Alink漫談(八) : 二分類評估 AUC、K-S、PRC、Precision、Recall、LiftChart 如何實現
目錄
- Alink漫談(八) : 二分類評估 AUC、K-S、PRC、Precision、Recall、LiftChart 如何實現
- 0x00 摘要
- 0x01 相關概念
- 0x02 示例代碼
- 0x03 批處理
- 3.1 EvalBinaryClassBatchOp
- 3.2 BaseEvalClassBatchOp
- 3.2.0 調用關係綜述
- 3.2.1 calLabelPredDetailLocal
- 3.2.1.1 flatMap
- 3.2.1.2 reduceGroup
- 3.2.1.3 mapPartition
- 3.2.2 ReduceBaseMetrics
- 3.2.3 SaveDataAsParams
- 3.2.4 計算混淆矩陣
- 3.2.4.1 原始矩陣
- 3.2.4.2 計算標籤
- 3.2.4.3 具體代碼
- 0x04 流處理
- 4.1 示例
- 4.1.1 主類
- 4.1.2 TimeMemSourceStreamOp
- 4.1.3 Source
- 4.2 BaseEvalClassStreamOp
- 4.2.1 PredDetailLabel
- 4.2.2 AllDataMerge
- 4.2.3 SaveDataStream
- 4.2.4 Union
- 4.2.4.2 windowOutput
- 0xFF 參考
0x00 摘要
Alink 是阿里巴巴基於實時計算引擎 Flink 研發的新一代機器學習算法平台，是業界首個同時支持批式算法、流式算法的機器學習平台。二分類評估是對二分類算法的預測結果進行效果評估。本文將剖析Alink中對應代碼實現。
0x01 相關概念
如果對本文某些概念有疑惑，可以參見之前文章 [白話解析] 通過實例來梳理概念 ：準確率 (Accuracy)、精準率(Precision)、召回率(Recall) 和 F值(F-Measure)
0x02 示例代碼
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());
}
}
程序輸出
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，二分類評估有批處理，流處理兩種實現，下面一一為大家介紹（ Alink 複雜之一在於大量精細的數據結構，所以下文會大量打印程序中變量以便大家理解）。
2.1 主要思路
-
把 [0,1] 分成假設 100000個桶(bin)。所以得到positiveBin / negativeBin 兩個100000的數組。
-
根據輸入給positiveBin / negativeBin賦值。positiveBin就是 TP + FP，negativeBin就是 TN + FN。這些是後續計算的基礎。
-
遍歷bins中每一個有意義的點，計算出totalTrue和totalFalse，並且在每一個點上計算該點的混淆矩陣，tpr，以及rocCurve，recallPrecisionCurve，liftChart在該點對應的數據；
-
依據曲線內容計算並且存儲 AUC/PRC/KS
具體後續還有詳細調用關係綜述。
0x03 批處理
3.1 EvalBinaryClassBatchOp
EvalBinaryClassBatchOp是二分類評估的實現，功能是計算二分類的評估指標(evaluation metrics)。
輸入有兩種：
- label column and predResult column
- label column and predDetail column。如果有predDetail，則predResult被忽略
我們例子中 "prefix1" 就是 label，"{\"prefix1\": 0.9, \"prefix0\": 0.1}" 就是 predDetail
Row.of("prefix1", "{\"prefix1\": 0.9, \"prefix0\": 0.1}")
具體類摘錄如下：
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類BaseEvalClassBatchOp中完成，所以我們會首先看BaseEvalClassBatchOp。
3.2 BaseEvalClassBatchOp
我們還是從 linkFrom 函數入手，其主要是做了幾件事：
- 獲取配置信息
- 從輸入中提取某些列：”label”，”detailInput”
- calLabelPredDetailLocal會按照partition分別計算evaluation metrics
- 綜合reduce上述計算結果
- SaveDataAsParams函數會把最終數值輸入到 output table
具體代碼如下
@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);
// 從輸入中提取某些列："label"，"detailInput"
DataSet<Row> data = in.select(new String[] {labelColName, predDetailColName}).getDataSet();
// 按照partition分別計算evaluation metrics
res = calLabelPredDetailLocal(data, positiveValue, binary);
break;
}
......
