricsCollector<BinaryClassMetrics> {
@Override
public BinaryClassMetrics collectMetrics() {
return new BinaryClassMetrics(this.collect().get(0));
}
}
可以看到，其主要工作都是在基类BaseEvalClassBatchOp中完成，所以我们会首先看BaseEvalClassBatchOp。
3.2 BaseEvalClassBatchOp
我们还是从 linkFrom 函数入手，其主要是做了几件事：
- 获取配置信息
- 从输入中提取某些列："label"，"detailInput"
- calLabelPredDetailLocal会按照partition分别计算evaluation metrics
- 综合reduce上述计算结果
- SaveDataAsParams函数会把最终数值输入到 output table
具体代码如下
@Override
public T linkFrom(BatchOperator<?>... inputs) {
BatchOperator<?> in = checkAndGetFirst(inputs);
String labelColName = this.get(MultiEvaluationParams.LABEL_COL);
String positiveValue = this.get(BinaryEvaluationParams.POS_LABEL_VAL_STR);
// Judge the evaluation type from params.
ClassificationEvaluationUtil.Type type = ClassificationEvaluationUtil.judgeEvaluationType(this.getParams());
DataSet<BaseMetricsSummary> res;
switch (type) {
case PRED_DETAIL: {
String predDetailColName = this.get(MultiEvaluationParams.PREDICTION_DETAIL_COL);
// 从输入中提取某些列："label"，"detailInput"
DataSet<Row> data = in.select(new String[] {labelColName, predDetailColName}).getDataSet();
// 按照partition分别计算evaluation metrics
res = calLabelPredDetailLocal(data, positiveValue, binary);
break;
}
......
}
// 综合reduce上述计算结果
DataSet<BaseMetricsSummary> metrics = res
.reduce(new EvaluationUtil.ReduceBaseMetrics());
// 把最终数值输入到 output table
this.setOutput(metrics.flatMap(new EvaluationUtil.SaveDataAsParams()),
new String[] {DATA_OUTPUT}, new TypeInformation[] {Types.STRING});
return (T)this;
}
// 执行中一些变量如下
labelColName = "label"
predDetailColName = "detailInput"
type = {ClassificationEvaluationUtil$Type@2532} "PRED_DETAIL"
binary = true
positiveValue = null
3.2.0 调用关系综述
因为后续代码调用关系复杂，所以先给出一个调用关系：
- 从输入中提取某些列："label"，"detailInput"，in.select(new String[] {labelColName, predDetailColName}).getDataSet()。因为可能输入还有其他列，而只有某些列是我们计算需要的，所以只提取这些列。
- 按照partition分别计算evaluation metrics，即调用 calLabelPredDetailLocal(data, positiveValue, binary);
- flatMap会从label列和prediction列中，取出所有labels（注意是取出labels的名字 ），发送给下游算子。
- reduceGroup主要功能是通过 buildLabelIndexLabelArray 去重 "labels名字"，然后给每一个label一个ID，得到一个 <labels, ID>的map，最后返回是二元组(map, labels)，即({prefix1=0, prefix0=1},[prefix1, prefix0])。从后文看，<labels, ID>Map看来是多分类才用到。二分类只用到了labels。
- mapPartition 分区调用 CalLabelDetailLocal 来计算混淆矩阵，主要是分区调用getDetailStatistics，前文中得到的二元组(map, labels)会作为参数传递进来 。
- getDetailStatistics 遍历 rows 数据，提取每一个item（比如 "prefix1,{"prefix1": 0.8, "prefix0": 0.2}"），然后通过updateBinaryMetricsSummary累积计算混淆矩阵所需数据。
- updateBinaryMetricsSummary 把 [0,1] 分成假设 100000个桶(bin)。所以得到positiveBin / negativeBin 两个100000的数组。positiveBin就是 TP + FP，negativeBin就是 TN + FN。
- 如果某个 sample 为 正例 (positive value) 的概率是 p, 则该 sample 对应的 bin index 就是 p * 100000。如果 p 被预测为正例 (positive value) ，则positiveBin[index]++，
- 否则就是被预测为负例(negative value) ，则negativeBin[index]++。
- updateBinaryMetricsSummary 把 [0,1] 分成假设 100000个桶(bin)。所以得到positiveBin / negativeBin 两个100000的数组。positiveBin就是 TP + FP，negativeBin就是 TN + FN。
- getDetailStatistics 遍历 rows 数据，提取每一个item（比如 "prefix1,{"prefix1": 0.8, "prefix0": 0.2}"），然后通过updateBinaryMetricsSummary累积计算混淆矩阵所需数据。
- 综合reduce上述计算结果，metrics = res.reduce(new EvaluationUtil.ReduceBaseMetrics());
- 具体计算是在BinaryMetricsSummary.merge，其作用就是Merge the bins, and add the logLoss。
- 把最终数值输入到 output table，setOutput(metrics.flatMap(new EvaluationUtil.SaveDataAsParams()..);
- 归并所有BaseMetrics后，得到total BaseMetrics，计算indexes存入params。collector.collect(t.toMetrics().serialize());
- 实际业务在BinaryMetricsSummary.toMetrics，即基于bin的信息计算，然后存储到params。
- extractMatrixThreCurve函数取出非空的bins，据此计算出ConfusionMatrix array（混淆矩阵）, threshold array, rocCurve/recallPrecisionCurve/LiftChart.
- 遍历bins中每一个有意义的点，计算出totalTrue和totalFalse，并且在每一个点上计算：
- curTrue += positiveBin[index]; curFalse += negativeBin[index];
- 得到该点的混淆矩阵 new ConfusionMatrix(new long[][] {{curTrue, curFalse}, {totalTrue - curTrue, totalFalse - curFalse}});
- 得到 tpr = (totalTrue == 0 ? 1.0 : 1.0 * curTrue / totalTrue);
- rocCurve，recallPrecisionCurve，liftChart在该点对应的数据；
- 依据曲线内容计算并且存储 AUC/PRC/KS
- 对生成的rocCurve/recallPrecisionCurve/LiftChart输出进行抽样
- 依据抽样后的输出存储 RocCurve/RecallPrecisionCurve/LiftChar
- 存储正例样本的度量指标
- 存储Logloss
- Pick the middle point where threshold is 0.5.
- extractMatrixThreCurve函数取出非空的bins，据此计算出ConfusionMatrix array（混淆矩阵）, threshold array, rocCurve/recallPrecisionCurve/LiftChart.
