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Make a grid

Next, you need to create a grid of values to search over when looking for the optimal hyperparameters. The submodule pyspark.ml.tuning includes a class called ParamGridBuilder that does just that (maybe you're starting to notice a pattern here; PySpark has a submodule for just about everything!).

You'll need to use the .addGrid() and .build() methods to create a grid that you can use for cross validation. The .addGrid() method takes a model parameter (an attribute of the model Estimator, lr, that you created a few exercises ago) and a list of values that you want to try. The .build() method takes no arguments, it just returns the grid that you'll use later.

Instruktioner

100 XP
  • Import the submodule pyspark.ml.tuning under the alias tune.
  • Call the class constructor ParamGridBuilder() with no arguments. Save this as grid.
  • Call the .addGrid() method on grid with lr.regParam as the first argument and np.arange(0, .1, .01) as the second argument. This second call is a function from the numpy module (imported as np) that creates a list of numbers from 0 to .1, incrementing by .01. Overwrite grid with the result.
  • Update grid again by calling the .addGrid() method a second time create a grid for lr.elasticNetParam that includes only the values [0, 1].
  • Call the .build() method on grid and overwrite it with the output.