def main(spark, model_file, data_file, user_file, track_file):
#load ALS model
als_model = ALSModel.load(model_file)
user_indexer = StringIndexer.load(user_file)
track_indexer = StringIndexer.load(track_file)
#read in test data as parquet
df_test = spark.read.parquet(data_file)
pipeline = Pipeline(stages=[user_indexer, track_indexer])
mapping = pipeline.fit(df_test)
df_test = mapping.transform(df_test)
########### PERFORM RANKING METRICS ###########
#create user actual items dataframe
actual_recs = df_test.groupBy('user_idx').agg(
F.collect_list('track_idx').alias('track_idx'))
#create user predicted items dataframe
user_subset = df_test.select('user_idx').distinct()
pred_recs = als_model.recommendForUserSubset(user_subset, 500)
pred_recs = pred_recs.select(
'user_idx',
F.col('recommendations.track_idx').alias('track_idx'))
#create user item RDD & join on users
perUserItemsRDD = pred_recs\
.join(actual_recs, on='user_idx').rdd\
.map(lambda row: (row[1], row[2]))
rankingMetrics = RankingMetrics(perUserItemsRDD)
#print results to the console
print("Ranking Metrics MAP: ", rankingMetrics.meanAveragePrecision)
