Description

Fit a binary classfication model.

Parameters

Name Description Type Required？ Default Value
algoType null Integer
learningRate learning rate for gbdt training(default 0.3) Double 0.3
minSumHessianPerLeaf minimum sum hessian for each leaf Double 0.0
numTrees Number of decision trees. Integer 100
minSamplesPerLeaf Minimal number of sample in one leaf. Integer 100
maxDepth depth of the tree Integer 6
subsamplingRatio Ratio of the training samples used for learning each decision tree. Double 1.0
featureSubsamplingRatio Ratio of the features used in each tree, in range (0, 1]. Double 1.0
groupCol Name of a grouping column String null
maxBins MAX number of bins for continuous feature Integer 128
featureCols Names of the feature columns used for training in the input table String[] ✓
labelCol Name of the label column in the input table String ✓
categoricalCols Names of the categorical columns used for training in the input table String[]
weightCol Name of the column indicating weight String null
maxLeaves max leaves of tree Integer 2147483647
minSampleRatioPerChild Minimal value of: (num of samples in child)/(num of samples in its parent). Double 0.0
minInfoGain minimum info gain when performing split Double 0.0

Script Example

  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.     GbdtTrainBatchOp()
  56.     .setLearningRate(1.0)
  57.     .setNumTrees(3)
  58.     .setMinSamplesPerLeaf(1)
  59.     .setLabelCol('label')
  60.     .setFeatureCols(['f0', 'f1', 'f2', 'f3'])
  61. )
  63. predictBatchOp = (
  64.     GbdtPredictBatchOp()
  65.     .setPredictionDetailCol('pred_detail')
  66.     .setPredictionCol('pred')
  67. )
  69. (
  70.     predictBatchOp
  71.     .linkFrom(
  72.         batchSource().link(trainOp),
  73.         batchSource()
  74.     )
  75.     .print()
  76. )
  78. predictStreamOp = (
  79.     GbdtPredictStreamOp(
  80.         batchSource().link(trainOp)
  81.     )
  82.     .setPredictionDetailCol('pred_detail')
  83.     .setPredictionCol('pred')
  84. )
  86. (
  87.     predictStreamOp
  88.     .linkFrom(
  89.         streamSource()
  90.     )
  91.     .print()
  92. )
  94. StreamOperator.execute()

Result

Batch prediction

  1.     f0 f1  f2  f3  label  pred                                        pred_detail
  2. 0  1.0  A   0   0      0     0  {"0":0.9849144951094335,"1":0.015085504890566462}
  3. 1  2.0  B   1   1      0     0  {"0":0.9849144951094335,"1":0.015085504890566462}
  4. 2  3.0  C   2   2      1     1   {"0":0.01508550489056637,"1":0.9849144951094336}
  5. 3  4.0  D   3   3      1     1   {"0":0.01508550489056637,"1":0.9849144951094336}

Stream Prediction

  1.     f0    f1    f2    f3    label    pred    pred_detail
  2. 0    1.0    A    0    0    0    0    {"0":0.9849144951094335,"1":0.015085504890566462}
  3. 1    3.0    C    2    2    1    1    {"0":0.01508550489056637,"1":0.9849144951094336}
  4. 2    2.0    B    1    1    0    0    {"0":0.9849144951094335,"1":0.015085504890566462}
  5. 3    4.0    D    3    3    1    1    {"0":0.01508550489056637,"1":0.9849144951094336}