Description

Make stream prediction based on the multilayer perceptron model fitted by MultilayerPerceptronTrainBatchOp.

Parameters

Name Description Type Required？ Default Value
vectorCol Name of a vector column String null
predictionCol Column name of prediction. String ✓
predictionDetailCol Column name of prediction result, it will include detailed info. String
reservedCols Names of the columns to be retained in the output table String[] null

Script Example

Code

  1. URL = "http://alink-dataset.cn-hangzhou.oss.aliyun-inc.com/csv/iris.csv"
  2. SCHEMA_STR = "sepal_length double, sepal_width double, petal_length double, petal_width double, category string";
  3. data = CsvSourceBatchOp().setFilePath(URL).setSchemaStr(SCHEMA_STR)
  5. classifier = MultilayerPerceptronClassifier()\
  6.             .setFeatureCols(Iris.getFeatureColNames())\
  7.             .setLabelCol(Iris.getLabelColName())\
  8.             .setLayers([4, 5, 3])\
  9.             .setMaxIter(100)\
  10.             .setPredictionCol("pred_label")\
  11.             .setPredictionDetailCol("pred_detail")
  13. classifier.fit(data).transform(Iris.getStreamData()).print();

Results

  1. 6.3000|3.3000|6.0000|2.5000|Iris-virginica|Iris-virginica|{"Iris-virginica":0.9433614954932688,"Iris-versicolor":0.056638504506731226,"Iris-setosa":3.008568854761749E-175}
  2. 5.6000|2.8000|4.9000|2.0000|Iris-virginica|Iris-virginica|{"Iris-virginica":0.9433614954932688,"Iris-versicolor":0.056638504506731226,"Iris-setosa":3.008568854761749E-175}
  3. 5.0000|3.3000|1.4000|0.2000|Iris-setosa|Iris-setosa|{"Iris-virginica":8.4E-323,"Iris-versicolor":4.0138401486628416E-173,"Iris-setosa":1.0}
  4. 5.8000|2.7000|5.1000|1.9000|Iris-virginica|Iris-virginica|{"Iris-virginica":0.9433614954932688,"Iris-versicolor":0.056638504506731226,"Iris-setosa":3.008568854761749E-175}
  5. 7.0000|3.2000|4.7000|1.4000|Iris-versicolor|Iris-versicolor|{"Iris-virginica":5.31328185381337E-80,"Iris-versicolor":1.0,"Iris-setosa":6.407249280059006E-44}