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[html source url](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/db/week7/14_ProbabilisticTopicModels/025_LDA_20NewsGroupsSmall.html) of this databricks notebook and its recorded Uji ![Image of Uji, Dogen's Time-Being](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/images/UjiTimeBeingDogen.png 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[LDA](https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.clustering.LDA)\n  - Python: [LDA](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.clustering.LDA)\n- [MLlib Programming Guide](http://spark.apache.org/docs/latest/mllib-clustering.html#latent-dirichlet-allocation-lda)\n- [ML Feature Extractors & Transformers](http://spark.apache.org/docs/latest/ml-features.html)\n- [Wikipedia: Latent Dirichlet 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</p>\n</iframe>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.45998162456E12,"submitTime":1.459981522985E12,"finishTime":1.459981624635E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":true,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"22c9de2f-537e-40e2-ad7b-1afaee55025e"},{"version":"CommandV1","origId":95454,"guid":"fcd035bf-8222-4516-bd61-dee1f5b30f0e","subtype":"command","commandType":"auto","position":0.828125,"command":"displayHTML(frameIt(\"https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation#Mathematical_definition\",910))","commandVersion":0,"state":"finished","results":{"type":"htmlSandbox","data":"<iframe 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</p>\n</iframe>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459981631639E12,"submitTime":1.45998153008E12,"finishTime":1.459981631729E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":true,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"7ef656e8-5b9d-498f-b21a-1d69cfca9a4f"},{"version":"CommandV1","origId":95455,"guid":"2dbe6e32-0197-434c-a1cf-072bba89a53d","subtype":"command","commandType":"auto","position":0.875,"command":"%md \n## Probabilistic Topic Modeling Example\n\nThis is an outline of our Topic Modeling workflow. Feel free to jump to any subtopic to find out more.\n- Step 0. Dataset Review\n- Step 1. Downloading and Loading Data into DBFS \n    - (Step 1. only needs to be done once per shard - see details at the end of the notebook for Step 1.)\n- Step 2. Loading the Data and Data Cleaning \n- Step 3. Text Tokenization\n- Step 4. Remove Stopwords\n- Step 5. Vector of Token Counts\n- Step 6. Create LDA model with Online Variational Bayes\n- Step 7. Review Topics\n- Step 8. Model Tuning - Refilter Stopwords\n- Step 9. Create LDA model with Expectation Maximization\n- Step 10. Visualize Results","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567737617E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"bb04caa1-1876-4129-8c9a-fcbd694fb13a"},{"version":"CommandV1","origId":95456,"guid":"2642ec6f-ad65-4754-af01-c233abe33159","subtype":"command","commandType":"auto","position":0.90625,"command":"%md \n\n## Step 0. Dataset Review\n\nIn this example, we will use the mini [20 Newsgroups dataset](http://kdd.ics.uci.edu/databases/20newsgroups/20newsgroups.html), which is a random subset of the original 20 Newsgroups dataset. Each newsgroup is stored in a subdirectory, with each article stored as a separate file.\n\n***\n***\n\nThe following is the markdown file `20newsgroups.data.md` of the original details on the dataset, obtained as follows:\n\n```%sh\n$ wget -k http://kdd.ics.uci.edu/databases/20newsgroups/20newsgroups.data.html\n--2016-04-07 10:31:51--  http://kdd.ics.uci.edu/databases/20newsgroups/20newsgroups.data.html\nResolving kdd.ics.uci.edu (kdd.ics.uci.edu)... 128.195.1.95\nConnecting to kdd.ics.uci.edu (kdd.ics.uci.edu)|128.195.1.95|:80... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 4371 (4.3K) [text/html]\nSaving to: '20newsgroups.data.html?\n\n100%[======================================>] 4,371       --.-K/s   in 0s      \n\n2016-04-07 10:31:51 (195 MB/s) - '20newsgroups.data.html? saved [4371/4371]\n\nConverting 20newsgroups.data.html... nothing to do.\nConverted 1 files in 0 seconds.\n\n$ pandoc -f html -t markdown 20newsgroups.data.html > 20newsgroups.data.md\n```\n### 20 Newsgroups\n\n#### Data Type\n\ntext\n\n#### Abstract\n\nThis data set consists of 20000 messages taken from 20 newsgroups.\n\n#### Sources\n\n##### Original Owner and Donor\n\n    Tom Mitchell\n    School of Computer Science\n    Carnegie Mellon University\n    tom.mitchell@cmu.edu\n\n**Date Donated:** September 9, 1999\n\n#### Data Characteristics\n\nOne thousand Usenet articles were taken from each of the following 20\nnewsgroups.\n\n        alt.atheism\n        comp.graphics\n        comp.os.ms-windows.misc\n        comp.sys.ibm.pc.hardware\n        comp.sys.mac.hardware\n        comp.windows.x\n        misc.forsale\n        rec.autos\n        rec.motorcycles\n        rec.sport.baseball\n        rec.sport.hockey\n        sci.crypt\n        sci.electronics\n        sci.med\n        sci.space\n        soc.religion.christian\n        talk.politics.guns\n        talk.politics.mideast\n        talk.politics.misc\n        talk.religion.misc\n\nApproximately 4% of the articles are crossposted. The articles are\ntypical postings and thus have headers including subject lines,\nsignature files, and quoted portions of other articles.\n\n#### Data Format\n\nEach newsgroup is stored in a subdirectory, with each article stored as\na separate file.\n\n#### Past Usage\n\nT. Mitchell. Machine Learning, McGraw Hill, 1997.\n\nT. Joachims (1996). [A probabilistic analysis of the Rocchio algorithm\nwith TFIDF for text\ncategorization](http://reports-archive.adm.cs.cmu.edu/anon/1996/CMU-CS-96-118.ps),\nComputer Science Technical Report CMU-CS-96-118. Carnegie Mellon\nUniversity.\n\n#### Acknowledgements, Copyright Information, and Availability\n\nYou may use this material free of charge for any educational purpose,\nprovided attribution is given in any lectures or publications that make\nuse of this material.\n\n#### References and Further Information\n\nNaive Bayes code for text classification is available from:\n[http://www.cs.cmu.edu/afs/cs/project/theo-11/www/naive-bayes.html](http://www.cs.cmu.edu/afs/cs/project/theo-11/www/naive-bayes.html)\n\n* * * * *\n\n[The UCI KDD Archive](http://kdd.ics.uci.edu/) \\\n [Information and Computer Science](http://www.ics.uci.edu/) \\\n [University of California, Irvine](http://www.uci.edu/) \\\n Irvine, CA 92697-3425 \\\n\nLast modified: September 9, 1999\n\n***\n***","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567737851E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b2bfd0ed-2c5e-4e9f-9f34-d69e389d2f7d"},{"version":"CommandV1","origId":95564,"guid":"8b92471e-4761-476c-80f9-e4af6da13953","subtype":"command","commandType":"auto","position":0.921875,"command":"%md\n**NOTE:** The mini dataset consists of 100 articles from the following 20 Usenet newsgroups:\n\n    alt.atheism\n    comp.graphics\n    comp.os.ms-windows.misc\n    comp.sys.ibm.pc.hardware\n    comp.sys.mac.hardware\n    comp.windows.x\n    misc.forsale\n    rec.autos\n    rec.motorcycles\n    rec.sport.baseball\n    rec.sport.hockey\n    sci.crypt\n    sci.electronics\n    sci.med\n    sci.space\n    soc.religion.christian\n    talk.politics.guns\n    talk.politics.mideast\n    talk.politics.misc\n    talk.religion.misc\n\nSome of the newsgroups seem pretty similar on first glance, such as *comp.sys.ibm.pc.hardware* and *comp.sys.mac.hardware*, which may affect our results.\n","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"ac689d08-b1c7-4265-8a57-2c4f8da2ef32"},{"version":"CommandV1","origId":95457,"guid":"6aa365aa-ed1d-4e27-9aaf-cf5d7aa1f74e","subtype":"command","commandType":"auto","position":0.9375,"command":"%md \n## Step 2. Loading the Data and Data Cleaning\n\nWe have already used the wget command to download the file, and put it in our distributed file system (this process takes about 10 minutes). To repeat these steps or to download data from another source follow the steps at the bottom of this worksheet on **Step 1. Downloading and Loading Data into DBFS**.\n\nLet's make sure these files are in dbfs now:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567738133E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"124bf62e-8c95-47ca-9297-eaa0e616f975"},{"version":"CommandV1","origId":95458,"guid":"fb5ca735-02b9-4d73-9ef1-ac69a25d0a73","subtype":"command","commandType":"auto","position":0.9921875,"command":"display(dbutils.fs.ls(\"dbfs:/datasets/mini_newsgroups\")) // this is where the data resides in dbfs (see below to download it first, if you go to a new shard!)","commandVersion":0,"state":"finished","results":{"type":"table","data":[["dbfs:/datasets/mini_newsgroups/alt.atheism/","alt.atheism/",0.0],["dbfs:/datasets/mini_newsgroups/comp.graphics/","comp.graphics/",0.0],["dbfs:/datasets/mini_newsgroups/comp.os.ms-windows.misc/","comp.os.ms-windows.misc/",0.0],["dbfs:/datasets/mini_newsgroups/comp.sys.ibm.pc.hardware/","comp.sys.ibm.pc.hardware/",0.0],["dbfs:/datasets/mini_newsgroups/comp.sys.mac.hardware/","comp.sys.mac.hardware/",0.0],["dbfs:/datasets/mini_newsgroups/comp.windows.x/","comp.windows.x/",0.0],["dbfs:/datasets/mini_newsgroups/misc.forsale/","misc.forsale/",0.0],["dbfs:/datasets/mini_newsgroups/rec.autos/","rec.autos/",0.0],["dbfs:/datasets/mini_newsgroups/rec.motorcycles/","rec.motorcycles/",0.0],["dbfs:/datasets/mini_newsgroups/rec.sport.baseball/","rec.sport.baseball/",0.0],["dbfs:/datasets/mini_newsgroups/rec.sport.hockey/","rec.sport.hockey/",0.0],["dbfs:/datasets/mini_newsgroups/sci.crypt/","sci.crypt/",0.0],["dbfs:/datasets/mini_newsgroups/sci.electronics/","sci.electronics/",0.0],["dbfs:/datasets/mini_newsgroups/sci.med/","sci.med/",0.0],["dbfs:/datasets/mini_newsgroups/sci.space/","sci.space/",0.0],["dbfs:/datasets/mini_newsgroups/soc.religion.christian/","soc.religion.christian/",0.0],["dbfs:/datasets/mini_newsgroups/talk.politics.guns/","talk.politics.guns/",0.0],["dbfs:/datasets/mini_newsgroups/talk.politics.mideast/","talk.politics.mideast/",0.0],["dbfs:/datasets/mini_newsgroups/talk.politics.misc/","talk.politics.misc/",0.0],["dbfs:/datasets/mini_newsgroups/talk.religion.misc/","talk.religion.misc/",0.0]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"path","type":"\"string\""},{"name":"name","type":"\"string\""},{"name":"size","type":"\"long\""}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"java.io.FileNotFoundException: /datasets/mini_newsgroups","error":"<div class=\"ansiout\">\tat com.databricks.backend.daemon.data.client.DbfsClient.send0(DbfsClient.scala:63)\n\tat com.databricks.backend.daemon.data.client.DbfsClient.sendIdempotent(DbfsClient.scala:40)\n\tat com.databricks.backend.daemon.data.client.DatabricksFileSystem.listStatus(DatabricksFileSystem.scala:174)\n\tat com.databricks.backend.daemon.dbutils.FSUtils$.ls(DBUtilsCore.scala:60)\n\tat com.databricks.dbutils_v1.package$fs$.ls(dbutils_v1.scala:44)</div>","startTime":1.459984432039E12,"submitTime":1.45998433036E12,"finishTime":1.459984432744E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"e870d3ae-da1c-43ad-8ee8-a88b0af8b30a"},{"version":"CommandV1","origId":95460,"guid":"532c6562-de5f-43eb-9e25-c58c9e425d01","subtype":"command","commandType":"auto","position":0.99609375,"command":"%md\nNow let us read in the data using `wholeTextFiles()`.\n\nRecall that the `wholeTextFiles()` command will read in the entire directory of text files, and return a key-value pair of (filePath, fileContent).\n\nAs we do not need the file paths in this example, we will apply a map function to extract the file contents, and then convert everything to lowercase.\n","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567738647E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"15b8d5ca-1302-491f-86d0-f3767263f3a1"},{"version":"CommandV1","origId":95461,"guid":"1f0bf855-e079-4bc6-b57e-c8f97425a375","subtype":"command","commandType":"auto","position":1.0,"command":"// Load text file, leave out file paths, convert all strings to lowercase\nval corpus = sc.wholeTextFiles(\"/datasets/mini_newsgroups/*\").map(_._2).map(_.toLowerCase()).cache() // let's cache","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">corpus: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[950] at map at &lt;console&gt;:35\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459984522599E12,"submitTime":1.459984420914E12,"finishTime":1.459984522742E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b629d3a7-45e0-4e94-ac06-740ea51f9f26"},{"version":"CommandV1","origId":95462,"guid":"590d061d-d1a7-488a-a1d0-51f76ed29aef","subtype":"command","commandType":"auto","position":1.125,"command":"corpus.count // there are 2000 documents in total - this action will take about 2 minutes","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res17: Long = 2000\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459984526394E12,"submitTime":1.459984424705E12,"finishTime":1.459984675335E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"f2591066-9a3e-4513-8384-c5d7899b54b6"},{"version":"CommandV1","origId":95464,"guid":"78ed7f95-7709-4dfd-8c4d-f6b09ee45739","subtype":"command","commandType":"auto","position":1.25,"command":"%md\nReview first 5 documents to get a sense for the data format.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567739354E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"01e88872-0843-4063-acbe-a52f0beda52d"},{"version":"CommandV1","origId":95465,"guid":"f229f515-d752-4721-b561-f0eb04a79345","subtype":"command","commandType":"auto","position":1.375,"command":"corpus.take(5)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res19: Array[String] = \nArray(&quot;xref: cantaloupe.srv.cs.cmu.edu alt.atheism:51121 soc.motss:139944 rec.scouting:5318\nnewsgroups: alt.atheism,soc.motss,rec.scouting\npath: cantaloupe.srv.cs.cmu.edu!crabapple.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!howland.reston.ans.net!wupost!uunet!newsgate.watson.ibm.com!yktnews.watson.ibm.com!watson!watson.ibm.com!strom\nfrom: strom@watson.ibm.com (rob strom)\nsubject: re: [soc.motss, et al.] &quot;princeton axes matching funds for boy scouts&quot;\nsender: @watson.ibm.com\nmessage-id: &lt;1993apr05.180116.43346@watson.ibm.com&gt;\ndate: mon, 05 apr 93 18:01:16 gmt\ndistribution: usa\nreferences: &lt;c47efs.3q47@austin.ibm.com&gt; &lt;1993mar22.033150.17345@cbnewsl.cb.att.com&gt; &lt;n4hy.93apr5120934@harder.ccr-p.ida.org&gt;\norganization: ibm research\nlines: 15\n\nin article &lt;n4hy.93apr5120934@harder.ccr-p.ida.org&gt;, n4hy@harder.ccr-p.ida.org (bob mcgwier) writes:\n\n|&gt; [1] however, i hate economic terrorism and political correctness\n|&gt; worse than i hate this policy.  \n\n\n|&gt; [2] a more effective approach is to stop donating\n|&gt; to any organizating that directly or indirectly supports gay rights issues\n|&gt; until they end the boycott on funding of scouts.  \n\ncan somebody reconcile the apparent contradiction between [1] and [2]?\n\n-- \nrob strom, strom@watson.ibm.com, (914) 784-7641\nibm research, 30 saw mill river road, p.o. box 704, yorktown heights, ny  10598\n&quot;, &quot;path: cantaloupe.srv.cs.cmu.edu!crabapple.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!howland.reston.ans.net!noc.near.net!news.centerline.com!uunet!olivea!sgigate!sgiblab!adagio.panasonic.com!nntp-server.caltech.edu!keith\nfrom: keith@cco.caltech.edu (keith allan schneider)\nnewsgroups: alt.atheism\nsubject: re: &gt;&gt;&gt;&gt;&gt;&gt;pompous ass\nmessage-id: &lt;1pi9btinnqa5@gap.caltech.edu&gt;\ndate: 2 apr 93 20:57:33 gmt\nreferences: &lt;1ou4koinne67@gap.caltech.edu&gt; &lt;1p72bkinnjt7@gap.caltech.edu&gt; &lt;93089.050046mvs104@psuvm.psu.edu&gt; &lt;1pa6ntinns5d@gap.caltech.edu&gt; &lt;1993mar30.210423.1302@bmerh85.bnr.ca&gt; &lt;1pcnqjinnpon@gap.caltech.edu&gt; &lt;kmr4.1344.733611641@po.cwru.edu&gt;\norganization: california institute of technology, pasadena\nlines: 9\nnntp-posting-host: punisher.caltech.edu\n\nkmr4@po.cwru.edu (keith m. ryan) writes:\n\n&gt;&gt;then why do people keep asking the same questions over and over?