- 实际业务在BinaryMetricsSummary.toMetrics，即基于bin的信息计算，然后存储到params。
- 归并所有BaseMetrics后，得到total BaseMetrics，计算indexes存入params。collector.collect(t.toMetrics().serialize());
3.2.1 calLabelPredDetailLocal
本函数按照partition分别计算评估指标 evaluation metrics。是的，这代码很短，但是有个地方需要注意。有时候越简单的地方越容易疏漏。容易疏漏点是：
第一行代码的结果 labels 是第二行代码的参数，而并非第二行主体。第二行代码主体和第一行代码主体一样，都是data。
private static DataSet<BaseMetricsSummary> calLabelPredDetailLocal(DataSet<Row> data, final String positiveValue, oolean binary) {
DataSet<Tuple2<Map<String, Integer>, String[]>> labels = data.flatMap(new FlatMapFunction<Row, String>() {
@Override
public void flatMap(Row row, Collector<String> collector) {
TreeMap<String, Double> labelProbMap;
if (EvaluationUtil.checkRowFieldNotNull(row)) {
labelProbMap = EvaluationUtil.extractLabelProbMap(row);
labelProbMap.keySet().forEach(collector::collect);
collector.collect(row.getField(0).toString());
}
}
}).reduceGroup(new EvaluationUtil.DistinctLabelIndexMap(binary, positiveValue));
return data
.rebalance()
.mapPartition(new CalLabelDetailLocal(binary))
.withBroadcastSet(labels, LABELS);
}
calLabelPredDetailLocal中具体分为三步骤：
- 在flatMap会从label列和prediction列中，取出所有labels（注意是取出labels的名字 ），发送给下游算子。
- reduceGroup的主要功能是去重 "labels名字"，然后给每一个label一个ID，最后结果是一个<labels, ID>Map。
- mapPartition 是分区调用 CalLabelDetailLocal 来计算混淆矩阵。
下面具体看看。
3.2.1.1 flatMap
在flatMap中，主要是从label列和prediction列中，取出所有labels（注意是取出labels的名字 ），发送给下游算子。
EvaluationUtil.extractLabelProbMap 作用就是解析输入的json，获得具体detailInput中的信息。
下游算子是reduceGroup，所以Flink runtime会对这些labels自动去重。如果对这部分有兴趣，可以参见我之前介绍reduce的文章。CSDN ： [源码解析] Flink的groupBy和reduce究竟做了什么 博客园 : [源码解析] Flink的groupBy和reduce究竟做了什么
程序中变量如下
row = {Row@8922} "prefix1,{"prefix1": 0.9, "prefix0": 0.1}"
fields = {Object[2]@8925}
0 = "prefix1"
1 = "{"prefix1": 0.9, "prefix0": 0.1}"
labelProbMap = {TreeMap@9008} size = 2
"prefix0" -> {Double@9015} 0.1
"prefix1" -> {Double@9017} 0.9
labelProbMap.keySet().forEach(collector::collect); //这里发送 "prefix0", "prefix1"
collector.collect(row.getField(0).toString()); // 这里发送 "prefix1"
// 因为下一个操作是reduceGroup，所以这些label会被runtime去重
3.2.1.2 reduceGroup
主要功能是通过buildLabelIndexLabelArray去重labels，然后给每一个label一个ID，最后结果是一个<labels, ID>的Map。
reduceGroup(new EvaluationUtil.DistinctLabelIndexMap(binary, positiveValue));
DistinctLabelIndexMap的作用是从label列和prediction列中，取出所有不同的labels，返回一个<labels, ID>的map，根据后续代码看，这个map是多分类才用到。Get all the distinct labels from label column and prediction column, and return the map of labels and their IDs.
前面已经提到，这里的参数rows已经被自动去重。
public static class DistinctLabelIndexMap implements
GroupReduceFunction<String, Tuple2<Map<String, Integer>, String[]>> {
......
@Override
public void reduce(Iterable<String> rows, Collector<Tuple2<Map<String, Integer>, String[]>> collector) throws Exception {
HashSet<String> labels = new HashSet<>();
rows.forEach(labels::add);
collector.collect(buildLabelIndexLabelArray(labels, binary, positiveValue));
}
}
// 变量为
labels = {HashSet@9008} size = 2
0 = "prefix1"
1 = "prefix0"
binary = true
buildLabelIndexLabelArray的作用是给每一个label一个ID，得到一个 <labels, ID>的map，最后返回是二元组(map, labels)，即({prefix1=0, prefix0=1},[prefix1, prefix0])。
// Give each label an ID, return a map of label and ID.
public static Tuple2<Map<String, Integer>, String[]> buildLabelIndexLabelArray(HashSet<String> set,boolean binary, String positiveValue) {
String[] labels = set.toArray(new String[0]);
Arrays.sort(labels, Collections.reverseOrder());
Map<String, Integer> map = new HashMap<>(labels.length);
if (binary && null != positiveValue) {
if (labels[1].equals(positiveValue)) {
labels[1] = labels[0];
labels[0] = positiveValue;
}
map.put(labels[0], 0);
map.put(labels[1], 1);
} else {
for (int i = 0; i < labels.length; i++) {
map.put(labels[i], i);
}
}
return Tuple2.of(map, labels);
}
// 程序变量如下
labels = {String[2]@9013}
0 = "prefix1"
1 = "prefix0"
map = {HashMap@9014} size = 2
"prefix1" -> {Integer@9020} 0
"prefix0" -> {Integer@9021} 1
3.2.1.3 mapPartition
这里主要功能是分区调用 CalLabelDetailLocal 来为后来计算混淆矩阵做准备。
return data
.rebalance()
.mapPartition(new CalLabelDetailLocal(binary)) //这里是业务所在
.withBroadcastSet(labels, LABELS);
具体工作是 CalLabelDetailLocal 完成的，其作用是分区调用getDetailStatistics
// Calculate the confusion matrix based on the label and predResult.
static class CalLabelDetailLocal extends RichMapPartitionFunction<Row, BaseMetricsSummary> {
private Tuple2<Map<String, Integer>, String[]> map;
private boolean binary;
@Override
public void open(Configuration parameters) throws Exception {
List<Tuple2<Map<String, Integer>, String[]>> list = getRuntimeContext().getBroadcastVariable(LABELS);
this.map = list.get(0);// 前文生成的二元组(map, labels)
}
@Override
public void mapPartition(Iterable<Row> rows, Collector<BaseMetricsSummary> collector) {
// 调用到了 getDetailStatistics
collector.collect(getDetailStatistics(rows, binary, map));
}
}
getDetailStatistics 的作用是：初始化分类评估的度量指标 base classification evaluation metrics，累积计算混淆矩阵需要的数据。主要就是遍历 rows 数据，提取每一个item（比如 "prefix1,{"prefix1": 0.8, "prefix0": 0.2}"），然后累积计算混淆矩阵所需数据。
// Initialize the base classification evaluation metrics. There are two cases: BinaryClassMetrics and MultiClassMetrics.
