[jira] [Updated] (MAHOUT-1319) seqdirectory -filter argument silently ignored when run as MR
[ https://issues.apache.org/jira/browse/MAHOUT-1319?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-1319: -- Attachment: (was: MAHOUT-1319.patch) seqdirectory -filter argument silently ignored when run as MR - Key: MAHOUT-1319 URL: https://issues.apache.org/jira/browse/MAHOUT-1319 Project: Mahout Issue Type: Bug Components: Integration Affects Versions: 0.8 Reporter: Liz Merkhofer Assignee: Suneel Marthi Labels: seqdirectory, text Fix For: 0.9 Attachments: MAHOUT-1319-custom-filter.patch Running seqdirectory (Sequence Files from Input Directory) from the command line and specifying a custom filter using the -filter parameter, the argument is ignored and the default PrefixAdditionFilter is used on the input. No exception is thrown. When the same command is run with -xm sequential, the filter is found and works as expected. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Commented] (MAHOUT-1319) seqdirectory -filter argument silently ignored when run as MR
[ https://issues.apache.org/jira/browse/MAHOUT-1319?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13853776#comment-13853776 ] Suneel Marthi commented on MAHOUT-1319: --- Uploading a new patch that takes a filter class that implements PathFilter. Unlike the sequential version the MR version already handles the keyprefix and chunk sizes without the need of a filter class (like PrefixAdditionFilter). With this patch it should be possible to pass in a CustomFilter that implements PathFilter to the MR version of seqdirectory. seqdirectory -filter argument silently ignored when run as MR - Key: MAHOUT-1319 URL: https://issues.apache.org/jira/browse/MAHOUT-1319 Project: Mahout Issue Type: Bug Components: Integration Affects Versions: 0.8 Reporter: Liz Merkhofer Assignee: Suneel Marthi Labels: seqdirectory, text Fix For: 0.9 Attachments: MAHOUT-1319-custom-filter.patch, MAHOUT-1319.patch Running seqdirectory (Sequence Files from Input Directory) from the command line and specifying a custom filter using the -filter parameter, the argument is ignored and the default PrefixAdditionFilter is used on the input. No exception is thrown. When the same command is run with -xm sequential, the filter is found and works as expected. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-1319) seqdirectory -filter argument silently ignored when run as MR
[ https://issues.apache.org/jira/browse/MAHOUT-1319?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-1319: -- Attachment: MAHOUT-1319.patch seqdirectory -filter argument silently ignored when run as MR - Key: MAHOUT-1319 URL: https://issues.apache.org/jira/browse/MAHOUT-1319 Project: Mahout Issue Type: Bug Components: Integration Affects Versions: 0.8 Reporter: Liz Merkhofer Assignee: Suneel Marthi Labels: seqdirectory, text Fix For: 0.9 Attachments: MAHOUT-1319-custom-filter.patch, MAHOUT-1319.patch Running seqdirectory (Sequence Files from Input Directory) from the command line and specifying a custom filter using the -filter parameter, the argument is ignored and the default PrefixAdditionFilter is used on the input. No exception is thrown. When the same command is run with -xm sequential, the filter is found and works as expected. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Created] (MAHOUT-1384) Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails.
Suneel Marthi created MAHOUT-1384: - Summary: Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. Key: MAHOUT-1384 URL: https://issues.apache.org/jira/browse/MAHOUT-1384 Project: Mahout Issue Type: Bug Components: Examples Affects Versions: 0.8 Reporter: Suneel Marthi Assignee: Suneel Marthi Priority: Minor Fix For: 0.9 Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. This is because the example files are not copied to HDFS for the MR version (like what's presently being done in cluster-reuters.sh). -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-1384) Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails.
[ https://issues.apache.org/jira/browse/MAHOUT-1384?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-1384: -- Status: Patch Available (was: Open) Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. - Key: MAHOUT-1384 URL: https://issues.apache.org/jira/browse/MAHOUT-1384 Project: Mahout Issue Type: Bug Components: Examples Affects Versions: 0.8 Reporter: Suneel Marthi Assignee: Suneel Marthi Priority: Minor Fix For: 0.9 Attachments: MAHOUT-1384.patch Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. This is because the example files are not copied to HDFS for the MR version (like what's presently being done in cluster-reuters.sh). -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-1384) Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails.
