Examples with MultilayerPerceptronClassifier com.alibaba.alink.pipeline.classification.MultilayerPerceptronClassifier used on opensource projects

Example 6 with MultilayerPerceptronClassifier

use of com.alibaba.alink.pipeline.classification.MultilayerPerceptronClassifier in project Alink by alibaba.

the class PipelineSaveAndLoadTest method testNestedSaveAndLoad.

@Test
public void testNestedSaveAndLoad() throws Exception {
    VectorAssembler va = new VectorAssembler().setSelectedCols(Iris.getFeatureColNames()).setOutputCol("features");
    MultilayerPerceptronClassifier classifier = new MultilayerPerceptronClassifier().setVectorCol("features").setLabelCol(Iris.getLabelColName()).setLayers(new int[] { 4, 5, 3 }).setMaxIter(2).setPredictionCol("pred_label").setPredictionDetailCol("pred_detail").setReservedCols(Iris.getLabelColName());
    Pipeline pipeline = new Pipeline().add(new Pipeline().add(va).add(classifier));
    Pipeline pipeline1 = Pipeline.collectLoad(pipeline.save());
    Assert.assertEquals(PipelineModel.collectLoad(pipeline1.fit(data).save()).transform(Iris.getBatchData()).count(), 150);
}

Example 7 with MultilayerPerceptronClassifier

use of com.alibaba.alink.pipeline.classification.MultilayerPerceptronClassifier in project Alink by alibaba.

the class PipelineSaveAndLoadTest method testLocalPredictor.

@Test
public void testLocalPredictor() throws Exception {
    VectorAssembler va = new VectorAssembler().setSelectedCols(Iris.getFeatureColNames()).setOutputCol("features");
    MultilayerPerceptronClassifier classifier = new MultilayerPerceptronClassifier().setVectorCol("features").setLabelCol(Iris.getLabelColName()).setLayers(new int[] { 4, 5, 3 }).setMaxIter(30).setPredictionCol("pred_label").setPredictionDetailCol("pred_detail").setReservedCols(Iris.getLabelColName());
    Pipeline pipeline = new Pipeline().add(va).add(classifier);
    PipelineModel model = pipeline.fit(data);
    FilePath filePath = new FilePath(folder.newFile().getAbsolutePath());
    model.save(filePath, true);
    BatchOperator.execute();
    LocalPredictor localPredictor = new LocalPredictor(filePath, new TableSchema(ArrayUtils.add(data.getColNames(), "features"), ArrayUtils.add(data.getColTypes(), VectorTypes.DENSE_VECTOR)));
    Row result = localPredictor.map(Row.of(5.1, 3.5, 1.4, 0.2, "Iris-setosanew", new DenseVector(new double[] { 5.1, 3.5, 1.4, 0.2 })));
    System.out.println(JsonConverter.toJson(result));
}

Example 8 with MultilayerPerceptronClassifier

use of com.alibaba.alink.pipeline.classification.MultilayerPerceptronClassifier in project Alink by alibaba.

the class ModelSaveAndLoadTest method testNestedSaveAndLoad.

@Test
public void testNestedSaveAndLoad() throws Exception {
    VectorAssembler va = new VectorAssembler().setSelectedCols(Iris.getFeatureColNames()).setOutputCol("features");
    MultilayerPerceptronClassifier classifier = new MultilayerPerceptronClassifier().setVectorCol("features").setLabelCol(Iris.getLabelColName()).setLayers(new int[] { 4, 5, 3 }).setMaxIter(2).setPredictionCol("pred_label").setPredictionDetailCol("pred_detail").setReservedCols(Iris.getLabelColName());
    Pipeline pipeline = new Pipeline().add(va).add(classifier);
    Pipeline pipeline2 = new Pipeline().add(pipeline);
    Assert.assertEquals(PipelineModel.collectLoad(pipeline2.fit(data).save()).transform(Iris.getBatchData()).count(), 150);
}

Example 9 with MultilayerPerceptronClassifier

use of com.alibaba.alink.pipeline.classification.MultilayerPerceptronClassifier in project Alink by alibaba.

the class PipelineSaveAndLoadTest method testNewSaveToFile.

@Test
public void testNewSaveToFile() throws Exception {
    VectorAssembler va = new VectorAssembler().setSelectedCols(Iris.getFeatureColNames()).setOutputCol("features");
    MultilayerPerceptronClassifier classifier = new MultilayerPerceptronClassifier().setVectorCol("features").setLabelCol(Iris.getLabelColName()).setLayers(new int[] { 4, 5, 3 }).setMaxIter(30).setPredictionCol("pred_label").setPredictionDetailCol("pred_detail").setReservedCols(Iris.getLabelColName());
    Pipeline pipeline = new Pipeline().add(va).add(classifier);
    PipelineModel model = pipeline.fit(data);
    FilePath filePath = new FilePath(folder.newFile().getAbsolutePath());
    model.save(filePath, true);
    BatchOperator.execute();
    PipelineModel modelLoaded = PipelineModel.load(filePath);
    Assert.assertEquals(modelLoaded.transform(Iris.getBatchData()).count(), 150);
}

Example 10 with MultilayerPerceptronClassifier

use of com.alibaba.alink.pipeline.classification.MultilayerPerceptronClassifier in project Alink by alibaba.

the class PipelineSaveAndLoadTest method testNewSaveToFileMultiFile.

@Test
public void testNewSaveToFileMultiFile() throws Exception {
    VectorAssembler va = new VectorAssembler().setSelectedCols(Iris.getFeatureColNames()).setOutputCol("features");
    MultilayerPerceptronClassifier classifier = new MultilayerPerceptronClassifier().setVectorCol("features").setLabelCol(Iris.getLabelColName()).setLayers(new int[] { 4, 5, 3 }).setMaxIter(30).setPredictionCol("pred_label").setPredictionDetailCol("pred_detail").setReservedCols(Iris.getLabelColName());
    Pipeline pipeline = new Pipeline().add(va).add(classifier);
    PipelineModel model = pipeline.fit(data);
    FilePath filePath = new FilePath(folder.newFile().getAbsolutePath());
    model.save(filePath, true, 2);
    BatchOperator.execute();
    PipelineModel modelLoaded = PipelineModel.load(filePath);
    Assert.assertEquals(modelLoaded.transform(Iris.getBatchData()).count(), 150);
}