private static BaseMetricsSummary getDetailStatistics(Iterable<Row> rows,
String positiveValue,
boolean binary,
Tuple2<Map<String, Integer>, String[]> tuple) {
BinaryMetricsSummary binaryMetricsSummary = null;
MultiMetricsSummary multiMetricsSummary = null;
Tuple2<Map<String, Integer>, String[]> labelIndexLabelArray = tuple; // 前文生成的二元组(map, labels)
Iterator<Row> iterator = rows.iterator();
Row row = null;
while (iterator.hasNext() && !checkRowFieldNotNull(row)) {
row = iterator.next();
}
Map<String, Integer> labelIndexMap = null;
if (binary) {
// 二分法在这里
binaryMetricsSummary = new BinaryMetricsSummary(
new long[ClassificationEvaluationUtil.DETAIL_BIN_NUMBER],
new long[ClassificationEvaluationUtil.DETAIL_BIN_NUMBER],
labelIndexLabelArray.f1, 0.0, 0L);
} else {
//
labelIndexMap = labelIndexLabelArray.f0; // 前文生成的<labels, ID>Map看来是多分类才用到。
multiMetricsSummary = new MultiMetricsSummary(
new long[labelIndexMap.size()][labelIndexMap.size()],
labelIndexLabelArray.f1, 0.0, 0L);
}
while (null != row) {
if (checkRowFieldNotNull(row)) {
TreeMap<String, Double> labelProbMap = extractLabelProbMap(row);
String label = row.getField(0).toString();
if (ArrayUtils.indexOf(labelIndexLabelArray.f1, label) >= 0) {
if (binary) {
// 二分法在这里
updateBinaryMetricsSummary(labelProbMap, label, binaryMetricsSummary);
} else {
updateMultiMetricsSummary(labelProbMap, label, labelIndexMap, multiMetricsSummary);
}
}
}
row = iterator.hasNext() ? iterator.next() : null;
}
return binary ? binaryMetricsSummary : multiMetricsSummary;
}
//变量如下
tuple = {Tuple2@9252} "({prefix1=0, prefix0=1},[prefix1, prefix0])"
f0 = {HashMap@9257} size = 2
"prefix1" -> {Integer@9264} 0
"prefix0" -> {Integer@9266} 1
f1 = {String[2]@9258}
0 = "prefix1"
1 = "prefix0"
row = {Row@9271} "prefix1,{"prefix1": 0.8, "prefix0": 0.2}"
fields = {Object[2]@9276}
0 = "prefix1"
1 = "{"prefix1": 0.8, "prefix0": 0.2}"
labelIndexLabelArray = {Tuple2@9240} "({prefix1=0, prefix0=1},[prefix1, prefix0])"
f0 = {HashMap@9288} size = 2
"prefix1" -> {Integer@9294} 0
"prefix0" -> {Integer@9296} 1
f1 = {String[2]@9242}
0 = "prefix1"
1 = "prefix0"
labelProbMap = {TreeMap@9342} size = 2
"prefix0" -> {Double@9378} 0.1
"prefix1" -> {Double@9380} 0.9
先回忆下混淆矩阵：
| 预测值 0 | 预测值 1 | |||
|---|---|---|---|---|
| 真实值 0 | TN | FP | ||
| 真实值 1 | FN | TP |
针对混淆矩阵，BinaryMetricsSummary 的作用是Save the evaluation data for binary classification。函数具体计算思路是：
-
把 [0,1] 分成ClassificationEvaluationUtil.DETAIL_BIN_NUMBER（100000）这么多桶(bin)。所以binaryMetricsSummary的positiveBin/negativeBin分别是两个100000的数组。如果某一个 sample 为 正例(positive value) 的概率是 p, 则该 sample 对应的 bin index 就是 p * 100000。如果 p 被预测为正例(positive value) ，则positiveBin[index]++，否则就是被预测为负例(negative value) ，则negativeBin[index]++。positiveBin就是 TP + FP，negativeBin就是 TN + FN。
-
所以这里会遍历输入，如果某一个输入（以
"prefix1", "{"prefix1": 0.9, "prefix0": 0.1}"为例），0.9 是prefix1(正例) 的概率，0.1 是为prefix0(负例) 的概率。- 既然这个算法选择了 prefix1(正例) ，所以就说明此算法是判别成 positive 的，所以在 positiveBin 的 90000 处 + 1。
- 假设这个算法选择了 prefix0(负例) ，则说明此算法是判别成 negative 的，所以应该在 negativeBin 的 90000 处 + 1。
具体对应我们示例代码的5个采样，分类如下：
Row.of("prefix1", "{"prefix1": 0.9, "prefix0": 0.1}"), positiveBin 90000处+1
Row.of("prefix1", "{"prefix1": 0.8, "prefix0": 0.2}"), positiveBin 80000处+1
Row.of("prefix1", "{"prefix1": 0.7, "prefix0": 0.3}"), positiveBin 70000处+1
Row.of("prefix0", "{"prefix1": 0.75, "prefix0": 0.25}"), negativeBin 75000处+1
Row.of("prefix0", "{"prefix1": 0.6, "prefix0": 0.4}") negativeBin 60000处+1
具体代码如下
public static void updateBinaryMetricsSummary(TreeMap<String, Double> labelProbMap,
String label,
BinaryMetricsSummary binaryMetricsSummary) {
binaryMetricsSummary.total++;
binaryMetricsSummary.logLoss += extractLogloss(labelProbMap, label);
double d = labelProbMap.get(binaryMetricsSummary.labels[0]);
int idx = d == 1.0 ? ClassificationEvaluationUtil.DETAIL_BIN_NUMBER - 1 :
(int)Math.floor(d * ClassificationEvaluationUtil.DETAIL_BIN_NUMBER);
if (idx >= 0 && idx < ClassificationEvaluationUtil.DETAIL_BIN_NUMBER) {
if (label.equals(binaryMetricsSummary.labels[0])) {
binaryMetricsSummary.positiveBin[idx] += 1;
} else if (label.equals(binaryMetricsSummary.labels[1])) {
binaryMetricsSummary.negativeBin[idx] += 1;
} else {
.....