[ https://issues.apache.org/jira/browse/MAHOUT-1384?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-1384: -- Attachment: MAHOUT-1384.patch Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. - Key: MAHOUT-1384 URL: https://issues.apache.org/jira/browse/MAHOUT-1384 Project: Mahout Issue Type: Bug Components: Examples Affects Versions: 0.8 Reporter: Suneel Marthi Assignee: Suneel Marthi Priority: Minor Fix For: 0.9 Attachments: MAHOUT-1384.patch Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. This is because the example files are not copied to HDFS for the MR version (like what's presently being done in cluster-reuters.sh). -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-1384) Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails in seqdirectory step.
[ https://issues.apache.org/jira/browse/MAHOUT-1384?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-1384: -- Summary: Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails in seqdirectory step. (was: Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails.) Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails in seqdirectory step. -- Key: MAHOUT-1384 URL: https://issues.apache.org/jira/browse/MAHOUT-1384 Project: Mahout Issue Type: Bug Components: Examples Affects Versions: 0.8 Reporter: Suneel Marthi Assignee: Suneel Marthi Priority: Minor Fix For: 0.9 Attachments: MAHOUT-1384.patch Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. This is because the example files are not copied to HDFS for the MR version (like what's presently being done in cluster-reuters.sh). -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-1384) Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails in seqdirectory step.
[ https://issues.apache.org/jira/browse/MAHOUT-1384?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-1384: -- Resolution: Fixed Status: Resolved (was: Patch Available) Fix committed to trunk. Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails in seqdirectory step. -- Key: MAHOUT-1384 URL: https://issues.apache.org/jira/browse/MAHOUT-1384 Project: Mahout Issue Type: Bug Components: Examples Affects Versions: 0.8 Reporter: Suneel Marthi Assignee: Suneel Marthi Priority: Minor Fix For: 0.9 Attachments: MAHOUT-1384.patch Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. This is because the example files are not copied to HDFS for the MR version (like what's presently being done in cluster-reuters.sh). -- This message was sent by Atlassian JIRA (v6.1.4#6159)
Re: Mahout 0.9 release
+1 for 1.0. This is more challenging than expected (the old hadoop 0.23 profile support is misleading) Sent from my iPhone On Dec 19, 2013, at 19:48, Andrew Musselman andrew.mussel...@gmail.com wrote: +1 On Thu, Dec 19, 2013 at 9:20 AM, Suneel Marthi suneel_mar...@yahoo.comwrote: +1 Sent from my iPhone On Dec 19, 2013, at 12:17 PM, Frank Scholten fr...@frankscholten.nl wrote: I am looking at M-1329 (Support for Hadoop 2.x) as we speak. This change requires quite some testing and I prefer to push this to 1.0. I am thinking of creating a unit test that starts miniclusters for each versions and runs a job in them. On Thu, Dec 19, 2013 at 12:28 AM, Suneel Marthi suneel_mar...@yahoo.com wrote: There's M-1329 that covers this. Hopefully it should make it for 0.9 Sent from my iPhone On Dec 18, 2013, at 6:20 PM, Isabel Drost-Fromm isa...@apache.org wrote: On Mon, 16 Dec 2013 23:16:36 +0200 Gokhan Capan gkhn...@gmail.com wrote: M-1354 (Support for Hadoop 2.x) - Patch available. Gokhan, any updates on this. Nope, still couldn't make it work. Should we push that for 1.0 then (if this is shortly before completion and there's too much in 1.0 to push for a release early next year, I'd also be happy to have a smaller release between now and Berlin Buzzwords that includes the fix...). Isabel
[jira] [Updated] (MAHOUT-1030) Regression: Clustered Points Should be WeightedPropertyVectorWritable not WeightedVectorWritable
[ https://issues.apache.org/jira/browse/MAHOUT-1030?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-1030: -- Fix Version/s: (was: 1.0) Regression: Clustered Points Should be WeightedPropertyVectorWritable not WeightedVectorWritable Key: MAHOUT-1030 URL: https://issues.apache.org/jira/browse/MAHOUT-1030 Project: Mahout Issue Type: Bug Components: Clustering, Integration Affects Versions: 0.7 Reporter: Jeff Eastman Assignee: Andrew Musselman Fix For: 0.9 Attachments: MAHOUT-1030.patch, MAHOUT-1030.patch, MAHOUT-1030.patch, MAHOUT-1030.patch, MAHOUT-1030.patch, MAHOUT-1030.patch, MAHOUT-1030.patch Looks like this won't make it into this build. Pretty widespread impact on code and tests and I don't know which properties were implemented in the old version. I will create a JIRA and post my interim results. On 6/8/12 12:21 PM, Jeff