Description

One-hot maps a serial of columns of category indices to a column of sparse binary vector. It will produce a model of one hot, and then it can transform data to binary format using this model.

Parameters

Name Description Type Required？ Default Value
discreteThresholdsArray discrete thresholds array Integer[]
discreteThresholds discrete thresholds array Integer Integer.MIN_VALUE
selectedCols Names of the columns used for processing String[]

Script Example

Script

  1. import numpy as np
  2. import pandas as pd
  3. data = np.array([
  4.     [1.1, True, "2", "A"],
  5.     [1.1, False, "2", "B"],
  6.     [1.1, True, "1", "B"],
  7.     [2.2, True, "1", "A"]
  8. ])
  9. df = pd.DataFrame({"double": data[:, 0], "bool": data[:, 1], "number": data[:, 2], "str": data[:, 3]})
  11. inOp1 = BatchOperator.fromDataframe(df, schemaStr='double double, bool boolean, number int, str string')
  12. inOp2 = StreamOperator.fromDataframe(df, schemaStr='double double, bool boolean, number int, str string')
  14. onehot = OneHotTrainBatchOp().setSelectedCols(["double", "bool", "number", "str"]).setDiscreteThresholds(2)
  15. predictBatch = OneHotPredictBatchOp().setSelectedCols(["double", "bool"]).setEncode("ASSEMBLED_VECTOR").setOutputCols(["pred"]).setDropLast(False)
  16. onehot.linkFrom(inOp1)
  17. predictBatch.linkFrom(onehot, inOp1)
  18. [model,predict] = collectToDataframes(onehot, predictBatch)
  19. print(model)
  20. print(predict)
  22. predictStream = OneHotPredictStreamOp(onehot).setSelectedCols(["double", "bool"]).setEncode("ASSEMBLED_VECTOR").setOutputCols(["vec"])
  23. predictStream.linkFrom(inOp2)
  24. predictStream.print(refreshInterval=-1)
  25. StreamOperator.execute()

Result

  1.    double   bool  number str            pred
  2. 0     1.1   True       2   A  $6$0:1.0 3:1.0
  3. 1     1.1  False       2   B  $6$0:1.0 5:1.0
  4. 2     1.1   True       1   B  $6$0:1.0 3:1.0
  5. 3     2.2   True       1   A  $6$2:1.0 3:1.0