Alink漫谈 : 模型的来龙去脉
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Alink漫谈(四) : 模型的来龙去脉
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。本文将从模型角度入手带领大家来再次深入Alink。
因为Alink的公开资料太少，所以以下均为自行揣测，肯定会有疏漏错误，希望大家指出，我会随时更新。
0x01 模型
之前的文章中，我们一直没有仔细说明Alink的模型，本篇我们就深入探究一下。套用下范伟的话：我既想知道模型是怎么来的,我又想知道模型是怎么没的。
1.1 模型包含内容
我们先想想，一个机器学习训练出来的模型，应该包含哪些内容。
- 流水线：因为一个模型可能包括多个阶段，比如转化，预测等，这样构成了一个流水线。
- 算法 ：这个是具体机器学习平台绑定的。比如在Flink就是某一个java算法类。
- 参数：这个是肯定要有的，机器学习很大一部分工作不就是做这个的嘛。
- 数据：这个其实也应该算参数的一种，也是训练出来的。比如说KMeans算法训练出来的各个中心点。
1.2 Alink的模型文件
让我们打开Alink的模型文件来验证下：
-1,"{""schema"":["""",""model_id BIGINT,model_info VARCHAR""],""param"":[""{""outputCol"":""\""features\"""",""selectedCols"":""[\""sepal_length\"",\""sepal_width\"",\""petal_length\"",\""petal_width\""]""}"",""{""vectorCol"":""\""features\"""",""maxIter"":""100"",""reservedCols"":""[\""category\""]"",""k"":""3"",""predictionCol"":""\""prediction_result\"""",""predictionDetailCol"":""\""prediction_detail\""""}""],""clazz"":[""com.alibaba.alink.pipeline.dataproc.vector.VectorAssembler"",""com.alibaba.alink.pipeline.clustering.KMeansModel""]}"
1,"0^{""vectorCol"":""""features"""",""latitudeCol"":null,""longitudeCol"":null,""distanceType"":""""EUCLIDEAN"""",""k"":""3"",""vectorSize"":""4""}"
1,"1048576^{""clusterId"":0,""weight"":39.0,""vec"":{""data"":[6.8538461538461535,3.0769230769230766,5.7153846153846155,2.0538461538461545]}}"
1,"2097152^{""clusterId"":1,""weight"":61.0,""vec"":{""data"":[5.883606557377049,2.740983606557377,4.388524590163936,1.4344262295081969]}}"
1,"3145728^{""clusterId"":2,""weight"":50.0,""vec"":{""data"":[5.006,3.418,1.4640000000000002,0.24400000000000005]}}"
我们看到了两个类名字：
com.alibaba.alink.pipeline.dataproc.vector.VectorAssembler
com.alibaba.alink.pipeline.clustering.KMeansModel
这就是我们提到的算法，Alink在执行过程中，可以根据这两个类名字来生成java类。而两个算法类看起来是可以构建成一个流水线。我们也能看到参数和数据。
但是有几个地方很奇怪：
- 1048576，2097152这些奇怪的数字是什么意思？
- 为什么文件的第一个数值是-1？然后第二行第一个数字是 1？怎么没有 中间的 0 ？
- 具体Alink是如何生成和加载模型的？
下面我们就一一排查。
0x02 流程图
我们首先给出一个流程图便于大家理解。这个图只是逻辑上的大致概念，和真实运行有区别。因为实际场景上是先生成执行计划，再具体操作。
* 下面只是逻辑上的大致概念，和真实运行有区别，因为实际场景上是先生成执行计划，再具体操作.
* 所以只是给大家一个概念。
*
*
* Pipeline.fit 训练
* |
* |
* +-----> KMeansTrainModelData [ centroids, params -- 中心点数据，参数]
* | // KMeansOutputModel.calc()中执行，生成中心点数据和参数
* |
* |
* +-----> Tuple2<Params, Iterable<String>> [ "Params"是模型元数据，Iterable<String>是模型具体数据 ]
* | // KMeansModelDataConverter.serializeModel()，进行序列化操作，包括 把数据转换成json，调用KMeansTrainModelData.toParams设置各种参数
* |
* |
* +-----> Collector<Row> [ Row可以有任意的field，基于position(zero-based)访问field ]
* | // ModelConverterUtils.appendMetaRow，ModelConverterUtils.appendDataRows
* |
* |
* +-----> List<Row> model [ collector.getRows() ]
* | // List<Row> model = completeResult.calc(context);
* |
* |
* +-----> DataSet<Row> [ 序列化算子计算结果 ]
* | // BaseComQueue.exec --- serializeModel(clearObjs(loopEnd))
* |
* |
* +-----> Table output [ AlgoOperator.output，就是算子组件的输出表 ]
* | // KMeansTrainBatchOp.linkFrom --- setOutput
* |
* |
* +-----> KMeansModel [ 模型，Find the closest cluster center for every point ]
* | // createModel(train(input).getOutputTable()) 这里设定模型参数
* | // KMeansModel.setModelData(Table modelData) 这里设定模型数据
* |
* |
* +-----> TransformerBase[] [ PipelineModel.transformers ]
* | // 这就是最终训练出来的流水线模型，KMeansModel是其中一个，KMeansModelMapper是KMeansModel的业务组件
* |
* |
* PipelineModel.save 存储
* |
* |
* +-----> BatchOperator [ 把transformers数组压缩成BatchOperator ]
* | // ModelExporterUtils.packTransformersArray
* |
* |
* +-----> 存储的模型文件 [ csv文件 ]
* | // PipelineModel.save --- CsvSinkBatchOp(path)
* |
* PipelineModel.load 加载
* |
* |
* +-----> 存储的模型文件 [ csv文件 ]
* | // PipelineModel.load --- CsvSourceBatchOp(path)
* |
* |
* +-----> KMeansModel [ 模型，Find the closest cluster center for every point ]
* | // 依据文件生成模型，(TransformerBase) clazz.getConstructor(Params.class)
* | // 设置数据((ModelBase) transformers[i]).setModelData(data.getOutputTable())
* |
* +-----> TransformerBase[] [ 从csv文件读取并恢复的transformers ]
* | // ModelExporterUtils.unpackTransformersArray(batchOp)
* |
* |
* +-----> PipelineModel [ 流水线模型 ]
* | // new PipelineModel(ModelExporterUtils.unpackTransformersArray(batchOp));
* |
* |
* PipelineModel.transform(data) 预测
* |
* |
* |
* +-----> ModelSource [ Load model data from ModelSource when open() ]