}
}
}
private static double extractLogloss(TreeMap<String, Double> labelProbMap, String label) {
Double prob = labelProbMap.get(label);
prob = null == prob ? 0. : prob;
return -Math.log(Math.max(Math.min(prob, 1 - LOG_LOSS_EPS), LOG_LOSS_EPS));
}
// 变量如下
ClassificationEvaluationUtil.DETAIL_BIN_NUMBER=100000
// 当 "prefix1", "{"prefix1": 0.9, "prefix0": 0.1}" 时候
labelProbMap = {TreeMap@9305} size = 2
"prefix0" -> {Double@9331} 0.1
"prefix1" -> {Double@9333} 0.9
d = 0.9
idx = 90000
binaryMetricsSummary = {BinaryMetricsSummary@9262}
labels = {String[2]@9242}
0 = "prefix1"
1 = "prefix0"
total = 1
positiveBin = {long[100000]@9263} // 90000处+1
negativeBin = {long[100000]@9264}
logLoss = 0.10536051565782628
// 当 "prefix0", "{"prefix1": 0.6, "prefix0": 0.4}" 时候
labelProbMap = {TreeMap@9514} size = 2
"prefix0" -> {Double@9546} 0.4
"prefix1" -> {Double@9547} 0.6
d = 0.6
idx = 60000
binaryMetricsSummary = {BinaryMetricsSummary@9262}
labels = {String[2]@9242}
0 = "prefix1"
1 = "prefix0"
total = 2
positiveBin = {long[100000]@9263}
negativeBin = {long[100000]@9264} // 60000处+1
logLoss = 1.0216512475319812
3.2.2 ReduceBaseMetrics
ReduceBaseMetrics作用是把局部计算的 BaseMetrics 聚合起来。
DataSet<BaseMetricsSummary> metrics = res
.reduce(new EvaluationUtil.ReduceBaseMetrics());
ReduceBaseMetrics如下
public static class ReduceBaseMetrics implements ReduceFunction<BaseMetricsSummary> {
@Override
public BaseMetricsSummary reduce(BaseMetricsSummary t1, BaseMetricsSummary t2) throws Exception {
return null == t1 ? t2 : t1.merge(t2);
}
}
具体计算是在BinaryMetricsSummary.merge，其作用就是Merge the bins, and add the logLoss。
@Override
public BinaryMetricsSummary merge(BinaryMetricsSummary binaryClassMetrics) {
for (int i = 0; i < this.positiveBin.length; i++) {
this.positiveBin[i] += binaryClassMetrics.positiveBin[i];
}
for (int i = 0; i < this.negativeBin.length; i++) {
this.negativeBin[i] += binaryClassMetrics.negativeBin[i];
}
this.logLoss += binaryClassMetrics.logLoss;
this.total += binaryClassMetrics.total;
return this;
}
// 程序变量是
this = {BinaryMetricsSummary@9316}
labels = {String[2]@9322}
0 = "prefix1"
1 = "prefix0"
total = 2
positiveBin = {long[100000]@9320}
negativeBin = {long[100000]@9323}
logLoss = 1.742969305058623
3.2.3 SaveDataAsParams
this.setOutput(metrics.flatMap(new EvaluationUtil.SaveDataAsParams()),
new String[] {DATA_OUTPUT}, new TypeInformation[] {Types.STRING});
当归并所有BaseMetrics之后，得到了total BaseMetrics，计算indexes，存入到params。
public static class SaveDataAsParams implements FlatMapFunction<BaseMetricsSummary, Row> {
@Override
public void flatMap(BaseMetricsSummary t, Collector<Row> collector) throws Exception {
collector.collect(t.toMetrics().serialize());
}
}
实际业务在BinaryMetricsSummary.toMetrics中完成，即基于bin的信息计算，得到confusionMatrix array, threshold array, rocCurve/recallPrecisionCurve/LiftChart等等，然后存储到params。
public BinaryClassMetrics toMetrics() {
Params params = new Params();
// 生成若干曲线，比如rocCurve/recallPrecisionCurve/LiftChart
Tuple3<ConfusionMatrix[], double[], EvaluationCurve[]> matrixThreCurve =
extractMatrixThreCurve(positiveBin, negativeBin, total);
// 依据曲线内容计算并且存储 AUC/PRC/KS
setCurveAreaParams(params, matrixThreCurve.f2);
// 对生成的rocCurve/recallPrecisionCurve/LiftChart输出进行抽样
Tuple3<ConfusionMatrix[], double[], EvaluationCurve[]> sampledMatrixThreCurve = sample(
PROBABILITY_INTERVAL, matrixThreCurve);
// 依据抽样后的输出存储 RocCurve/RecallPrecisionCurve/LiftChar
setCurvePointsParams(params, sampledMatrixThreCurve);
ConfusionMatrix[] matrices = sampledMatrixThreCurve.f0;
// 存储正例样本的度量指标
setComputationsArrayParams(params, sampledMatrixThreCurve.f1, sampledMatrixThreCurve.f0);
// 存储Logloss
setLoglossParams(params, logLoss, total);
// Pick the middle point where threshold is 0.5.
int middleIndex = getMiddleThresholdIndex(sampledMatrixThreCurve.f1);
setMiddleThreParams(params, matrices[middleIndex], labels);
return new BinaryClassMetrics(params);
}
extractMatrixThreCurve是全文重点。这里是 Extract the bins who are not empty, keep the middle threshold 0.5，然后初始化了 RocCurve, Recall-Precision Curve and Lift Curve，计算出ConfusionMatrix array（混淆矩阵）, threshold array, rocCurve/recallPrecisionCurve/LiftChart.。
/**
* Extract the bins who are not empty, keep the middle threshold 0.5.
* Initialize the RocCurve, Recall-Precision Curve and Lift Curve.
* RocCurve: (FPR, TPR), starts with (0,0). Recall-Precision Curve: (recall, precision), starts with (0, p), p is the precision with the lowest. LiftChart: (TP+FP/total, TP), starts with (0,0). confusion matrix = [TP FP][FN * TN].
*
* @param positiveBin positiveBins.
* @param negativeBin negativeBins.
* @param total sample number
* @return ConfusionMatrix array, threshold array, rocCurve/recallPrecisionCurve/LiftChart.