}
// 綜合reduce上述計算結果
DataSet<BaseMetricsSummary> metrics = res
.reduce(new EvaluationUtil.ReduceBaseMetrics());
// 把最終數值輸入到 output table
this.setOutput(metrics.flatMap(new EvaluationUtil.SaveDataAsParams()),
new String[] {DATA_OUTPUT}, new TypeInformation[] {Types.STRING});
return (T)this;
}
// 執行中一些變量如下
labelColName = "label"
predDetailColName = "detailInput"
type = {ClassificationEvaluationUtil$Type@2532} "PRED_DETAIL"
binary = true
positiveValue = null
3.2.0 調用關係綜述
因為後續代碼調用關係複雜，所以先給出一個調用關係：
- 從輸入中提取某些列：”label”，”detailInput”，in.select(new String[] {labelColName, predDetailColName}).getDataSet()。因為可能輸入還有其他列，而只有某些列是我們計算需要的，所以只提取這些列。
- 按照partition分別計算evaluation metrics，即調用 calLabelPredDetailLocal(data, positiveValue, binary);
- flatMap會從label列和prediction列中，取出所有labels（注意是取出labels的名字 ），發送給下游算子。
- reduceGroup主要功能是通過 buildLabelIndexLabelArray 去重 “labels名字”，然後給每一個label一個ID，得到一個 <labels, ID>的map，最後返回是二元組(map, labels)，即({prefix1=0, prefix0=1},[prefix1, prefix0])。從後文看，<labels, ID>Map看來是多分類才用到。二分類只用到了labels。
- mapPartition 分區調用 CalLabelDetailLocal 來計算混淆矩陣，主要是分區調用getDetailStatistics，前文中得到的二元組(map, labels)會作為參數傳遞進來 。
- getDetailStatistics 遍歷 rows 數據，提取每一個item（比如 “prefix1,{“prefix1”: 0.8, “prefix0”: 0.2}”），然後通過updateBinaryMetricsSummary累積計算混淆矩陣所需數據。
- updateBinaryMetricsSummary 把 [0,1] 分成假設 100000個桶(bin)。所以得到positiveBin / negativeBin 兩個100000的數組。positiveBin就是 TP + FP，negativeBin就是 TN + FN。
- 如果某個 sample 為 正例 (positive value) 的概率是 p, 則該 sample 對應的 bin index 就是 p * 100000。如果 p 被預測為正例 (positive value) ，則positiveBin[index]++，
- 否則就是被預測為負例(negative value) ，則negativeBin[index]++。
- 綜合reduce上述計算結果，metrics = res.reduce(new EvaluationUtil.ReduceBaseMetrics());
- 具體計算是在BinaryMetricsSummary.merge，其作用就是Merge the bins, and add the logLoss。
- 把最終數值輸入到 output table，setOutput(metrics.flatMap(new EvaluationUtil.SaveDataAsParams()..);
- 歸併所有BaseMetrics后，得到total BaseMetrics，計算indexes存入params。collector.collect(t.toMetrics().serialize());
- 實際業務在BinaryMetricsSummary.toMetrics，即基於bin的信息計算，然後存儲到params。
- extractMatrixThreCurve函數取出非空的bins，據此計算出ConfusionMatrix array（混淆矩陣）, threshold array, rocCurve/recallPrecisionCurve/LiftChart.
- 遍歷bins中每一個有意義的點，計算出totalTrue和totalFalse，並且在每一個點上計算：
- curTrue += positiveBin[index]; curFalse += negativeBin[index];
- 得到該點的混淆矩陣 new ConfusionMatrix(new long[][] {{curTrue, curFalse}, {totalTrue – curTrue, totalFalse – curFalse}});
- 得到 tpr = (totalTrue == 0 ? 1.0 : 1.0 * curTrue / totalTrue);
- rocCurve，recallPrecisionCurve，liftChart在該點對應的數據；
- 依據曲線內容計算並且存儲 AUC/PRC/KS
- 對生成的rocCurve/recallPrecisionCurve/LiftChart輸出進行抽樣
- 依據抽樣后的輸出存儲 RocCurve/RecallPrecisionCurve/LiftChar
- 存儲正例樣本的度量指標
- 存儲Logloss
- Pick the middle point where threshold is 0.5.