* | // ModelMapperAdapter.open --- List<Row> modelRows = this.modelSource.getModelRows(getRuntimeContext());
* |
* +-----> Tuple2<Params, Iterable<String>> [ metaAndData ]
* | // SimpleModelDataConverter.load
* |
* |
* +-----> KMeansTrainModelData [ 反序列化 ]
* | // KMeansModelDataConverter.deserializeModel(Params params, Iterable<String> data)
* |
* |
* +-----> KMeansTrainModelData [ Load KMeansTrainModelData from saved model ]
* | // KMeansModelMapper.loadModel
* | // KMeansTrainModelData.loadModelForTrain(Params params, Iterable<String> data)
* |
* |
* +-----> KMeansPredictModelData [ Model data for KMeans trainData ]
* | // 将训练模型数据转换为预测模型数据，里面包含centroids
* | // KMeansUtil.transformTrainDataToPredictData(trainModelData);
* |
* |
* +-----> Row row [ "5.0,3.2,1.2,0.2,Iris-setosa,5.0 3.2 1.2 0.2" ]
* | // row是预测目标数据，ModelMapperAdapter.map
* |
* |
* +-----> Row row [ "0|0.4472728134421832 0.35775115900088217 0.19497602755693455" ]
* | // 预测结果，KMeansModelMapper.map
* |
* |
0x03 生成模型
我们还是用KMeans算法来做示例，看看模型数据是什么样子，如何转换成Alink需要的样子。
VectorAssembler va = new VectorAssembler()
.setSelectedCols(new String[]{"sepal_length", "sepal_width", "petal_length", "petal_width"})
.setOutputCol("features");
KMeans kMeans = new KMeans().setVectorCol("features").setK(3)
.setPredictionCol("prediction_result")
.setPredictionDetailCol("prediction_detail")
.setReservedCols("category")
.setMaxIter(100);
Pipeline pipeline = new Pipeline().add(va).add(kMeans);
pipeline.fit(data);
从之前文章中大家可以知道，KMeans训练最重要的类是KMeansTrainBatchOp。KMeansTrainBatchOp在算法迭代结束时候，使用.closeWith(new KMeansOutputModel(distanceType, vectorColName, latitudeColName, longitudeColName))来输出模型。
3.1 生成模型
所以我们重点就要看 KMeansOutputModel 类。其calc函数的作用就是把中心点和参数转化为模型。
- 首先是调用serializeModel将中心点序列化成json。这里记做 (1)，下面代码注释会对应指出。
- 其次save函数会进行序列化，生成了一个Tuple2 <Params, Iterable
>。Params是参数，Iterable 是模型的具体数据，就是中心点的集合。这里记做 (2)，下面代码注释会对应指出。 - 然后save函数把参数和数据分开存储。这里记做 (3)，下面注释会对应指出。
- 最后collector就是模型数据。这里记做 (4)，下面注释会对应指出。
/**
* Tranform the centroids to KmeansModel.
*/
public class KMeansOutputModel extends CompleteResultFunction {
private DistanceType distanceType;
private String vectorColName;
private String latitudeColName;
private String longtitudeColName;
@Override
public List <Row> calc(ComContext context) {
KMeansTrainModelData modelData = new KMeansTrainModelData();
... 各种赋值操作
modelData.params = new KMeansTrainModelData.ParamSummary();
modelData.params.k = k;
modelData.params.vectorColName = vectorColName;
...
// 我们可以看出来，在此处，计算出来的中心点和各种参数已经被添加到KMeansTrainModelData之中。
modelData = {KMeansTrainModelData@11319}
centroids = {ArrayList@11327} size = 3
0 = {KMeansTrainModelData$ClusterSummary@11330}
clusterId = 0
weight = 38.0
vec = {DenseVector@11333} "6.849999999999999 3.0736842105263156 5.742105263157895 2.071052631578947"
1 = {KMeansTrainModelData$ClusterSummary@11331}
2 = {KMeansTrainModelData$ClusterSummary@11332}
params = {KMeansTrainModelData$ParamSummary@11328}
k = 3
vectorSize = 4
distanceType = {DistanceType@11287} "EUCLIDEAN"
vectorColName = "features"
latitudeColName = null
longtitudeColName = null
RowCollector collector = new RowCollector();
// save函数中将进行(1)(2)(3)，后续代码中会具体给出(1)(2)(3)的位置
new KMeansModelDataConverter().save(modelData, collector);
// KMeansModelDataConverter实现了SimpleModelDataConverter，所以save就调用到了KMeansModelDataConverter.save，其调用serializeModel将中心点转换jason。最后生成了一个Tuple2 <Params, Iterable<String>>
// (4) 这时候collector就是模型数据。
return collector.getRows();
// 我们能看出来，模型数据已经和模型文件的内容有几分相似了。里面有之前提到的奇怪数字。
collector = {RowCollector@11321}
rows = {ArrayList@11866} size = 4
0 = {Row@11737} "0,{"vectorCol":""features"","latitudeCol":null,"longitudeCol":null,"distanceType":""EUCLIDEAN"","k":"3","vectorSize":"4"}"
1 = {Row@11801} "1048576,{"clusterId":0,"weight":38.0,"vec":{"data":[6.849999999999999,3.0736842105263156,5.742105263157895,2.071052631578947]}}"
2 = {Row@11868} "2097152,{"clusterId":1,"weight":50.0,"vec":{"data":[5.006,3.4179999999999997,1.4640000000000002,0.24400000000000002]}}"
3 = {Row@11869} "3145728,{"clusterId":2,"weight":62.0,"vec":{"data":[5.901612903225806,2.7483870967741937,4.393548387096773,1.4338709677419355]}}"
}
}
具体转化是在KMeansModelDataConverter和其基类SimpleModelDataConverter中完成。首先是调用serializeModel将中心点序列化成json，形成了一个json列表。
/**
* KMeans Model.
