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https://issues.apache.org/jira/browse/SPARK-10384?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-10384:
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Description:
It would be nice to define univariate statistics as UDAFs. This JIRA discusses
general implementation and tracks the process of subtasks. Univariate
statistics include:
continuous: min, max, range, variance, stddev, median, quantiles, skewness, and
kurtosis
categorical: number of categories, mode
If we define them as UDAFs, it would be quite flexible to use them with
DataFrames, e.g.,
{code}
df.groupBy("key").agg(min("x"), min("y"), variance("x"), skewness("x"))
{code}
Note that some univariate statistics depend on others, e.g., variance might
depend on mean and count. It would be nice if SQL can optimize the sequence to
avoid duplicate computation.
Univariate statistics for continuous variables:
* ~~min~~
* max
* range
* sample variance
* population variance
* sample standard deviation
* population standard deviation
* skewness
* kurtosis
* approximate median
* approximate quantiles
Univariate statistics for categorical variables:
* mode: https://en.wikipedia.org/wiki/Mode_(statistics)
* number of categories
was:
It would be nice to define univariate statistics as UDAFs. This JIRA discusses
general implementation and tracks the process of subtasks. Univariate
statistics include:
continuous: min, max, range, variance, stddev, median, quantiles, skewness, and
kurtosis
categorical: number of categories, mode
If we define them as UDAFs, it would be quite flexible to use them with
DataFrames, e.g.,
{code}
df.groupBy("key").agg(min("x"), min("y"), variance("x"), skewness("x"))
{code}
Note that some univariate statistics depend on others, e.g., variance might
depend on mean and count. It would be nice if SQL can optimize the sequence to
avoid duplicate computation.
Univariate statistics for continuous variables:
* min
* max
* range
* sample variance
* population variance
* sample standard deviation
* population standard deviation
* skewness
* kurtosis
* approximate median
* approximate quantiles
Univariate statistics for categorical variables:
* mode: https://en.wikipedia.org/wiki/Mode_(statistics)
* number of categories
> Univariate statistics as UDAFs
> ------------------------------
>
> Key: SPARK-10384
> URL: https://issues.apache.org/jira/browse/SPARK-10384
> Project: Spark
> Issue Type: Umbrella
> Components: ML, SQL
> Reporter: Xiangrui Meng
> Assignee: Burak Yavuz
>
> It would be nice to define univariate statistics as UDAFs. This JIRA
> discusses general implementation and tracks the process of subtasks.
> Univariate statistics include:
> continuous: min, max, range, variance, stddev, median, quantiles, skewness,
> and kurtosis
> categorical: number of categories, mode
> If we define them as UDAFs, it would be quite flexible to use them with
> DataFrames, e.g.,
> {code}
> df.groupBy("key").agg(min("x"), min("y"), variance("x"), skewness("x"))
> {code}
> Note that some univariate statistics depend on others, e.g., variance might
> depend on mean and count. It would be nice if SQL can optimize the sequence
> to avoid duplicate computation.
> Univariate statistics for continuous variables:
> * ~~min~~
> * max
> * range
> * sample variance
> * population variance
> * sample standard deviation
> * population standard deviation
> * skewness
> * kurtosis
> * approximate median
> * approximate quantiles
> Univariate statistics for categorical variables:
> * mode: https://en.wikipedia.org/wiki/Mode_(statistics)
> * number of categories
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