model.py

from pyspark.ml.recommendation import ALS 
from pyspark.ml.evaluation import RegressionEvaluator 

training, test = indexed.randomSplit([0.8,0.1])

als = ALS(maxIter = 10 , 
          regParam = 0.9, 
          userCol = "user_id", 
          itemCol = "isbn_indexed", 
          ratingCol = "book_rating",
          nonnegative = True, 
          coldStartStrategy = 'drop')
model = als.fit(training)

from pyspark.ml.evaluation import RegressionEvaluator 


predictions = model.transform(test)
evaluator = RegressionEvaluator(metricName = 'rmse', labelCol = 'book_rating', predictionCol = "prediction")
rmse = evaluator.evaluate(predictions)
print("Root mean squared error:  ",rmse)