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https://issues.apache.org/jira/browse/SPARK-38139?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17489521#comment-17489521
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zhengruifeng commented on SPARK-38139:
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I think it is ok to adjust the tol in this case
> ml.recommendation.ALS doctests failures
> ---------------------------------------
>
> Key: SPARK-38139
> URL: https://issues.apache.org/jira/browse/SPARK-38139
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 3.3.0
> Reporter: Maciej Szymkiewicz
> Priority: Major
>
> In my dev setups, ml.recommendation:ALS test consistently converges to value
> lower than expected and fails with:
> {code:python}
> File "/path/to/spark/python/pyspark/ml/recommendation.py", line 322, in
> __main__.ALS
> Failed example:
> predictions[0]
> Expected:
> Row(user=0, item=2, newPrediction=0.69291...)
> Got:
> Row(user=0, item=2, newPrediction=0.6929099559783936)
> {code}
> In can correct for that, but it creates some noise, so if anyone else
> experiences this, we could drop a digit from the results
> {code}
> diff --git a/python/pyspark/ml/recommendation.py
> b/python/pyspark/ml/recommendation.py
> index f0628fb922..b8e2a6097d 100644
> --- a/python/pyspark/ml/recommendation.py
> +++ b/python/pyspark/ml/recommendation.py
> @@ -320,7 +320,7 @@ class ALS(JavaEstimator, _ALSParams, JavaMLWritable,
> JavaMLReadable):
> >>> test = spark.createDataFrame([(0, 2), (1, 0), (2, 0)], ["user",
> "item"])
> >>> predictions = sorted(model.transform(test).collect(), key=lambda r:
> r[0])
> >>> predictions[0]
> - Row(user=0, item=2, newPrediction=0.69291...)
> + Row(user=0, item=2, newPrediction=0.6929...)
> >>> predictions[1]
> Row(user=1, item=0, newPrediction=3.47356...)
> >>> predictions[2]
> {code}
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