[jira] [Commented] (SPARK-12372) Document limitations of MLlib local linear algebra
[ https://issues.apache.org/jira/browse/SPARK-12372?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15064476#comment-15064476 ] Christos Iraklis Tsatsoulis commented on SPARK-12372: - You are very welcome > Document limitations of MLlib local linear algebra > -- > > Key: SPARK-12372 > URL: https://issues.apache.org/jira/browse/SPARK-12372 > Project: Spark > Issue Type: Documentation > Components: Documentation, MLlib >Affects Versions: 1.5.2 >Reporter: Christos Iraklis Tsatsoulis > > This JIRA is now for documenting limitations of MLlib's local linear algebra > types. Basically, we should make it clear in the user guide that they > provide simple functionality but are not a full-fledged local linear library. > We should also recommend libraries for users to use in the meantime: > probably Breeze for Scala (and Java?) and numpy/scipy for Python. > *Original JIRA title*: Unary operator "-" fails for MLlib vectors > *Original JIRA text, as an example of the need for better docs*: > Consider the following snippet in pyspark 1.5.2: > {code:none} > >>> from pyspark.mllib.linalg import Vectors > >>> x = Vectors.dense([0.0, 1.0, 0.0, 7.0, 0.0]) > >>> x > DenseVector([0.0, 1.0, 0.0, 7.0, 0.0]) > >>> -x > Traceback (most recent call last): > File "", line 1, in > TypeError: func() takes exactly 2 arguments (1 given) > >>> y = Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]) > >>> y > DenseVector([2.0, 0.0, 3.0, 4.0, 5.0]) > >>> x-y > DenseVector([-2.0, 1.0, -3.0, 3.0, -5.0]) > >>> -y+x > Traceback (most recent call last): > File "", line 1, in > TypeError: func() takes exactly 2 arguments (1 given) > >>> -1*x > DenseVector([-0.0, -1.0, -0.0, -7.0, -0.0]) > {code} > Clearly, the unary operator {{-}} (minus) for vectors fails, giving errors > for expressions like {{-x}} and {{-y+x}}, despite the fact that {{x-y}} > behaves as expected. > The last operation, {{-1*x}}, although mathematically "correct", includes > minus signs for the zero entries, which again is normally not expected. -- 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-12372) Unary operator "-" fails for MLlib vectors
[ https://issues.apache.org/jira/browse/SPARK-12372?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15060858#comment-15060858 ] Christos Iraklis Tsatsoulis commented on SPARK-12372: - If this is the case, then a warning/clarification in the documentation wouldn't hurt - Spark users are not supposed to be aware of the internal "ongoing discussions" between Spark developers (BTW, any relevant link would be very welcome - I could not find any mention in MLlib & Breeze docs, neither in the recent preprint papers on linalg & MLlib). All in all, I suggest you re-open the issue with a different type (it's not a bug, as you say), and the required resolution being a notification in the relevant docs ("don't try this..., because..."). > Unary operator "-" fails for MLlib vectors > -- > > Key: SPARK-12372 > URL: https://issues.apache.org/jira/browse/SPARK-12372 > Project: Spark > Issue Type: Bug > Components: MLlib, PySpark >Affects Versions: 1.5.2 >Reporter: Christos Iraklis Tsatsoulis > > Consider the following snippet in pyspark 1.5.2: > {code:none} > >>> from pyspark.mllib.linalg import Vectors > >>> x = Vectors.dense([0.0, 1.0, 0.0, 7.0, 0.0]) > >>> x > DenseVector([0.0, 1.0, 0.0, 7.0, 0.0]) > >>> -x > Traceback (most recent call last): > File "", line 1, in > TypeError: func() takes exactly 2 arguments (1 given) > >>> y = Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]) > >>> y > DenseVector([2.0, 0.0, 3.0, 4.0, 5.0]) > >>> x-y > DenseVector([-2.0, 1.0, -3.0, 3.0, -5.0]) > >>> -y+x > Traceback (most recent call last): > File "", line 1, in > TypeError: func() takes exactly 2 arguments (1 given) > >>> -1*x > DenseVector([-0.0, -1.0, -0.0, -7.0, -0.0]) > {code} > Clearly, the unary operator {{-}} (minus) for vectors fails, giving errors > for expressions like {{-x}} and {{-y+x}}, despite the fact that {{x-y}} > behaves as expected. > The last operation, {{-1*x}}, although mathematically "correct", includes > minus signs for the zero entries, which again is normally not expected. -- 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] [Created] (SPARK-12372) Unary operator "-" fails for MLlib vectors
