ALS(Python)

from pyspark.ml.evaluation import RegressionEvaluator

from pyspark.ml.recommendation import ALS

from pyspark.sql import Row

lines = spark.read.text("/FileStore/tables/sample_movielens_ratings.txt").rdd

parts = lines.map(lambda row: row.value.split("::"))

ratingsRDD = parts.map(lambda p: Row(userId=int(p[0]), movieId=int(p[1]),

                                     rating=float(p[2]), timestamp=long(p[3])))

ratings = spark.createDataFrame(ratingsRDD)

(training, test) = ratings.randomSplit([0.8, 0.2])

# Build the recommendation model using ALS on the training data

# Note we set cold start strategy to 'drop' to ensure we don't get NaN evaluation metrics

als = ALS(maxIter=5, rank=10, regParam=0.01, userCol="userId", itemCol="movieId", ratingCol="rating",

          coldStartStrategy="drop")

model = als.fit(training)

#PMML 

# Evaluate the model by computing the RMSE on the test data

predictions = model.transform(test)

evaluator = RegressionEvaluator(metricName="rmse", labelCol="rating",

                                predictionCol="prediction")

rmse = evaluator.evaluate(predictions)

print("Root-mean-square error = " + str(rmse))

# Generate top 10 movie recommendations for each user

userRecs = model.recommendForAllUsers(10)

# Generate top 10 user recommendations for each movie

movieRecs = model.recommendForAllItems(10)