[jira] [Comment Edited] (SPARK-17025) Cannot persist PySpark ML Pipeline model that includes custom Transformer
[
https://issues.apache.org/jira/browse/SPARK-17025?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16855948#comment-16855948
]
John Bauer edited comment on SPARK-17025 at 6/4/19 11:12 PM:
-
[~Hadar] [~yug95] [~ralucamaria.b...@gmail.com] I wrote a minimal example of a
PySpark estimator/model pair which can be saved and loaded at
[ImputeNormal|https://github.com/JohnHBauer/ImputeNormal]
It imputes missing values from a normal distribution, using mean and standard
deviation parameters estimated from the data, so it might be useful for that
too.
Let me know if it helps you,
was (Author: johnhbauer):
[~Hadar] [~yug95] [~ralucamaria.b...@gmail.com] I wrote a minimal example of a
PySpark estimator/model pair which can be saved and loaded at
[ImputeNormal|https://github.com/JohnHBauer/ImputeNormal] which imputes missing
values from a normal distribution using parameters estimated from the data.
Let me know if it helps you,
> Cannot persist PySpark ML Pipeline model that includes custom Transformer
> -
>
> Key: SPARK-17025
> URL: https://issues.apache.org/jira/browse/SPARK-17025
> Project: Spark
> Issue Type: New Feature
> Components: ML, PySpark
>Affects Versions: 2.0.0
>Reporter: Nicholas Chammas
>Assignee: Ajay Saini
>Priority: Minor
> Fix For: 2.3.0
>
>
> Following the example in [this Databricks blog
> post|https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html]
> under "Python tuning", I'm trying to save an ML Pipeline model.
> This pipeline, however, includes a custom transformer. When I try to save the
> model, the operation fails because the custom transformer doesn't have a
> {{_to_java}} attribute.
> {code}
> Traceback (most recent call last):
> File ".../file.py", line 56, in
> model.bestModel.save('model')
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 222, in save
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 217, in write
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/util.py",
> line 93, in __init__
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 254, in _to_java
> AttributeError: 'PeoplePairFeaturizer' object has no attribute '_to_java'
> {code}
> Looking at the source code for
> [ml/base.py|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/base.py],
> I see that not even the base Transformer class has such an attribute.
> I'm assuming this is missing functionality that is intended to be patched up
> (i.e. [like
> this|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/classification.py#L1421-L1433]).
> I'm not sure if there is an existing JIRA for this (my searches didn't turn
> up clear results).
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[jira] [Comment Edited] (SPARK-17025) Cannot persist PySpark ML Pipeline model that includes custom Transformer
[
https://issues.apache.org/jira/browse/SPARK-17025?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16855948#comment-16855948
]
John Bauer edited comment on SPARK-17025 at 6/4/19 11:10 PM:
-
[~Hadar] [~yug95] [~ralucamaria.b...@gmail.com] I wrote a minimal example of a
PySpark estimator/model pair which can be saved and loaded at
[ImputeNormal|https://github.com/JohnHBauer/ImputeNormal] which imputes missing
values from a normal distribution using parameters estimated from the data.
Let me know if it helps you,
was (Author: johnhbauer):
[~Hadar] [~yug95] I wrote a minimal example of a PySpark estimator/model pair
which can be saved and loaded at
[ImputeNormal|https://github.com/JohnHBauer/ImputeNormal] which imputes missing
values from a normal distribution using parameters estimated from the data.
Let me know if it helps you,
> Cannot persist PySpark ML Pipeline model that includes custom Transformer
> -
>
> Key: SPARK-17025
> URL: https://issues.apache.org/jira/browse/SPARK-17025
> Project: Spark
> Issue Type: New Feature
> Components: ML, PySpark
>Affects Versions: 2.0.0
>Reporter: Nicholas Chammas
>Assignee: Ajay Saini
>Priority: Minor
> Fix For: 2.3.0
>
>
> Following the example in [this Databricks blog
> post|https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html]
> under "Python tuning", I'm trying to save an ML Pipeline model.
> This pipeline, however, includes a custom transformer. When I try to save the
> model, the operation fails because the custom transformer doesn't have a
> {{_to_java}} attribute.
> {code}
> Traceback (most recent call last):
> File ".../file.py", line 56, in
> model.bestModel.save('model')
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 222, in save
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 217, in write
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/util.py",
> line 93, in __init__
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 254, in _to_java
> AttributeError: 'PeoplePairFeaturizer' object has no attribute '_to_java'
> {code}
> Looking at the source code for
> [ml/base.py|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/base.py],
> I see that not even the base Transformer class has such an attribute.
> I'm assuming this is missing functionality that is intended to be patched up
> (i.e. [like
> this|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/classification.py#L1421-L1433]).
> I'm not sure if there is an existing JIRA for this (my searches didn't turn
> up clear results).
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[jira] [Comment Edited] (SPARK-17025) Cannot persist PySpark ML Pipeline model that includes custom Transformer
[
https://issues.apache.org/jira/browse/SPARK-17025?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16101984#comment-16101984
]
Ajay Saini edited comment on SPARK-17025 at 7/26/17 5:40 PM:
-
I'm currently working on a solution to this that involves building custom
persistence support for Python-only pipeline stages. As of now, you cannot
persist a pipeline stage in Python unless there is a Java implementation of
that stage. The framework I'm working on will make it much easier to implement
Python-only persistence of custom stages so that they don't need to rely on
Java.
