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Posted to reviews@spark.apache.org by VinceShieh <gi...@git.apache.org> on 2017/02/14 04:45:19 UTC
[GitHub] spark pull request #16922: [SPARK-19590][pyspark][ML] update the document fo...
GitHub user VinceShieh opened a pull request:
https://github.com/apache/spark/pull/16922
[SPARK-19590][pyspark][ML] update the document for QuantileDiscretize\u2026
## 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 <vi...@intel.com>
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 <vi...@intel.com>
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 <vi...@intel.com>
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[GitHub] spark issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
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[GitHub] spark issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
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[GitHub] spark pull request #16922: [SPARK-19590][pyspark][ML] Update the document fo...
Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:
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[GitHub] spark issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
Posted by AmplabJenkins <gi...@git.apache.org>.
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[GitHub] spark issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/16922
**[Test build #72851 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/72851/testReport)** for PR 16922 at commit [`9ce7cb8`](https://github.com/apache/spark/commit/9ce7cb867c8e1986bc75500fcbc4c5f0e4103f06).
* This patch **fails Python style tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
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[GitHub] spark issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
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[GitHub] spark pull request #16922: [SPARK-19590][pyspark][ML] Update the document fo...
Posted by holdenk <gi...@git.apache.org>.
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...
Posted by VinceShieh <gi...@git.apache.org>.
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 issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
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**[Test build #72908 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/72908/testReport)** for PR 16922 at commit [`56e708f`](https://github.com/apache/spark/commit/56e708f6b15d51578f7579dd7672767705ccf19a).
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[GitHub] spark issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
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* This patch **fails Python style tests**.
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* This patch passes all tests.
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[GitHub] spark issue #16922: [SPARK-19590][pyspark][ML] Update the document for Quant...
Posted by holdenk <gi...@git.apache.org>.
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/16922
Thanks for doing the follow up Python work, merged to master :)
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