\n&gt;because you rarely ever answer them.\n\nnope, i've answered each question posed, and most were answered multiple\ntimes.\n\nkeith\n&quot;, &quot;path: cantaloupe.srv.cs.cmu.edu!crabapple.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!howland.reston.ans.net!noc.near.net!news.centerline.com!uunet!olivea!sgigate!sgiblab!adagio.panasonic.com!nntp-server.caltech.edu!keith\nfrom: keith@cco.caltech.edu (keith allan schneider)\nnewsgroups: alt.atheism\nsubject: re: &gt;&gt;&gt;&gt;&gt;&gt;pompous ass\nmessage-id: &lt;1pi9jkinnqe2@gap.caltech.edu&gt;\ndate: 2 apr 93 21:01:40 gmt\nreferences: &lt;1ou4koinne67@gap.caltech.edu&gt; &lt;1p72bkinnjt7@gap.caltech.edu&gt; &lt;93089.050046mvs104@psuvm.psu.edu&gt; &lt;1pa6ntinns5d@gap.caltech.edu&gt; &lt;1993mar30.205919.26390@blaze.cs.jhu.edu&gt; &lt;1pcnp3innpom@gap.caltech.edu&gt; &lt;1pdjip$jsi@fido.asd.sgi.com&gt;\norganization: california institute of technology, pasadena\nlines: 14\nnntp-posting-host: punisher.caltech.edu\n\nlivesey@solntze.wpd.sgi.com (jon livesey) writes:\n\n&gt;&gt;&gt;how long does it [the motto] have to stay around before it becomes the\n&gt;&gt;&gt;default?  ...  where's the cutoff point? \n&gt;&gt;i don't know where the exact cutoff is, but it is at least after a few\n&gt;&gt;years, and surely after 40 years.\n&gt;why does the notion of default not take into account changes\n&gt;in population makeup?     \n\nspecifically, which changes are you talking about?  are you arguing\nthat the motto is interpreted as offensive by a larger portion of the\npopulation now than 40 years ago?\n\nkeith\n&quot;, &quot;path: cantaloupe.srv.cs.cmu.edu!crabapple.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!howland.reston.ans.net!wupost!sdd.hp.com!sgiblab!adagio.panasonic.com!nntp-server.caltech.edu!keith\nfrom: keith@cco.caltech.edu (keith allan schneider)\nnewsgroups: alt.atheism\nsubject: re: &lt;political atheists?\ndate: 2 apr 1993 21:22:59 gmt\norganization: california institute of technology, pasadena\nlines: 44\nmessage-id: &lt;1piarjinnqsa@gap.caltech.edu&gt;\nreferences: &lt;1p9bseinni6o@gap.caltech.edu&gt; &lt;1pamva$b6j@fido.asd.sgi.com&gt; &lt;1pcq4pinnqp1@gap.caltech.edu&gt; &lt;11702@vice.ico.tek.com&gt;\nnntp-posting-host: punisher.caltech.edu\n\nbobbe@vice.ico.tek.com (robert beauchaine) writes:\n\n&gt;&gt;but, you don't know that capital punishment is wrong, so it isn't the same\n&gt;&gt;as shooting.  a better analogy would be that you continue to drive your car,\n&gt;&gt;realizing that sooner or later, someone is going to be killed in an automobile\n&gt;&gt;accident.  you *know* people get killed as a result of driving, yet you\n&gt;&gt;continue to do it anyway.\n&gt;uh uh.  you do not know that you will be the one to do the\n&gt;killing.  i'm not sure i'd drive a car if i had sufficient evidence to\n&gt;conclude that i would necessarily kill someone during my lifetime.\n\nyes, and everyone thinks as you do.  no one thinks that he is going to cause\nor be involved in a fatal accident, but the likelihood is surprisingly high.\njust because you are the man on the firing squad whose gun is shooting\nblanks does not mean that you are less guilty.\n\n&gt;i don't know about jon, but i say *all* taking of human life is\n&gt;murder.  and i say murder is wrong in all but one situation:  when\n&gt;it is the only action that will prevent another murder, either of\n&gt;myself or another.\n\nyou mean that killing is wrong in all but one situtation?  and, you should\nnote that that situation will never occur.  there are always other options\nthank killing.  why don't you just say that all killing is wrong.  this\nis basically what you are saying.\n\n&gt;i'm getting a bit tired of your probabilistic arguments.\n\nare you attempting to be condescending?\n\n&gt;that the system usually works pretty well is small consolation to\n&gt;the poor innocent bastard getting the lethal injection.  is your\n&gt;personal value of human life based solely on a statistical approach?\n&gt;you sound like an unswerving adherent to the needs of the many\n&gt;outweighing the needs of the few, so fuck the few.\n\nbut, most people have found the risk to be acceptable.  you are probably\nmuch more likely to die in a plane crash, or even using an electric\nblender, than you are to be executed as an innocent.  i personally think\nthat the risk is acceptable, but in an ideal moral system, no such risk\nis acceptable.  &quot;acceptable&quot; is the fudge factor necessary in such an\napproximation to the ideal.\n\nkeith\n&quot;, &quot;path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!husc-news.harvard.edu!kuhub.cc.ukans.edu!wupost!howland.reston.ans.net!zaphod.mps.ohio-state.edu!sol.ctr.columbia.edu!ursa!pooh!halat\nnewsgroups: alt.atheism\nsubject: re: there must be a creator! (maybe)\nmessage-id: &lt;30066@ursa.bear.com&gt;\nfrom: halat@pooh.bears (jim halat)\ndate: 1 apr 93 21:24:35 gmt\nreply-to: halat@pooh.bears (jim halat)\nsender: news@bear.com\nreferences: &lt;16ba1e927.drporter@suvm.syr.edu&gt;\nlines: 24\n\nin article &lt;16ba1e927.drporter@suvm.syr.edu&gt;, drporter@suvm.syr.edu (brad porter) writes:\n&gt;\n&gt;   science is wonderful at answering most of our questions.  i'm not the type\n&gt;to question scientific findings very often, but...  personally, i find the\n&gt;theory of evolution to be unfathomable.  could humans, a highly evolved,\n&gt;complex organism that thinks, learns, and develops truly be an organism\n&gt;that resulted from random genetic mutations and natural selection?\n\n[...stuff deleted...]\n\ncomputers are an excellent example...of evolution without &quot;a&quot; creator.\nwe did not &quot;create&quot; computers.  we did not create the sand that goes\ninto the silicon that goes into the integrated circuits that go into\nprocessor board.  we took these things and put them together in an\ninteresting way. just like plants &quot;create&quot; oxygen using light through \nphotosynthesis.  it's a much bigger leap to talk about something that\ncreated &quot;everything&quot; from nothing.  i find it unfathomable to resort\nto believing in a creator when a much simpler alternative exists: we\nsimply are incapable of understanding our beginnings -- if there even\nwere beginnings at all.  and that's ok with me.  the present keeps me\nperfectly busy.\n\n-jim halat\n\n&quot;)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459984776941E12,"submitTime":1.459984675237E12,"finishTime":1.459984777108E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"0a60928c-18b0-493c-9232-0b9b65a75968"},{"version":"CommandV1","origId":95467,"guid":"a6371259-e214-4018-9a2b-93b5f974fa2a","subtype":"command","commandType":"auto","position":1.5,"command":"%md\nTo review a random document in the corpus uncomment and evaluate the following cell.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567739915E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"57779767-6d61-4989-865d-54bcadba887f"},{"version":"CommandV1","origId":95468,"guid":"21fbbd33-6fa6-4513-ab86-04775ae431d3","subtype":"command","commandType":"auto","position":2.0,"command":"corpus.takeSample(false, 1)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res21: Array[String] = \nArray(&quot;xref: cantaloupe.srv.cs.cmu.edu alt.politics.clinton:28802 talk.politics.misc:176904 soc.history:20523\npath: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!ogicse!uwm.edu!wupost!uunet!optilink!cramer\nfrom: cramer@optilink.com (clayton cramer)\nnewsgroups: alt.politics.clinton,talk.politics.misc,soc.history\nsubject: re: lincoln &amp; slavery (re: top ten tricks you can play on the american voter)\nmessage-id: &lt;15238@optilink.com&gt;\ndate: 5 apr 93 20:30:48 gmt\narticle-i.d.: optilink.15238\nreferences: &lt;1993mar30.103106.18328@hemlock.cray.com&gt; &lt;1993apr2.154232.29527@princeton.edu&gt;\norganization: optilink corporation, petaluma, ca\nlines: 16\n\nin article &lt;1993apr2.154232.29527@princeton.edu&gt;, glhewitt@phoenix.princeton.edu (gary livingston hewitt) writes:\n&gt; in article &lt;1993apr2.055109.5833@rigel.econ.uga.edu&gt; depken@rigel.econ.uga.edu (craig depken) writes:\n&gt; [to which is concluded...]\n&gt; &gt;the south only wanted free trade!!! \n&gt; \n&gt; no, they wanted slavery.  if free trade was in their economic interests\n&gt; under that regime (which it was), then free trade they wanted too.  but\n\n&gt; gary l hewitt                          glhewitt@phoenix.princeton.edu\n\nof course, free trade and slavery don't make much sense together in\na phrase anyway.  perhaps mr. depken meant, &quot;low import tariffs,&quot; but\nthat is quite a bit less than &quot;free trade.&quot;\n-- \nclayton e. cramer {uunet,pyramid}!optilink!cramer  my opinions, all mine!\nrelations between people to be by mutual consent, or not at all.\n&quot;)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Cancelled","error":null,"startTime":1.459984866022E12,"submitTime":1.459984764312E12,"finishTime":1.459984866204E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"9e2e5020-561f-41a9-a69e-5d4e79518a0d"},{"version":"CommandV1","origId":95470,"guid":"0110301e-59a6-4aec-b9a0-ff0458ec402f","subtype":"command","commandType":"auto","position":2.375,"command":"%md\nNote that the document begins with a header containing some metadata that we don't need, and we are only interested in the body of the document. 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-- \nrob strom, strom@watson.ibm.com, (914) 784-7641\nibm research, 30 saw mill river road, p.o. box 704, yorktown heights, ny  10598\n&quot;, &quot;kmr4@po.cwru.edu (keith m. ryan) writes: &gt;&gt;then why do people keep asking the same questions over and over?\n&gt;because you rarely ever answer them. nope, i've answered each question posed, and most were answered multiple\ntimes. keith\n&quot;, &quot;livesey@solntze.wpd.sgi.com (jon livesey) writes: &gt;&gt;&gt;how long does it [the motto] have to stay around before it becomes the\n&gt;&gt;&gt;default?  ...  where's the cutoff point? \n&gt;&gt;i don't know where the exact cutoff is, but it is at least after a few\n&gt;&gt;years, and surely after 40 years.\n&gt;why does the notion of default not take into account changes\n&gt;in population makeup?      specifically, which changes are you talking about?  are you arguing\nthat the motto is interpreted as offensive by a larger portion of the\npopulation now than 40 years ago? keith\n&quot;, &quot;bobbe@vice.ico.tek.com (robert beauchaine) writes: &gt;&gt;but, you don't know that capital punishment is wrong, so it isn't the same\n&gt;&gt;as shooting.  a better analogy would be that you continue to drive your car,\n&gt;&gt;realizing that sooner or later, someone is going to be killed in an automobile\n&gt;&gt;accident.  you *know* people get killed as a result of driving, yet you\n&gt;&gt;continue to do it anyway.\n&gt;uh uh.  you do not know that you will be the one to do the\n&gt;killing.  i'm not sure i'd drive a car if i had sufficient evidence to\n&gt;conclude that i would necessarily kill someone during my lifetime. yes, and everyone thinks as you do.  no one thinks that he is going to cause\nor be involved in a fatal accident, but the likelihood is surprisingly high.\njust because you are the man on the firing squad whose gun is shooting\nblanks does not mean that you are less guilty. &gt;i don't know about jon, but i say *all* taking of human life is\n&gt;murder.  and i say murder is wrong in all but one situation:  when\n&gt;it is the only action that will prevent another murder, either of\n&gt;myself or another. you mean that killing is wrong in all but one situtation?  and, you should\nnote that that situation will never occur.  there are always other options\nthank killing.  why don't you just say that all killing is wrong.  this\nis basically what you are saying. &gt;i'm getting a bit tired of your probabilistic arguments. are you attempting to be condescending? &gt;that the system usually works pretty well is small consolation to\n&gt;the poor innocent bastard getting the lethal injection.  is your\n&gt;personal value of human life based solely on a statistical approach?\n&gt;you sound like an unswerving adherent to the needs of the many\n&gt;outweighing the needs of the few, so fuck the few. but, most people have found the risk to be acceptable.  you are probably\nmuch more likely to die in a plane crash, or even using an electric\nblender, than you are to be executed as an innocent.  i personally think\nthat the risk is acceptable, but in an ideal moral system, no such risk\nis acceptable.  &quot;acceptable&quot; is the fudge factor necessary in such an\napproximation to the ideal. keith\n&quot;, in article &lt;16ba1e927.drporter@suvm.syr.edu&gt;, drporter@suvm.syr.edu (brad porter) writes:\n&gt;\n&gt;   science is wonderful at answering most of our questions.  i'm not the type\n&gt;to question scientific findings very often, but...  personally, i find the\n&gt;theory of evolution to be unfathomable.  could humans, a highly evolved,\n&gt;complex organism that thinks, learns, and develops truly be an organism\n&gt;that resulted from random genetic mutations and natural selection? [...stuff deleted...] computers are an excellent example...of evolution without &quot;a&quot; creator.\nwe did not &quot;create&quot; computers.  we did not create the sand that goes\ninto the silicon that goes into the integrated circuits that go into\nprocessor board.  we took these things and put them together in an\ninteresting way. just like plants &quot;create&quot; oxygen using light through \nphotosynthesis.  it's a much bigger leap to talk about something that\ncreated &quot;everything&quot; from nothing.  