* Save the id, center point and point number of clusters.
*/
public class KMeansModelDataConverter extends SimpleModelDataConverter<KMeansTrainModelData, KMeansPredictModelData> {
public KMeansModelDataConverter() {}
@Override
public Tuple2<Params, Iterable<String>> serializeModel(KMeansTrainModelData modelData) {
List <String> data = new ArrayList <>();
for (ClusterSummary centroid : modelData.centroids) {
data.add(JsonConverter.toJson(centroid)); // (1)，把中心点转换生成json
}
return Tuple2.of(modelData.params.toParams(), data);
}
@Override
public KMeansPredictModelData deserializeModel(Params params, Iterable<String> data) {
KMeansTrainModelData trainModelData = KMeansUtil.loadModelForTrain(params, data);
return KMeansUtil.transformTrainDataToPredictData(trainModelData);
}
}
其次进行序列化操作，生成Tuple2<Params, Iterable
/**
* The abstract class for a kind of {@link ModelDataConverter} where the model data can serialize to
* "Tuple2&jt;Params, Iterable&jt;String>>". Here "Params" is the meta data of the model, and "Iterable&jt;String>" is
* concrete data of the model.
*/
public abstract class SimpleModelDataConverter<M1, M2> implements ModelDataConverter<M1, M2> {
@Override
public M2 load(List<Row> rows) {
Tuple2<Params, Iterable<String>> metaAndData = ModelConverterUtils.extractModelMetaAndData(rows);
return deserializeModel(metaAndData.f0, metaAndData.f1);
}
@Override
public void save(M1 modelData, Collector<Row> collector) {
// (2)，序列化生成Tuple2
Tuple2<Params, Iterable<String>> model = serializeModel(modelData);
// 此时模型数据是一个元祖Tuple2<Params, Iterable<String>>
model = {Tuple2@11504} "(Params {vectorCol="features", latitudeCol=null, longitudeCol=null, distanceType="EUCLIDEAN", k=3, vectorSize=4},[{"clusterId":0,"weight":38.0,"vec":{"data":[6.849999999999999,3.0736842105263156,5.742105263157895,2.071052631578947]}}, {"clusterId":1,"weight":50.0,"vec":{"data":[5.006,3.4179999999999997,1.4640000000000002,0.24400000000000002]}}, {"clusterId":2,"weight":62.0,"vec":{"data":[5.901612903225806,2.7483870967741937,4.393548387096773,1.4338709677419355]}}])"
// (3) 分开发送参数和数据
ModelConverterUtils.appendMetaRow(model.f0, collector, 2);
ModelConverterUtils.appendDataRows(model.f1, collector, 2);
}
}
然后分开存储参数和数据。
/**
* Collector of Row type data.
*/
public class RowCollector implements Collector<Row> {
private List<Row> rows;
@Override
public void collect(Row row) {
rows.add(row); // 把数据存储起来
}
}
// 调用栈是
collect:37, RowCollector (com.alibaba.alink.common.utils)
collect:12, RowCollector (com.alibaba.alink.common.utils)
appendStringData:270, ModelConverterUtils (com.alibaba.alink.common.model)
appendMetaRow:35, ModelConverterUtils (com.alibaba.alink.common.model)
save:57, SimpleModelDataConverter (com.alibaba.alink.common.model)
calc:76, KMeansOutputModel (com.alibaba.alink.operator.common.clustering.kmeans)
mapPartition:287, BaseComQueue$4 (com.alibaba.alink.common.comqueue)
3.2 转换DataSet
模型数据是要转换成 DataSet，即 a collection of rows。其转换目的是为了让模型数据在Alink中更好的传输和被利用。
把模型数据中的string转换为 row数据的时候，可能会遇到string过长的问题，所以Alink就将String分割转存为多行row。这时候就用ModelConverterUtils的getModelId，getStringIndex函数来分割。
这时候得到的model Id就是计算出来的1048576，就是模型文件中的那个奇怪数字。
后续load模型时候也会用同样思路从row转换回模型string。
// A utility class for converting model data to a collection of rows.
class ModelConverterUtils {
/**
* Maximum number of slices a string can split to.
