@ emr als example output
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.recommendation.ALS
defined class Rating
parseRating: (str: String)Rating
ratings: org.apache.spark.sql.DataFrame = [userId: int, movieId: int ... 2 more fields]
training: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [userId: int, movieId: int ... 2 more fields]
test: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [userId: int, movieId: int ... 2 more fields]
als: org.apache.spark.ml.recommendation.ALS = als_e2f5f7124ee5
model: org.apache.spark.ml.recommendation.ALSModel = als_e2f5f7124ee5
res15: model.type = als_e2f5f7124ee5
predictions: org.apache.spark.sql.DataFrame = [userId: int, movieId: int ... 3 more fields]
evaluator: org.apache.spark.ml.evaluation.RegressionEvaluator = regEval_f81139bbd9fa
rmse: Double = 1.7279256519713435
Root-mean-square error = 1.7279256519713435
userRecs: org.apache.spark.sql.DataFrame = [userId: int, recommendations: array<struct<movieId:int,rating:float>>]
movieRecs: org.apache.spark.sql.DataFrame = [movieId: int, recommendations: array<struct<userId:int,rating:float>>]
users: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [userId: int]
userSubsetRecs: org.apache.spark.sql.DataFrame = [userId: int, recommendations: array<struct<movieId:int,rating:float>>]
movies: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [movieId: int]
movieSubSetRecs: org.apache.spark.sql.DataFrame = [movieId: int, recommendations: array<struct<userId:int,rating:float>>]
+------+--------------------+
|userId| recommendations|
+------+--------------------+
| 28|[[92, 5.126849], ...|
| 26|[[94, 5.2986035],...|
| 27|[[38, 3.8033946],...|
| 12|[[69, 5.6789775],...|
| 22|[[53, 5.566115], ...|
| 1|[[87, 3.8826718],...|
| 13|[[93, 3.3000138],...|
| 6|[[76, 4.760107], ...|
| 16|[[90, 4.942918], ...|
| 3|[[51, 4.708821], ...|
| 20|[[75, 3.8126705],...|
| 5|[[55, 4.8531346],...|
| 19|[[32, 4.0812955],...|
| 15|[[46, 4.5400715],...|
| 17|[[46, 5.069146], ...|
| 9|[[49, 4.9833574],...|
| 4|[[53, 5.5690856],...|
| 8|[[29, 5.460162], ...|
| 23|[[92, 5.6384144],...|
| 7|[[85, 3.819065], ...|
+------+--------------------+
only showing top 20 rows
+-------+--------------------+
|movieId| recommendations|
+-------+--------------------+
| 31|[[12, 3.8914654],...|
| 85|[[16, 4.8915253],...|
| 65|[[23, 5.0863047],...|
| 53|[[4, 5.5690856], ...|
| 78|[[4, 1.4995669], ...|
| 34|[[2, 3.7822325], ...|
| 81|[[18, 2.8832386],...|
| 28|[[18, 4.6643767],...|
| 76|[[14, 4.893073], ...|
| 26|[[4, 3.1977046], ...|
| 27|[[11, 5.21796], [...|
| 44|[[18, 4.006497], ...|
| 12|[[28, 4.7016525],...|
| 91|[[9, 3.9811857], ...|
| 22|[[26, 4.7620754],...|
| 93|[[8, 4.819683], [...|
| 47|[[23, 4.328814], ...|
| 1|[[18, 4.889136], ...|
| 52|[[24, 5.0816426],...|
| 13|[[9, 4.249684], [...|
+-------+--------------------+
only showing top 20 rows
+------+--------------------+
|userId| recommendations|
+------+--------------------+
| 26|[[94, 5.2986035],...|
| 27|[[38, 3.8033946],...|
| 15|[[46, 4.5400715],...|
+------+--------------------+
+-------+--------------------+
|movieId| recommendations|
+-------+--------------------+
| 53|[[4, 5.5690856], ...|
| 22|[[26, 4.7620754],...|
| 3|[[14, 2.8827474],...|
+-------+--------------------+