i find it unfathomable to resort\nto believing in a creator when a much simpler alternative exists: we\nsimply are incapable of understanding our beginnings -- if there even\nwere beginnings at all.  and that's ok with me.  the present keeps me\nperfectly busy. -jim halat)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command 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SparkContext has been shutdown","error":"java.lang.IllegalStateException: SparkContext has been shutdown\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1827)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1848)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1861)\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:212)\n\tat org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165)\n\tat org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)\n\tat org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1523)\n\tat org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1523)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)\n\tat org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2110)\n\tat org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1522)\n\tat org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1529)\n\tat org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1399)\n\tat org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1398)\n\tat org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2123)\n\tat org.apache.spark.sql.DataFrame.head(DataFrame.scala:1398)\n\tat org.apache.spark.sql.DataFrame.take(DataFrame.scala:1480)\n\tat com.databricks.backend.daemon.driver.OutputAggregator$.withOutputAggregation0(OutputAggregator.scala:70)\n\tat com.databricks.backend.daemon.driver.OutputAggregator$.withOutputAggregation(OutputAggregator.scala:42)\n\tat com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$2.apply(ScalaDriverLocal.scala:170)\n\tat com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$2.apply(ScalaDriverLocal.scala:162)\n\tat scala.Option.map(Option.scala:145)\n\tat com.databricks.backend.daemon.driver.ScalaDriverLocal.repl(ScalaDriverLocal.scala:162)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:177)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$3.apply(DriverWrapper.scala:485)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$3.apply(DriverWrapper.scala:485)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:482)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:384)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:195)\n\tat java.lang.Thread.run(Thread.java:745)\n","startTime":1.45998747111E12,"submitTime":1.459987368983E12,"finishTime":1.45998747201E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"d8c6cfd4-7eb9-4569-9c3c-c5262490ad44"},{"version":"CommandV1","origId":95487,"guid":"4bed156b-80ba-40e6-b91f-ec9173053e5b","subtype":"command","commandType":"auto","position":7.044921875,"command":"%md \n## Step 4. Remove Stopwords\n\nWe can easily remove stopwords using the StopWordsRemover(). \n\n","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567743732E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"ae2a0942-2f04-4ac2-81e9-be858c1cf4c5"},{"version":"CommandV1","origId":95488,"guid":"4fa1b560-7d17-4cbb-96b8-4dffb4d58198","subtype":"command","commandType":"auto","position":7.05615234375,"command":"displayHTML(frameIt(\"http://spark.apache.org/docs/latest/ml-features.html#stopwordsremover\",600))","commandVersion":0,"state":"finished","results":{"type":"htmlSandbox","data":"<iframe \n src=\"http://spark.apache.org/docs/latest/ml-features.html#stopwordsremover\"\n width=\"95%\" height=\"600\"\n sandbox>\n  <p>\n    <a href=\"http://spark.apache.org/docs/latest/ml-features.html\">\n      Fallback link for browsers that, unlikely, don't support frames\n    </a>\n  </p>\n</iframe>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459987965664E12,"submitTime":1.45998786381E12,"finishTime":1.459987965751E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":true,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"bf75c687-fa82-465d-b1f3-8450433b532f"},{"version":"CommandV1","origId":95490,"guid":"21aea0a5-cf66-49e3-bdb0-ee4cc0bfa4e2","subtype":"command","commandType":"auto","position":7.0673828125,"command":"%md\nIf a list of stopwords is not provided, the StopWordsRemover() will use [this list of stopwords](http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words), also shown below, by default. \n\n``` a,about,above,across,after,afterwards,again,against,all,almost,alone,along,already,also,although,always,am,among,amongst,amoungst,amount,an,and,another,any,anyhow,anyone,anything,anyway,anywhere,\nare,around,as,at,back,be,became,because,become,becomes,becoming,been,before,beforehand,behind,being,below,beside,besides,between,beyond,bill,both,bottom,but,by,call,can,cannot,cant,co,computer,con,could,\ncouldnt,cry,de,describe,detail,do,done,down,due,during,each,eg,eight,either,eleven,else,elsewhere,empty,enough,etc,even,ever,every,everyone,everything,everywhere,except,few,fifteen,fify,fill,find,fire,first,\nfive,for,former,formerly,forty,found,four,from,front,full,further,get,give,go,had,has,hasnt,have,he,hence,her,here,hereafter,hereby,herein,hereupon,hers,herself,him,himself,his,how,however,hundred,i,ie,if,\nin,inc,indeed,interest,into,is,it,its,itself,keep,last,latter,latterly,least,less,ltd,made,many,may,me,meanwhile,might,mill,mine,more,moreover,most,mostly,move,much,must,my,myself,name,namely,neither,never,\nnevertheless,next,nine,no,nobody,none,noone,nor,not,nothing,now,nowhere,of,off,often,on,once,one,only,onto,or,other,others,otherwise,our,ours,ourselves,out,over,own,part,per,perhaps,please,put,rather,re,same,\nsee,seem,seemed,seeming,seems,serious,several,she,should,show,side,since,sincere,six,sixty,so,some,somehow,someone,something,sometime,sometimes,somewhere,still,such,system,take,ten,than,that,the,their,them,\nthemselves,then,thence,there,thereafter,thereby,therefore,therein,thereupon,these,they,thick,thin,third,this,those,though,three,through,throughout,thru,thus,to,together,too,top,toward,towards,twelve,twenty,two,\nun,under,until,up,upon,us,very,via,was,we,well,were,what,whatever,when,whence,whenever,where,whereafter,whereas,whereby,wherein,whereupon,wherever,whether,which,while,whither,who,whoever,whole,whom,whose,why,will,\nwith,within,without,would,yet,you,your,yours,yourself,yourselves\n```\n\nYou can use `getStopWords()` to see the list of stopwords that will be used.\n\nIn this example, we will specify a list of stopwords for the StopWordsRemover() to use. We do this so that we can add on to the list later on.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567744238E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"eda2bbc8-f9ef-439e-bfb9-b0b105edb2c3"},{"version":"CommandV1","origId":96017,"guid":"4e564e15-d2c6-4a2d-9734-ff89721d6ab2","subtype":"command","commandType":"auto","position":7.07861328125,"command":"display(dbutils.fs.ls(\"dbfs:/tmp/stopwords\")) // check if the file already exists from earlier wget and dbfs-load","commandVersion":0,"state":"finished","results":{"type":"table","data":[["dbfs:/tmp/stopwords","stopwords",2237.0]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"path","type":"\"string\""},{"name":"name","type":"\"string\""},{"name":"size","type":"\"long\""}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"java.lang.IllegalArgumentException: Hostname not allowed in dbfs uri. Please use 'dbfs:/' instead of 'dbfs://' in uri: dbfs://tmp/stopwords","error":"<div class=\"ansiout\">\tat com.databricks.backend.daemon.data.client.DBFS.getAuth(DatabricksFileSystem.scala:26)\n\tat com.databricks.backend.daemon.data.client.DBFS.getAuth(DatabricksFileSystem.scala:22)\n\tat com.databricks.backend.daemon.data.client.DatabricksFileSystem.initialize(DatabricksFileSystem.scala:190)\n\tat org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:1446)\n\tat org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:67)\n\tat org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:1464)\n\tat org.apache.hadoop.fs.FileSystem.get(FileSystem.java:263)\n\tat org.apache.hadoop.fs.Path.getFileSystem(Path.java:187)\n\tat com.databricks.backend.daemon.dbutils.FSUtils$.getFS(DBUtilsCore.scala:168)\n\tat com.databricks.backend.daemon.dbutils.FSUtils$.ls(DBUtilsCore.scala:60)\n\tat com.databricks.dbutils_v1.package$fs$.ls(dbutils_v1.scala:44)</div>","startTime":1.459988686669E12,"submitTime":1.459988584769E12,"finishTime":1.459988687028E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"561f81f5-acc0-4ec9-8d5e-72b9fe043317"},{"version":"CommandV1","origId":96018,"guid":"91651c4c-8e59-49b5-ab12-7dcd5e8ed07c","subtype":"command","commandType":"auto","position":7.084228515625,"command":"%md\nIf the file `dbfs:/tmp/stopwords` already exists then skip the next two cells, otherwise download and load it into DBFS by uncommenting and evaluating the next two cells.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"807accc5-d591-4317-a035-fb043d21246a"},{"version":"CommandV1","origId":95491,"guid":"68c5e3bb-061d-4c3d-9b76-e9f46edadaf7","subtype":"command","commandType":"auto","position":7.08984375,"command":"//%sh wget http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words -O /tmp/stopwords # uncomment '//' at the beginning and repeat only if needed again","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">--2016-04-07 00:21:20--  http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words\nResolving ir.dcs.gla.ac.uk (ir.dcs.gla.ac.uk)... 130.209.240.253\nConnecting to ir.dcs.gla.ac.uk (ir.dcs.gla.ac.uk)|130.209.240.253|:80... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 2237 (2.2K) [text/plain]\nSaving to: &apos;/tmp/stopwords&apos;\n\n     0K ..                                                    100%  310M=0s\n\n2016-04-07 00:21:21 (310 MB/s) - &apos;/tmp/stopwords&apos; saved [2237/2237]\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459988480616E12,"submitTime":1.459988378724E12,"finishTime":1.459988481839E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"c4bcaae1-4648-4111-85ca-2b698e7b5506"},{"version":"CommandV1","origId":95492,"guid":"8eef964e-fc58-401c-afc7-77b2c0322c05","subtype":"command","commandType":"auto","position":7.1123046875,"command":"//%fs cp file:/tmp/stopwords dbfs:/tmp/stopwords # uncomment '//' at the beginning and repeat only if needed again","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res31: Boolean = true\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459988488421E12,"submitTime":1.459988386528E12,"finishTime":1.459988489141E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"8cd7d819-f349-49de-af49-5127433317d0"},{"version":"CommandV1","origId":95493,"guid":"f11f74a7-cfd5-433e-a95b-47827f24a291","subtype":"command","commandType":"auto","position":7.134765625,"command":"// List of stopwords\nval stopwords = sc.textFile(\"/tmp/stopwords\").collect()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">stopwords: Array[String] = Array(a, about, above, across, after, afterwards, again, against, all, almost, alone, along, already, also, although, always, am, among, amongst, amoungst, amount, an, and, another, any, anyhow, anyone, anything, anyway, anywhere, are, around, as, at, back, be, became, because, become, becomes, becoming, been, before, beforehand, behind, being, below, beside, besides, between, beyond, bill, both, bottom, but, by, call, can, cannot, cant, co, computer, con, could, couldnt, cry, de, describe, detail, do, done, down, due, during, each, eg, eight, either, eleven, else, elsewhere, empty, enough, etc, even, ever, every, everyone, everything, everywhere, except, few, fifteen, fify, fill, find, fire, first, five, for, former, formerly, forty, found, four, from, front, full, further, get, give, go, had, has, hasnt, have, he, hence, her, here, hereafter, hereby, herein, hereupon, hers, herself, him, himself, his, how, however, hundred, i, ie, if, in, inc, indeed, interest, into, is, it, its, itself, keep, last, latter, latterly, least, less, ltd, made, many, may, me, meanwhile, might, mill, mine, more, moreover, most, mostly, move, much, must, my, myself, name, namely, neither, never, nevertheless, next, nine, no, nobody, none, noone, nor, not, nothing, now, nowhere, of, off, often, on, once, one, only, onto, or, other, others, otherwise, our, ours, ourselves, out, over, own, part, per, perhaps, please, put, rather, re, same, see, seem, seemed, seeming, seems, serious, several, she, should, show, side, since, sincere, six, sixty, so, some, somehow, someone, something, sometime, sometimes, somewhere, still, such, system, take, ten, than, that, the, their, them, themselves, then, thence, there, thereafter, thereby, therefore, therein, thereupon, these, they, thick, thin, third, this, those, though, three, through, throughout, thru, thus, to, together, too, top, toward, towards, twelve, twenty, two, un, under, until, up, upon, us, very, via, was, we, well, were, what, whatever, when, whence, whenever, where, whereafter, whereas, whereby, wherein, whereupon, wherever, whether, which, while, whither, who, whoever, whole, whom, whose, why, will, with, within, without, would, yet, you, your, yours, yourself, yourselves)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: dbfs:/tmp/stopwords","error":"<div class=\"ansiout\">\tat org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:197)\n\tat org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:208)\n\tat org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:207)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)\n\tat scala.Option.getOrElse(Option.scala:120)\n\tat org.apache.spark.rdd.RDD.partitions(RDD.scala:237)\n\tat org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)\n\tat org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)\n\tat scala.Option.getOrElse(Option.scala:120)\n\tat org.apache.spark.rdd.RDD.partitions(RDD.scala:237)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1922)\n\tat org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:905)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:306)\n\tat org.apache.spark.rdd.RDD.collect(RDD.scala:904)</div>","startTime":1.459988816641E12,"submitTime":1.459988714747E12,"finishTime":1.459988817309E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"303441ff-a997-48aa-807e-21ce26942f2b"},{"version":"CommandV1","origId":96019,"guid":"8bd88427-ab2e-4372-9f04-9ed26cbefafd","subtype":"command","commandType":"auto","position":7.1572265625,"command":"stopwords.length // find the number of stopwords in the scala Array[String]","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res35: Int = 319\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:39: error: Int does not take parameters\n              stopwords.length()\n                              ^\n</div>","error":null,"startTime":1.459988858165E12,"submitTime":1.459988756267E12,"finishTime":1.459988858235E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"f9130545-b184-4408-ad25-a93722c414d1"},{"version":"CommandV1","origId":96020,"guid":"431a38aa-6dbe-400f-bfe6-a42904270a0f","subtype":"command","commandType":"auto","position":7.16845703125,"command":"%md\nFinally, we can just remove the stopwords using the `StopWordsRemover` as follows:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"ec87ea39-4b78-461e-b544-0fae88fa244b"},{"version":"CommandV1","origId":95494,"guid":"e80189a7-7c25-4992-b441-9adc0e01da27","subtype":"command","commandType":"auto","position":7.1796875,"command":"import org.apache.spark.ml.feature.StopWordsRemover\n\n// Set params for StopWordsRemover\nval remover = new StopWordsRemover()\n.setStopWords(stopwords) // This parameter is optional\n.setInputCol(\"tokens\")\n.setOutputCol(\"filtered\")\n\n// Create new DF with Stopwords removed\nval filtered_df = remover.transform(tokenized_df)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">import org.apache.spark.ml.feature.StopWordsRemover\nremover: org.apache.spark.ml.feature.StopWordsRemover = stopWords_5e925c9e2cca\nfiltered_df: org.apache.spark.sql.DataFrame = [corpus: string, id: bigint, tokens: array&lt;string&gt;, filtered: array&lt;string&gt;]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.45998900048E12,"submitTime":1.459988898559E12,"finishTime":1.459989000592E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"4affd4ac-dda3-4c0b-b07c-85dc97a14c11"},{"version":"CommandV1","origId":95496,"guid":"2f351e93-6a36-4bda-8ff2-e9327c374a44","subtype":"command","commandType":"auto","position":7.384765625,"command":"%md \n## Step 5. Vector of Token Counts\n\nLDA takes in a vector of token counts as input. We can use the `CountVectorizer()` to easily convert our text documents into vectors of token counts.\n\nThe `CountVectorizer` will return `(VocabSize, Array(Indexed Tokens), Array(Token Frequency))`.\n\nTwo handy parameters to note:\n  - `setMinDF`: Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.\n  - `setMinTF`: Specifies the minimum number of times a term has to appear in a document to be included in the vocabulary.\n","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567745377E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b569843b-e11a-41a9-bf49-c531da5b9a59"},{"version":"CommandV1","origId":95497,"guid":"25789f78-5941-4a35-a43b-a243f3a079ac","subtype":"command","commandType":"auto","position":7.6923828125,"command":"displayHTML(frameIt(\"http://spark.apache.org/docs/latest/ml-features.html#countvectorizer\",700))","commandVersion":0,"state":"finished","results":{"type":"htmlSandbox","data":"<iframe \n src=\"http://spark.apache.org/docs/latest/ml-features.html#countvectorizer\"\n width=\"95%\" height=\"700\"\n sandbox>\n  <p>\n    <a href=\"http://spark.apache.org/docs/latest/ml-features.html\">\n      Fallback link for browsers that, unlikely, don't support frames\n    </a>\n  </p>\n</iframe>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459989730719E12,"submitTime":1.459989628774E12,"finishTime":1.459989730788E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":true,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"7f2996cf-aeb9-4917-ab6a-c9c9b32ce6bc"},{"version":"CommandV1","origId":95498,"guid":"0ab97c91-f8aa-4f61-b366-37fb8ea77293","subtype":"command","commandType":"auto","position":8.0,"command":"import org.apache.spark.ml.feature.CountVectorizer\n\n// Set params for CountVectorizer\nval vectorizer = new CountVectorizer()\n.setInputCol(\"filtered\")\n.setOutputCol(\"features\")\n.setVocabSize(10000) \n.setMinDF(5) // the minimum number of different documents a term must appear in to be included in the vocabulary.