*/
static Tuple3<ConfusionMatrix[], double[], EvaluationCurve[]> extractMatrixThreCurve(long[] positiveBin, long[] negativeBin, long total) {
ArrayList<Integer> effectiveIndices = new ArrayList<>();
long totalTrue = 0, totalFalse = 0;
// 计算totalTrue，totalFalse，effectiveIndices
for (int i = 0; i < ClassificationEvaluationUtil.DETAIL_BIN_NUMBER; i++) {
if (0L != positiveBin[i] || 0L != negativeBin[i]
|| i == ClassificationEvaluationUtil.DETAIL_BIN_NUMBER / 2) {
effectiveIndices.add(i);
totalTrue += positiveBin[i];
totalFalse += negativeBin[i];
}
}
// 以我们例子，得到
effectiveIndices = {ArrayList@9273} size = 6
0 = {Integer@9277} 50000 //这里加入了中间点
1 = {Integer@9278} 60000
2 = {Integer@9279} 70000
3 = {Integer@9280} 75000
4 = {Integer@9281} 80000
5 = {Integer@9282} 90000
totalTrue = 3
totalFalse = 2
// 继续初始化，生成若干curve
final int length = effectiveIndices.size();
final int newLen = length + 1;
final double m = 1.0 / ClassificationEvaluationUtil.DETAIL_BIN_NUMBER;
EvaluationCurvePoint[] rocCurve = new EvaluationCurvePoint[newLen];
EvaluationCurvePoint[] recallPrecisionCurve = new EvaluationCurvePoint[newLen];
EvaluationCurvePoint[] liftChart = new EvaluationCurvePoint[newLen];
ConfusionMatrix[] data = new ConfusionMatrix[newLen];
double[] threshold = new double[newLen];
long curTrue = 0;
long curFalse = 0;
// 以我们例子，得到
length = 6
newLen = 7
m = 1.0E-5
// 计算, 其中rocCurve，recallPrecisionCurve，liftChart 都可以从代码中看出
for (int i = 1; i < newLen; i++) {
int index = effectiveIndices.get(length - i);
curTrue += positiveBin[index];
curFalse += negativeBin[index];
threshold[i] = index * m;
// 计算出混淆矩阵
data[i] = new ConfusionMatrix(
new long[][] {{curTrue, curFalse}, {totalTrue - curTrue, totalFalse - curFalse}});
double tpr = (totalTrue == 0 ? 1.0 : 1.0 * curTrue / totalTrue);
// 比如当 90000 这点，得到 curTrue = 1 curFalse = 0 i = 1 index = 90000 tpr = 0.3333333333333333。totalTrue = 3 totalFalse = 2，
// 我们也知道，TPR = TP / (TP + FN) ，所以可以计算 tpr = 1 / 3
rocCurve[i] = new EvaluationCurvePoint(totalFalse == 0 ? 1.0 : 1.0 * curFalse / totalFalse, tpr, threshold[i]);
recallPrecisionCurve[i] = new EvaluationCurvePoint(tpr, curTrue + curTrue == 0 ? 1.0 : 1.0 * curTrue / (curTrue + curFalse), threshold[i]);
liftChart[i] = new EvaluationCurvePoint(1.0 * (curTrue + curFalse) / total, curTrue, threshold[i]);
}
// 以我们例子，得到
curTrue = 3
curFalse = 2
threshold = {double[7]@9349}
0 = 0.0
1 = 0.9
2 = 0.8
3 = 0.7500000000000001
4 = 0.7000000000000001
5 = 0.6000000000000001
6 = 0.5
rocCurve = {EvaluationCurvePoint[7]@9315}
1 = {EvaluationCurvePoint@9440}
x = 0.0
y = 0.3333333333333333
p = 0.9
2 = {EvaluationCurvePoint@9448}
x = 0.0
y = 0.6666666666666666
p = 0.8
3 = {EvaluationCurvePoint@9449}
x = 0.5
y = 0.6666666666666666
p = 0.7500000000000001
4 = {EvaluationCurvePoint@9450}
x = 0.5
y = 1.0
p = 0.7000000000000001
5 = {EvaluationCurvePoint@9451}
x = 1.0
y = 1.0
p = 0.6000000000000001
6 = {EvaluationCurvePoint@9452}
x = 1.0
y = 1.0
p = 0.5
recallPrecisionCurve = {EvaluationCurvePoint[7]@9320}
1 = {EvaluationCurvePoint@9444}
x = 0.3333333333333333
y = 1.0
p = 0.9
2 = {EvaluationCurvePoint@9453}
x = 0.6666666666666666
y = 1.0
p = 0.8
3 = {EvaluationCurvePoint@9454}
x = 0.6666666666666666
y = 0.6666666666666666
p = 0.7500000000000001
4 = {EvaluationCurvePoint@9455}
x = 1.0
y = 0.75
p = 0.7000000000000001
5 = {EvaluationCurvePoint@9456}
x = 1.0
y = 0.6
p = 0.6000000000000001
6 = {EvaluationCurvePoint@9457}
x = 1.0
y = 0.6
p = 0.5
liftChart = {EvaluationCurvePoint[7]@9325}
1 = {EvaluationCurvePoint@9458}
x = 0.2
y = 1.0
p = 0.9
2 = {EvaluationCurvePoint@9459}
x = 0.4
y = 2.0
p = 0.8
3 = {EvaluationCurvePoint@9460}
x = 0.6
y = 2.0
p = 0.7500000000000001
4 = {EvaluationCurvePoint@9461}
x = 0.8
y = 3.0
p = 0.7000000000000001
5 = {EvaluationCurvePoint@9462}
x = 1.0
y = 3.0
p = 0.6000000000000001
6 = {EvaluationCurvePoint@9463}
x = 1.0
y = 3.0
p = 0.5
data = {ConfusionMatrix[7]@9339}
0 = {ConfusionMatrix@9486}
longMatrix = {LongMatrix@9488}
matrix = {long[2][]@9491}
0 = {long[2]@9492}
0 = 0
1 = 0
1 = {long[2]@9493}
0 = 3
1 = 2
rowNum = 2
colNum = 2
labelCnt = 2
total = 5
actualLabelFrequency = {long[2]@9489}
0 = 3
1 = 2
predictLabelFrequency = {long[2]@9490}
0 = 0
1 = 5
tpCount = 2.0
tnCount = 2.0
fpCount = 3.0
fnCount = 3.0
1 = {ConfusionMatrix@9435}
longMatrix = {LongMatrix@9469}
matrix = {long[2][]@9472}
0 = {long[2]@9474}
0 = 1
1 = 0
1 = {long[2]@9475}
0 = 2
1 = 2
rowNum = 2
colNum = 2
labelCnt = 2
total = 5
actualLabelFrequency = {long[2]@9470}
0 = 3
1 = 2
predictLabelFrequency = {long[2]@9471}
0 = 1
1 = 4
tpCount = 3.0
tnCount = 3.0
fpCount = 2.0
fnCount = 2.0
......