3.2.1 calLabelPredDetailLocal
本函數按照partition分別計算評估指標 evaluation metrics。是的，這代碼很短，但是有個地方需要注意。有時候越簡單的地方越容易疏漏。容易疏漏點是：
第一行代碼的結果 labels 是第二行代碼的參數，而並非第二行主體。第二行代碼主體和第一行代碼主體一樣，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中具體分為三步驟：
- 在flatMap會從label列和prediction列中，取出所有labels（注意是取出labels的名字 ），發送給下游算子。
- reduceGroup的主要功能是去重 “labels名字”，然後給每一個label一個ID，最後結果是一個<labels, ID>Map。
- mapPartition 是分區調用 CalLabelDetailLocal 來計算混淆矩陣。
下面具體看看。
3.2.1.1 flatMap
在flatMap中，主要是從label列和prediction列中，取出所有labels（注意是取出labels的名字 ），發送給下游算子。
EvaluationUtil.extractLabelProbMap 作用就是解析輸入的json，獲得具體detailInput中的信息。
下游算子是reduceGroup，所以Flink runtime會對這些labels自動去重。如果對這部分有興趣，可以參見我之前介紹reduce的文章。CSDN ： [源碼解析] Flink的groupBy和reduce究竟做了什麼 博客園 : [源碼解析] Flink的groupBy和reduce究竟做了什麼
程序中變量如下
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); //這裏發送 "prefix0", "prefix1"
collector.collect(row.getField(0).toString()); // 這裏發送 "prefix1"
// 因為下一個操作是reduceGroup，所以這些label會被runtime去重
3.2.1.2 reduceGroup
主要功能是通過buildLabelIndexLabelArray去重labels，然後給每一個label一個ID，最後結果是一個<labels, ID>的Map。
reduceGroup(new EvaluationUtil.DistinctLabelIndexMap(binary, positiveValue));
DistinctLabelIndexMap的作用是從label列和prediction列中，取出所有不同的labels，返回一個<labels, ID>的map，根據後續代碼看，這個map是多分類才用到。Get all the distinct labels from label column and prediction column, and return the map of labels and their IDs.
前面已經提到，這裏的參數rows已經被自動去重。
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));
}
}
// 變量為
labels = {HashSet@9008} size = 2
0 = "prefix1"
1 = "prefix0"
binary = true
buildLabelIndexLabelArray的作用是給每一個label一個ID，得到一個 <labels, ID>的map，最後返回是二元組(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);
}
// 程序變量如下
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
這裏主要功能是分區調用 CalLabelDetailLocal 來為後來計算混淆矩陣做準備。
return data
.rebalance()
.mapPartition(new CalLabelDetailLocal(binary)) //這裡是業務所在
.withBroadcastSet(labels, LABELS);
具體工作是 CalLabelDetailLocal 完成的，其作用是分區調用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);// 前文生成的二元組(map, labels)
}
@Override
public void mapPartition(Iterable<Row> rows, Collector<BaseMetricsSummary> collector) {
// 調用到了 getDetailStatistics
collector.collect(getDetailStatistics(rows, binary, map));
}
}
getDetailStatistics 的作用是：初始化分類評估的度量指標 base classification evaluation metrics，累積計算混淆矩陣需要的數據。主要就是遍歷 rows 數據，提取每一個item（比如 “prefix1,{“prefix1”: 0.8, “prefix0”: 0.2}”），然後累積計算混淆矩陣所需數據。
// 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; // 前文生成的二元組(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) {
// 二分法在這裏
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看來是多分類才用到。
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) {
// 二分法在這裏
updateBinaryMetricsSummary(labelProbMap, label, binaryMetricsSummary);
} else {
updateMultiMetricsSummary(labelProbMap, label, labelIndexMap, multiMetricsSummary);
}
}
}
row = iterator.hasNext() ? iterator.next() : null;
}
return binary ? binaryMetricsSummary : multiMetricsSummary;
}
//變量如下
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
先回憶下混淆矩陣：
|
|
|
預測值 0 |
預測值 1 |
|
|
真實值 0 |
TN |
FP |
|
|
真實值 1 |
FN |
TP |
針對混淆矩陣，BinaryMetricsSummary 的作用是Save the evaluation data for binary classification。函數具體計算思路是：
-
把 [0,1] 分成ClassificationEvaluationUtil.DETAIL_BIN_NUMBER（100000）這麼多桶(bin)。所以binaryMetricsSummary的positiveBin/negativeBin分別是兩個100000的數組。如果某一個 sample 為 正例(positive value) 的概率是 p, 則該 sample 對應的 bin index 就是 p * 100000。如果 p 被預測為正例(positive value) ，則positiveBin[index]++，否則就是被預測為負例(negative value) ，則negativeBin[index]++。positiveBin就是 TP + FP，negativeBin就是 TN + FN。