\n.fit(filtered_df)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">import org.apache.spark.ml.feature.CountVectorizer\nvectorizer: org.apache.spark.ml.feature.CountVectorizerModel = cntVec_0b64dd06e4a7\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:44: error: not found: value filtered_df\n       .fit(filtered_df)\n            ^\n</div>","error":null,"startTime":1.459989806659E12,"submitTime":1.459989704709E12,"finishTime":1.459989809099E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"65583956-e83d-4718-8c27-e5a56b856bae"},{"version":"CommandV1","origId":95499,"guid":"7aea6948-8ee7-459d-9458-4b040559f356","subtype":"command","commandType":"auto","position":8.75,"command":"// Create vector of token counts\nval countVectors = vectorizer.transform(filtered_df).select(\"id\", \"features\")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">countVectors: org.apache.spark.sql.DataFrame = [id: bigint, features: vector]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459989830614E12,"submitTime":1.459989728618E12,"finishTime":1.459989830795E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"00cd224b-7251-4712-9426-a805041cf5b9"},{"version":"CommandV1","origId":95500,"guid":"d63288e5-d40f-464f-97d6-e5ef89e36683","subtype":"command","commandType":"auto","position":8.84375,"command":"// see the first countVectors\ncountVectors.take(1)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res38: Array[org.apache.spark.sql.Row] = Array([0,(6139,[0,1,145,231,317,496,499,522,569,605,762,824,852,858,945,1134,1693,2003,2087,2446,2763,3163,3500,3840,4621,5464,5579],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0])])\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Cancelled","error":null,"startTime":1.459989835928E12,"submitTime":1.459989733975E12,"finishTime":1.459989836197E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"34f26ce6-6a64-4635-b58a-23a910e254b9"},{"version":"CommandV1","origId":95502,"guid":"c1dd474e-800c-4b98-8b3c-fb9d9dac7f73","subtype":"command","commandType":"auto","position":8.9375,"command":"%md \nTo use the LDA algorithm in the MLlib library, we have to convert the DataFrame back into an RDD.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567746843E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"1b88d696-fe1c-4504-94d5-d22edbebdbad"},{"version":"CommandV1","origId":95503,"guid":"73b37105-6ba1-4f8f-bce3-f11bc9573b70","subtype":"command","commandType":"auto","position":9.125,"command":"// Convert DF to RDD\nimport org.apache.spark.mllib.linalg.Vector\n\nval lda_countVector = countVectors.map { case Row(id: Long, countVector: Vector) => (id, countVector) }","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">import org.apache.spark.mllib.linalg.Vector\nlda_countVector: org.apache.spark.rdd.RDD[(Long, org.apache.spark.mllib.linalg.Vector)] = MapPartitionsRDD[2071] at map at &lt;console&gt;:62\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:41: error: not found: value countVectors\n       val lda_countVector = countVectors.map { case Row(id: Long, countVector: Vector) =&gt; (id, countVector) }\n                             ^\n</div>","error":null,"startTime":1.459989973489E12,"submitTime":1.45998987154E12,"finishTime":1.459989973987E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"f75ad5a7-e1ac-4834-ad0c-18a1808f4fdf"},{"version":"CommandV1","origId":95504,"guid":"fbadb3d9-26aa-4a86-a03c-d1efc7bfb4b0","subtype":"command","commandType":"auto","position":9.39453125,"command":"// format: Array(id, (VocabSize, Array(indexedTokens), Array(Token Frequency)))\nlda_countVector.take(1)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res39: Array[(Long, org.apache.spark.mllib.linalg.Vector)] = Array((0,(6139,[0,1,145,231,317,496,499,522,569,605,762,824,852,858,945,1134,1693,2003,2087,2446,2763,3163,3500,3840,4621,5464,5579],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0])))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.45998997934E12,"submitTime":1.459989876933E12,"finishTime":1.459989979471E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"4e2d1cbc-bf34-4387-bbbc-2415aa706bc4"},{"version":"CommandV1","origId":95505,"guid":"df1ddac7-7235-495f-bb25-79310fd6d1da","subtype":"command","commandType":"auto","position":9.4208984375,"command":"%md\n## Let's get an overview of LDA in Spark's MLLIB\n","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567747473E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"6957ab5c-4e40-4e0e-b881-690b1ec8b562"},{"version":"CommandV1","origId":95506,"guid":"295e45d4-c1c5-4f74-ad4b-3a43982105d4","subtype":"command","commandType":"auto","position":9.43408203125,"command":"displayHTML(frameIt(\"http://spark.apache.org/docs/latest/mllib-clustering.html#latent-dirichlet-allocation-lda\",800))","commandVersion":0,"state":"finished","results":{"type":"htmlSandbox","data":"<iframe \n src=\"http://spark.apache.org/docs/latest/mllib-clustering.html#latent-dirichlet-allocation-lda\"\n width=\"95%\" height=\"800\"\n sandbox>\n  <p>\n    <a href=\"http://spark.apache.org/docs/latest/ml-features.html\">\n      Fallback link for browsers that, unlikely, don't support frames\n    </a>\n  </p>\n</iframe>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:35: error: too many arguments for method displayHTML: (html: String)Unit\n              displayHTML(&quot;http://spark.apache.org/docs/latest/mllib-clustering.html#latent-dirichlet-allocation-lda&quot;,600)\n                         ^\n</div>","error":null,"startTime":1.459989983895E12,"submitTime":1.459989881944E12,"finishTime":1.45998998398E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":true,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"e02c9e84-63b0-4a47-9768-f84398c84bd0"},{"version":"CommandV1","origId":95507,"guid":"c3157e5a-bcdb-405e-911b-6ca0ad9ced88","subtype":"command","commandType":"auto","position":9.447265625,"command":"%md \n## Create LDA model with Online Variational Bayes\n\nWe will now set the parameters for LDA. We will use the OnlineLDAOptimizer() here, which implements Online Variational Bayes.\n\nChoosing the number of topics for your LDA model requires a bit of domain knowledge. As we know that there are 20 unique newsgroups in our dataset, we will set numTopics to be 20.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567747875E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"c87bf956-66c2-412a-9d52-495e50d6f9b9"},{"version":"CommandV1","origId":95508,"guid":"ea7d9883-8979-457e-8653-9f263d06aa89","subtype":"command","commandType":"auto","position":9.4736328125,"command":"val numTopics = 20","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">numTopics: Int = 20\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459998840304E12,"submitTime":1.459998737856E12,"finishTime":1.459998840382E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"c4f34e5e-80c5-45d5-8921-711b6231e1ad"},{"version":"CommandV1","origId":95510,"guid":"857a9ebf-d946-4a96-a622-1b54e5dc0bfb","subtype":"command","commandType":"auto","position":9.48681640625,"command":"%md\nWe will set the parameters needed to build our LDA model. We can also setMiniBatchFraction for the OnlineLDAOptimizer, which sets the fraction of corpus sampled and used at each iteration. In this example, we will set this to 0.8.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567748384E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"587b31fb-0311-4c04-980b-e97966891cf1"},{"version":"CommandV1","origId":95511,"guid":"50c0c8ee-5ef6-438d-a1e7-a9f922ffcf79","subtype":"command","commandType":"auto","position":9.5,"command":"import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}\n\n// Set LDA params\nval lda = new LDA()\n.setOptimizer(new OnlineLDAOptimizer().setMiniBatchFraction(0.8))\n.setK(numTopics)\n.setMaxIterations(3)\n.setDocConcentration(-1) // use default values\n.setTopicConcentration(-1) // use default values","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}\nlda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@eac463e\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459998843524E12,"submitTime":1.459998741131E12,"finishTime":1.459998843595E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"17988632-40a6-4731-947b-1af011dc7e92"},{"version":"CommandV1","origId":95513,"guid":"08acce5d-bf1a-4cc7-9cc1-fb9d541d8061","subtype":"command","commandType":"auto","position":10.125,"command":"%md \nCreate the LDA model with Online Variational Bayes.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567748861E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"36996dfe-ccbc-4e4d-a3bb-5e8b238aa870"},{"version":"CommandV1","origId":95514,"guid":"4c887172-285d-4a9f-81e3-287aba30a576","subtype":"command","commandType":"auto","position":10.75,"command":"val ldaModel = lda.run(lda_countVector)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.LocalLDAModel@17cdda2f\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459998847311E12,"submitTime":1.459998744914E12,"finishTime":1.459998858885E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"33f00120-5134-4d69-9bed-42edbbae9d82"},{"version":"CommandV1","origId":95515,"guid":"e29ee790-0726-4860-baa3-88db11c5380c","subtype":"command","commandType":"auto","position":10.9453125,"command":"%md\nWatch **Online Learning for Latent Dirichlet Allocation** in NIPS2010 by Matt Hoffman (right click and open in new tab)\n\n[![Matt Hoffman's NIPS 2010 Talk Online LDA]](http://videolectures.net/nips2010_hoffman_oll/thumb.jpg)](http://videolectures.net/nips2010_hoffman_oll/)\n  \nAlso see the paper on *Online varioational Bayes* by Matt linked for more details (from the above URL): [http://videolectures.net/site/normal_dl/tag=83534/nips2010_1291.pdf](http://videolectures.net/site/normal_dl/tag=83534/nips2010_1291.pdf)","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567749276E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"5810714c-9b49-452d-976e-ddc722c26cab"},{"version":"CommandV1","origId":95516,"guid":"a36ae7a4-a660-4587-9c5c-5de14d96a8eb","subtype":"command","commandType":"auto","position":11.140625,"command":"%md \nNote that using the OnlineLDAOptimizer returns us a [LocalLDAModel](http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.clustering.LocalLDAModel), which stores the inferred topics of your corpus.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567749533E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"c650c34f-365f-45b5-a543-7a15548f0b2b"},{"version":"CommandV1","origId":95517,"guid":"7d1e984b-eb95-4880-a58f-778de794abb2","subtype":"command","commandType":"auto","position":11.53125,"command":"%md \n## Review Topics\n\nWe can now review the results of our LDA model. We will print out all 20 topics with their corresponding term probabilities.\n\nNote that you will get slightly different results every time you run an LDA model since LDA includes some randomization.\n\nLet us review results of LDA model with Online Variational Bayes, step by step.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567749786E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"febab2cb-8121-4820-8bf5-3434a9a766f5"},{"version":"CommandV1","origId":97055,"guid":"0d8e900a-d0cb-4f95-bba7-9662330f0aea","subtype":"command","commandType":"auto","position":11.765625,"command":"val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 5)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(478, 1, 1682, 0, 3745),Array(9.113505975133812E-4, 4.557975050322383E-4, 4.545489267706039E-4, 4.517781600778419E-4, 3.875005679653611E-4)), (Array(450, 712, 286, 233, 754),Array(9.560934452627691E-4, 7.721352315532168E-4, 7.365686286929136E-4, 7.261231518866587E-4, 7.03472271111425E-4)), (Array(1032, 563, 228, 0, 1),Array(3.882093450661003E-4, 3.5521819889472835E-4, 3.4201719900067235E-4, 3.2621996108433174E-4, 3.06663766931284E-4)), (Array(12, 120, 3, 27, 177),Array(6.761823130004531E-4, 4.88767757122783E-4, 4.5170174356735097E-4, 4.4375158315670477E-4, 3.8849662686257563E-4)), (Array(2, 0, 1, 4, 6),Array(0.003185472256415464, 0.0028493746211403818, 0.0024740442989260093, 0.0020037015976216055, 0.001595009369564834)), (Array(4, 2, 0, 1, 720),Array(0.0012709967409024006, 0.0011417151166524557, 0.0010544405219545623, 0.0010123746625196367, 9.183163186203722E-4)), (Array(12, 19, 57, 27, 51),Array(0.0030145818015698753, 0.0027283501614456473, 0.0022697949407339117, 0.001957271343418948, 0.001689397935001074)), (Array(0, 1, 486, 4, 837),Array(9.719543375938563E-4, 9.384111927087899E-4, 9.151134433994827E-4, 7.292694949168934E-4, 7.16882384152936E-4)), (Array(0, 1, 3, 5, 4),Array(0.0049509407010048835, 0.004309684532799592, 0.004226895276919982, 0.003860958897466652, 0.003258510113328494)), (Array(57, 464, 19, 1269, 38),Array(0.0013473956332889431, 7.811660285252943E-4, 7.543364883734577E-4, 6.831981945107086E-4, 6.643231361416098E-4)), (Array(21, 7, 1453, 303, 5),Array(3.510970467986506E-4, 3.3481103671203827E-4, 3.2301448027176907E-4, 3.1577485277004295E-4, 2.889910790672993E-4)), (Array(460, 37, 57, 38, 5),Array(0.0010610343509692497, 8.951293725811999E-4, 8.127580436613238E-4, 6.672359017113721E-4, 6.302458613228979E-4)), (Array(327, 21, 604, 671, 460),Array(8.501983533823928E-4, 5.984675843879712E-4, 5.716261864850354E-4, 5.124119655442778E-4, 5.097727393744755E-4)), (Array(0, 192, 1, 2, 4),Array(0.0010234258584622495, 8.233201873198843E-4, 6.456132122400078E-4, 6.162267426510803E-4, 5.592993817731324E-4)), (Array(0, 1, 1667, 2, 70),Array(6.538387359327251E-4, 6.045873520031561E-4, 4.690604380535848E-4, 4.007175747250603E-4, 3.9172745963307956E-4)), (Array(21, 36, 19, 213, 80),Array(0.0026386196389207883, 0.0017291183661896717, 0.0014003435827566765, 9.623622562319358E-4, 9.145098416444801E-4)), (Array(0, 39, 3, 138, 5),Array(8.597506128242443E-4, 7.573021788182564E-4, 7.408552035199535E-4, 7.054438338378976E-4, 6.384903314038909E-4)), (Array(1445, 2165, 207, 275, 1480),Array(7.516226853347455E-4, 6.912305186758577E-4, 6.750169138283474E-4, 6.722331745121359E-4, 6.511753081551685E-4)), (Array(12, 207, 0, 275, 1),Array(0.0010123301455581717, 9.368948818110908E-4, 9.091319475326166E-4, 7.465621152838189E-4, 7.077802649686397E-4)), (Array(0, 235, 1, 981, 50),Array(4.366116275317424E-4, 3.4249974914102473E-4, 3.322447535370271E-4, 2.9827059873849444E-4, 2.891325102583416E-4)))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459998900885E12,"submitTime":1.459998798476E12,"finishTime":1.459998901098E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"8a65333a-75d3-4987-8863-dd33f965ec07"},{"version":"CommandV1","origId":97056,"guid":"281d2e2f-256f-4726-acad-e28730bcac8f","subtype":"command","commandType":"auto","position":11.8828125,"command":"val vocabList = vectorizer.vocabulary","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">vocabList: Array[String] = Array(people, time, good, used, windows, want, work, right, problem, need, really, image, data, information, better, believe, using, software, years, year, mail, sure, point, drive, program, available, space, file, power, help, government, things, question, doesn, number, case, world, look, read, version, come, thing, long, different, best, jpeg, fact, university, real, probably, didn, true, state, course, files, high, possible, actually, list, 