threshold[0] = 1.0;
data[0] = new ConfusionMatrix(new long[][] {{0, 0}, {totalTrue, totalFalse}});
rocCurve[0] = new EvaluationCurvePoint(0, 0, threshold[0]);
recallPrecisionCurve[0] = new EvaluationCurvePoint(0, recallPrecisionCurve[1].getY(), threshold[0]);
liftChart[0] = new EvaluationCurvePoint(0, 0, threshold[0]);
return Tuple3.of(data, threshold, new EvaluationCurve[] {new EvaluationCurve(rocCurve),
new EvaluationCurve(recallPrecisionCurve), new EvaluationCurve(liftChart)});
}
3.2.4 计算混淆矩阵
这里再给大家讲讲混淆矩阵如何计算，这里思路比较绕。
3.2.4.1 原始矩阵
调用之处是：
// 调用之处
data[i] = new ConfusionMatrix(
new long[][] {{curTrue, curFalse}, {totalTrue - curTrue, totalFalse - curFalse}});
// 调用时候各种赋值
i = 1
index = 90000
totalTrue = 3
totalFalse = 2
curTrue = 1
curFalse = 0
得到原始矩阵，以下都有cur，说明只针对当前点来说。
| curTrue = 1 | curFalse = 0 |
| totalTrue - curTrue = 2 | totalFalse - curFalse = 2 |
3.2.4.2 计算标签
后续ConfusionMatrix计算中，由此可以得到
actualLabelFrequency = longMatrix.getColSums();
predictLabelFrequency = longMatrix.getRowSums();
actualLabelFrequency = {long[2]@9322}
0 = 3
1 = 2
predictLabelFrequency = {long[2]@9323}
0 = 1
1 = 4
可以看出来，Alink算法认为：每列的sum和实际标签有关；每行sum和预测标签有关。
得到新矩阵如下
| predictLabelFrequency | |||
|---|---|---|---|
| curTrue = 1 | curFalse = 0 | 1 = curTrue + curFalse | |
| totalTrue - curTrue = 2 | totalFalse - curFalse = 2 | 4 = total - curTrue - curFalse | |
| actualLabelFrequency | 3 = totalTrue | 2 = totalFalse |
后续计算将要基于这些来计算：
计算中就用到longMatrix 对角线上的数据，即longMatrix(0)(0)和 longMatrix(1)(1)。一定要注意，这里考虑的都是 当前状态 (画重点强调)。
longMatrix(0)(0) ：curTrue
longMatrix(1)(1) ：totalFalse - curFalse
totalFalse ：( TN + FN )
totalTrue ：( TP + FP )
double numTrueNegative(Integer labelIndex) {
// labelIndex为 0 时候，return 1 + 5 - 1 - 3 = 2;
// labelIndex为 1 时候，return 2 + 5 - 4 - 2 = 1;
return null == labelIndex ? tnCount : longMatrix.getValue(labelIndex, labelIndex) + total - predictLabelFrequency[labelIndex] - actualLabelFrequency[labelIndex];
}
double numTruePositive(Integer labelIndex) {
// labelIndex为 0 时候，return 1; 这个是 curTrue，就是真实标签是True，判别也是True。是TP
// labelIndex为 1 时候，return 2; 这个是 totalFalse - curFalse，总判别错 - 当前判别错。这就意味着“本来判别错了但是当前没有发现”，所以认为在当前状态下，这也算是TP
return null == labelIndex ? tpCount : longMatrix.getValue(labelIndex, labelIndex);
}
double numFalseNegative(Integer labelIndex) {
// labelIndex为 0 时候，return 3 - 1;
// actualLabelFrequency[0] = totalTrue。所以return totalTrue - curTrue，即当前“全部正确”中没有“判别为正确”，这个就可以认为是“判别错了且判别为负”
// labelIndex为 1 时候，return 2 - 2;
// actualLabelFrequency[1] = totalFalse。所以return totalFalse - ( totalFalse - curFalse ) = curFalse
return null == labelIndex ? fnCount : actualLabelFrequency[labelIndex] - longMatrix.getValue(labelIndex, labelIndex);
}
double numFalsePositive(Integer labelIndex) {
// labelIndex为 0 时候，return 1 - 1;
// predictLabelFrequency[0] = curTrue + curFalse。
// 所以 return = curTrue + curFalse - curTrue = curFalse = current( TN + FN ) 这可以认为是判断错了实际是正确标签
// labelIndex为 1 时候，return 4 - 2;
// predictLabelFrequency[1] = total - curTrue - curFalse。
// 所以 return = total - curTrue - curFalse - (totalFalse - curFalse) = totalTrue - curTrue = ( TP + FP ) - currentTP = currentFP
return null == labelIndex ? fpCount : predictLabelFrequency[labelIndex] - longMatrix.getValue(labelIndex, labelIndex);
}
// 最后得到
tpCount = 3.0
tnCount = 3.0
fpCount = 2.0
fnCount = 2.0
3.2.4.3 具体代码
// 具体计算
public ConfusionMatrix(LongMatrix longMatrix) {
longMatrix = {LongMatrix@9297}
0 = {long[2]@9324}
0 = 1
1 = 0
1 = {long[2]@9325}
0 = 2
1 = 2
this.longMatrix = longMatrix;
labelCnt = this.longMatrix.getRowNum();
// 这里就是计算
actualLabelFrequency = longMatrix.getColSums();
predictLabelFrequency = longMatrix.getRowSums();
actualLabelFrequency = {long[2]@9322}
0 = 3
1 = 2
predictLabelFrequency = {long[2]@9323}
0 = 1
1 = 4
labelCnt = 2
total = 5
total = longMatrix.getTotal();
for (int i = 0; i < labelCnt; i++) {
tnCount += numTrueNegative(i);
tpCount += numTruePositive(i);
fnCount += numFalseNegative(i);
fpCount += numFalsePositive(i);
}
}
0x04 流处理
4.1 示例
Alink原有python示例代码中，Stream部分是没有输出的，因为MemSourceStreamOp没有和时间相关联，而Alink中没有提供基于时间的StreamOperator，所以只能自己仿照MemSourceBatchOp写了一个。虽然代码有些丑，但是至少可以提供输出，这样就能够调试。
4.1.1 主类
public class EvalBinaryClassExampleStream {
AlgoOperator getData(boolean isBatch) {
Row[] rows = new Row[]{
Row.of("prefix1", "{"prefix1": 0.9, "prefix0": 0.1}")
};
String[] schema = new String[]{"label", "detailInput"};
if (isBatch) {
return new MemSourceBatchOp(rows, schema);
} else {
return new TimeMemSourceStreamOp(rows, schema, new EvalBinaryStreamSource());
}
}
public static void main(String[] args) throws Exception {
EvalBinaryClassExampleStream test = new EvalBinaryClassExampleStream();