*/
static final long MAX_NUM_SLICES = 1024L * 1024L;
private static long getModelId(int stringIndex, int sliceIndex) {
return MAX_NUM_SLICES * stringIndex + sliceIndex;
}
private static int getStringIndex(long modelId) {
return (int) ((modelId) / MAX_NUM_SLICES);
}
}
row = {Row@11714} "1048576,{"clusterId":0,"weight":62.0,"vec":{"data":[5.901612903225806,2.7483870967741932,4.393548387096773,1.4338709677419355]}}"
fields = {Object[2]@11724}
0 = {Long@11725} 1048576
1 = "{"clusterId":0,"weight":62.0,"vec":{"data":[5.901612903225806,2.7483870967741932,4.393548387096773,1.4338709677419355]}}"
// 相关调用栈如下
appendStringData:270, ModelConverterUtils (com.alibaba.alink.common.model)
appendDataRows:52, ModelConverterUtils (com.alibaba.alink.common.model)
save:58, SimpleModelDataConverter (com.alibaba.alink.common.model)
calc:76, KMeansOutputModel (com.alibaba.alink.operator.common.clustering.kmeans)
mapPartition:287, BaseComQueue$4 (com.alibaba.alink.common.comqueue)
run:103, MapPartitionDriver (org.apache.flink.runtime.operators)
...
run:748, Thread (java.lang)
3.3 存储为Table
前面KMeansOutputModel最终返回的是一个DataSet，这里将把这个DataSet转化为Table存储在流水线中。
public final class KMeansTrainBatchOp extends BatchOperator <KMeansTrainBatchOp>
public KMeansTrainBatchOp linkFrom(BatchOperator <?>... inputs) {
DataSet <Row> finalCentroid = iterateICQ(initCentroid, data,
vectorSize, maxIter, tol, distance, distanceType, vectorColName, null, null);
// 这里存储为Table
this.setOutput(finalCentroid, new KMeansModelDataConverter().getModelSchema());
return this;
}
this = {KMeansTrainBatchOp@5130} "UnnamedTable$1"
params = {Params@5143} "Params {vectorCol="features", maxIter=100, reservedCols=["category"], k=3, predictionCol="prediction_result", predictionDetailCol="prediction_detail"}"
output = {TableImpl@5188} "UnnamedTable$1"
tableEnvironment = {BatchTableEnvironmentImpl@5190}
operationTree = {DataSetQueryOperation@5191}
operationTreeBuilder = {OperationTreeBuilder@5192}
lookupResolver = {LookupCallResolver@5193}
tableName = "UnnamedTable$1"
sideOutputs = null
我们可以看到，在Alink运行时候，模型数据都统一转化为Table类型。这部分原因可能是因为Alink想要统一处理DataSet和DataStream，即批和流都要用一个思路或者代码来处理。而Flink目前已经用Table来统一整合二者，所以Alink就针对此统一用Table。参见如下：
public abstract class ModelBase<M extends ModelBase<M>> extends TransformerBase<M>
implements Model<M> {
protected Table modelData;
}
public abstract class AlgoOperator<T extends AlgoOperator<T>>
implements WithParams<T>, HasMLEnvironmentId<T>, Serializable {
// Params for algorithms.
private Params params;
// The table held by operator.
private Table output = null;
// The side outputs of operator that be similar to the stream‘s side outputs.
private Table[] sideOutputs = null;
}
0x04 存储模型
4.1 存储代码
我们修改一下代码，调用save函数把流水线模型存储起来。Alink目前是把模型文件存储成特殊格式的csv文件。
Pipeline pipeline = new Pipeline().add(va).add(kMeans);
pipeline.fit(data).save("./kmeans.csv");
流水线存储代码如下：
public class PipelineModel extends ModelBase<PipelineModel> implements LocalPredictable {
// Pack the pipeline model to a BatchOperator.
public BatchOperator save() {
return ModelExporterUtils.packTransformersArray(transformers);
}
}
我们可以看到，流水线最终调用到 ModelExporterUtils.packTransformersArray，所以我们就重点看看这个函数。这里可以解答模型文件中的问题：为什么第一个数值是-1？然后是 1？怎么没有 中间的 0 ？
模型文件中每行第一个数字对应的是transformer的index。config是特殊的所以index设置为-1，下面代码中有指出。
模型文件中的1 就是说明第二个transformer KMeansModel具有数据，具体数据内容就在index 1对应这行 。
为什么模型文件没有 0 就是因为第一个transformer VectorAssembler没有自己的数据，所以就不包括了。
class ModelExporterUtils {
//Pack an array of transformers to a BatchOperator.