1993, game, little, news, group, david, send, wrong, graphics, based, able, support, place, free, called, subject, post, john, reason, color, second, great, card, having, public, email, info, following, start, hard, science, says, example, means, code, evidence, person, note, maybe, general, president, heard, problems, quite, mean, source, life, systems, price, order, window, standard, access, jesus, claim, paul, getting, looking, control, trying, simply, disk, seen, times, team, book, play, chip, local, encryption, idea, truth, church, research, opinions, issue, given, images, wrote, display, large, thought, makes, remember, national, doing, format, away, nasa, change, human, home, small, saying, current, mark, interested, today, area, internet, word, original, agree, left, memory, microsoft, works, machine, instead, kind, hardware, working, request, sort, higher, money, later, programs, questions, israel, mike, pretty, hand, guess, netcom, address, include, technology, cause, matter, uiuc, type, video, speed, wire, days, server, usually, view, earth, package, april, open, stuff, unless, told, christian, important, similar, house, major, size, faith, known, provide, body, phone, ground, rights, michael, health, american, apple, feel, including, center, answer, bible, lines, user, cost, text, check, security, mind, anybody, understand, care, copy, wouldn, live, certainly, started, level, network, women, message, mouse, running, study, clinton, making, position, company, came, board, screen, groups, talking, single, common, white, wiring, monitor, likely, christians, test, special, quality, black, light, effect, nice, medical, members, hope, sources, certain, posted, uucp, fine, hear, canada, cars, write, clear, difference, history, couple, build, police, love, launch, press, books, situation, jewish, specific, sense, particular, words, talk, unix, anti, stop, posting, model, religion, discussion, school, contact, private, built, frank, turkish, keys, cable, taking, simple, service, features, legal, sound, consider, short, night, children, reference, argument, tools, comes, scsi, device, application, comments, jews, applications, clipper, doubt, tried, objective, force, process, theory, self, experience, usenet, games, expect, steve, early, tape, uses, needed, interesting, station, easy, death, exactly, manager, killed, value, turn, correct, according, needs, amiga, ones, response, states, drug, considered, reading, james, language, wanted, shuttle, insurance, personal, term, goes, koresh, strong, past, form, opinion, future, taken, result, sorry, mentioned, rules, especially, religious, hell, happy, society, plus, went, country, design, written, various, author, guns, drivers, asked, results, analysis, haven, latest, longer, gets, advance, aren, parts, cases, york, main, section, laws, previous, accept, input, looks, week, mode, required, christ, weapons, washington, community, robert, numbers, head, fast, disease, option, series, circuit, office, offer, macintosh, driver, andrew, period, venus, israeli, range, exist, clock, players, runs, values, department, toronto, involved, moral, allow, organization, brian, sell, colors, knows, picture, months, choice, half, takes, currently, suggest, dave, armenians, cards, federal, individual, object, wasn, hockey, took, includes, candida, policy, total, title, directly, protect, follow, americans, food, equipment, assume, close, purpose, recently, devices, statement, present, happened, happen, users, media, provides, deal, require, reasons, scientific, christianity, road, shall, dead, lost, action, speak, couldn, bike, save, wants, city, george, goal, lead, obviously, completely, california, mission, useful, details, baseball, voice, engineering, league, complete, uunet, terms, batf, condition, court, easily, rest, responsible, administration, ways, international, sent, clearly, compatible, disclaimer, output, water, algorithm, business, appreciated, freedom, kill, issues, digital, pass, hours, fans, figure, error, story, project, deleted, coming, operating, processing, companies, context, average, trade, appropriate, leave, berkeley, season, face, carry, events, port, difficult, addition, performance, building, basis, final, requires, heart, points, armenian, political, lower, player, king, convert, page, environment, apply, knowledge, resolution, field, willing, volume, actual, stanford, site, sale, suppose, related, limited, entries, advice, commercial, sounds, worth, orbit, lots, basic, defense, anonymous, directory, claims, supposed, designed, explain, handle, included, turkey, reply, enforcement, inside, family, ability, signal, young, neutral, forget, changes, wonder, suspect, radio, necessary, programming, wait, understanding, paper, friend, thinking, shot, month, services, greek, installed, create, printer, land, russian, finally, homosexuality, natural, morality, formats, names, machines, report, peter, setting, eric, homosexual, normal, interface, population, hold, break, comment, rutgers, reasonable, arab, logic, miles, secure, east, supply, product, communications, comp, percent, escrow, avoid, lives, colorado, types, room, allowed, multiple, developed, peace, cancer, million, library, expensive, cheap, gary, soon, recent, cubs, agencies, mention, supported, event, gives, soviet, looked, release, obvious, street, physics, suggestions, doctor, technical, caused, happens, table, return, archive, treatment, bitnet, chance, mass, radar, outside, entire, friends, folks, choose, print, install, generally, development, diet, exists, areas, concept, requests, straight, child, learn, supports, behavior, ideas, morning, member, electrical, illegal, weeks, social, wish, smith, trouble, muslim, jack, meaning, united, remove, stand, bring, thank, unit, reality, engine, worked, practice, drives, motif, west, asking, appear, provided, pick, studies, attempt, possibly, answers, decided, near, henry, trust, existence, changed, modem, bits, errors, false, continue, middle, belief, compound, extra, arguments, proper, particularly, class, congress, guys, contains, yeah, safe, facts, loss, worse, heat, appears, thread, andy, pages, manual, function, attack, fonts, aware, privacy, operations, armenia, constitution, institute, frame, command, drugs, stupid, wide, nature, conference, independent, unfortunately, hands, wall, distribution, approach, speaking, edge, division, shouldn, knew, effective, indiana, shows, compression, safety, crime, serial, added, 1992, 1990, modern, ethernet, solution, civil, bought, turks, floppy, appreciate, abortion, cramer, blood, blue, letter, plastic, regular, writing, spend, allows, utility, hello, rate, virginia, wondering, germany, gave, record, faster, simms, operation, arms, internal, playing, step, giving, views, switch, tool, decision, atheism, surface, articles, actions, font, additional, method, described, base, concerned, scott, stated, kids, played, prevent, rule, late, quote, buying, capability, places, products, attitude, costs, patients, controller, fair, connected, north, military, keith, cover, levels, highly, considering, resources, turned, apparently, valid, hate, countries, shell, western, poor, excellent, market, necessarily, wires, created, america, richard, boston, calls, brought, functions, received, account, creation, watch, majority, cwru, forward, student, released, driving, authority, committee, chips, quick, protection, notice, produce, sold, arizona, complex, visual, absolutely, arabs, fairly, murder, keyboard, plan, moon, minutes, boxes, lord, properly, greatly, killing, shareware, larry, islam, vote, panel, rangers, effort, options, review, dangerous, holy, intelligence, acts, tests, nation, property, damage, begin, living, electronics, failed)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459998921831E12,"submitTime":1.459998819003E12,"finishTime":1.459998921912E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"73a6216e-2f37-418a-af20-d9c95c9e8dfd"},{"version":"CommandV1","origId":97057,"guid":"0e4d225f-978e-4506-9388-ca3c1de97a74","subtype":"command","commandType":"auto","position":11.94140625,"command":"val topics = topicIndices.map { case (terms, termWeights) =>\n  terms.map(vocabList(_)).zip(termWeights)\n}","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">topics: Array[Array[(String, Double)]] = Array(Array((took,9.113505975133812E-4), (time,4.557975050322383E-4), (extension,4.545489267706039E-4), (people,4.517781600778419E-4), (titles,3.875005679653611E-4)), Array((players,9.560934452627691E-4), (release,7.721352315532168E-4), (history,7.365686286929136E-4), (mind,7.261231518866587E-4), (social,7.03472271111425E-4)), Array((connection,3.882093450661003E-4), (deleted,3.5521819889472835E-4), (user,3.4201719900067235E-4), (people,3.2621996108433174E-4), (time,3.06663766931284E-4)), Array((data,6.761823130004531E-4), (disk,4.88767757122783E-4), (used,4.5170174356735097E-4), (file,4.4375158315670477E-4), (questions,3.8849662686257563E-4)), Array((good,0.003185472256415464), (people,0.0028493746211403818), (time,0.0024740442989260093), (windows,0.0020037015976216055), (work,0.001595009369564834)), Array((windows,0.0012709967409024006), (good,0.0011417151166524557), (people,0.0010544405219545623), (time,0.0010123746625196367), (happens,9.183163186203722E-4)), Array((data,0.0030145818015698753), (year,0.0027283501614456473), (actually,0.0022697949407339117), (file,0.001957271343418948), (true,0.001689397935001074)), Array((people,9.719543375938563E-4), (time,9.384111927087899E-4), (follow,9.151134433994827E-4), (windows,7.292694949168934E-4), (speaking,7.16882384152936E-4)), Array((people,0.0049509407010048835), (time,0.004309684532799592), (used,0.004226895276919982), (want,0.003860958897466652), (windows,0.003258510113328494)), Array((actually,0.0013473956332889431), (months,7.811660285252943E-4), (year,7.543364883734577E-4), (club,6.831981945107086E-4), (read,6.643231361416098E-4)), Array((sure,3.510970467986506E-4), (right,3.3481103671203827E-4), (brothers,3.2301448027176907E-4), (stop,3.1577485277004295E-4), (want,2.889910790672993E-4)), Array((sell,0.0010610343509692497), (look,8.951293725811999E-4), (actually,8.127580436613238E-4), (read,6.672359017113721E-4), (want,6.302458613228979E-4)), Array((argument,8.501983533823928E-4), (sure,5.984675843879712E-4), (site,5.716261864850354E-4), (hold,5.124119655442778E-4), (sell,5.097727393744755E-4)), Array((people,0.0010234258584622495), (speed,8.233201873198843E-4), (time,6.456132122400078E-4), (good,6.162267426510803E-4), (windows,5.592993817731324E-4)), Array((people,6.538387359327251E-4), (time,6.045873520031561E-4), (comparison,4.690604380535848E-4), (good,4.007175747250603E-4), (support,3.9172745963307956E-4)), Array((sure,0.0026386196389207883), (world,0.0017291183661896717), (year,0.0014003435827566765), (provide,9.623622562319358E-4), (great,9.145098416444801E-4)), Array((people,8.597506128242443E-4), (version,7.573021788182564E-4), (used,7.408552035199535E-4), (display,7.054438338378976E-4), (want,6.384903314038909E-4)), Array((sleep,7.516226853347455E-4), (forever,6.912305186758577E-4), (similar,6.750169138283474E-4), (sources,6.722331745121359E-4), (raised,6.511753081551685E-4)), Array((data,0.0010123301455581717), (similar,9.368948818110908E-4), (people,9.091319475326166E-4), (sources,7.465621152838189E-4), (time,7.077802649686397E-4)), Array((people,4.366116275317424E-4), (understand,3.4249974914102473E-4), (time,3.322447535370271E-4), (islam,2.9827059873849444E-4), (didn,2.891325102583416E-4)))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459998940199E12,"submitTime":1.459998837536E12,"finishTime":1.459998940446E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"a184c8cb-37a3-4153-9de2-afcafb52a502"},{"version":"CommandV1","origId":97062,"guid":"c0bc4db6-65c3-4290-ae4a-c309a4d4d3a9","subtype":"command","commandType":"auto","position":11.9560546875,"command":"%md\nFeel free to take things apart to understand!","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"828120b1-6e0f-476f-85e4-eec6a6b35d47"},{"version":"CommandV1","origId":97058,"guid":"73fb3190-40fc-4744-a991-91187df1ee56","subtype":"command","commandType":"auto","position":11.970703125,"command":"topicIndices(0)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res54: (Array[Int], Array[Double]) = (Array(478, 1, 1682, 0, 3745),Array(9.113505975133812E-4, 4.557975050322383E-4, 4.545489267706039E-4, 4.517781600778419E-4, 3.875005679653611E-4))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459999011282E12,"submitTime":1.4599989086E12,"finishTime":1.45999901136E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"76194bf9-fff8-4623-9428-5ac95c34f113"},{"version":"CommandV1","origId":97059,"guid":"9eed2002-62b6-4c85-96a0-fd8695cd75ce","subtype":"command","commandType":"auto","position":11.9853515625,"command":"topicIndices(0)._1","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res56: Array[Int] = Array(478, 1, 1682, 0, 3745)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:77: error: (Array[Int], Array[Double]) does not take parameters\n              topicIndices(0)(0)\n                             ^\n</div>","error":null,"startTime":1.459999057416E12,"submitTime":1.459998955014E12,"finishTime":1.459999057505E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"d539f391-d646-4b7f-a11d-1cc796702857"},{"version":"CommandV1","origId":97060,"guid":"693ad0a5-27de-4fbd-804f-c6cc46c8e086","subtype":"command","commandType":"auto","position":11.99267578125,"command":"topicIndices(0)._1(0)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res57: Int = 478\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459999083712E12,"submitTime":1.45999898102E12,"finishTime":1.459999083806E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"704c30d4-ac12-4806-bf51-16954092809a"},{"version":"CommandV1","origId":97061,"guid":"bda87413-1e06-4e4b-9b9d-b6a18ff57280","subtype":"command","commandType":"auto","position":11.996337890625,"command":"vocabList(topicIndices(0)._1(0))","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res58: String = took\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459999118504E12,"submitTime":1.459999016096E12,"finishTime":1.45999911859E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"9842fdd9-54e0-438e-90fd-aad0b71a42b7"},{"version":"CommandV1","origId":97063,"guid":"2c2ac7a4-f8e1-438f-b630-45bb540d41c3","subtype":"command","commandType":"auto","position":11.9981689453125,"command":"%md\nReview Results of LDA model with Online Variational Bayes - Doing all four steps earlier at once.