This fix will be sent out later this week.
was (Author: ajaysaini):
I'm currently working on a solution to this that involves building custom
persistence support for Python-only pipeline stages. As of now, you cannot
persist a pipeline stage in Python unless there is a Java implementation of
that stage. The framework I'm working on will make it much easier to implement
Python-only persistence of custom stages so that they don't need to rely on
Java.
> Cannot persist PySpark ML Pipeline model that includes custom Transformer
> -
>
> Key: SPARK-17025
> URL: https://issues.apache.org/jira/browse/SPARK-17025
> Project: Spark
> Issue Type: New Feature
> Components: ML, PySpark
>Affects Versions: 2.0.0
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Following the example in [this Databricks blog
> post|https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html]
> under "Python tuning", I'm trying to save an ML Pipeline model.
> This pipeline, however, includes a custom transformer. When I try to save the
> model, the operation fails because the custom transformer doesn't have a
> {{_to_java}} attribute.
> {code}
> Traceback (most recent call last):
> File ".../file.py", line 56, in
> model.bestModel.save('model')
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 222, in save
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 217, in write
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/util.py",
> line 93, in __init__
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 254, in _to_java
> AttributeError: 'PeoplePairFeaturizer' object has no attribute '_to_java'
> {code}
> Looking at the source code for
> [ml/base.py|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/base.py],
> I see that not even the base Transformer class has such an attribute.
> I'm assuming this is missing functionality that is intended to be patched up
> (i.e. [like
> this|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/classification.py#L1421-L1433]).
> I'm not sure if there is an existing JIRA for this (my searches didn't turn
> up clear results).
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[jira] [Comment Edited] (SPARK-17025) Cannot persist PySpark ML Pipeline model that includes custom Transformer
[
https://issues.apache.org/jira/browse/SPARK-17025?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15417788#comment-15417788
]
Nicholas Chammas edited comment on SPARK-17025 at 8/11/16 7:33 PM:
---
cc [~josephkb], [~mengxr]
I guess a first step would be to add a {{_to_java}} method to the base
{{Transformer}} class that simply raises {{NotImplementedError}}.
Ultimately though, is there a way to have the base class handle this work
automatically, or do custom transformers need to each implement their own
{{_to_java}} method?
was (Author: nchammas):
cc [~josephkb], [~mengxr]
I guess a first step be to add a {{_to_java}} method to the base Transformer
class that simply raises {{NotImplementedError}}.
Is there a way to have the base class handle this work automatically, or do
custom transformers need to each implement their own {{_to_java}} method?
> Cannot persist PySpark ML Pipeline model that includes custom Transformer
> -
>
> Key: SPARK-17025
> URL: https://issues.apache.org/jira/browse/SPARK-17025
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
>Affects Versions: 2.0.0
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Following the example in [this Databricks blog
> post|https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html]
> under "Python tuning", I'm trying to save an ML Pipeline model.
> This pipeline, however, includes a custom transformer. When I try to save the
> model, the operation fails because the custom transformer doesn't have a
> {{_to_java}} attribute.
> {code}
> Traceback (most recent call last):
> File ".../file.py", line 56, in
> model.bestModel.save('model')
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 222, in save
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 217, in write
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/util.py",
> line 93, in __init__
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 254, in _to_java
> AttributeError: 'PeoplePairFeaturizer' object has no attribute '_to_java'
> {code}
> Looking at the source code for
> [ml/base.py|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/base.py],
> I see that not even the base Transformer class has such an attribute.
> I'm assuming this is missing functionality that is intended to be patched up
> (i.e. [like
> this|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/classification.py#L1421-L1433]).
> I'm not sure if there is an existing JIRA for this (my searches didn't turn
> up clear results).
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[jira] [Comment Edited] (SPARK-17025) Cannot persist PySpark ML Pipeline model that includes custom Transformer
[
https://issues.apache.org/jira/browse/SPARK-17025?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15417788#comment-15417788
]
Nicholas Chammas edited comment on SPARK-17025 at 8/11/16 7:27 PM:
---
cc [~josephkb], [~mengxr]
I guess a first step be to add a {{_to_java}} method to the base Transformer
class that simply raises {{NotImplementedError}}.
Is there a way to have the base class handle this work automatically, or do
custom transformers need to each implement their own {{_to_java}} method?
was (Author: nchammas):
cc [~josephkb] [~mengxr]
> Cannot persist PySpark ML Pipeline model that includes custom Transformer
> -
>
> Key: SPARK-17025
> URL: https://issues.apache.org/jira/browse/SPARK-17025
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
>Affects Versions: 2.0.0
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Following the example in [this Databricks blog
> post|https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html]
> under "Python tuning", I'm trying to save an ML Pipeline model.
> This pipeline, however, includes a custom transformer. When I try to save the
> model, the operation fails because the custom transformer doesn't have a
> {{_to_java}} attribute.
> {code}
> Traceback (most recent call last):
> File ".../file.py", line 56, in
> model.bestModel.save('model')
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 222, in save
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 217, in write
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/util.py",
> line 93, in __init__
> File
> "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
> line 254, in _to_java
> AttributeError: 'PeoplePairFeaturizer' object has no attribute '_to_java'
> {code}
> Looking at the source code for
> [ml/base.py|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/base.py],
> I see that not even the base Transformer class has such an attribute.
> I'm assuming this is missing functionality that is intended to be patched up
> (i.e. [like
> this|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/classification.py#L1421-L1433]).
> I'm not sure if there is an existing JIRA for this (my searches didn't turn
> up clear results).
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