[jira] [Commented] (SPARK-13568) Create feature transformer to impute missing values
[ https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15186714#comment-15186714 ] Apache Spark commented on SPARK-13568: -- User 'hhbyyh' has created a pull request for this issue: https://github.com/apache/spark/pull/11601 > Create feature transformer to impute missing values > --- > > Key: SPARK-13568 > URL: https://issues.apache.org/jira/browse/SPARK-13568 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Nick Pentreath >Priority: Minor > > It is quite common to encounter missing values in data sets. It would be > useful to implement a {{Transformer}} that can impute missing data points, > similar to e.g. {{Imputer}} in > [scikit-learn|http://scikit-learn.org/dev/modules/preprocessing.html#imputation-of-missing-values]. > Initially, options for imputation could include {{mean}}, {{median}} and > {{most frequent}}, but we could add various other approaches. Where possible > existing DataFrame code can be used (e.g. for approximate quantiles etc). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-13568) Create feature transformer to impute missing values
[ https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15177368#comment-15177368 ] Nick Pentreath commented on SPARK-13568: Ok - the Imputer will need to compute column stats ignoring NaNs, so SPARK-13639 should add that (whether as default behaviour, or an optional argument) > Create feature transformer to impute missing values > --- > > Key: SPARK-13568 > URL: https://issues.apache.org/jira/browse/SPARK-13568 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Nick Pentreath >Priority: Minor > > It is quite common to encounter missing values in data sets. It would be > useful to implement a {{Transformer}} that can impute missing data points, > similar to e.g. {{Imputer}} in > [scikit-learn|http://scikit-learn.org/dev/modules/preprocessing.html#imputation-of-missing-values]. > Initially, options for imputation could include {{mean}}, {{median}} and > {{most frequent}}, but we could add various other approaches. Where possible > existing DataFrame code can be used (e.g. for approximate quantiles etc). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-13568) Create feature transformer to impute missing values
[ https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15172423#comment-15172423 ] yuhao yang commented on SPARK-13568: Yes, I'm working on support numeric values too. And I agree about the imputation for vector should check the elements in the vector. I intends to support the 3 use cases you mentioned. I'll send a PR today or tomorrow after some refine and performance benchmark. Thanks > Create feature transformer to impute missing values > --- > > Key: SPARK-13568 > URL: https://issues.apache.org/jira/browse/SPARK-13568 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Nick Pentreath >Priority: Minor > > It is quite common to encounter missing values in data sets. It would be > useful to implement a {{Transformer}} that can impute missing data points, > similar to e.g. {{Imputer}} in > [scikit-learn|http://scikit-learn.org/dev/modules/preprocessing.html#imputation-of-missing-values]. > Initially, options for imputation could include {{mean}}, {{median}} and > {{most frequent}}, but we could add various other approaches. Where possible > existing DataFrame code can be used (e.g. for approximate quantiles etc). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-13568) Create feature transformer to impute missing values
[ https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15172328#comment-15172328 ] Nick Pentreath commented on SPARK-13568: Sure, go ahead. However, taking a quick look at your branch, I think the approach needs a bit of discussion. I think the Imputer should handle numeric and/or vector columns. If a vector column, the idea is not to impute an entire vector when it is null, but rather the missing (null / NaN) values that may be present in each vector. I guess if a vector column itself has missing values (i.e. entire vector is null), then the result would look something like what you have done. I tend to think that usage within a pipeline is more likely to be imputing missing values from a set of numeric columns, before applying further transformations into feature vectors. However, we can potentially support all three use cases. > Create feature transformer to impute missing values > --- > > Key: SPARK-13568 > URL: https://issues.apache.org/jira/browse/SPARK-13568 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Nick Pentreath >Priority: Minor > > It is quite common to encounter missing values in data sets. It would be > useful to implement a {{Transformer}} that can impute missing data points, > similar to e.g. {{Imputer}} in > [scikit-learn|http://scikit-learn.org/dev/modules/preprocessing.html#imputation-of-missing-values]. > Initially, options for imputation could include {{mean}}, {{median}} and > {{most frequent}}, but we could add various other approaches. Where possible > existing DataFrame code can be used (e.g. for approximate quantiles etc). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-13568) Create feature transformer to impute missing values
[ https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15172216#comment-15172216 ] yuhao yang commented on SPARK-13568: Hi Nick, can I work on this since I kind of already have... I got an implementation at https://github.com/hhbyyh/spark/blob/imputer/mllib/src/main/scala/org/apache/spark/ml/feature/Imputer.scala > Create feature transformer to impute missing values > --- > > Key: SPARK-13568 > URL: https://issues.apache.org/jira/browse/SPARK-13568 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Nick Pentreath >Priority: Minor > > It is quite common to encounter missing values in data sets. It would be > useful to implement a {{Transformer}} that can impute missing data points, > similar to e.g. {{Imputer}} in > [scikit-learn|http://scikit-learn.org/dev/modules/preprocessing.html#imputation-of-missing-values]. > Initially, options for imputation could include {{mean}}, {{median}} and > {{most frequent}}, but we could add various other approaches. Where possible > existing DataFrame code can be used (e.g. for approximate quantiles etc). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org