static BatchOperator packTransformersArray(TransformerBase[] transformers) {
int numTransformers = transformers.length;
String[] clazzNames = new String[numTransformers];
String[] params = new String[numTransformers];
String[] schemas = new String[numTransformers];
for (int i = 0; i < numTransformers; i++) {
clazzNames[i] = transformers[i].getClass().getCanonicalName();
params[i] = transformers[i].getParams().toJson();
schemas[i] = "";
if (transformers[i] instanceof PipelineModel) {
schemas[i] = CsvUtil.schema2SchemaStr(PIPELINE_MODEL_SCHEMA);
} else if (transformers[i] instanceof ModelBase) {
long envId = transformers[i].getMLEnvironmentId();
BatchOperator data = BatchOperator.fromTable(((ModelBase) transformers[i]).getModelData());
data.setMLEnvironmentId(envId);
data = data.link(new VectorSerializeBatchOp().setMLEnvironmentId(envId));
schemas[i] = CsvUtil.schema2SchemaStr(data.getSchema());
}
}
Map<String, Object> config = new HashMap<>();
config.put("clazz", clazzNames);
config.put("param", params);
config.put("schema", schemas);
// 这里就对应着模型文件的第一个数值 -1，就是config对应的index就是-1。
Row row = Row.of(-1L, JsonConverter.toJson(config));
// 这个时候我们可以看到，schema, param, clazz 就是对应着模型文件中的输出，我们距离目标更近了一步
config = {HashMap@5432} size = 3
"schema" -> {String[2]@5431}
key = "schema"
value = {String[2]@5431}
0 = ""
1 = "model_id BIGINT,model_info VARCHAR"
"param" -> {String[2]@5430}
key = "param"
value = {String[2]@5430}
0 = "{"outputCol":""features"","selectedCols":"["sepal_length","sepal_width","petal_length","petal_width"]"}"
1 = "{"vectorCol":""features"","maxIter":"100","reservedCols":"["category"]","k":"3","predictionCol":""prediction_result"","predictionDetailCol":""prediction_detail""}"
"clazz" -> {String[2]@5429}
key = "clazz"
value = {String[2]@5429}
0 = "com.alibaba.alink.pipeline.dataproc.vector.VectorAssembler"
1 = "com.alibaba.alink.pipeline.clustering.KMeansModel"
BatchOperator packed = new MemSourceBatchOp(Collections.singletonList(row), PIPELINE_MODEL_SCHEMA)
.setMLEnvironmentId(transformers.length > 0 ? transformers[0].getMLEnvironmentId() :
MLEnvironmentFactory.DEFAULT_ML_ENVIRONMENT_ID);
for (int i = 0; i < numTransformers; i++) {
BatchOperator data = null;
final long envId = transformers[i].getMLEnvironmentId();
if (transformers[i] instanceof PipelineModel) {
data = packTransformersArray(((PipelineModel) transformers[i]).transformers);
} else if (transformers[i] instanceof ModelBase) {
data = BatchOperator.fromTable(((ModelBase) transformers[i]).getModelData())
.setMLEnvironmentId(envId);
data = data.link(new VectorSerializeBatchOp().setMLEnvironmentId(envId));
}
if (data != null) {
// 这对应模型文件中的1, 为什么模型文件没有 0就是因为VectorAssembler没有自己的数据，所以就不包括了。
packed = new UnionAllBatchOp().setMLEnvironmentId(envId).linkFrom(packed, packBatchOp(data, i));
}
}
return packed;
}
}
0x05 读取模型
下面代码作用是：读取模型，然后进行转换。
BatchOperator data = new CsvSourceBatchOp().setFilePath(URL).setSchemaStr(SCHEMA_STR);
PipelineModel pipeline = PipelineModel.load("./kmeans.csv");
pipeline.transform(data).print();
读取模型文件，然后转换成PipelineModel。
public class PipelineModel extends ModelBase<PipelineModel> implements LocalPredictable {
//Load the pipeline model from a path.
public static PipelineModel load(String path) {
return load(new CsvSourceBatchOp(path, PIPELINE_MODEL_SCHEMA));
}
//Load the pipeline model from a BatchOperator.
public static PipelineModel load(BatchOperator batchOp) {
return new PipelineModel(ModelExporterUtils.unpackTransformersArray(batchOp));
}
public PipelineModel(TransformerBase[] transformers) {
super(null);
if (null == transformers) {
this.transformers = new TransformerBase[]{};
} else {
List<TransformerBase> flattened = new ArrayList<>();
flattenTransformers(transformers, flattened);
this.transformers = flattened.toArray(new TransformerBase[0]);
}
}
}
// 相关调用栈如下
unpackTransformersArray:91, ModelExporterUtils (com.alibaba.alink.pipeline)
load:149, PipelineModel (com.alibaba.alink.pipeline)
load:142, PipelineModel (com.alibaba.alink.pipeline)
main:22, KMeansExample2 (com.alibaba.alink)
以下是为导入导出用到的功能类，比如导入导出transformer。我们能够看到大致功能如下：
- 从index为-1处获取配置信息。
- 从配置信息中获取了算法类，参数，shema等信息。
- 根据算法类，生成所有transformer。
- 每次生成一个新transformer时候，会读取文件中对应行内容，unpack该行内容，生成模型对应的数据，然后赋值给transformer。注意的是，解析出来的数据被包装成一个BatchOperator。
class ModelExporterUtils {
// Unpack transformers array from a BatchOperator.