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"c644813a-31ec-462c-8161-7fe03a0519a5"},{"version":"CommandV1","origId":95518,"guid":"a4d67a83-3599-4690-91d5-d7b9347ebce0","subtype":"command","commandType":"auto","position":12.0,"command":"val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 5)\nval vocabList = vectorizer.vocabulary\nval topics = topicIndices.map { case (terms, termWeights) =>\n  terms.map(vocabList(_)).zip(termWeights)\n}\nprintln(s\"$numTopics topics:\")\ntopics.zipWithIndex.foreach { case (topic, i) =>\n  println(s\"TOPIC $i\")\n  topic.foreach { case (term, weight) => println(s\"$term\\t$weight\") }\n  println(s\"==========\")\n}","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">20 topics:\nTOPIC 0\nperiod\t7.330684538278144E-4\npower\t6.132462790531913E-4\nplay\t5.384103408306002E-4\nwrites\t5.190717860644124E-4\ndoes\t4.0842511583718383E-4\n==========\nTOPIC 1\nwomen\t6.183565463707178E-4\nclothing\t5.44590919628385E-4\narticle\t5.141720331879428E-4\nwrites\t4.836126687689027E-4\nchurch\t4.6106838085887376E-4\n==========\nTOPIC 2\nauthor\t4.2234487237330864E-4\ndesign\t4.1974078616202284E-4\nyear\t3.9716313690794263E-4\narticle\t3.923870240081209E-4\nbible\t3.8995373590960475E-4\n==========\nTOPIC 3\npicture\t7.340051789666939E-4\narticle\t4.7457980117106806E-4\nwrites\t4.3477418192419383E-4\ndrive\t4.2807982486122906E-4\ngood\t4.2481788232450374E-4\n==========\nTOPIC 4\narticle\t7.914810204487556E-4\nwrites\t7.557389954263873E-4\nknow\t6.077256925719123E-4\nwright\t5.536793958170185E-4\nlike\t5.33544121683129E-4\n==========\nTOPIC 5\nleafs\t6.09961128007918E-4\nwrites\t5.375757360214054E-4\narticle\t4.758295342758499E-4\ninput\t4.453294602805684E-4\ndata\t4.2990145274448345E-4\n==========\nTOPIC 6\nisrael\t7.571207379452645E-4\njewish\t6.65354600101315E-4\nwrites\t6.383328772453376E-4\narab\t6.249407968625211E-4\napril\t6.043521307500501E-4\n==========\nTOPIC 7\ncubs\t8.269719423563971E-4\nsuck\t8.238850844049989E-4\nwrites\t6.095129204456378E-4\nmathew\t4.5011183923159166E-4\nmantis\t4.455423576528236E-4\n==========\nTOPIC 8\npeople\t5.609722906901543E-4\nknow\t4.868781106315686E-4\ntime\t4.331604244957274E-4\narticle\t3.7054274063109765E-4\nlike\t3.7036469046352157E-4\n==========\nTOPIC 9\nwrites\t5.132711963317823E-4\narticle\t4.3054771158263763E-4\npeople\t4.270729605242108E-4\nwant\t3.877634611875376E-4\njust\t3.791226066345781E-4\n==========\nTOPIC 10\nwrites\t0.0054312283960385645\narticle\t0.004678097342056468\npeople\t0.003939283778914211\njust\t0.003800708985235169\nlike\t0.003719825123729371\n==========\nTOPIC 11\nworking\t5.312682127697326E-4\nwindows\t4.8551422285305517E-4\nproblem\t4.807546066926888E-4\namendment\t4.659289868038814E-4\nlike\t4.4757832769700133E-4\n==========\nTOPIC 12\nwindows\t6.253746613374257E-4\nwrites\t4.900408860598469E-4\nfact\t4.568554161162082E-4\nsoftware\t4.478590759679576E-4\narticle\t4.134477354210795E-4\n==========\nTOPIC 13\nwrites\t7.738256279709485E-4\narticle\t6.555828771652631E-4\ntime\t5.738061424608107E-4\ntruth\t5.379517365953391E-4\nknow\t4.985161905298162E-4\n==========\nTOPIC 14\nimage\t0.004183654396019808\nprogram\t0.0017574730245397377\ndata\t0.0017318859189716527\nfile\t0.0014852541281729795\nimages\t0.0014083673802810987\n==========\nTOPIC 15\nwrites\t8.327069562101253E-4\narticle\t6.158606846937626E-4\njust\t4.7455659395936374E-4\nlike\t4.472272178483073E-4\ndoes\t4.3304290936932243E-4\n==========\nTOPIC 16\nlaunch\t0.001447659926679004\nspace\t0.0012696272256431682\nshuttle\t8.306056563717886E-4\nvenus\t7.927154546427446E-4\nmission\t6.1334113594564E-4\n==========\nTOPIC 17\nwindows\t0.004913479109017912\nmicrosoft\t0.0022161433032952768\nmouse\t0.0014163394288429392\nsoftware\t0.0013036479294347187\ngraphics\t0.001302894816173263\n==========\nTOPIC 18\nwrites\t8.098591997124465E-4\narticle\t6.541902233757504E-4\nfaith\t6.54026359519847E-4\nknow\t6.010310938376768E-4\njesus\t5.835655286195288E-4\n==========\nTOPIC 19\narticle\t4.065642421766462E-4\njust\t3.451713989340494E-4\nwrites\t3.4177839900304685E-4\nrussotto\t3.390146087255311E-4\nbelief\t3.2134550562201296E-4\n==========\ntopicIndices: Array[(Array[Int], Array[Double])] = Array((Array(459, 39, 138, 0, 7),Array(7.330684538278144E-4, 6.132462790531913E-4, 5.384103408306002E-4, 5.190717860644124E-4, 4.0842511583718383E-4)), (Array(258, 2911, 1, 0, 144),Array(6.183565463707178E-4, 5.44590919628385E-4, 5.141720331879428E-4, 4.836126687689027E-4, 4.6106838085887376E-4)), (Array(417, 414, 29, 1, 240),Array(4.2234487237330864E-4, 4.1974078616202284E-4, 3.9716313690794263E-4, 3.923870240081209E-4, 3.8995373590960475E-4)), (Array(478, 1, 0, 34, 9),Array(7.340051789666939E-4, 4.7457980117106806E-4, 4.3477418192419383E-4, 4.2807982486122906E-4, 4.2481788232450374E-4)), (Array(1, 0, 4, 1093, 5),Array(7.914810204487556E-4, 7.557389954263873E-4, 6.077256925719123E-4, 5.536793958170185E-4, 5.33544121683129E-4)), (Array(1186, 0, 1, 437, 21),Array(6.09961128007918E-4, 5.375757360214054E-4, 4.758295342758499E-4, 4.453294602805684E-4, 4.2990145274448345E-4)), (Array(192, 309, 0, 692, 214),Array(7.571207379452645E-4, 6.65354600101315E-4, 6.383328772453376E-4, 6.249407968625211E-4, 6.043521307500501E-4)), (Array(720, 1073, 0, 1393, 1917),Array(8.269719423563971E-4, 8.238850844049989E-4, 6.095129204456378E-4, 4.5011183923159166E-4, 4.455423576528236E-4)), (Array(2, 4, 8, 1, 5),Array(5.609722906901543E-4, 4.868781106315686E-4, 4.331604244957274E-4, 3.7054274063109765E-4, 3.7036469046352157E-4)), (Array(0, 1, 2, 13, 3),Array(5.132711963317823E-4, 4.3054771158263763E-4, 4.270729605242108E-4, 3.877634611875376E-4, 3.791226066345781E-4)), (Array(0, 1, 2, 3, 5),Array(0.0054312283960385645, 0.004678097342056468, 0.003939283778914211, 0.003800708985235169, 0.003719825123729371)), (Array(183, 12, 16, 1148, 5),Array(5.312682127697326E-4, 4.8551422285305517E-4, 4.807546066926888E-4, 4.659289868038814E-4, 4.4757832769700133E-4)), (Array(12, 0, 58, 27, 1),Array(6.253746613374257E-4, 4.900408860598469E-4, 4.568554161162082E-4, 4.478590759679576E-4, 4.134477354210795E-4)), (Array(0, 1, 8, 143, 4),Array(7.738256279709485E-4, 6.555828771652631E-4, 5.738061424608107E-4, 5.379517365953391E-4, 4.985161905298162E-4)), (Array(19, 35, 21, 38, 149),Array(0.004183654396019808, 0.0017574730245397377, 0.0017318859189716527, 0.0014852541281729795, 0.0014083673802810987)), (Array(0, 1, 3, 5, 7),Array(8.327069562101253E-4, 6.158606846937626E-4, 4.7455659395936374E-4, 4.472272178483073E-4, 4.3304290936932243E-4)), (Array(305, 37, 390, 460, 539),Array(0.001447659926679004, 0.0012696272256431682, 8.306056563717886E-4, 7.927154546427446E-4, 6.1334113594564E-4)), (Array(12, 177, 260, 27, 80),Array(0.004913479109017912, 0.0022161433032952768, 0.0014163394288429392, 0.0013036479294347187, 0.001302894816173263)), (Array(0, 1, 225, 4, 125),Array(8.098591997124465E-4, 6.541902233757504E-4, 6.54026359519847E-4, 6.010310938376768E-4, 5.835655286195288E-4)), (Array(1, 3, 0, 3004, 810),Array(4.065642421766462E-4, 3.451713989340494E-4, 3.4177839900304685E-4, 3.390146087255311E-4, 3.2134550562201296E-4)))\nvocabList: Array[String] = Array(writes, article, people, just, know, like, think, does, time, good, make, used, windows, want, work, right, problem, need, really, image, said, data, going, information, better, believe, using, software, years, year, mail, sure, point, thanks, drive, program, available, space, file, power, help, government, things, question, doesn, number, case, world, look, read, line, version, come, thing, long, different, best, jpeg, fact, university, real, probably, didn, true, state, course, files, high, possible, actually, list, 1993, game, little, news, group, david, send, tell, wrong, graphics, based, able, support, place, free, called, subject, post, john, reason, color, second, great, card, having, public, email, info, following, start, hard, science, says, example, means, code, evidence, person, note, maybe, general, president, heard, problems, quite, mean, source, life, systems, price, order, window, standard, access, jesus, claim, paul, getting, looking, control, trying, simply, disk, seen, times, team, book, play, chip, local, encryption, idea, truth, church, research, opinions, issue, given, images, wrote, display, large, thought, makes, remember, national, doing, format, away, nasa, change, human, home, small, saying, current, mark, interested, today, area, internet, word, original, agree, left, memory, microsoft, works, machine, instead, kind, hardware, working, request, sort, higher, money, entry, later, programs, questions, israel, mike, pretty, hand, guess, netcom, address, include, technology, cause, matter, uiuc, type, video, speed, wire, days, server, usually, view, earth, package, april, open, stuff, unless, told, 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department, toronto, involved, moral, allow, organization, brian, sell, colors, knows, picture, months, choice, half, takes, currently, suggest, dave, armenians, cards, federal, individual, object, wasn, hockey, took, includes, candida, policy, total, title, directly, protect, follow, americans, food, equipment, assume, close, purpose, recently, devices, statement, present, happened, happen, users, media, provides, deal, require, reasons, scientific, christianity, road, shall, dead, lost, action, speak, couldn, bike, save, wants, city, george, goal, lead, obviously, completely, california, mission, useful, details, baseball, voice, engineering, league, complete, uunet, terms, batf, condition, court, easily, rest, responsible, administration, ways, international, sent, clearly, compatible, disclaimer, output, water, algorithm, business, appreciated, freedom, kill, issues, digital, pass, hours, newsgroup, fans, figure, error, story, project, deleted, coming, operating, processing, companies, context, average, trade, appropriate, leave, berkeley, season, face, carry, events, port, difficult, addition, performance, building, basis, final, requires, heart, points, armenian, political, lower, player, king, convert, page, environment, apply, knowledge, resolution, field, willing, volume, actual, stanford, site, sale, suppose, related, limited, entries, advice, commercial, sounds, worth, orbit, lots, basic, defense, anonymous, directory, claims, supposed, designed, explain, handle, included, turkey, reply, enforcement, inside, family, ability, signal, young, neutral, forget, changes, wonder, suspect, radio, necessary, programming, wait, understanding, paper, friend, thinking, shot, month, services, greek, installed, create, printer, land, russian, finally, homosexuality, natural, morality, formats, names, machines, report, peter, setting, eric, homosexual, normal, interface, population, hold, break, comment, rutgers, reasonable, arab, logic, miles, secure, east, supply, product, communications, comp, percent, escrow, avoid, lives, colorado, types, room, allowed, multiple, developed, peace, cancer, million, library, expensive, cheap, gary, soon, recent, cubs, agencies, mention, supported, event, gives, soviet, looked, release, obvious, street, physics, suggestions, doctor, technical, caused, happens, table, return, archive, treatment, bitnet, chance, mass, radar, outside, entire, friends, folks, choose, print, install, generally, development, diet, exists, areas, concept, requests, straight, child, learn, supports, behavior, ideas, morning, member, electrical, illegal, weeks, social, wish, smith, trouble, muslim, jack, meaning, united, remove, stand, bring, thank, unit, reality, engine, worked, practice, drives, motif, west, asking, appear, provided, pick, studies, attempt, possibly, answers, decided, near, henry, trust, existence, changed, modem, bits, errors, false, continue, middle, belief, compound, extra, arguments, proper, particularly, class, congress, guys, contains, yeah, safe, facts, loss, worse, heat, appears, thread, andy, pages, manual, function, attack, fonts, aware, privacy, operations, armenia, constitution, institute, frame, command, drugs, stupid, wide, nature, conference, independent, unfortunately, hands, wall, distribution, approach, speaking, edge, division, shouldn, knew, effective, indiana, shows, compression, safety, crime, serial, added, 1992, 1990, modern, ethernet, solution, civil, bought, turks, floppy, appreciate, abortion, cramer, blood, blue, letter, plastic, regular, writing, spend, allows, utility, hello, rate, virginia, wondering, germany, gave, record, faster, simms, operation, arms, internal, playing, step, giving, views, switch, tool, decision, atheism, surface, articles, actions, font, additional, method, described, base, concerned, scott, stated, kids, played, prevent, rule, late, quote, buying, capability, places, products, attitude, costs, patients, controller, fair, connected, north, military, keith, cover, levels, highly, considering, resources, turned, apparently, valid, hate, countries, shell, western, poor, excellent, market, necessarily, wires, created, america, richard, boston, calls, brought, functions, received, account, creation, watch, majority, cwru, forward, student, released, driving, authority, committee, chips, quick, protection, notice, produce, sold, arizona, complex, visual, absolutely, arabs, fairly, murder, keyboard, plan, moon, minutes, boxes, lord, properly, greatly, killing, shareware, larry, islam, vote, panel)\ntopics: Array[Array[(String, Double)]] = Array(Array((period,7.330684538278144E-4), (power,6.132462790531913E-4), (play,5.384103408306002E-4), (writes,5.190717860644124E-4), (does,4.0842511583718383E-4)), Array((women,6.183565463707178E-4), (clothing,5.44590919628385E-4), (article,5.141720331879428E-4), (writes,4.836126687689027E-4), (church,4.6106838085887376E-4)), Array((author,4.2234487237330864E-4), (design,4.1974078616202284E-4), (year,3.9716313690794263E-4), (article,3.923870240081209E-4), (bible,3.8995373590960475E-4)), Array((picture,7.340051789666939E-4), (article,4.7457980117106806E-4), (writes,4.3477418192419383E-4), (drive,4.2807982486122906E-4), (good,4.2481788232450374E-4)), Array((article,7.914810204487556E-4), (writes,7.557389954263873E-4), (know,6.077256925719123E-4), (wright,5.536793958170185E-4), (like,5.33544121683129E-4)), Array((leafs,6.09961128007918E-4), (writes,5.375757360214054E-4), (article,4.758295342758499E-4), (input,4.453294602805684E-4), (data,4.2990145274448345E-4)), Array((israel,7.571207379452645E-4), (jewish,6.65354600101315E-4), (writes,6.383328772453376E-4), (arab,6.249407968625211E-4), (april,6.043521307500501E-4)), Array((cubs,8.269719423563971E-4), (suck,8.238850844049989E-4), (writes,6.095129204456378E-4), (mathew,4.5011183923159166E-4), (mantis,4.455423576528236E-4)), Array((people,5.609722906901543E-4), (know,4.868781106315686E-4), (time,4.331604244957274E-4), (article,3.7054274063109765E-4), (like,3.7036469046352157E-4)), Array((writes,5.132711963317823E-4), (article,4.3054771158263763E-4), (people,4.270729605242108E-4), (want,3.877634611875376E-4), (just,3.791226066345781E-4)), Array((writes,0.0054312283960385645), (article,0.004678097342056468), (people,0.003939283778914211), (just,0.003800708985235169), (like,0.003719825123729371)), Array((working,5.312682127697326E-4), (windows,4.8551422285305517E-4), (problem,4.807546066926888E-4), (amendment,4.659289868038814E-4), (like,4.4757832769700133E-4)), Array((windows,6.253746613374257E-4), (writes,4.900408860598469E-4), (fact,4.568554161162082E-4), (software,4.478590759679576E-4), (article,4.134477354210795E-4)), Array((writes,7.738256279709485E-4), (article,6.555828771652631E-4), (time,5.738061424608107E-4), (truth,5.379517365953391E-4), (know,4.985161905298162E-4)), Array((image,0.004183654396019808), (program,0.0017574730245397377), (data,0.0017318859189716527), (file,0.0014852541281729795), (images,0.0014083673802810987)), Array((writes,8.327069562101253E-4), (article,6.158606846937626E-4), (just,4.7455659395936374E-4), (like,4.472272178483073E-4), (does,4.3304290936932243E-4)), Array((launch,0.001447659926679004), (space,0.0012696272256431682), (shuttle,8.306056563717886E-4), (venus,7.927154546427446E-4), (mission,6.1334113594564E-4)), Array((windows,0.004913479109017912), (microsoft,0.0022161433032952768), (mouse,0.0014163394288429392), (software,0.0013036479294347187), (graphics,0.001302894816173263)), Array((writes,8.098591997124465E-4), (article,6.541902233757504E-4), (faith,6.54026359519847E-4), (know,6.010310938376768E-4), (jesus,5.835655286195288E-4)), Array((article,4.065642421766462E-4), (just,3.451713989340494E-4), (writes,3.4177839900304685E-4), (russotto,3.390146087255311E-4), (belief,3.2134550562201296E-4)))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459996073336E12,"submitTime":1.459995971076E12,"finishTime":1.459996074096E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"5dd54cb3-4000-448e-8db5-33aaaad6f3b2"},{"version":"CommandV1","origId":95519,"guid":"a2457424-41de-4f10-a692-f2252ec49c89","subtype":"command","commandType":"auto","position":12.5,"command":"%md\nGoing through the results, you may notice that some of the topic words returned are actually stopwords that are specific to our dataset (for eg: \"writes\", \"article\"...). Let's try improving our model.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567750223E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"e7731f75-7d0f-4183-8a57-972833b80039"},{"version":"CommandV1","origId":95520,"guid":"a5a22994-4732-47a1-a183-271ff08ebb32","subtype":"command","commandType":"auto","position":13.0,"command":"%md \n## Step 8. Model Tuning - Refilter Stopwords\n\nWe will try to improve the results of our model by identifying some stopwords that are specific to our dataset. We will filter these stopwords out and rerun our LDA model to see if we get better results.