[jira] [Updated] (SPARK-6345) Model update propagation during prediction in Streaming Regression
[
https://issues.apache.org/jira/browse/SPARK-6345?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiangrui Meng updated SPARK-6345:
-
Assignee: Jeremy Freeman
Model update propagation during prediction in Streaming Regression
--
Key: SPARK-6345
URL: https://issues.apache.org/jira/browse/SPARK-6345
Project: Spark
Issue Type: Bug
Components: MLlib, Streaming
Reporter: Jeremy Freeman
Assignee: Jeremy Freeman
During streaming regression analyses (Streaming Linear Regression and
Streaming Logistic Regression), model updates based on training data are not
being reflected in subsequent calls to predictOn or predictOnValues, despite
updates themselves occurring successfully. It may be due to recent changes to
model declaration, and I have a working fix prepared to be submitted ASAP
(alongside expanded test coverage).
A temporary workaround is to retrieve and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
...print or other side effects...
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
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[jira] [Updated] (SPARK-6345) Model update propagation during prediction in Streaming Regression
[
https://issues.apache.org/jira/browse/SPARK-6345?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jeremy Freeman updated SPARK-6345:
--
Description:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve the and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
was:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve the updated model within a foreachRDD, as in:
{code}
model.trainOn(trainingData)
testData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
Model update propagation during prediction in Streaming Regression
--
Key: SPARK-6345
URL: https://issues.apache.org/jira/browse/SPARK-6345
Project: Spark
Issue Type: Bug
Components: MLlib, Streaming
Reporter: Jeremy Freeman
During streaming regression analyses (Streaming Linear Regression and
Streaming Logistic Regression), model updates based on training data are not
being reflected in subsequent calls to predictOn or predictOnValues, despite
updates themselves occurring successfully. It may be due to recent changes to
model declaration, and I have a working fix prepared to be submitted ASAP
(alongside expanded test coverage).
A temporary workaround is retrieve the and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
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[jira] [Updated] (SPARK-6345) Model update propagation during prediction in Streaming Regression
[
https://issues.apache.org/jira/browse/SPARK-6345?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jeremy Freeman updated SPARK-6345:
--
Description:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
...print or other side effects...
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
was:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve the and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
...print or other side effects...
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
Model update propagation during prediction in Streaming Regression
--
Key: SPARK-6345
URL: https://issues.apache.org/jira/browse/SPARK-6345
Project: Spark
Issue Type: Bug
Components: MLlib, Streaming
Reporter: Jeremy Freeman
During streaming regression analyses (Streaming Linear Regression and
Streaming Logistic Regression), model updates based on training data are not
being reflected in subsequent calls to predictOn or predictOnValues, despite
updates themselves occurring successfully. It may be due to recent changes to
model declaration, and I have a working fix prepared to be submitted ASAP
(alongside expanded test coverage).
A temporary workaround is retrieve and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
...print or other side effects...
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
--
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[jira] [Updated] (SPARK-6345) Model update propagation during prediction in Streaming Regression
[
https://issues.apache.org/jira/browse/SPARK-6345?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jeremy Freeman updated SPARK-6345:
--
Description:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is to retrieve and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
...print or other side effects...
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
was:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
...print or other side effects...
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
Model update propagation during prediction in Streaming Regression
--
Key: SPARK-6345
URL: https://issues.apache.org/jira/browse/SPARK-6345
Project: Spark
Issue Type: Bug
Components: MLlib, Streaming
Reporter: Jeremy Freeman
During streaming regression analyses (Streaming Linear Regression and
Streaming Logistic Regression), model updates based on training data are not
being reflected in subsequent calls to predictOn or predictOnValues, despite
updates themselves occurring successfully. It may be due to recent changes to
model declaration, and I have a working fix prepared to be submitted ASAP
(alongside expanded test coverage).
A temporary workaround is to retrieve and use the updated model within a
foreachRDD, as in:
{code}
model.trainOn(trainingData)
testingData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
...print or other side effects...
}
{code}
Or within a transform, as in:
{code}
model.trainOn(trainingData)
val predictions = testingData.transform { rdd =
val latest = model.latestModel()
rdd.map(lp = (lp.label, latest.predict(lp.features)))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
--
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[jira] [Updated] (SPARK-6345) Model update propagation during prediction in Streaming Regression
[
https://issues.apache.org/jira/browse/SPARK-6345?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jeremy Freeman updated SPARK-6345:
--
Description:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve the updated model within a foreachRDD, as in:
{code}
model.trainOn(trainingData)
testData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features)
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
was:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve the updated model within a foreachRDD, as in:
{code}
model.predictOn(trainingData)
testData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features)
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
Model update propagation during prediction in Streaming Regression
--
Key: SPARK-6345
URL: https://issues.apache.org/jira/browse/SPARK-6345
Project: Spark
Issue Type: Bug
Components: MLlib, Streaming
Reporter: Jeremy Freeman
During streaming regression analyses (Streaming Linear Regression and
Streaming Logistic Regression), model updates based on training data are not
being reflected in subsequent calls to predictOn or predictOnValues, despite
updates themselves occurring successfully. It may be due to recent changes to
model declaration, and I have a working fix prepared to be submitted ASAP
(alongside expanded test coverage).
A temporary workaround is retrieve the updated model within a foreachRDD, as
in:
{code}
model.trainOn(trainingData)
testData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features)
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
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To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
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[jira] [Updated] (SPARK-6345) Model update propagation during prediction in Streaming Regression
[
https://issues.apache.org/jira/browse/SPARK-6345?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jeremy Freeman updated SPARK-6345:
--
Description:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve the updated model within a foreachRDD, as in:
{code}
model.trainOn(trainingData)
testData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
was:
During streaming regression analyses (Streaming Linear Regression and Streaming
Logistic Regression), model updates based on training data are not being
reflected in subsequent calls to predictOn or predictOnValues, despite updates
themselves occurring successfully. It may be due to recent changes to model
declaration, and I have a working fix prepared to be submitted ASAP (alongside
expanded test coverage).
A temporary workaround is retrieve the updated model within a foreachRDD, as in:
{code}
model.trainOn(trainingData)
testData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features)
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
Model update propagation during prediction in Streaming Regression
--
Key: SPARK-6345
URL: https://issues.apache.org/jira/browse/SPARK-6345
Project: Spark
Issue Type: Bug
Components: MLlib, Streaming
Reporter: Jeremy Freeman
During streaming regression analyses (Streaming Linear Regression and
Streaming Logistic Regression), model updates based on training data are not
being reflected in subsequent calls to predictOn or predictOnValues, despite
updates themselves occurring successfully. It may be due to recent changes to
model declaration, and I have a working fix prepared to be submitted ASAP
(alongside expanded test coverage).
A temporary workaround is retrieve the updated model within a foreachRDD, as
in:
{code}
model.trainOn(trainingData)
testData.foreachRDD{ rdd =
val latest = model.latestModel()
val predictions = rdd.map(lp = latest.predict(lp.features))
}
{code}
Note that this does not affect Streaming KMeans, which works as expected for
combinations of training and prediction.
--
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