static TransformerBase[] unpackTransformersArray(BatchOperator batchOp) {
String configStr;
try {
// 从index为-1处获取配置信息。
List<Row> rows = batchOp.as(new String[]{"f1", "f2"}).where("f1=-1").collect();
Preconditions.checkArgument(rows.size() == 1, "Invalid model.");
configStr = (String) rows.get(0).getField(1);
} catch (Exception e) {
throw new RuntimeException("Fail to collect model config.");
}
// 这里从配置信息中获取了算法类，参数，shema等信息
String[] clazzNames = JsonConverter.fromJson(JsonPath.read(configStr, "$.clazz").toString(), String[].class);
String[] params = JsonConverter.fromJson(JsonPath.read(configStr, "$.param").toString(), String[].class);
String[] schemas = JsonConverter.fromJson(JsonPath.read(configStr, "$.schema").toString(), String[].class);
// 遍历，生成所有transformer。
int numTransformers = clazzNames.length;
TransformerBase[] transformers = new TransformerBase[numTransformers];
for (int i = 0; i < numTransformers; i++) {
try {
Class clazz = Class.forName(clazzNames[i]);
transformers[i] = (TransformerBase) clazz.getConstructor(Params.class).newInstance(
Params.fromJson(params[i])
.set(HasMLEnvironmentId.ML_ENVIRONMENT_ID, batchOp.getMLEnvironmentId()));
} catch (Exception e) {
throw new RuntimeException("Fail to re construct transformer.", e);
}
BatchOperator packed = batchOp.as(new String[]{"f1", "f2"}).where("f1=" + i);
if (transformers[i] instanceof PipelineModel) {
BatchOperator data = unpackBatchOp(packed, CsvUtil.schemaStr2Schema(schemas[i]));
transformers[i] = new PipelineModel(unpackTransformersArray(data))
.setMLEnvironmentId(batchOp.getMLEnvironmentId());
} else if (transformers[i] instanceof ModelBase) {
BatchOperator data = unpackBatchOp(packed, CsvUtil.schemaStr2Schema(schemas[i]));
// 这里会设置模型数据。
((ModelBase) transformers[i]).setModelData(data.getOutputTable());
}
}
return transformers;
}
}
最后生成的transformers如下：
transformers = {TransformerBase[2]@9340}
0 = {VectorAssembler@9383}
mapperBuilder = {VectorAssembler$lambda@9385}
params = {Params@9386} "Params {outputCol="features", selectedCols=["sepal_length","sepal_width","petal_length","petal_width"], MLEnvironmentId=0}"
1 = {KMeansModel@9384}
mapperBuilder = {KMeansModel$lambda@9388}
modelData = {TableImpl@9389} "UnnamedTable$1"
params = {Params@9390} "Params {vectorCol="features", maxIter=100, reservedCols=["category"], k=3, MLEnvironmentId=0, predictionCol="prediction_result", predictionDetailCol="prediction_detail"}"
0x06 预测
pipeline.transform(data).print();是预测的代码。
6.1 生成runtime rapper
预测算法需要被包装成RichMapFunction，才能够被Flink引用。
VectorAssembler是起到转换csv文件作用。KMeansModel是用来预测。预测时候会调用到KMeansModel.transform，其又会调用到linkFrom，这里生成了runtime rapper。
public abstract class MapModel<T extends MapModel<T>>
extends ModelBase<T> implements LocalPredictable {
@Override
public BatchOperator transform(BatchOperator input) {
return new ModelMapBatchOp(this.mapperBuilder, this.params)
.linkFrom(BatchOperator.fromTable(this.getModelData())
.setMLEnvironmentId(input.getMLEnvironmentId()), input);
}
}
// this.getModelData()是模型数据，对应linkFrom的输入参数inputs[0]
// input 这个是待处理的数据。，对应linkFrom的输入参数inputs[1]
// 模型数据就是之前从csv中取出来设置的。
public abstract class ModelBase<M extends ModelBase<M>> extends TransformerBase<M>
implements Model<M> {
public Table getModelData() {
return this.modelData;
}
}
ModelMapBatchOp.linkFrom 代码中，会生成ModelMapperAdapter。此时会把模型信息作为广播变量存起来。这样在后续预测时候就可以先load模型数据。
public class ModelMapBatchOp<T extends ModelMapBatchOp<T>> extends BatchOperator<T> {
private static final String BROADCAST_MODEL_TABLE_NAME = "broadcastModelTable";
// (modelScheme, dataSchema, params) -> ModelMapper
private final TriFunction<TableSchema, TableSchema, Params, ModelMapper> mapperBuilder;
public ModelMapBatchOp(TriFunction<TableSchema, TableSchema, Params, ModelMapper> mapperBuilder, Params params) {
super(params);
this.mapperBuilder = mapperBuilder;
}
@Override
public T linkFrom(BatchOperator<?>... inputs) {
BroadcastVariableModelSource modelSource = new BroadcastVariableModelSource(BROADCAST_MODEL_TABLE_NAME);
ModelMapper mapper = this.mapperBuilder.apply(
inputs[0].getSchema(),
inputs[1].getSchema(),
this.getParams());
DataSet<Row> modelRows = inputs[0].getDataSet().rebalance();
// 这里会广播变量
DataSet<Row> resultRows = inputs[1].getDataSet()
.map(new ModelMapperAdapter(mapper, modelSource))
.withBroadcastSet(modelRows, BROADCAST_MODEL_TABLE_NAME);
TableSchema outputSchema = mapper.getOutputSchema();
this.setOutput(resultRows, outputSchema);
return (T) this;
}
}
6.2 加载模型
当预测时候，ModelMapperAdapter会在open函数先加载模型。
public class ModelMapperAdapter extends RichMapFunction<Row, Row> implements Serializable {
@Override
public void open(Configuration parameters) throws Exception {
List<Row> modelRows = this.modelSource.getModelRows(getRuntimeContext());
this.mapper.loadModel(modelRows);
}
}
// 加载出来的模型数据举例如下
modelRows = {ArrayList@10100} size = 4
0 = {Row@10103} "2097152,{"clusterId":1,"weight":62.0,"vec":{"data":[5.901612903225806,2.7483870967741932,4.393548387096773,1.4338709677419355]}}"
1 = {Row@10104} "0,{"vectorCol":""features"","latitudeCol":null,"longitudeCol":null,"distanceType":""EUCLIDEAN"","k":"3","vectorSize":"4"}"
2 = {Row@10105} "3145728,{"clusterId":2,"weight":50.0,"vec":{"data":[5.005999999999999,3.418,1.4639999999999997,0.24400000000000002]}}"
3 = {Row@10106} "1048576,{"clusterId":0,"weight":38.0,"vec":{"data":[6.85,3.0736842105263156,5.742105263157894,2.0710526315789477]}}"
this.mapper.loadModel(modelRows) 会调用KMeansModelMapper.loadModel，其最后调用到
- ModelConverterUtils.extractModelMetaAndData 来进行反序列化，把DataSet转换回Tuple。
- 最终调用到KMeansUtil.KMeansTrainModelData生成用来预测的模型KMeansTrainModelData
/**
* The abstract class for a kind of {@link ModelDataConverter} where the model data can serialize to "Tuple2&jt;Params, Iterable&jt;String>>". Here "Params" is the meta data of the model, and "Iterable&jt;String>" is concrete data of the model.
