[MINOR][ML][PYSPARK] ALS example cleanup

Cleans up ALS examples by removing unnecessary casts to double for `rating` and `prediction` columns, since `RegressionEvaluator` now supports `Double` & `Float` input types.

## How was this patch tested?

Manual compile and run with `run-example ml.ALSExample` and `spark-submit examples/src/main/python/ml/als_example.py`.

Author: Nick Pentreath <nickp@za.ibm.com>

Closes #12892 from MLnick/als-examples-cleanup.

1 files changed, 3 insertions, 6 deletions

diff --git a/examples/src/main/python/ml/als_example.py b/examples/src/main/python/ml/als_example.py
index ff0829b0dd..1a979ff5b5 100644
--- a/examples/src/main/python/ml/als_example.py
+++ b/examples/src/main/python/ml/als_example.py
@@ -48,12 +48,9 @@ if __name__ == "__main__":
     model = als.fit(training)
 
     # Evaluate the model by computing the RMSE on the test data
-    rawPredictions = model.transform(test)
-    predictions = rawPredictions\
-        .withColumn("rating", rawPredictions.rating.cast("double"))\
-        .withColumn("prediction", rawPredictions.prediction.cast("double"))
-    evaluator =\
-        RegressionEvaluator(metricName="rmse", labelCol="rating", predictionCol="prediction")
+    predictions = model.transform(test)
+    evaluator = RegressionEvaluator(metricName="rmse", labelCol="rating",
+                                    predictionCol="prediction")
     rmse = evaluator.evaluate(predictions)
     print("Root-mean-square error = " + str(rmse))
     # $example off$