-
所以這裡會遍歷輸入，如果某一個輸入（以"prefix1", "{\"prefix1\": 0.9, \"prefix0\": 0.1}"為例），0.9 是prefix1(正例) 的概率，0.1 是為prefix0(負例) 的概率。
- 既然這個算法選擇了 prefix1(正例) ，所以就說明此算法是判別成 positive 的，所以在 positiveBin 的 90000 處 + 1。
- 假設這個算法選擇了 prefix0(負例) ，則說明此算法是判別成 negative 的，所以應該在 negativeBin 的 90000 處 + 1。
具體對應我們示例代碼的5個採樣，分類如下：
Row.of("prefix1", "{\"prefix1\": 0.9, \"prefix0\": 0.1}"), positiveBin 90000處+1
Row.of("prefix1", "{\"prefix1\": 0.8, \"prefix0\": 0.2}"), positiveBin 80000處+1
Row.of("prefix1", "{\"prefix1\": 0.7, \"prefix0\": 0.3}"), positiveBin 70000處+1
Row.of("prefix0", "{\"prefix1\": 0.75, \"prefix0\": 0.25}"), negativeBin 75000處+1
Row.of("prefix0", "{\"prefix1\": 0.6, \"prefix0\": 0.4}") negativeBin 60000處+1
具體代碼如下
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));
}
// 變量如下
ClassificationEvaluationUtil.DETAIL_BIN_NUMBER=100000
// 當 "prefix1", "{\"prefix1\": 0.9, \"prefix0\": 0.1}" 時候
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處+1
negativeBin = {long[100000]@9264}
logLoss = 0.10536051565782628
// 當 "prefix0", "{\"prefix1\": 0.6, \"prefix0\": 0.4}" 時候
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處+1
logLoss = 1.0216512475319812
3.2.2 ReduceBaseMetrics
ReduceBaseMetrics作用是把局部計算的 BaseMetrics 聚合起來。
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);
}
}
具體計算是在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;
}
// 程序變量是
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});
當歸併所有BaseMetrics之後，得到了total BaseMetrics，計算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());
}
}
實際業務在BinaryMetricsSummary.toMetrics中完成，即基於bin的信息計算，得到confusionMatrix array, threshold array, rocCurve/recallPrecisionCurve/LiftChart等等，然後存儲到params。
public BinaryClassMetrics toMetrics() {
Params params = new Params();
// 生成若干曲線，比如rocCurve/recallPrecisionCurve/LiftChart
Tuple3<ConfusionMatrix[], double[], EvaluationCurve[]> matrixThreCurve =
extractMatrixThreCurve(positiveBin, negativeBin, total);
// 依據曲線內容計算並且存儲 AUC/PRC/KS
setCurveAreaParams(params, matrixThreCurve.f2);
// 對生成的rocCurve/recallPrecisionCurve/LiftChart輸出進行抽樣
Tuple3<ConfusionMatrix[], double[], EvaluationCurve[]> sampledMatrixThreCurve = sample(
PROBABILITY_INTERVAL, matrixThreCurve);
// 依據抽樣后的輸出存儲 RocCurve/RecallPrecisionCurve/LiftChar
setCurvePointsParams(params, sampledMatrixThreCurve);
ConfusionMatrix[] matrices = sampledMatrixThreCurve.f0;
// 存儲正例樣本的度量指標
setComputationsArrayParams(params, sampledMatrixThreCurve.f1, sampledMatrixThreCurve.f0);
// 存儲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是全文重點。這裡是 Extract the bins who are not empty, keep the middle threshold 0.5，然後初始化了 RocCurve, Recall-Precision Curve and Lift Curve，計算出ConfusionMatrix array（混淆矩陣）, 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;
// 計算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];
}
}
// 以我們例子，得到
effectiveIndices = {ArrayList@9273} size = 6
0 = {Integer@9277} 50000 //這裏加入了中間點
1 = {Integer@9278} 60000
2 = {Integer@9279} 70000
3 = {Integer@9280} 75000
4 = {Integer@9281} 80000
5 = {Integer@9282} 90000
totalTrue = 3
totalFalse = 2
// 繼續初始化，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;
// 以我們例子，得到
length = 6
newLen = 7
m = 1.0E-5
// 計算, 其中rocCurve，recallPrecisionCurve，liftChart 都可以從代碼中看出
for (int i = 1; i < newLen; i++) {
int index = effectiveIndices.get(length - i);
curTrue += positiveBin[index];
curFalse += negativeBin[index];