*/
public abstract class SimpleModelDataConverter<M1, M2> implements ModelDataConverter<M1, M2> {
@Override
public M2 load(List<Row> rows) {
Tuple2<Params, Iterable<String>> metaAndData = ModelConverterUtils.extractModelMetaAndData(rows);
return deserializeModel(metaAndData.f0, metaAndData.f1);
}
}
metaAndData = {Tuple2@10267} "(Params {vectorCol="features", latitudeCol=null, longitudeCol=null, distanceType="EUCLIDEAN", k=3, vectorSize=4},com.alibaba.alink.common.model.ModelConverterUtils$StringDataIterable@7e9c1b42)"
f0 = {Params@10252} "Params {vectorCol="features", latitudeCol=null, longitudeCol=null, distanceType="EUCLIDEAN", k=3, vectorSize=4}"
params = {HashMap@10273} size = 6
"vectorCol" -> ""features""
"latitudeCol" -> null
"longitudeCol" -> null
"distanceType" -> ""EUCLIDEAN""
"k" -> "3"
"vectorSize" -> "4"
f1 = {ModelConverterUtils$StringDataIterable@10262}
iterator = {ModelConverterUtils$StringDataIterator@10272}
modelRows = {ArrayList@10043} size = 4
order = {Integer[4]@10388}
curr = "{"clusterId":0,"weight":38.0,"vec":{"data":[6.85,3.0736842105263156,5.742105263157894,2.0710526315789477]}}"
listPos = 2
可以看到getModelRows就是从广播变量中读取数据。
public class BroadcastVariableModelSource implements ModelSource {
public List<Row> getModelRows(RuntimeContext runtimeContext) {
return runtimeContext.getBroadcastVariable(modelVariableName);
}
}
6.3 预测
最后预测是在ModelMapperAdapter的map函数。这实际上是 flink根据用户代码生成的执行计划进行相应处理后自己执行的。
/**
* Adapt a {@link ModelMapper} to run within flink.
* <p>
* This adapter class hold the target {@link ModelMapper} and it‘s {@link ModelSource}. Upon open(),
* it will load model rows from {@link ModelSource} into {@link ModelMapper}.
*/
public class ModelMapperAdapter extends RichMapFunction<Row, Row> implements Serializable {
@Override
public Row map(Row row) throws Exception {
return this.mapper.map(row);
}
}
mapper实际调用到KMeansModelMapper，这里就用到了模型数据。
// Find the closest cluster center for every point.
public class KMeansModelMapper extends ModelMapper {
@Override
public Row map(Row row){
Vector record = KMeansUtil.getKMeansPredictVector(colIdx, row);
......
if(isPredDetail){
double[] probs = KMeansUtil.getProbArrayFromDistanceArray(clusterDistances);
DenseVector vec = new DenseVector(probs.length);
for(int i = 0; i < this.modelData.params.k; i++){
// 这里就用到了模型数据进行预测
vec.set((int)this.modelData.getClusterId(i), probs[i]);
}
res.add(vec.toString());
}
return outputColsHelper.getResultRow(row, Row.of(res.toArray(new Object[0])));
}
}
// 模型数据如下
this = {KMeansModelMapper@10822}
modelData = {KMeansPredictModelData@10828}
centroids = {FastDistanceMatrixData@10842}
vectors = {DenseMatrix@10843} "mat[4,3]:
5.006,6.85,5.901612903225807
3.418,3.0736842105263156,2.7483870967741937
1.4639999999999997,5.742105263157894,4.393548387096774
0.24400000000000002,2.0710526315789473,1.4338709677419355
"
label = {DenseMatrix@10844} "mat[1,3]:
38.945592000000005,93.63106648199445,63.74191987513008
"
rows = {Row[3]@10845}
params = {KMeansTrainModelData$ParamSummary@10829}
k = 3
vectorSize = 4
distanceType = {DistanceType@10849} "EUCLIDEAN"
vectorColName = "features"
latitudeColName = null
longtitudeColName = null
0x07 流式预测
我们知道Alink是可以支持批式预测和流式预测。我们看看流式预测是怎么处理的。下面就是KMeans的流式预测。
public class KMeansExampleStream {
AlgoOperator getData(boolean isBatch) {
Row[] array = new Row[] {
Row.of(0, "0 0 0"),
Row.of(1, "0.1,0.1,0.1"),
Row.of(2, "0.2,0.2,0.2"),
Row.of(3, "9 9 9"),
Row.of(4, "9.1 9.1 9.1"),
Row.of(5, "9.2 9.2 9.2")
};
if (isBatch) {
return new MemSourceBatchOp(
Arrays.asList(array), new String[] {"id", "vec"});
} else {
return new MemSourceStreamOp(
Arrays.asList(array), new String[] {"id", "vec"});
}
}
public static void main(String[] args) throws Exception {
KMeansExampleStream ks = new KMeansExampleStream();
BatchOperator inOp1 = (BatchOperator)ks.getData(true);
StreamOperator inOp2 = (StreamOperator)ks.getData(false);
KMeansTrainBatchOp trainBatch = new KMeansTrainBatchOp().setVectorCol("vec").setK(2);
KMeansPredictBatchOp predictBatch = new KMeansPredictBatchOp().setPredictionCol("pred");
trainBatch.linkFrom(inOp1);
KMeansPredictStreamOp predictStream = new KMeansPredictStreamOp(trainBatch).setPredictionCol("pred");
predictStream.linkFrom(inOp2);
predictStream.print(-1,5);
StreamOperator.execute();
}
}
predictStream.linkFrom是我们这里的要点，其调用到ModelMapStreamOp。ModelMapStreamOp这个类的代码虽然少，但是条理非常清晰，非常适合学习。
- 首先相关继承关系如下
KMeansPredictStreamOp extends ModelMapStreamOp - 其次能看出来，流预测所依赖的数据模型依然是一个批处理产生的模型
BatchOperator model。 - mapperBuilder是业务模型算子，其构造是通过(modelScheme, dataSchema, params) 得出来的，这恰恰就是机器学习的几个要素。
- KMeansModelMapper就是具体模型算子 ：
KMeansModelMapper extends ModelMapper。
// Find the closest cluster center for every point.