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567750425E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"62c7009e-594b-4ee8-a711-6aaef1f397f4"},{"version":"CommandV1","origId":95521,"guid":"7c186f09-ef48-49af-aceb-58a186f4ac54","subtype":"command","commandType":"auto","position":15.0,"command":"val add_stopwords = Array(\"article\", \"writes\", \"entry\", \"date\", \"udel\", \"said\", \"tell\", \"think\", \"know\", \"just\", \"newsgroup\", \"line\", \"like\", \"does\", \"going\", \"make\", \"thanks\")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">add_stopwords: Array[String] = Array(article, writes, entry, date, udel, said, tell, think, know, just, newsgroup, line, like, does, going, make, thanks)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459996153208E12,"submitTime":1.459996050936E12,"finishTime":1.45999615334E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"388f925b-b2b6-41f8-bcee-19562c287dd2"},{"version":"CommandV1","origId":95522,"guid":"25f73514-7eb4-45d2-98b0-364e476962c3","subtype":"command","commandType":"auto","position":16.0,"command":"// Combine newly identified stopwords to our exising list of stopwords\nval new_stopwords = stopwords.union(add_stopwords)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">new_stopwords: Array[String] = Array(a, about, above, across, after, afterwards, again, against, all, almost, alone, along, already, also, although, always, am, among, amongst, amoungst, amount, an, and, another, any, anyhow, anyone, anything, anyway, anywhere, are, around, as, at, back, be, became, because, become, becomes, becoming, been, before, beforehand, behind, being, below, beside, besides, between, beyond, bill, both, bottom, but, by, call, can, cannot, cant, co, computer, con, could, couldnt, cry, de, describe, detail, do, done, down, due, during, each, eg, eight, either, eleven, else, elsewhere, empty, enough, etc, even, ever, every, everyone, everything, everywhere, except, few, fifteen, fify, fill, find, fire, first, five, for, former, formerly, forty, found, four, from, front, full, further, get, give, go, had, has, hasnt, have, he, hence, her, here, hereafter, hereby, herein, hereupon, hers, herself, him, himself, his, how, however, hundred, i, ie, if, in, inc, indeed, interest, into, is, it, its, itself, keep, last, latter, latterly, least, less, ltd, made, many, may, me, meanwhile, might, mill, mine, more, moreover, most, mostly, move, much, must, my, myself, name, namely, neither, never, nevertheless, next, nine, no, nobody, none, noone, nor, not, nothing, now, nowhere, of, off, often, on, once, one, only, onto, or, other, others, otherwise, our, ours, ourselves, out, over, own, part, per, perhaps, please, put, rather, re, same, see, seem, seemed, seeming, seems, serious, several, she, should, show, side, since, sincere, six, sixty, so, some, somehow, someone, something, sometime, sometimes, somewhere, still, such, system, take, ten, than, that, the, their, them, themselves, then, thence, there, thereafter, thereby, therefore, therein, thereupon, these, they, thick, thin, third, this, those, though, three, through, throughout, thru, thus, to, together, too, top, toward, towards, twelve, twenty, two, un, under, until, up, upon, us, very, via, was, we, well, were, what, whatever, when, whence, whenever, where, whereafter, whereas, whereby, wherein, whereupon, wherever, whether, which, while, whither, who, whoever, whole, whom, whose, why, will, with, within, without, would, yet, you, your, yours, yourself, yourselves, article, writes, entry, date, udel, said, tell, think, know, just, newsgroup, line, like, does, going, make, thanks)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459996156765E12,"submitTime":1.459996054494E12,"finishTime":1.459996156913E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"3b71f48e-9c42-4424-b362-d305cc67cc7d"},{"version":"CommandV1","origId":95523,"guid":"141886b7-ed3e-44ca-ab42-8ac95eaeb54d","subtype":"command","commandType":"auto","position":18.0,"command":"import org.apache.spark.ml.feature.StopWordsRemover\n\n// Set Params for StopWordsRemover with new_stopwords\nval remover = new StopWordsRemover()\n.setStopWords(new_stopwords)\n.setInputCol(\"tokens\")\n.setOutputCol(\"filtered\")\n\n// Create new df with new list of stopwords removed\nval new_filtered_df = remover.transform(tokenized_df)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">import org.apache.spark.ml.feature.StopWordsRemover\nremover: org.apache.spark.ml.feature.StopWordsRemover = stopWords_1ca9315ed099\nnew_filtered_df: org.apache.spark.sql.DataFrame = [corpus: string, id: bigint, tokens: array&lt;string&gt;, filtered: array&lt;string&gt;]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:40: error: not found: value tokenized_df\n       val new_filtered_df = remover.transform(tokenized_df)\n                                               ^\n</div>","error":null,"startTime":1.459996167427E12,"submitTime":1.459996065141E12,"finishTime":1.459996167548E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"2ac603e0-16da-44fb-a811-c52a9a03e6ca"},{"version":"CommandV1","origId":95524,"guid":"0ecd00ef-1a89-4866-acaa-8fe58182e6f4","subtype":"command","commandType":"auto","position":19.0,"command":"// Set Params for CountVectorizer\nval vectorizer = new CountVectorizer()\n.setInputCol(\"filtered\")\n.setOutputCol(\"features\")\n.setVocabSize(10000)\n.setMinDF(5)\n.fit(new_filtered_df)\n\n// Create new df of countVectors\nval new_countVectors = vectorizer.transform(new_filtered_df).select(\"id\", \"features\")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">vectorizer: org.apache.spark.ml.feature.CountVectorizerModel = cntVec_a230d19021f9\nnew_countVectors: org.apache.spark.sql.DataFrame = [id: bigint, features: vector]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:48: error: not found: type CountVectorizer\n       val vectorizer = new CountVectorizer()\n                            ^\n</div>","error":null,"startTime":1.459996194514E12,"submitTime":1.459996092232E12,"finishTime":1.459996195625E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"46926e14-1e33-43ff-bdee-579eb2a048f1"},{"version":"CommandV1","origId":95525,"guid":"9a6163cb-6ac1-46be-bd71-703593446090","subtype":"command","commandType":"auto","position":20.0,"command":"// Convert DF to RDD\nval new_lda_countVector = new_countVectors.map { case Row(id: Long, countVector: Vector) => (id, countVector) }","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">new_lda_countVector: org.apache.spark.rdd.RDD[(Long, org.apache.spark.mllib.linalg.Vector)] = MapPartitionsRDD[3334] at map at &lt;console&gt;:68\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:53: error: type Vector takes type parameters\n       val new_lda_countVector = new_countVectors.map { case Row(id: Long, countVector: Vector) =&gt; (id, countVector) }\n                                                                                        ^\n</div>","error":null,"startTime":1.45999620249E12,"submitTime":1.459996100215E12,"finishTime":1.45999620268E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"870a39ba-0f3d-4714-afa5-9d7e87a23e7b"},{"version":"CommandV1","origId":95527,"guid":"00cfab58-aa25-47d8-aede-e75ee8c5f5c9","subtype":"command","commandType":"auto","position":20.25,"command":"%md\nWe will also increase MaxIterations to 10 to see if we get better results.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.45256775192E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"339772c6-88de-4e3d-9e62-0a0c5d664d7b"},{"version":"CommandV1","origId":95528,"guid":"648facf4-2db7-46df-9a29-7041609d8232","subtype":"command","commandType":"auto","position":20.5,"command":"// Set LDA parameters\nval new_lda = new LDA()\n.setOptimizer(new OnlineLDAOptimizer().setMiniBatchFraction(0.8))\n.setK(numTopics)\n.setMaxIterations(10)\n.setDocConcentration(-1) // use default values\n.setTopicConcentration(-1) // use default values","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">new_lda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@3f0bde6\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:33: error: not found: type OnlineLDAOptimizer\n       .setOptimizer(new OnlineLDAOptimizer().setMiniBatchFraction(0.8))\n                         ^\n</div>","error":null,"startTime":1.459996211398E12,"submitTime":1.459996109137E12,"finishTime":1.459996211481E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"645761c2-df97-409c-a409-2937ff6ef03b"},{"version":"CommandV1","origId":96752,"guid":"b3f2eb85-c089-4d51-b8bb-3c79fcf8bd2d","subtype":"command","commandType":"auto","position":20.75,"command":"%md\n#### How to find what the default values are?\n\nDive into the source!!!\n\n1. Let's find the default value for `docConcentration` now.\n1. Got to Apache Spark package Root: [https://spark.apache.org/docs/latest/api/scala/#package](https://spark.apache.org/docs/latest/api/scala/#package)\n* search for 'ml' in the search box on the top left (ml is for ml library)\n* Then find the `LDA` by scrolling below on the left to mllib's `clustering` methods and click on `LDA`\n* Then click on the source code link which should take you here:\n  * [https://github.com/apache/spark/blob/v1.6.1/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala](https://github.com/apache/spark/blob/v1.6.1/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala)\n  * Now, simply go to the right function and see the following comment block:\n  \n  ```\n  /**\n   * Concentration parameter (commonly named \"alpha\") for the prior placed on documents'\n   * distributions over topics (\"theta\").\n   *\n   * This is the parameter to a Dirichlet distribution, where larger values mean more smoothing\n   * (more regularization).\n   *\n   * If not set by the user, then docConcentration is set automatically. If set to\n   * singleton vector [alpha], then alpha is replicated to a vector of length k in fitting.\n   * Otherwise, the [[docConcentration]] vector must be length k.\n   * (default = automatic)\n   *\n   * Optimizer-specific parameter settings:\n   *  - EM\n   *     - Currently only supports symmetric distributions, so all values in the vector should be\n   *       the same.\n   *     - Values should be > 1.0\n   *     - default = uniformly (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows\n   *       from Asuncion et al. (2009), who recommend a +1 adjustment for EM.\n   *  - Online\n   *     - Values should be >= 0\n   *     - default = uniformly (1.0 / k), following the implementation from\n   *       [[https://github.com/Blei-Lab/onlineldavb]].\n   * @group param\n   */\n  ```","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b38fe72a-d6bc-40a5-a528-f64b4f9a615e"},{"version":"CommandV1","origId":96753,"guid":"abf441d0-ac61-4c66-9b9b-7b7372c52989","subtype":"command","commandType":"auto","position":20.875,"command":"%md\n\n**HOMEWORK:** Try to find the default value for `TopicConcentration`.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"206d0757-1849-46eb-b5da-419e96f28239"},{"version":"CommandV1","origId":95529,"guid":"2a409b98-ad9d-46dd-baa8-f1104baf927f","subtype":"command","commandType":"auto","position":21.0,"command":"// Create LDA model with stopwords refiltered\nval new_ldaModel = new_lda.run(new_lda_countVector)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">new_ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.LocalLDAModel@62905291\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Cancelled","error":null,"startTime":1.459997087863E12,"submitTime":1.459996985545E12,"finishTime":1.459997110307E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"1fc314df-ca8d-4c45-b601-3de829944995"},{"version":"CommandV1","origId":95530,"guid":"dfd961cd-08db-4395-80f8-b440fc5cdc84","subtype":"command","commandType":"auto","position":22.0,"command":"val topicIndices = new_ldaModel.describeTopics(maxTermsPerTopic = 5)\nval vocabList = vectorizer.vocabulary\nval topics = topicIndices.map { case (terms, termWeights) =>\n  terms.map(vocabList(_)).zip(termWeights)\n}\nprintln(s\"$numTopics topics:\")\ntopics.zipWithIndex.foreach { case (topic, i) =>\n  println(s\"TOPIC $i\")\n  topic.foreach { case (term, weight) => println(s\"$term\\t$weight\") }\n  println(s\"==========\")\n}","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">20 topics:\nTOPIC 0\ntime\t3.2301501733831547E-4\npolygon\t2.757354909838181E-4\nused\t2.654483818633533E-4\nright\t2.609681122741453E-4\nsafety\t2.555461113590312E-4\n==========\nTOPIC 1\npeople\t0.003951273714608944\njesus\t0.0032637924981120574\ntruth\t0.0030727202813964774\nclaim\t0.0028706299469536084\nchristians\t0.0025538786366397143\n==========\nTOPIC 2\nencryption\t0.005316422834121803\nobjective\t0.002991273333367653\nchip\t0.0027757287945837064\nclipper\t0.002012250578300165\ntechnology\t0.002001134169400598\n==========\nTOPIC 3\ncandida\t0.004414249809838281\nyeast\t0.0016877442394059603\nbody\t0.0015543313441443174\ndoctor\t0.0013447410367361569\nbacteria\t0.001331759291274523\n==========\nTOPIC 4\nsimms\t5.475231919063244E-4\ndisc\t5.274794023991899E-4\npitch\t4.8461515406604896E-4\ntime\t4.4018984687835467E-4\nused\t4.2193583900747175E-4\n==========\nTOPIC 5\npeople\t5.180544695268785E-4\ndisease\t4.3932168319708496E-4\nused\t3.7204624454097174E-4\ndavid\t3.708942763352488E-4\nright\t3.5828089274062274E-4\n==========\nTOPIC 6\ncards\t5.811861323938364E-4\ncolors\t3.9033297880507824E-4\nteam\t3.8068705498256337E-4\ncolor\t3.7720959799918537E-4\npeople\t3.766534832091429E-4\n==========\nTOPIC 7\ncubs\t0.002989600100099179\nsuck\t0.002165896577654509\nedge\t8.897338746311301E-4\nbetter\t4.586257607768128E-4\ngood\t3.7240640082069693E-4\n==========\nTOPIC 8\nwindows\t5.74890058710956E-4\nuiuc\t5.269991344592961E-4\npeople\t4.6894594738859744E-4\ncard\t4.4159043747457475E-4\nhard\t4.3643961474169136E-4\n==========\nTOPIC 9\npitt\t6.888917961022905E-4\nround\t3.5911178227999345E-4\nwash\t3.578458469164246E-4\nversion\t3.204457453513077E-4\nwork\t3.028987091869664E-4\n==========\nTOPIC 10\nconvenient\t0.0015581222086621636\nabortion\t0.001399744165824835\ninsurance\t0.001136301188610057\nberkeley\t9.071974771094637E-4\npremiums\t8.898195501611404E-4\n==========\nTOPIC 11\npeople\t0.00111446107424767\nbrake\t8.720332539076117E-4\ncylinder\t8.295916751229445E-4\nlord\t8.020986659645837E-4\nmaster\t7.385561845243469E-4\n==========\nTOPIC 12\ntheory\t7.219925969610627E-4\nfacts\t4.4780129637149045E-4\nevidence\t3.7647653439782557E-4\nspace\t3.2546428509197485E-4\nfact\t3.0635854965576704E-4\n==========\nTOPIC 13\ngame\t0.0057741025745637505\nteam\t0.004198948084653449\nyear\t0.003804651124454971\nplay\t0.0028473796331209313\ntime\t0.0021502589954345633\n==========\nTOPIC 14\njpeg\t0.009841626752204761\nfile\t0.005045954276754632\nimage\t0.00488603842989107\ncolor\t0.003659967408335233\nformat\t0.0032992541694430366\n==========\nTOPIC 15\nturkish\t0.0014266383186648003\ngreek\t0.0011870797498038858\ncyprus\t9.612857642231201E-4\nturks\t6.572283488506592E-4\npopulation\t4.6256576443266323E-4\n==========\nTOPIC 16\ndrive\t7.951106239215275E-4\nmotorola\t6.327188868676713E-4\nsync\t3.6229465027526043E-4\nlaunch\t3.5588576685879485E-4\nscsi\t3.3914936678971065E-4\n==========\nTOPIC 17\npeople\t0.0020630565384552554\ncramer\t0.0020371723638211957\nhomosexual\t0.0017387175382115126\njpeg\t0.0012225645437688017\noptilink\t0.0012135014715535155\n==========\nTOPIC 18\npeople\t0.0051467172160733566\ntime\t0.0037344162093453327\nwindows\t0.0032625617270205993\nused\t0.002961411951622898\ngood\t0.002745567943973728\n==========\nTOPIC 19\nscsi\t6.862948857617457E-4\ndrive\t5.003093593162511E-4\ncars\t4.790266547915217E-4\nmathew\t4.3436039867124945E-4\nlist\t3.674850446577185E-4\n==========\ntopicIndices: Array[(Array[Int], Array[Double])] = Array((Array(1, 1840, 3, 7, 846),Array(3.2301501733831547E-4, 2.757354909838181E-4, 2.654483818633533E-4, 2.609681122741453E-4, 2.555461113590312E-4)), (Array(0, 112, 130, 113, 264),Array(0.003951273714608944, 0.0032637924981120574, 0.0030727202813964774, 0.0028706299469536084, 0.0025538786366397143)), (Array(128, 339, 126, 336, 186),Array(0.005316422834121803, 0.002991273333367653, 0.0027757287945837064, 0.002012250578300165, 0.002001134169400598)), (Array(480, 1381, 214, 717, 1737),Array(0.004414249809838281, 0.0016877442394059603, 0.0015543313441443174, 0.0013447410367361569, 0.001331759291274523)), (Array(879, 3113, 1838, 1, 3),Array(5.475231919063244E-4, 5.274794023991899E-4, 4.8461515406604896E-4, 4.4018984687835467E-4, 