als_functions.py

# For loop will automatically create and store ALS models
for r in ranks:
    for mi in maxIters:
        for rp in regParams:
            for a in alphas:
                model_list.append(ALS(userCol= "userId", itemCol= "songId", ratingCol= "num_plays", rank = r, maxIter = mi, regParam = rp, alpha = a, coldStartStrategy="drop", nonnegative = True, implicitPrefs = True))

# Print the model list, and the length of model_list
print (model_list, "Length of model_list: ", len(model_list))

# Validate
len(model_list) == (len(ranks)*len(maxIters)*len(regParams)*len(alphas))

Now that you have all of your hyperparameter values specified, let's have Spark build enough models to test each combination. To facilitate this, a for loop is provided here. Follow the instructions below to automatically create these ALS models. In subsequent exercises you will run these models on test datasets to see which one performs the best.