Description

Generalized Linear Model. https://en.wikipedia.org/wiki/Generalized_linear_model.

Parameters

Name Description Type Required？ Default Value
family the name of family which is a description of the error distribution. Supported options: gaussian, binomial, poisson, gamma and tweedie String “gaussian”
variancePower The power in the variance function of the Tweedie distribution. It describe the relationship between the variance and mean of the distribution Double 0.0
link The name of link functionSupported options: cloglog, identity, inverse, log, logit, power, probit and sqrt String null
linkPower Param for the index in the power link function. Double 1.0
offsetCol The col name of offset String null
fitIntercept Sets if we should fit the intercept Boolean true
regParam Sets the regularization parameter for L2 regularization Double 0.0
epsilon epsilon Double 1.0E-5
weightCol Name of the column indicating weight String null
maxIter Maximum iterations, The default value is 10 Integer 10
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 ✓

Script Example

Code

  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')
  19. featureColNames = ["lot1", "lot2"]
  20. labelColName = "u"
  22. # train
  23. train = GlmTrainBatchOp()\
  24.                 .setFamily("gamma")\
  25.                 .setLink("Log")\
  26.                 .setRegParam(0.3)\
  27.                 .setMaxIter(5)\
  28.                 .setFeatureCols(featureColNames)\
  29.                 .setLabelCol(labelColName)
  31. source.link(train)
  33. # predict
  34. predict =  GlmPredictBatchOp()\
  35.                 .setPredictionCol("pred")
  37. predict.linkFrom(train, source)
  40. # eval
  41. eval =  GlmEvaluationBatchOp()\
  42.                 .setFamily("gamma")\
  43.                 .setLink("Log")\
  44.                 .setRegParam(0.3)\
  45.                 .setMaxIter(5)\
  46.                 .setFeatureCols(featureColNames)\
  47.                 .setLabelCol(labelColName)
  49. eval.linkFrom(train, source)
  51. predict.print()
  52. eval.print()

Results

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