Description

Gaussian Mixture prediction based on the model fitted by GmmTrainBatchOp.

Parameters

Name Description Type Required？ Default Value
vectorCol Name of a vector column String ✓
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.     so_iris = CsvSourceStreamOp().setFilePath(URL).setSchemaStr(SCHEMA_STR)
  4.     bo_iris = CsvSourceBatchOp().setFilePath(URL).setSchemaStr(SCHEMA_STR)
  6.     va = VectorAssembler() \
  7.         .setSelectedCols(['sepal_length', 'sepal_width', 'petal_length', 'petal_width']) \
  8.         .setOutputCol('features')
  10.     gmm = GaussianMixture() \
  11.         .setVectorCol('features') \
  12.         .setK(3) \
  13.         .setMaxIter(100) \
  14.         .setPredictionCol('cluster_id') \
  15.         .setPredictionDetailCol('cluster_detail')
  17.     pipeline = Pipeline().add(va).add(gmm)
  18.     model = pipeline.fit(bo_iris)
  19.     model.transform(so_iris).link(CsvSinkStreamOp().setFilePath('/tmp/gmm_pred.csv'))
  20.     StreamOperator.execute()

Results

  1. 5.0,3.2,1.2,0.2,Iris-setosa,5.0 3.2 1.2 0.2,0,1.0 1.0046812472553649E-25 4.0513728391732534E-35
  2. 6.6,3.0,4.4,1.4,Iris-versicolor,6.6 3.0 4.4 1.4,1,6.378045617038367E-82 0.9998755827709217 1.244172290783167E-4
  3. 5.4,3.9,1.3,0.4,Iris-setosa,5.4 3.9 1.3 0.4,0,1.0 2.549038258209056E-36 4.426016725123766E-44
  4. 5.0,2.3,3.3,1.0,Iris-versicolor,5.0 2.3 3.3 1.0,1,4.222741118453436E-36 0.9999377852049277 6.22147950723104E-5
  5. 5.5,3.5,1.3,0.2,Iris-setosa,5.5 3.5 1.3 0.2,0,1.0 9.30170187388185E-33 8.269822554170461E-42