threshold[i] = index * m;
// 計算出混淆矩陣
data[i] = new ConfusionMatrix(
new long[][] {{curTrue, curFalse}, {totalTrue - curTrue, totalFalse - curFalse}});
double tpr = (totalTrue == 0 ? 1.0 : 1.0 * curTrue / totalTrue);
// 比如當 90000 這點，得到 curTrue = 1 curFalse = 0 i = 1 index = 90000 tpr = 0.3333333333333333。totalTrue = 3 totalFalse = 2，
// 我們也知道，TPR = TP / (TP + FN) ，所以可以計算 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]);
}
// 以我們例子，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 計算混淆矩陣
這裏再給大家講講混淆矩陣如何計算，這裏思路比較繞。
3.2.4.1 原始矩陣
調用之處是：
// 調用之處
data[i] = new ConfusionMatrix(
new long[][] {{curTrue, curFalse}, {totalTrue - curTrue, totalFalse - curFalse}});
// 調用時候各種賦值
i = 1
index = 90000
totalTrue = 3
totalFalse = 2
curTrue = 1
curFalse = 0
得到原始矩陣，以下都有cur，說明只針對當前點來說。
|
|
| curTrue = 1 |
curFalse = 0 |
| totalTrue – curTrue = 2 |
totalFalse – curFalse = 2 |
3.2.4.2 計算標籤
後續ConfusionMatrix計算中，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
可以看出來，Alink算法認為：每列的sum和實際標籤有關；每行sum和預測標籤有關。
得到新矩陣如下
|
|
|
predictLabelFrequency |
|
curTrue = 1 |
curFalse = 0 |
1 = curTrue + curFalse |
|
totalTrue – curTrue = 2 |
totalFalse – curFalse = 2 |
4 = total – curTrue – curFalse |
| actualLabelFrequency |
3 = totalTrue |
2 = totalFalse |
|
後續計算將要基於這些來計算：
計算中就用到longMatrix 對角線上的數據，即longMatrix(0)(0)和 longMatrix(1)(1)。一定要注意，這裏考慮的都是 當前狀態 (畫重點強調)。
longMatrix(0)(0) ：curTrue
longMatrix(1)(1) ：totalFalse – curFalse
totalFalse ：( TN + FN )
totalTrue ：( TP + FP )
double numTrueNegative(Integer labelIndex) {
// labelIndex為 0 時候，return 1 + 5 - 1 - 3 = 2;
// labelIndex為 1 時候，return 2 + 5 - 4 - 2 = 1;
return null == labelIndex ? tnCount : longMatrix.getValue(labelIndex, labelIndex) + total - predictLabelFrequency[labelIndex] - actualLabelFrequency[labelIndex];
}
double numTruePositive(Integer labelIndex) {
// labelIndex為 0 時候，return 1; 這個是 curTrue，就是真實標籤是True，判別也是True。是TP
// labelIndex為 1 時候，return 2; 這個是 totalFalse - curFalse，總判別錯 - 當前判別錯。這就意味着“本來判別錯了但是當前沒有發現”，所以認為在當前狀態下，這也算是TP
return null == labelIndex ? tpCount : longMatrix.getValue(labelIndex, labelIndex);
}
double numFalseNegative(Integer labelIndex) {
// labelIndex為 0 時候，return 3 - 1;
// actualLabelFrequency[0] = totalTrue。所以return totalTrue - curTrue，即當前“全部正確”中沒有“判別為正確”，這個就可以認為是“判別錯了且判別為負”
// labelIndex為 1 時候，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為 0 時候，return 1 - 1;
// predictLabelFrequency[0] = curTrue + curFalse。
// 所以 return = curTrue + curFalse - curTrue = curFalse = current( TN + FN ) 這可以認為是判斷錯了實際是正確標籤
// labelIndex為 1 時候，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);
}
// 最後得到
tpCount = 3.0
tnCount = 3.0
fpCount = 2.0
fnCount = 2.0
3.2.4.3 具體代碼
// 具體計算
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();
// 這裏就是計算
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 流處理
4.1 示例
Alink原有python示例代碼中，Stream部分是沒有輸出的，因為MemSourceStreamOp沒有和時間相關聯，而Alink中沒有提供基於時間的StreamOperator，所以只能自己仿照MemSourceBatchOp寫了一個。雖然代碼有些丑，但是至少可以提供輸出，這樣就能夠調試。
4.1.1 主類
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
這個是我自己炮製的。借鑒了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
定時提供Row，加入了隨機數，讓概率有變化。
class EvalBinaryStreamSource extends RichSourceFunction[Row] {
override def run(ctx: SourceFunction.SourceContext[Row]) = {
while (true) {
val rdm = Math.random() // 這裏加入了隨機數，讓概率有變化
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"當前值：$row")
ctx.collect(row)
}
Thread.sleep(1000)
}
}
override def cancel() = ???
