以下是我用来训练我的模型的代码段。在此之后如何以及在何处可以保存我的模型,并读回比FileExporter类其他?它是只在一个文件或者我可以将其存储在缓存和访问回来?
IgniteCache<Integer, double[]> cache = ignite.getOrCreateCache("MLData_IRIS");
// extracting sepal length, sepal width, petal length, petal width
IgniteBiFunction<Integer, double[], Vector> featureExtractor = new RangeExtractor(1, 5);
IgniteBiFunction<Integer, double[], Double> labelExtractor = new PointExtractor(0);
System.out.println(">>> Create new training dataset splitter object.");
TrainTestSplit<Integer, double[]> split = new TrainTestDatasetSplitter<Integer, double[]>()
.split(0.5, 0.5);
IgniteBiPredicate<Integer, double[]> testData = split.getTestFilter();
IgniteBiPredicate<Integer, double[]> trainData = split.getTrainFilter();
// Set up the trainer
KMeansTrainer trainer = new KMeansTrainer()
.withDistance(new EuclideanDistance()) //other metrics are HammingDistance, ManhattanDistance
.withAmountOfClusters(3) // number clusters want to create
.withMaxIterations(100)
.withEpsilon(1.0E-4D)
.withSeed(1234L);
long t1 = System.currentTimeMillis();
KMeansModel mdl = trainer.fit(
ignite,
cache,
trainData,
featureExtractor,
labelExtractor
);
long t2 = System.currentTimeMillis();
System.out.println("time taken to build the model : " + (t2 - t1) + " ms");
System.out.println(">>> --------------------------------------------");
System.out.println(">>> trained model: " + mdl.toString(true));
现在点燃只有这种机制 - FileExporter。
但是,对于2.8版本中,我们已经实现了模型存储。
样品保存模型:
ModelStorage storage = new ModelStorageFactory().getModelStorage(ignite);
storage.mkdirs("/");
storage.putFile("/my_model", serializedMdl);
ModelDescriptor desc = new ModelDescriptor(
"MyModel",
"My Cool Model",
new ModelSignature("", "", ""),
new ModelStorageModelReader("/my_model"),
new IgniteModelParser<>()
);
ModelDescriptorStorage descStorage = new ModelDescriptorStorageFactory().getModelDescriptorStorage(ignite);
descStorage.put("my_model", desc);
样品加载模型:
Ignite ignite = Ignition.ignite();
ModelDescriptorStorage descStorage = new ModelDescriptorStorageFactory().getModelDescriptorStorage(ignite);
ModelDescriptor desc = descStorage.get(mdl);
Model<byte[], byte[]> infMdl = new SingleModelBuilder().build(desc.getReader(), desc.getParser());
Vector input = VectorUtils.of(x);
try {
return deserialize(infMdl.predict(serialize(input)));
}
catch (IOException | ClassNotFoundException e) {
throw new RuntimeException(e);
}
其中X - 是双打和MDL的载体 - 是型号名称。
注意:此API将提供与发行2.8。但是,你可以,如果你从主分支建立的Ignite现在尝试。