Eastman wrote: That's a reversion that evidently got in when the new ClusterClassificationDriver was introduced. It should be a pretty easy fix and I will see if I can make the change before Paritosh cuts the release bits tonight. On 6/7/12 1:00 PM, Pat Ferrel wrote: It appears that in kmeans the clusteredPoints are now written as WeightedVectorWritable where in mahout 0.6 they were WeightedPropertyVectorWritable? This means that the distance from the centroid is no longer stored here? Why? I hope I'm wrong because that is not a welcome change. How is one to order clustered docs by distance from cluster centroid? I'm sure I could calculate the distance but that would mean looking up the centroid for the cluster id given in the above WeightedVectorWritable, which means iterating through all the clusters for each clustered doc. In my case the number of clusters could be fairly large. Am I missing something? -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Commented] (MAHOUT-976) Implement Multilayer Perceptron
[ https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13853937#comment-13853937 ] Suneel Marthi commented on MAHOUT-976: -- Can this be marked as Duplicate of M-1265 since the code for M-1265 was committed to trunk? Implement Multilayer Perceptron --- Key: MAHOUT-976 URL: https://issues.apache.org/jira/browse/MAHOUT-976 Project: Mahout Issue Type: New Feature Affects Versions: 0.7 Reporter: Christian Herta Assignee: Ted Dunning Priority: Minor Labels: multilayer, networks, neural, perceptron Fix For: Backlog Attachments: MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch Original Estimate: 80h Remaining Estimate: 80h Implement a multi layer perceptron * via Matrix Multiplication * Learning by Backpropagation; implementing tricks by Yann LeCun et al.: Efficent Backprop * arbitrary number of hidden layers (also 0 - just the linear model) * connection between proximate layers only * different cost and activation functions (different activation function in each layer) * test of backprop by gradient checking * normalization of the inputs (storeable) as part of the model First: * implementation stocastic gradient descent like gradient machine * simple gradient descent incl. momentum Later (new jira issues): * Distributed Batch learning (see below) * Stacked (Denoising) Autoencoder - Feature Learning * advanced cost minimazation like 2nd order methods, conjugate gradient etc. Distribution of learning can be done by (batch learning): 1 Partioning of the data in x chunks 2 Learning the weight changes as matrices in each chunk 3 Combining the matrixes and update of the weights - back to 2 Maybe this procedure can be done with random parts of the chunks (distributed quasi online learning). Batch learning with delta-bar-delta heuristics for adapting the learning rates. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Created] (MAHOUT-1385) Caching Encoders don't cache
Johannes Schulte created MAHOUT-1385: Summary: Caching Encoders don't cache Key: MAHOUT-1385 URL: https://issues.apache.org/jira/browse/MAHOUT-1385 Project: Mahout Issue Type: Bug Affects Versions: 0.8 Reporter: Johannes Schulte Priority: Minor The Caching... line of encoders contains code of caching the hash code terms added to the vector. However, the method hashForProbe inside this classes is never called as the signature has String for the parameter original form (instead of byte[] like other encoders). Changing this to byte[] however would lose the java String internal caching of the Strings hash code , that is used as a key in the cache map, triggering another hash code calculation. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-1385) Caching Encoders don't cache
[ https://issues.apache.org/jira/browse/MAHOUT-1385?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Johannes Schulte updated MAHOUT-1385: - Attachment: MAHOUT-1385-test.patch No solution but demonstration of the defect Caching Encoders don't cache Key: MAHOUT-1385 URL: https://issues.apache.org/jira/browse/MAHOUT-1385 Project: Mahout Issue Type: Bug Affects Versions: 0.8 Reporter: Johannes Schulte Priority: Minor Attachments: MAHOUT-1385-test.patch The Caching... line of encoders contains code of caching the hash code terms added to the vector. However, the method hashForProbe inside this classes is never called as the signature has String for the parameter original form (instead of byte[] like other encoders). Changing this to byte[] however would lose the java String internal caching of the Strings hash code , that is used as a key in the cache map, triggering another hash code calculation. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Commented] (MAHOUT-1384) Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails in seqdirectory step.
