• GLM流式预测
    • 功能介绍
    • 参数说明
    • 脚本示例
      • 脚本代码
      • 脚本运行结果

    GLM流式预测

    功能介绍

    使用GLM模型，对数据进行流式预测

    参数说明

    名称 中文名称 描述 类型 是否必须？ 默认值
    linkPredResultCol 连接函数结果的列名 连接函数结果的列名 String null
    predictionCol 预测结果列名 预测结果列名 String ✓

    脚本示例

    脚本代码

    2. # data
    3. data = np.array([
    4.     [1.6094,118.0000,69.0000,1.0000,2.0000],
    5.     [2.3026,58.0000,35.0000,1.0000,2.0000],
    6.     [2.7081,42.0000,26.0000,1.0000,2.0000],
    7.     [2.9957,35.0000,21.0000,1.0000,2.0000],
    8.     [3.4012,27.0000,18.0000,1.0000,2.0000],
    9.     [3.6889,25.0000,16.0000,1.0000,2.0000],
    10.     [4.0943,21.0000,13.0000,1.0000,2.0000],
    11.     [4.3820,19.0000,12.0000,1.0000,2.0000],
    12.     [4.6052,18.0000,12.0000,1.0000,2.0000]
    13. ])
    16. df = pd.DataFrame({"u": data[:, 0], "lot1": data[:, 1], "lot2": data[:, 2], "offset": data[:, 3], "weights": data[:, 4]})
    17. source = dataframeToOperator(df, schemaStr='u double, lot1 double, lot2 double, offset double, weights double', op_type='batch')
    20. featureColNames = ["lot1", "lot2"]
    21. labelColName = "u"
    23. # train
    24. train = GlmTrainBatchOp()\
    25.                 .setFamily("gamma")\
    26.                 .setLink("Log")\
    27.                 .setRegParam(0.3)\
    28.                 .setMaxIter(5)\
    29.                 .setFeatureCols(featureColNames)\
    30.                 .setLabelCol(labelColName)
    32. source.link(train)
    34. #  batch predict
    35. predict =  GlmPredictBatchOp()\
    36.                 .setPredictionCol("pred")
    38. predict.linkFrom(train, source)
    39. predict.print()
    41. # eval
    42. eval =  GlmEvaluationBatchOp()\
    43.                 .setFamily("gamma")\
    44.                 .setLink("Log")\
    45.                 .setRegParam(0.3)\
    46.                 .setMaxIter(5)\
    47.                 .setFeatureCols(featureColNames)\
    48.                 .setLabelCol(labelColName)
    50. eval.linkFrom(train, source)
    51. eval.print()
    54. # stream predict
    55. source_stream = dataframeToOperator(df, schemaStr='u double, lot1 double, lot2 double, offset double, weights double', op_type='stream')
    57. predict_stream =  GlmPredictStreamOp(train)\
    58.                 .setPredictionCol("pred")
    60. predict_stream.linkFrom(source_stream)
    61. predict_stream.print()

    脚本运行结果

    u lot1 lot2 offset weights pred
    0 1.6094 118.0 69.0 1.0 2.0 0.378525
    1 2.3026 58.0 35.0 1.0 2.0 0.970639
    2 2.7081 42.0 26.0 1.0 2.0 1.126458
    3 2.9957 35.0 21.0 1.0 2.0 1.227753
    4 3.4012 27.0 18.0 1.0 2.0 1.258898
    5 3.6889 25.0 16.0 1.0 2.0 1.305654
    6 4.0943 21.0 13.0 1.0 2.0 1.367991
    7 4.3820 19.0 12.0 1.0 2.0 1.383571
    8 4.6052 18.0 12.0 1.0 2.0 1.375774