public final class KMeansPredictStreamOp extends ModelMapStreamOp <KMeansPredictStreamOp>
implements KMeansPredictParams <KMeansPredictStreamOp> {
// @param model trained from kMeansBatchOp
public KMeansPredictStreamOp(BatchOperator model) {
this(model, new Params());
}
public KMeansPredictStreamOp(BatchOperator model, Params params) {
super(model, KMeansModelMapper::new, params);
}
}
具体深入代码 ，我们可以看到：
- 首先 ，把DataSet的数据一次性都取出来，因为都取出来容易造成内存问题，所以 DataSet.collect 注释中有警告：Convenience method to get the elements of a DataSet as a List. As DataSet can contain a lot of data, this method should be used with caution.
- 其次，通过如下代码
this.mapperBuilder.apply(modelSchema, in.getSchema(), this.getParams());构建业务模型KMeansModelMapper。 - 然后，
new ModelMapperAdapter(mapper, modelSource)会建立一个 RichFunction 作为运行适配层。 - 最后，输入的流数据源 in 会通过
in.getDataStream().map((new ModelMapperAdapter(mapper, modelSource));来完成预测。 - 实际上，这时候只是生成stream graph，具体计算是后续flink会根据graph再进行处理。
public class ModelMapStreamOp<T extends ModelMapStreamOp <T>> extends StreamOperator<T> {
private final BatchOperator model;
// (modelScheme, dataSchema, params) -> ModelMapper
private final TriFunction<TableSchema, TableSchema, Params, ModelMapper> mapperBuilder;
public ModelMapStreamOp(BatchOperator model,
TriFunction<TableSchema, TableSchema, Params, ModelMapper> mapperBuilder,
Params params) {
super(params);
this.model = model;
this.mapperBuilder = mapperBuilder;
}
@Override
public T linkFrom(StreamOperator<?>... inputs) {
StreamOperator<?> in = checkAndGetFirst(inputs);
TableSchema modelSchema = this.model.getSchema();
try {
// 把模型数据全都取出来
DataBridge modelDataBridge = DirectReader.collect(model);
DataBridgeModelSource modelSource = new DataBridgeModelSource(modelDataBridge);
ModelMapper mapper = this.mapperBuilder.apply(modelSchema, in.getSchema(), this.getParams());
// 生成runtime适配层和预测算子。把预测结果返回。
// 实际上，这时候只是生成stream graph，具体计算是后续flink会根据graph再进行处理。
DataStream <Row> resultRows = in.getDataStream().map(new ModelMapperAdapter(mapper, modelSource));
TableSchema resultSchema = mapper.getOutputSchema();
this.setOutput(resultRows, resultSchema);
return (T) this;
} catch (Exception ex) {
throw new RuntimeException(ex);
}
}
}
0x08 总结
现在我们已经梳理了Alink模型的来龙去脉，让我们再次拿出模型文件内容来验证。
- 第一行是元数据信息，其中包含schema, 算法类名称，元参数。Alink可以通过这些信息生成流水线的transformer。
- 后续行是算法类所需要的模型数据。每一行对应一个算法类。Alink会取出这些数据来设置到transformer中。
- 后续行的模型数据是具体算法相关。
- 第一行特殊之处在于其index是 -1。后续数据行的index从0开始，如果某一个transformer没有数据，则没有对应行，跳过index。
这样Alink就可以根据模型文件生成流水线模型。
-1,"{""schema"":["""",""model_id BIGINT,model_info VARCHAR""],""param"":[""{""outputCol"":""\""features\"""",""selectedCols"":""[\""sepal_length\"",\""sepal_width\"",\""petal_length\"",\""petal_width\""]""}"",""{""vectorCol"":""\""features\"""",""maxIter"":""100"",""reservedCols"":""[\""category\""]"",""k"":""3"",""predictionCol"":""\""prediction_result\"""",""predictionDetailCol"":""\""prediction_detail\""""}""],""clazz"":[""com.alibaba.alink.pipeline.dataproc.vector.VectorAssembler"",""com.alibaba.alink.pipeline.clustering.KMeansModel""]}"
1,"0^{""vectorCol"":""""features"""",""latitudeCol"":null,""longitudeCol"":null,""distanceType"":""""EUCLIDEAN"""",""k"":""3"",""vectorSize"":""4""}"
1,"1048576^{""clusterId"":0,""weight"":39.0,""vec"":{""data"":[6.8538461538461535,3.0769230769230766,5.7153846153846155,2.0538461538461545]}}"
1,"2097152^{""clusterId"":1,""weight"":61.0,""vec"":{""data"":[5.883606557377049,2.740983606557377,4.388524590163936,1.4344262295081969]}}"
1,"3145728^{""clusterId"":2,""weight"":50.0,""vec"":{""data"":[5.006,3.418,1.4640000000000002,0.24400000000000005]}}"
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