[GitHub] spark pull request #16922: [SPARK-19590][pyspark][ML] Update the document fo...

[asfgit]

Github user asfgit closed the pull request at:

https://github.com/apache/spark/pull/16922


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[GitHub] spark pull request #16922: [SPARK-19590][pyspark][ML] Update the document fo...

[VinceShieh]

Github user VinceShieh commented on a diff in the pull request:

https://github.com/apache/spark/pull/16922#discussion_r101183452
  
--- Diff: python/pyspark/ml/feature.py ---
@@ -1178,7 +1178,17 @@ class QuantileDiscretizer(JavaEstimator, 
HasInputCol, HasOutputCol, JavaMLReadab
 
 `QuantileDiscretizer` takes a column with continuous features and 
outputs a column with binned
 categorical features. The number of bins can be set using the 
:py:attr:`numBuckets` parameter.
-The bin ranges are chosen using an approximate algorithm (see the 
documentation for
+It is possible that the number of buckets used will be less than this 
value, for example, if
+there are too few distinct values of the input to create enough 
distinct quantiles.
+
+NaN handling: Note also that
+QuantileDiscretizer will raise an error when it finds NaN values in 
the dataset, but the user
+can also choose to either keep or remove NaN values within the dataset 
by setting
+`handleInvalid`. If the user chooses to keep NaN values, they will be 
handled specially and
--- End diff --

yeah, sure. Thanks for pointing that out... ;)


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[GitHub] spark pull request #16922: [SPARK-19590][pyspark][ML] Update the document fo...

[holdenk]

Github user holdenk commented on a diff in the pull request:

https://github.com/apache/spark/pull/16922#discussion_r101097983
  
--- Diff: python/pyspark/ml/feature.py ---
@@ -1178,7 +1178,17 @@ class QuantileDiscretizer(JavaEstimator, 
HasInputCol, HasOutputCol, JavaMLReadab
 
 `QuantileDiscretizer` takes a column with continuous features and 
outputs a column with binned
 categorical features. The number of bins can be set using the 
:py:attr:`numBuckets` parameter.
-The bin ranges are chosen using an approximate algorithm (see the 
documentation for
+It is possible that the number of buckets used will be less than this 
value, for example, if
+there are too few distinct values of the input to create enough 
distinct quantiles.
+
+NaN handling: Note also that
+QuantileDiscretizer will raise an error when it finds NaN values in 
the dataset, but the user
+can also choose to either keep or remove NaN values within the dataset 
by setting
+`handleInvalid`. If the user chooses to keep NaN values, they will be 
handled specially and
--- End diff --

could we maybe link this with a py attr like we did with numBuckets?


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[GitHub] spark pull request #16922: [SPARK-19590][pyspark][ML] update the document fo...

[VinceShieh]

GitHub user VinceShieh opened a pull request:

https://github.com/apache/spark/pull/16922

[SPARK-19590][pyspark][ML] update the document for QuantileDiscretize…

## What changes were proposed in this pull request?
This PR is to document the changes on QuantileDiscretizer in pyspark for PR:
https://github.com/apache/spark/pull/15428

## How was this patch tested?
No test needed

Signed-off-by: VinceShieh 

You can merge this pull request into a Git repository by running:

$ git pull https://github.com/VinceShieh/spark spark-19590

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/16922.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #16922


commit 25bdc0f09f763b993ff78cb6f86a4a567eae4872
Author: VinceShieh 
Date:   2017-02-14T04:37:43Z

[SPARK-19590][pyspark][ML] update the document for QuantileDiscretizer in 
pyspark

This PR is to document the change on QuantileDiscretizer in pyspark for PR:
https://github.com/apache/spark/pull/15428

Signed-off-by: VinceShieh 




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