Christos Iraklis Tsatsoulis created SPARK-12372: --- Summary: Unary operator "-" fails for MLlib vectors Key: SPARK-12372 URL: https://issues.apache.org/jira/browse/SPARK-12372 Project: Spark Issue Type: Bug Components: MLlib, PySpark Affects Versions: 1.5.2 Reporter: Christos Iraklis Tsatsoulis Consider the following snippet in pyspark 1.5.2: {code:none} >>> from pyspark.mllib.linalg import Vectors >>> x = Vectors.dense([0.0, 1.0, 0.0, 7.0, 0.0]) >>> x DenseVector([0.0, 1.0, 0.0, 7.0, 0.0]) >>> -x Traceback (most recent call last): File "", line 1, in TypeError: func() takes exactly 2 arguments (1 given) >>> y = Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]) >>> y DenseVector([2.0, 0.0, 3.0, 4.0, 5.0]) >>> x-y DenseVector([-2.0, 1.0, -3.0, 3.0, -5.0]) >>> -y+x Traceback (most recent call last): File "", line 1, in TypeError: func() takes exactly 2 arguments (1 given) >>> -1*x DenseVector([-0.0, -1.0, -0.0, -7.0, -0.0]) {code} Clearly, the unary operator {{-}} (minus) for vectors fails, giving errors for expressions like {{-x}} and {{-y+x}}, despite the fact that {{x-y}} behaves as expected. The last operation, {{-1*x}}, although mathematically "correct", includes minus signs for the zero entries, which again is normally not expected. -- 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] [Comment Edited] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14996533#comment-14996533 ] Christos Iraklis Tsatsoulis edited comment on SPARK-11530 at 11/9/15 1:50 PM: -- I edited it to target both; there are PCA.scala scripts for both ML & MLlib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib. was (Author: ctsats): I edited it to target both; there are PCA.scala scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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] [Comment Edited] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14996533#comment-14996533 ] Christos Iraklis Tsatsoulis edited comment on SPARK-11530 at 11/9/15 1:45 PM: -- I edited it to target both; there are PCA.scala scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib was (Author: ctsats): I edited it to target both; there are `PCA.scala` scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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] [Comment Edited] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14996533#comment-14996533 ] Christos Iraklis Tsatsoulis edited comment on SPARK-11530 at 11/9/15 1:40 PM: -- I edited it to target both; there are `PCA.scala` scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib was (Author: ctsats): I edited it to target both; there are PCA.scala scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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] [Comment Edited] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14996533#comment-14996533 ] Christos Iraklis Tsatsoulis edited comment on SPARK-11530 at 11/9/15 1:37 PM: -- I edited it to target both; there are PCA.scala scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib was (Author: ctsats): I edited it to target both; there are ``PCA.scala`` scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14996533#comment-14996533 ] Christos Iraklis Tsatsoulis commented on SPARK-11530: - I edited it to target both; there are ``PCA.scala`` scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christos Iraklis Tsatsoulis updated SPARK-11530: Component/s: MLlib > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14994136#comment-14994136 ] Christos Iraklis Tsatsoulis commented on SPARK-11530: - Thanks Sean. Unfortunately, I don't speak Scala - I'm actually a data scientist and not a developer. Thought that it was worthy raising the issue, even if I cannot resolve it myself. Hope that's OK... > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christos Iraklis Tsatsoulis updated SPARK-11530: Description: For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. For details, see these SO questions http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ (pyspark) http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ was: For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ for details. > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christos Iraklis Tsatsoulis updated SPARK-11530: Description: For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ for details. was: For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/) and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ for details. > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > See this SO question > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ > for details. -- 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] [Created] (SPARK-11530) Return eigenvalues with PCA model
Christos Iraklis Tsatsoulis created SPARK-11530: --- Summary: Return eigenvalues with PCA model Key: SPARK-11530 URL: https://issues.apache.org/jira/browse/SPARK-11530 Project: Spark Issue Type: Improvement Components: ML Affects Versions: 1.5.1 Reporter: Christos Iraklis Tsatsoulis For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/) and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ for details. -- 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