[ https://issues.apache.org/jira/browse/MAHOUT-1384?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13854018#comment-13854018 ] Hudson commented on MAHOUT-1384: SUCCESS: Integrated in Mahout-Quality #2377 (See [https://builds.apache.org/job/Mahout-Quality/2377/]) MAHOUT-1384: Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails in seqdirectory step. (smarthi: rev 1552538) * /mahout/trunk/CHANGELOG * /mahout/trunk/examples/bin/classify-20newsgroups.sh Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails in seqdirectory step. -- Key: MAHOUT-1384 URL: https://issues.apache.org/jira/browse/MAHOUT-1384 Project: Mahout Issue Type: Bug Components: Examples Affects Versions: 0.8 Reporter: Suneel Marthi Assignee: Suneel Marthi Priority: Minor Fix For: 0.9 Attachments: MAHOUT-1384.patch Executing the MR version of Naive Bayes/CNB of classify_20newgroups.sh fails. This is because the example files are not copied to HDFS for the MR version (like what's presently being done in cluster-reuters.sh). -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-1319) seqdirectory -filter argument silently ignored when run as MR
[ https://issues.apache.org/jira/browse/MAHOUT-1319?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-1319: -- Resolution: Fixed Status: Resolved (was: Patch Available) Patch committed to trunk. seqdirectory -filter argument silently ignored when run as MR - Key: MAHOUT-1319 URL: https://issues.apache.org/jira/browse/MAHOUT-1319 Project: Mahout Issue Type: Bug Components: Integration Affects Versions: 0.8 Reporter: Liz Merkhofer Assignee: Suneel Marthi Labels: seqdirectory, text Fix For: 0.9 Attachments: MAHOUT-1319-custom-filter.patch, MAHOUT-1319.patch Running seqdirectory (Sequence Files from Input Directory) from the command line and specifying a custom filter using the -filter parameter, the argument is ignored and the default PrefixAdditionFilter is used on the input. No exception is thrown. When the same command is run with -xm sequential, the filter is found and works as expected. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Commented] (MAHOUT-976) Implement Multilayer Perceptron
[ https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13854566#comment-13854566 ] Ted Dunning commented on MAHOUT-976: Seems like a dupe to me. Yexi has incorporated the good bits. Implement Multilayer Perceptron --- Key: MAHOUT-976 URL: https://issues.apache.org/jira/browse/MAHOUT-976 Project: Mahout Issue Type: New Feature Affects Versions: 0.7 Reporter: Christian Herta Assignee: Ted Dunning Priority: Minor Labels: multilayer, networks, neural, perceptron Fix For: Backlog Attachments: MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch Original Estimate: 80h Remaining Estimate: 80h Implement a multi layer perceptron * via Matrix Multiplication * Learning by Backpropagation; implementing tricks by Yann LeCun et al.: Efficent Backprop * arbitrary number of hidden layers (also 0 - just the linear model) * connection between proximate layers only * different cost and activation functions (different activation function in each layer) * test of backprop by gradient checking * normalization of the inputs (storeable) as part of the model First: * implementation stocastic gradient descent like gradient machine * simple gradient descent incl. momentum Later (new jira issues): * Distributed Batch learning (see below) * Stacked (Denoising) Autoencoder - Feature Learning * advanced cost minimazation like 2nd order methods, conjugate gradient etc. Distribution of learning can be done by (batch learning): 1 Partioning of the data in x chunks 2 Learning the weight changes as matrices in each chunk 3 Combining the matrixes and update of the weights - back to 2 Maybe this procedure can be done with random parts of the chunks (distributed quasi online learning). Batch learning with delta-bar-delta heuristics for adapting the learning rates. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-976) Implement Multilayer Perceptron
