Description

The batch operator that predict the data using the random forest model.

Parameters

Name Description Type Required？ Default Value
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. import numpy as np
  2. import pandas as pd
  3. from pyalink.alink import *
  6. def exampleData():
  7.     return np.array([
  8.         [1.0, "A", 0, 0, 0],
  9.         [2.0, "B", 1, 1, 0],
  10.         [3.0, "C", 2, 2, 1],
  11.         [4.0, "D", 3, 3, 1]
  12.     ])
  15. def sourceFrame():
  16.     data = exampleData()
  17.     return pd.DataFrame({
  18.         "f0": data[:, 0],
  19.         "f1": data[:, 1],
  20.         "f2": data[:, 2],
  21.         "f3": data[:, 3],
  22.         "label": data[:, 4]
  23.     })
  26. def batchSource():
  27.     return dataframeToOperator(
  28.         sourceFrame(),
  29.         schemaStr='''
  30.     f0 double, 
  31.     f1 string, 
  32.     f2 int, 
  33.     f3 int, 
  34.     label int
  35.     ''',
  36.         op_type='batch'
  37.     )
  40. def streamSource():
  41.     return dataframeToOperator(
  42.         sourceFrame(),
  43.         schemaStr='''
  44.     f0 double, 
  45.     f1 string, 
  46.     f2 int, 
  47.     f3 int, 
  48.     label int
  49.     ''',
  50.         op_type='stream'
  51.     )
  54. trainOp = (
  55.     RandomForestTrainBatchOp()
  56.     .setLabelCol('label')
  57.     .setFeatureCols(['f0', 'f1', 'f2', 'f3'])
  58. )
  60. predictBatchOp = (
  61.     RandomForestPredictBatchOp()
  62.     .setPredictionDetailCol('pred_detail')
  63.     .setPredictionCol('pred')
  64. )
  66. (
  67.     predictBatchOp
  68.     .linkFrom(
  69.         batchSource().link(trainOp),
  70.         batchSource()
  71.     )
  72.     .print()
  73. )
  75. predictStreamOp = (
  76.     RandomForestPredictStreamOp(
  77.         batchSource().link(trainOp)
  78.     )
  79.     .setPredictionDetailCol('pred_detail')
  80.     .setPredictionCol('pred')
  81. )
  83. (
  84.     predictStreamOp
  85.     .linkFrom(
  86.         streamSource()
  87.     )
  88.     .print()
  89. )
  91. StreamOperator.execute()

Result

  1.     f0 f1  f2  f3  label  pred        pred_detail
  2. 0  1.0  A   0   0      0     0  {"0":1.0,"1":0.0}
  3. 1  2.0  B   1   1      0     0  {"0":1.0,"1":0.0}
  4. 2  3.0  C   2   2      1     1  {"0":0.0,"1":1.0}
  5. 3  4.0  D   3   3      1     1  {"0":0.0,"1":1.0}