StreamOperator streamData = (StreamOperator) test.getData(false);
StreamOperator sOp = new EvalBinaryClassStreamOp()
.setLabelCol("label")
.setPredictionDetailCol("detailInput")
.setTimeInterval(1)
.linkFrom(streamData);
sOp.print();
StreamOperator.execute();
}
}
4.1.2 TimeMemSourceStreamOp
这个是我自己炮制的。借鉴了MemSourceStreamOp。
public final class TimeMemSourceStreamOp extends StreamOperator<TimeMemSourceStreamOp> {
public TimeMemSourceStreamOp(Row[] rows, String[] colNames, EvalBinaryStrSource source) {
super(null);
init(source, Arrays.asList(rows), colNames);
}
private void init(EvalBinaryStreamSource source, List <Row> rows, String[] colNames) {
Row first = rows.iterator().next();
int arity = first.getArity();
TypeInformation <?>[] types = new TypeInformation[arity];
for (int i = 0; i < arity; ++i) {
types[i] = TypeExtractor.getForObject(first.getField(i));
}
init(source, colNames, types);
}
private void init(EvalBinaryStreamSource source, String[] colNames, TypeInformation <?>[] colTypes) {
DataStream <Row> dastr = MLEnvironmentFactory.get(getMLEnvironmentId())
.getStreamExecutionEnvironment().addSource(source);
StringBuilder sbd = new StringBuilder();
sbd.append(colNames[0]);
for (int i = 1; i < colNames.length; i++) {
sbd.append(",").append(colNames[i]);
}
this.setOutput(dastr, colNames, colTypes);
}
@Override
public TimeMemSourceStreamOp linkFrom(StreamOperator<?>... inputs) {
return null;
}
}
4.1.3 Source
定时提供Row，加入了随机数，让概率有变化。
class EvalBinaryStreamSource extends RichSourceFunction[Row] {
override def run(ctx: SourceFunction.SourceContext[Row]) = {
while (true) {
val rdm = Math.random() // 这里加入了随机数，让概率有变化
val rows: Array[Row] = Array[Row](
Row.of("prefix1", "{"prefix1": " + rdm + ", "prefix0": " + (1-rdm) + "}"),
Row.of("prefix1", "{"prefix1": 0.8, "prefix0": 0.2}"),
Row.of("prefix1", "{"prefix1": 0.7, "prefix0": 0.3}"),
Row.of("prefix0", "{"prefix1": 0.75, "prefix0": 0.25}"),
Row.of("prefix0", "{"prefix1": 0.6, "prefix0": 0.4}"))
for(row <- rows) {
println(s"当前值：$row")
ctx.collect(row)
}
Thread.sleep(1000)
}
}
override def cancel() = ???
}
4.2 BaseEvalClassStreamOp
Alink流处理类是 EvalBinaryClassStreamOp，主要工作在其基类 BaseEvalClassStreamOp，所以我们重点看后者。
public class BaseEvalClassStreamOp<T extends BaseEvalClassStreamOp<T>> extends StreamOperator<T> {
@Override
public T linkFrom(StreamOperator<?>... inputs) {
StreamOperator<?> in = checkAndGetFirst(inputs);
String labelColName = this.get(MultiEvaluationStreamParams.LABEL_COL);
String positiveValue = this.get(BinaryEvaluationStreamParams.POS_LABEL_VAL_STR);
Integer timeInterval = this.get(MultiEvaluationStreamParams.TIME_INTERVAL);
ClassificationEvaluationUtil.Type type = ClassificationEvaluationUtil.judgeEvaluationType(this.getParams());
DataStream<BaseMetricsSummary> statistics;
switch (type) {
case PRED_RESULT: {
......
}
case PRED_DETAIL: {
String predDetailColName = this.get(MultiEvaluationStreamParams.PREDICTION_DETAIL_COL);
//
PredDetailLabel eval = new PredDetailLabel(positiveValue, binary);
// 获取输入数据，重点是timeWindowAll
statistics = in.select(new String[] {labelColName, predDetailColName})
.getDataStream()
.timeWindowAll(Time.of(timeInterval, TimeUnit.SECONDS))
.apply(eval);
break;
}
}
// 把各个窗口的数据累积到 totalStatistics，注意，这里是新变量了。
DataStream<BaseMetricsSummary> totalStatistics = statistics
.map(new EvaluationUtil.AllDataMerge())
.setParallelism(1); // 并行度设置为1
// 基于两种 bins 计算&序列化，得到当前的 statistics
DataStream<Row> windowOutput = statistics.map(
new EvaluationUtil.SaveDataStream(ClassificationEvaluationUtil.WINDOW.f0));
// 基于bins计算&序列化，得到累积的 totalStatistics
DataStream<Row> allOutput = totalStatistics.map(
new EvaluationUtil.SaveDataStream(ClassificationEvaluationUtil.ALL.f0));
// "当前" 和 "累积" 做联合，最终返回
DataStream<Row> union = windowOutput.union(allOutput);
this.setOutput(union,
new String[] {ClassificationEvaluationUtil.STATISTICS_OUTPUT, DATA_OUTPUT},
new TypeInformation[] {Types.STRING, Types.STRING});
return (T)this;
}
}
具体业务是：
- PredDetailLabel 会进行去重标签名字 和 累积计算混淆矩阵所需数据
- buildLabelIndexLabelArray 去重 "labels名字"，然后给每一个label一个ID，最后结果是一个<labels, ID>Map。
- getDetailStatistics 遍历 rows 数据，提取每一个item（比如 "prefix1,{"prefix1": 0.8, "prefix0": 0.2}"），然后通过updateBinaryMetricsSummary累积计算混淆矩阵所需数据。
- 根据标签从Window中获取数据 statistics = in.select().getDataStream().timeWindowAll() .apply(eval);
- EvaluationUtil.AllDataMerge 把各个窗口的数据累积到 totalStatistics 。
- 得到windowOutput -------- EvaluationUtil.SaveDataStream，对"当前数据statistics"做处理。实际业务在BinaryMetricsSummary.toMetrics，即基于bin的信息计算，然后存储到params，并序列化返回Row。
- extractMatrixThreCurve函数取出非空的bins，据此计算出ConfusionMatrix array（混淆矩阵）, threshold array, rocCurve/recallPrecisionCurve/LiftChart.
