功能介绍

  • gbdt(Gradient Boosting Decision Trees)二分类，是经典的基于boosting的有监督学习模型，可以用来解决二分类问题

  • 支持连续特征和离散特征

  • 支持数据采样和特征采样

  • 目标分类必须是两个

参数说明

名称 中文名称 描述 类型 是否必须？ 默认值
predictionCol 预测结果列名 预测结果列名 String ✓
predictionDetailCol 预测详细信息列名 预测详细信息列名 String
reservedCols 算法保留列名 算法保留列 String[] null

脚本示例

运行脚本

  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()

脚本结果

批预测结果

  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}