Description

Lasso regression pipeline op.

Parameters

Name Description Type Required？ Default Value
optimMethod optimization method String null
lambda punish factor. Double ✓
reservedCols Names of the columns to be retained in the output table String[] null
predictionCol Column name of prediction. String ✓
vectorCol Name of a vector column String null
withIntercept Whether has intercept or not, default is true Boolean true
maxIter Maximum iterations, The default value is 100 Integer 100
epsilon Convergence tolerance for iterative algorithms (>= 0), The default value is 1.0e-06 Double 1.0E-6
featureCols Names of the feature columns used for training in the input table String[] null
labelCol Name of the label column in the input table String ✓
weightCol Name of the column indicating weight String null
vectorCol Name of a vector column String null
standardization Whether standardize training data or not, default is true Boolean true

Script Example

Script

  1. data = np.array([
  2.     [2, 1, 1],
  3.     [3, 2, 1],
  4.     [4, 3, 2],
  5.     [2, 4, 1],
  6.     [2, 2, 1],
  7.     [4, 3, 2],
  8.     [1, 2, 1],
  9.     [5, 3, 3]])
  10. df = pd.DataFrame({"f0": data[:, 0], 
  11.                    "f1": data[:, 1],
  12.                    "label": data[:, 2]})
  14. batchData = dataframeToOperator(df, schemaStr='f0 int, f1 int, label int', op_type='batch')
  15. colnames = ["f0","f1"]
  16. lasso = LassoRegression().setFeatureCols(colnames).setLambda(0.1).setLabelCol("label").setPredictionCol("pred")
  17. model = lasso.fit(batchData)
  18. model.transform(batchData).print()

Result

f0 f1 label pred
2 1 1 0.830304
3 2 1 1.377312
4 3 2 1.924320
2 4 1 1.159119
2 2 1 0.939909
4 3 2 1.924320
1 2 1 0.502506
5 3 3 2.361724