[ https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-976: - Resolution: Duplicate Fix Version/s: 0.9 Status: Resolved (was: Patch Available) Implement Multilayer Perceptron --- Key: MAHOUT-976 URL: https://issues.apache.org/jira/browse/MAHOUT-976 Project: Mahout Issue Type: New Feature Affects Versions: 0.7 Reporter: Christian Herta Assignee: Ted Dunning Priority: Minor Labels: multilayer, networks, neural, perceptron Fix For: Backlog, 0.9 Attachments: MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch Original Estimate: 80h Remaining Estimate: 80h Implement a multi layer perceptron * via Matrix Multiplication * Learning by Backpropagation; implementing tricks by Yann LeCun et al.: Efficent Backprop * arbitrary number of hidden layers (also 0 - just the linear model) * connection between proximate layers only * different cost and activation functions (different activation function in each layer) * test of backprop by gradient checking * normalization of the inputs (storeable) as part of the model First: * implementation stocastic gradient descent like gradient machine * simple gradient descent incl. momentum Later (new jira issues): * Distributed Batch learning (see below) * Stacked (Denoising) Autoencoder - Feature Learning * advanced cost minimazation like 2nd order methods, conjugate gradient etc. Distribution of learning can be done by (batch learning): 1 Partioning of the data in x chunks 2 Learning the weight changes as matrices in each chunk 3 Combining the matrixes and update of the weights - back to 2 Maybe this procedure can be done with random parts of the chunks (distributed quasi online learning). Batch learning with delta-bar-delta heuristics for adapting the learning rates. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
[jira] [Updated] (MAHOUT-976) Implement Multilayer Perceptron
[ https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Suneel Marthi updated MAHOUT-976: - Fix Version/s: (was: Backlog) Implement Multilayer Perceptron --- Key: MAHOUT-976 URL: https://issues.apache.org/jira/browse/MAHOUT-976 Project: Mahout Issue Type: New Feature Affects Versions: 0.7 Reporter: Christian Herta Assignee: Ted Dunning Priority: Minor Labels: multilayer, networks, neural, perceptron Fix For: 0.9 Attachments: MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch Original Estimate: 80h Remaining Estimate: 80h Implement a multi layer perceptron * via Matrix Multiplication * Learning by Backpropagation; implementing tricks by Yann LeCun et al.: Efficent Backprop * arbitrary number of hidden layers (also 0 - just the linear model) * connection between proximate layers only * different cost and activation functions (different activation function in each layer) * test of backprop by gradient checking * normalization of the inputs (storeable) as part of the model First: * implementation stocastic gradient descent like gradient machine * simple gradient descent incl. momentum Later (new jira issues): * Distributed Batch learning (see below) * Stacked (Denoising) Autoencoder - Feature Learning * advanced cost minimazation like 2nd order methods, conjugate gradient etc. Distribution of learning can be done by (batch learning): 1 Partioning of the data in x chunks 2 Learning the weight changes as matrices in each chunk 3 Combining the matrixes and update of the weights - back to 2 Maybe this procedure can be done with random parts of the chunks (distributed quasi online learning). Batch learning with delta-bar-delta heuristics for adapting the learning rates. -- This message was sent by Atlassian JIRA (v6.1.4#6159)
Mahout 0.9 Release - code freeze
We fixed all the bugs planned for 0.9 and the code's been committed to trunk. the plan is to freeze the trunk this sunday in preparation for 0.9 release. Please let this group know if there's any code that needs to make it to trunk before the code freeze date, otherwise please hold off from committing new code to trunk. Thank u.
Re: Getting off MODERATE list?
Hmm I should probably be ON that list, but clearly am not. Not being on the list, I probably can't help. Isabel, Grant, Are you guys on this? Can you boot Otis and add me? On Fri, Dec 20, 2013 at 4:27 PM, Otis Gospodnetic otis.gospodne...@gmail.com wrote: Hi, Anyone knows how I can get off Mahout moderator list? Would be an awesome Christmas present. :) Any pointers would be greatly appreciated. Thanks, Otis -- Performance Monitoring * Log Analytics * Search Analytics Solr Elasticsearch Support * http://sematext.com/ -- Forwarded message -- From: dev-reject-1387561207.4887.lfipnnphncnmnkkno...@mahout.apache.org Date: Fri, Dec 20, 2013 at 12:40 PM Subject: MODERATE for dev@mahout.apache.org To: Cc: dev-allow-tc.1387561207.llgedpelfhophepigcda-pat= occamsmachete@mahout.apache.org To approve: dev-accept-1387561207.4887.lfipnnphncnmnkkno...@mahout.apache.org To reject: dev-reject-1387561207.4887.lfipnnphncnmnkkno...@mahout.apache.org To give a reason to reject: %%% Start comment %%% End comment
