Tell Spark how to tune your ALS model

• Import RegressionEvaluator from pyspark.ml.evaluation and ParamGridBuilder and CrossValidator from pyspark.ml.tuning.
• Build a ParamGrid called param_grid using the ParamGridBuilder provided. Call the .addGrid() method on each hyperparameter by providing the name of the model and the name of each hyperparameter (ex: .addGrid(als.rank, []). Do this for the rank, maxIter and regParam hyperparameters. Also provide the respective lists of hyperparameter values that Spark should try, as provided here:

 rank: [10, 50, 100, 150]  
 maxIter: [5, 50, 100, 200]  
 regParam: [.01, .05, .1, .15]  

• Create a RegressionEvaluator called evaluator. Set the metricName to "rmse", set the labelCol to "rating", and tell Spark that when it generates predictions to call the predictionCol "prediction".
• Run len(param_grid) to confirm that the param_grid was created and to confirm that the right number of hyperparameter combinations will be tested. It should be equal to the number of rank values * the number of maxIter values * the number of regParam values in the ParamGridBuilder.

# Import the requisite items
from pyspark.ml.evaluation import ____
from pyspark.ml.____ import ____, ____

# Add hyperparameters and their respective values to param_grid
____ = ParamGridBuilder() \
            .addGrid(als.rank, [____, ____, ____, ____]) \
            .addGrid(als.____, [____, ____, ____, ____]) \
            .addGrid(als.____, [____, ____, ____, ____]) \
            .build()
           
# Define evaluator as RMSE and print length of evaluator
____ = RegressionEvaluator(metricName="____", labelCol="____", predictionCol="____") 
print ("Num models to be tested: ", len(param_grid))