- 依据曲线内容计算并且存储 AUC/PRC/KS
- 对生成的rocCurve/recallPrecisionCurve/LiftChart输出进行抽样
- 依据抽样后的输出存储 RocCurve/RecallPrecisionCurve/LiftChar
- 存储正例样本的度量指标
- 存储Logloss
- Pick the middle point where threshold is 0.5.
- 得到allOutput -------- EvaluationUtil.SaveDataStream , 对"累积数据totalStatistics"做处理。
- 详细处理流程同windowOutput。
- windowOutput 和 allOutput 做联合。最终返回 DataStream
union = windowOutput.union(allOutput);
4.2.1 PredDetailLabel
static class PredDetailLabel implements AllWindowFunction<Row, BaseMetricsSummary, TimeWindow> {
@Override
public void apply(TimeWindow timeWindow, Iterable<Row> rows, Collector<BaseMetricsSummary> collector) throws Exception {
HashSet<String> labels = new HashSet<>();
// 首先还是获取 labels 名字
for (Row row : rows) {
if (EvaluationUtil.checkRowFieldNotNull(row)) {
labels.addAll(EvaluationUtil.extractLabelProbMap(row).keySet());
labels.add(row.getField(0).toString());
}
}
labels = {HashSet@9757} size = 2
0 = "prefix1"
1 = "prefix0"
// 之前介绍过，buildLabelIndexLabelArray 去重 "labels名字"，然后给每一个label一个ID，最后结果是一个<labels, ID>Map。
// getDetailStatistics 遍历 rows 数据，累积计算混淆矩阵所需数据（ "TP + FN" / "TN + FP"）。
if (labels.size() > 0) {
collector.collect(
getDetailStatistics(rows, binary, buildLabelIndexLabelArray(labels, binary, positiveValue)));
}
}
}
4.2.2 AllDataMerge
EvaluationUtil.AllDataMerge 把各个窗口的数据累积
/**
* Merge data from different windows.
*/
public static class AllDataMerge implements MapFunction<BaseMetricsSummary, BaseMetricsSummary> {
private BaseMetricsSummary statistics;
@Override
public BaseMetricsSummary map(BaseMetricsSummary value) {
this.statistics = (null == this.statistics ? value : this.statistics.merge(value));
return this.statistics;
}
}
4.2.3 SaveDataStream
SaveDataStream具体调用的函数之前批处理介绍过，实际业务在BinaryMetricsSummary.toMetrics，即基于bin的信息计算，存储到params。
这里与批处理不同的是直接就把"构建出的度量信息“返回给用户。
public static class SaveDataStream implements MapFunction<BaseMetricsSummary, Row> {
@Override
public Row map(BaseMetricsSummary baseMetricsSummary) throws Exception {
BaseMetricsSummary metrics = baseMetricsSummary;
BaseMetrics baseMetrics = metrics.toMetrics();
Row row = baseMetrics.serialize();
return Row.of(funtionName, row.getField(0));
}
}
// 最后得到的 row 其实就是最终返回给用户的度量信息
row = {Row@10008} "{"PRC":"0.9164636268708667","SensitivityArray":"[0.38461538461538464,0.6923076923076923,0.6923076923076923,1.0,1.0,1.0]","ConfusionMatrix":"[[13,8],[0,0]]","MacroRecall":"0.5","MacroSpecificity":"0.5","FalsePositiveRateArray":"[0.0,0.0,0.5,0.5,1.0,1.0]" ...... 还有很多其他的
4.2.4 Union
DataStream<Row> windowOutput = statistics.map(
new EvaluationUtil.SaveDataStream(ClassificationEvaluationUtil.WINDOW.f0));
DataStream<Row> allOutput = totalStatistics.map(
new EvaluationUtil.SaveDataStream(ClassificationEvaluationUtil.ALL.f0));
DataStream<Row> union = windowOutput.union(allOutput);
最后返回两种统计数据
4.2.4.1 allOutput
all|{"PRC":"0.7341146115890359","SensitivityArray":"[0.3333333333333333,0.3333333333333333,0.6666666666666666,0.7333333333333333,0.8,0.8,0.8666666666666667,0.8666666666666667,0.9333333333333333,1.0]","ConfusionMatrix":"[[13,10],[2,0]]","MacroRecall":"0.43333333333333335","MacroSpecificity":"0.43333333333333335","FalsePositiveRateArray":"[0.0,0.5,0.5,0.5,0.5,1.0,1.0,1.0,1.0,1.0]","TruePositiveRateArray":"[0.3333333333333333,0.3333333333333333,0.6666666666666666,0.7333333333333333,0.8,0.8,0.8666666666666667,0.8666666666666667,0.9333333333333333,1.0]","AUC":"0.5666666666666667","MacroAccuracy":"0.52", ......
4.2.4.2 windowOutput
window|{"PRC":"0.7638888888888888","SensitivityArray":"[0.3333333333333333,0.3333333333333333,0.6666666666666666,1.0,1.0,1.0]","ConfusionMatrix":"[[3,2],[0,0]]","MacroRecall":"0.5","MacroSpecificity":"0.5","FalsePositiveRateArray":"[0.0,0.5,0.5,0.5,1.0,1.0]","TruePositiveRateArray":"[0.3333333333333333,0.3333333333333333,0.6666666666666666,1.0,1.0,1.0]","AUC":"0.6666666666666666","MacroAccuracy":"0.6","RecallArray":"[0.3333333333333333,0.3333333333333333,0.6666666666666666,1.0,1.0,1.0]","KappaArray":"[0.28571428571428564,-0.15384615384615377,0.1666666666666666,0.5454545454545455,0.0,0.0]","MicroFalseNegativeRate":"0.4","WeightedRecall":"0.6","WeightedPrecision":"0.36","Recall":"1.0","MacroPrecision":"0.3",......
0xFF 参考
[[白话解析] 通过实例来梳理概念 ：准确率 (Accuracy)、精准率(Precision)、召回率(Recall) 和 F值(F-Measure)](