Description

MinMaxScaler transforms a dataSet of rows, rescaling each feature to a specific range [min, max). (often [0, 1]). MinMaxScalerPredict will scale the dataSet with model which trained from MaxAbsTrain.

Parameters

Name Description Type Required？ Default Value
outputCols Names of the output columns String[] null

Script Example

Script

  1. data = np.array([
  2.             ["a", 10.0, 100],
  3.             ["b", -2.5, 9],
  4.             ["c", 100.2, 1],
  5.             ["d", -99.9, 100],
  6.             ["a", 1.4, 1],
  7.             ["b", -2.2, 9],
  8.             ["c", 100.9, 1]
  9. ])
  11. colnames = ["col1", "col2", "col3"]
  12. selectedColNames = ["col2", "col3"]
  14. df = pd.DataFrame({"col1": data[:, 0], "col2": data[:, 1], "col3": data[:, 2]})
  15. inOp = dataframeToOperator(df, schemaStr='col1 string, col2 double, col3 long', op_type='batch')
  18. # train
  19. trainOp = MinMaxScalerTrainBatchOp()\
  20.            .setSelectedCols(selectedColNames)
  22. trainOp.linkFrom(inOp)
  24. # batch predict
  25. predictOp = MinMaxScalerPredictBatchOp()
  26. predictOp.linkFrom(trainOp, inOp).print()
  28. # stream predict
  29. sinOp = dataframeToOperator(df, schemaStr='col1 string, col2 double, col3 long', op_type='stream')
  31. predictStreamOp = MinMaxScalerPredictStreamOp(trainOp)
  32. predictStreamOp.linkFrom(sinOp).print()
  35. StreamOperator.execute()

Results

  1.   col1      col2      col3
  2. 0    a  0.547311  1.000000
  3. 1    b  0.485060  0.080808
  4. 2    c  0.996514  0.000000
  5. 3    d  0.000000  1.000000
  6. 4    a  0.504482  0.000000
  7. 5    b  0.486554  0.080808
  8. 6    c  1.000000  0.000000