}
4.2 BaseEvalClassStreamOp
Alink流處理類是 EvalBinaryClassStreamOp，主要工作在其基類 BaseEvalClassStreamOp，所以我們重點看後者。
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);
// 獲取輸入數據，重點是timeWindowAll
statistics = in.select(new String[] {labelColName, predDetailColName})
.getDataStream()
.timeWindowAll(Time.of(timeInterval, TimeUnit.SECONDS))
.apply(eval);
break;
}
}
// 把各個窗口的數據累積到 totalStatistics，注意，這裡是新變量了。
DataStream<BaseMetricsSummary> totalStatistics = statistics
.map(new EvaluationUtil.AllDataMerge())
.setParallelism(1); // 并行度設置為1
// 基於兩種 bins 計算&序列化，得到當前的 statistics
DataStream<Row> windowOutput = statistics.map(
new EvaluationUtil.SaveDataStream(ClassificationEvaluationUtil.WINDOW.f0));
// 基於bins計算&序列化，得到累積的 totalStatistics
DataStream<Row> allOutput = totalStatistics.map(
new EvaluationUtil.SaveDataStream(ClassificationEvaluationUtil.ALL.f0));
// "當前" 和 "累積" 做聯合，最終返回
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;
}
}
具體業務是：
- PredDetailLabel 會進行去重標籤名字 和 累積計算混淆矩陣所需數據
- buildLabelIndexLabelArray 去重 “labels名字”，然後給每一個label一個ID，最後結果是一個<labels, ID>Map。
- getDetailStatistics 遍歷 rows 數據，提取每一個item（比如 “prefix1,{“prefix1”: 0.8, “prefix0”: 0.2}”），然後通過updateBinaryMetricsSummary累積計算混淆矩陣所需數據。
- 根據標籤從Window中獲取數據 statistics = in.select().getDataStream().timeWindowAll() .apply(eval);
- EvaluationUtil.AllDataMerge 把各個窗口的數據累積到 totalStatistics 。
- 得到windowOutput ——– EvaluationUtil.SaveDataStream，對”當前數據statistics”做處理。實際業務在BinaryMetricsSummary.toMetrics，即基於bin的信息計算，然後存儲到params，並序列化返回Row。
- extractMatrixThreCurve函數取出非空的bins，據此計算出ConfusionMatrix array（混淆矩陣）, threshold array, rocCurve/recallPrecisionCurve/LiftChart.
- 依據曲線內容計算並且存儲 AUC/PRC/KS
- 對生成的rocCurve/recallPrecisionCurve/LiftChart輸出進行抽樣
- 依據抽樣后的輸出存儲 RocCurve/RecallPrecisionCurve/LiftChar
- 存儲正例樣本的度量指標
- 存儲Logloss
- Pick the middle point where threshold is 0.5.
- 得到allOutput ——– EvaluationUtil.SaveDataStream , 對”累積數據totalStatistics”做處理。
- windowOutput 和 allOutput 做聯合。最終返回 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<>();
// 首先還是獲取 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"
// 之前介紹過，buildLabelIndexLabelArray 去重 "labels名字"，然後給每一個label一個ID，最後結果是一個<labels, ID>Map。
// getDetailStatistics 遍歷 rows 數據，累積計算混淆矩陣所需數據（ "TP + FN" / "TN + FP"）。
if (labels.size() > 0) {
collector.collect(
getDetailStatistics(rows, binary, buildLabelIndexLabelArray(labels, binary, positiveValue)));
}
}
}
4.2.2 AllDataMerge
EvaluationUtil.AllDataMerge 把各個窗口的數據累積
/**
* 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具體調用的函數之前批處理介紹過，實際業務在BinaryMetricsSummary.toMetrics，即基於bin的信息計算，存儲到params。
這裏與批處理不同的是直接就把”構建出的度量信息“返回給用戶。
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));
}
}
// 最後得到的 row 其實就是最終返回給用戶的度量信息
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]" ...... 還有很多其他的
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);
最後返回兩種統計數據
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 參考
[[白話解析] 通過實例來梳理概念 ：準確率 (Accuracy)、精準率(Precision)、召回率(Recall) 和 F值(F-Measure)](
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