from pyspark.ml.classification import LogisticRegression from pyspark.ml.tuning import ParamGridBuilder # create a grid of hyperparameters for Logistic Regression to be tuned paramGrid = ParamGridBuilder() \ .addGrid(LogisticRegression.regParam, [0.1, 0.01]) \ .addGrid(LogisticRegression.elasticNetParam, [0.0, 0.5, 1.0]) \ .build()
from pyspark.ml.recommendation import ALS from pyspark.ml.tuning import ParamGridBuilder # create a grid of hyperparameters for ALS to be tuned paramGrid = ParamGridBuilder() \ .addGrid(ALS.rank, [10, 50, 100]) \ .addGrid(ALS.maxIter, [5, 10]) \ .addGrid(ALS.regParam, [0.1, 0.01]) \ .build()This example creates a grid of hyperparameters for the Alternating Least Squares (ALS) model used for collaborative filtering. It uses the `ParamGridBuilder` class to create a grid of values for the `rank`, `maxIter`, and `regParam` hyperparameters. In conclusion, the ParamGridBuilder is a class in the pyspark.ml.tuning library which is used for creating a grid-search over hyperparameters in Spark ML models. Using the `addGrid()` method, different values for each hyperparameter can be specified, and calling the `build()` method will create the combination of all parameters.