4.2193583900747175E-4)), (Array(0, 435, 3, 64, 7),Array(5.180544695268785E-4, 4.3932168319708496E-4, 3.7204624454097174E-4, 3.708942763352488E-4, 3.5828089274062274E-4)), (Array(472, 461, 123, 78, 0),Array(5.811861323938364E-4, 3.9033297880507824E-4, 3.8068705498256337E-4, 3.7720959799918537E-4, 3.766534832091429E-4)), (Array(704, 1057, 838, 14, 2),Array(0.002989600100099179, 0.002165896577654509, 8.897338746311301E-4, 4.586257607768128E-4, 3.7240640082069693E-4)), (Array(4, 189, 0, 81, 88),Array(5.74890058710956E-4, 5.269991344592961E-4, 4.6894594738859744E-4, 4.4159043747457475E-4, 4.3643961474169136E-4)), (Array(1666, 1008, 4463, 39, 6),Array(6.888917961022905E-4, 3.5911178227999345E-4, 3.578458469164246E-4, 3.204457453513077E-4, 3.028987091869664E-4)), (Array(1221, 860, 376, 573, 2599),Array(0.0015581222086621636, 0.001399744165824835, 0.001136301188610057, 9.071974771094637E-4, 8.898195501611404E-4)), (Array(0, 2182, 2422, 975, 1195),Array(0.00111446107424767, 8.720332539076117E-4, 8.295916751229445E-4, 8.020986659645837E-4, 7.385561845243469E-4)), (Array(342, 806, 94, 26, 46),Array(7.219925969610627E-4, 4.4780129637149045E-4, 3.7647653439782557E-4, 3.2546428509197485E-4, 3.0635854965576704E-4)), (Array(60, 123, 19, 125, 1),Array(0.0057741025745637505, 0.004198948084653449, 0.003804651124454971, 0.0028473796331209313, 0.0021502589954345633)), (Array(45, 27, 11, 78, 145),Array(0.009841626752204761, 0.005045954276754632, 0.00488603842989107, 0.003659967408335233, 0.0032992541694430366)), (Array(313, 650, 2241, 857, 670),Array(0.0014266383186648003, 0.0011870797498038858, 9.612857642231201E-4, 6.572283488506592E-4, 4.6256576443266323E-4)), (Array(23, 1952, 1808, 291, 330),Array(7.951106239215275E-4, 6.327188868676713E-4, 3.6229465027526043E-4, 3.5588576685879485E-4, 3.3914936678971065E-4)), (Array(0, 861, 667, 45, 1314),Array(0.0020630565384552554, 0.0020371723638211957, 0.0017387175382115126, 0.0012225645437688017, 0.0012135014715535155)), (Array(0, 1, 4, 3, 2),Array(0.0051467172160733566, 0.0037344162093453327, 0.0032625617270205993, 0.002961411951622898, 0.002745567943973728)), (Array(330, 23, 282, 1377, 58),Array(6.862948857617457E-4, 5.003093593162511E-4, 4.790266547915217E-4, 4.3436039867124945E-4, 3.674850446577185E-4)))\nvocabList: Array[String] = Array(people, time, good, used, windows, want, work, right, problem, need, really, image, data, information, better, believe, using, software, years, year, mail, sure, point, drive, program, available, space, file, power, help, government, things, question, doesn, number, case, world, look, read, version, come, thing, long, different, best, jpeg, fact, university, real, probably, didn, true, state, course, files, high, possible, actually, list, 1993, game, little, news, group, david, send, wrong, graphics, based, able, support, place, free, called, subject, post, john, reason, color, second, great, card, having, public, email, info, following, start, hard, science, says, example, means, code, evidence, person, note, maybe, general, president, heard, problems, quite, mean, source, life, systems, price, order, window, standard, access, jesus, claim, paul, getting, looking, control, trying, simply, disk, seen, times, team, book, play, chip, local, encryption, idea, truth, church, research, opinions, issue, given, images, wrote, display, large, thought, makes, remember, national, doing, format, away, nasa, change, human, home, small, saying, current, mark, interested, today, area, internet, word, original, agree, left, memory, microsoft, works, machine, instead, kind, hardware, working, request, sort, higher, money, later, programs, questions, israel, mike, pretty, hand, guess, netcom, address, include, technology, cause, matter, uiuc, type, video, speed, wire, days, server, usually, view, earth, package, april, open, stuff, unless, told, christian, important, similar, house, major, size, faith, known, provide, body, phone, ground, rights, michael, health, american, apple, feel, including, center, answer, bible, lines, user, cost, text, check, security, mind, anybody, understand, care, copy, wouldn, live, certainly, started, level, network, women, message, mouse, running, study, clinton, making, position, company, came, board, screen, groups, talking, single, common, white, wiring, monitor, likely, christians, test, special, quality, black, light, effect, nice, medical, members, hope, sources, certain, posted, uucp, fine, hear, canada, cars, write, clear, difference, history, couple, build, police, love, launch, press, books, situation, jewish, specific, sense, particular, words, talk, unix, anti, stop, posting, model, religion, discussion, school, contact, private, built, frank, turkish, keys, cable, taking, simple, service, features, legal, sound, consider, short, night, children, reference, argument, tools, comes, scsi, device, application, comments, jews, applications, clipper, doubt, tried, objective, force, process, theory, self, experience, usenet, games, expect, steve, early, tape, uses, needed, interesting, station, easy, death, exactly, manager, killed, value, turn, correct, according, needs, amiga, ones, response, states, drug, considered, reading, james, language, wanted, shuttle, insurance, personal, term, goes, koresh, strong, past, form, opinion, future, taken, result, sorry, mentioned, rules, especially, religious, hell, happy, society, plus, went, country, design, written, various, author, guns, drivers, asked, results, analysis, haven, latest, longer, gets, advance, aren, parts, cases, york, main, section, laws, previous, accept, input, looks, week, mode, required, christ, weapons, washington, community, robert, numbers, head, fast, disease, option, series, circuit, office, offer, macintosh, driver, andrew, period, venus, israeli, range, exist, clock, players, runs, values, department, toronto, involved, moral, allow, organization, brian, sell, colors, knows, picture, months, choice, half, takes, currently, suggest, dave, armenians, cards, federal, individual, object, wasn, hockey, took, includes, candida, policy, total, title, directly, protect, follow, americans, food, equipment, assume, close, purpose, recently, devices, statement, present, happened, happen, users, media, provides, deal, require, reasons, scientific, christianity, road, shall, dead, lost, action, speak, couldn, bike, save, wants, city, george, goal, lead, obviously, completely, california, mission, useful, details, baseball, voice, engineering, league, complete, uunet, terms, batf, condition, court, easily, rest, responsible, administration, ways, international, sent, clearly, compatible, disclaimer, output, water, algorithm, business, appreciated, freedom, kill, issues, digital, pass, hours, fans, figure, error, story, project, deleted, coming, operating, processing, companies, context, average, trade, appropriate, leave, berkeley, season, face, carry, events, port, difficult, addition, performance, building, basis, final, requires, heart, points, armenian, political, lower, player, king, convert, page, environment, apply, knowledge, resolution, field, willing, volume, actual, stanford, site, sale, suppose, related, limited, entries, advice, commercial, sounds, worth, orbit, lots, basic, defense, anonymous, directory, claims, supposed, designed, explain, handle, included, turkey, reply, enforcement, inside, family, ability, signal, young, neutral, forget, changes, wonder, suspect, radio, necessary, programming, wait, understanding, paper, friend, thinking, shot, month, services, greek, installed, create, printer, land, russian, finally, homosexuality, natural, morality, formats, names, machines, report, peter, setting, eric, homosexual, normal, interface, population, hold, break, comment, rutgers, reasonable, arab, logic, miles, secure, east, supply, product, communications, comp, percent, escrow, avoid, lives, colorado, types, room, allowed, multiple, developed, peace, cancer, million, library, expensive, cheap, gary, soon, recent, cubs, agencies, mention, supported, event, gives, soviet, looked, release, obvious, street, physics, suggestions, doctor, technical, caused, happens, table, return, archive, treatment, bitnet, chance, mass, radar, outside, entire, friends, folks, choose, print, install, generally, development, diet, exists, areas, concept, requests, straight, child, learn, supports, behavior, ideas, morning, member, electrical, illegal, weeks, social, wish, smith, trouble, muslim, jack, meaning, united, remove, stand, bring, thank, unit, reality, engine, worked, practice, drives, motif, west, asking, appear, provided, pick, studies, attempt, possibly, answers, decided, near, henry, trust, existence, changed, modem, bits, errors, false, continue, middle, belief, compound, extra, arguments, proper, particularly, class, congress, guys, contains, yeah, safe, facts, loss, worse, heat, appears, thread, andy, pages, manual, function, attack, fonts, aware, privacy, operations, armenia, constitution, institute, frame, command, drugs, stupid, wide, nature, conference, independent, unfortunately, hands, wall, distribution, approach, speaking, edge, division, shouldn, knew, effective, indiana, shows, compression, safety, crime, serial, added, 1992, 1990, modern, ethernet, solution, civil, bought, turks, floppy, appreciate, abortion, cramer, blood, blue, letter, plastic, regular, writing, spend, allows, utility, hello, rate, virginia, wondering, germany, gave, record, faster, simms, operation, arms, internal, playing, step, giving, views, switch, tool, decision, atheism, surface, articles, actions, font, additional, method, described, base, concerned, scott, stated, kids, played, prevent, rule, late, quote, buying, capability, places, products, attitude, costs, patients, controller, fair, connected, north, military, keith, cover, levels, highly, considering, resources, turned, apparently, valid, hate, countries, shell, western, poor, excellent, market, necessarily, wires, created, america, richard, boston, calls, brought, functions, received, account, creation, watch, majority, cwru, forward, student, released, driving, authority, committee, chips, quick, protection, notice, produce, sold, arizona, complex, visual, absolutely, arabs, fairly, murder, keyboard, plan, moon, minutes, boxes, lord, properly, greatly, killing, shareware, larry, islam, vote, panel, rangers, effort, options, review, dangerous, holy, intelligence, acts, tests, nation, property, damage, begin, living, electronics, failed)\ntopics: Array[Array[(String, Double)]] = Array(Array((time,3.2301501733831547E-4), (polygon,2.757354909838181E-4), (used,2.654483818633533E-4), (right,2.609681122741453E-4), (safety,2.555461113590312E-4)), Array((people,0.003951273714608944), (jesus,0.0032637924981120574), (truth,0.0030727202813964774), (claim,0.0028706299469536084), (christians,0.0025538786366397143)), Array((encryption,0.005316422834121803), (objective,0.002991273333367653), (chip,0.0027757287945837064), (clipper,0.002012250578300165), (technology,0.002001134169400598)), Array((candida,0.004414249809838281), (yeast,0.0016877442394059603), (body,0.0015543313441443174), (doctor,0.0013447410367361569), (bacteria,0.001331759291274523)), Array((simms,5.475231919063244E-4), (disc,5.274794023991899E-4), (pitch,4.8461515406604896E-4), (time,4.4018984687835467E-4), (used,4.2193583900747175E-4)), Array((people,5.180544695268785E-4), (disease,4.3932168319708496E-4), (used,3.7204624454097174E-4), (david,3.708942763352488E-4), (right,3.5828089274062274E-4)), Array((cards,5.811861323938364E-4), (colors,3.9033297880507824E-4), (team,3.8068705498256337E-4), (color,3.7720959799918537E-4), (people,3.766534832091429E-4)), Array((cubs,0.002989600100099179), (suck,0.002165896577654509), (edge,8.897338746311301E-4), (better,4.586257607768128E-4), (good,3.7240640082069693E-4)), Array((windows,5.74890058710956E-4), (uiuc,5.269991344592961E-4), (people,4.6894594738859744E-4), (card,4.4159043747457475E-4), (hard,4.3643961474169136E-4)), Array((pitt,6.888917961022905E-4), (round,3.5911178227999345E-4), (wash,3.578458469164246E-4), (version,3.204457453513077E-4), (work,3.028987091869664E-4)), Array((convenient,0.0015581222086621636), (abortion,0.001399744165824835), (insurance,0.001136301188610057), (berkeley,9.071974771094637E-4), (premiums,8.898195501611404E-4)), Array((people,0.00111446107424767), (brake,8.720332539076117E-4), (cylinder,8.295916751229445E-4), (lord,8.020986659645837E-4), (master,7.385561845243469E-4)), Array((theory,7.219925969610627E-4), (facts,4.4780129637149045E-4), (evidence,3.7647653439782557E-4), (space,3.2546428509197485E-4), (fact,3.0635854965576704E-4)), Array((game,0.0057741025745637505), (team,0.004198948084653449), (year,0.003804651124454971), (play,0.0028473796331209313), (time,0.0021502589954345633)), Array((jpeg,0.009841626752204761), (file,0.005045954276754632), (image,0.00488603842989107), (color,0.003659967408335233), (format,0.0032992541694430366)), Array((turkish,0.0014266383186648003), (greek,0.0011870797498038858), (cyprus,9.612857642231201E-4), (turks,6.572283488506592E-4), (population,4.6256576443266323E-4)), Array((drive,7.951106239215275E-4), (motorola,6.327188868676713E-4), (sync,3.6229465027526043E-4), (launch,3.5588576685879485E-4), (scsi,3.3914936678971065E-4)), Array((people,0.0020630565384552554), (cramer,0.0020371723638211957), (homosexual,0.0017387175382115126), (jpeg,0.0012225645437688017), (optilink,0.0012135014715535155)), Array((people,0.0051467172160733566), (time,0.0037344162093453327), (windows,0.0032625617270205993), (used,0.002961411951622898), (good,0.002745567943973728)), Array((scsi,6.862948857617457E-4), (drive,5.003093593162511E-4), (cars,4.790266547915217E-4), (mathew,4.3436039867124945E-4), (list,3.674850446577185E-4)))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:55: error: not found: value new_ldaModel\n              val topicIndices = new_ldaModel.describeTopics(maxTermsPerTopic = 5)\n                                 ^\n</div>","error":null,"startTime":1.459997158271E12,"submitTime":1.459997055953E12,"finishTime":1.459997158784E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"232dbc3d-49db-4796-a1df-32be0b45bf9f"},{"version":"CommandV1","origId":95531,"guid":"a98baf84-4441-451c-9e9a-0129591e3661","subtype":"command","commandType":"auto","position":22.5,"command":"%md \nWe managed to get better results here. We can easily infer that topic 0 is about religion, topic 1 is about health, and topic 3 is about computers.\n\n```\nTOPIC 0\njesus\t0.0025991279808337086\njews\t0.0010588991588900212\nchristian\t8.051251021840198E-4\npeople\t7.752528303484914E-4\nmuslims\t7.618771378180496E-4\n\nTOPIC 1\nfood\t0.0020522039748626236\ndisease\t0.001845073142734646\ncancer\t0.0017833493426782912\nscience\t0.001399564327418778\nhealth\t0.0012375975892372289\n\nTOPIC 3\nwindows\t0.0053426084488505535\nimage\t0.0040386364479657755\nfile\t0.0037715493560291557\nsoftware\t0.003582419843166839\nprogram\t0.0033343163496265915\n```","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567752751E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"775f3125-58fe-476e-8d38-16be72f68f62"},{"version":"CommandV1","origId":95532,"guid":"462995f6-e36e-4c2d-b391-7bd6741f23d4","subtype":"command","commandType":"auto","position":23.0,"command":"%md \n## Step 9. Create LDA model with Expectation Maximization\n\nLet's try creating an LDA model with Expectation Maximization on the data that has been refiltered for additional stopwords. We will also increase MaxIterations here to 100 to see if that improves results.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567753004E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"e932e0bc-d827-44d1-b7f5-818b165be4fe"},{"version":"CommandV1","origId":96754,"guid":"8f9216a4-33cc-4a2d-82eb-301b16fd60d9","subtype":"command","commandType":"auto","position":23.5,"command":"displayHTML(frameIt(\"http://spark.apache.org/docs/latest/mllib-clustering.html#latent-dirichlet-allocation-lda\",800))","commandVersion":0,"state":"finished","results":{"type":"htmlSandbox","data":"<iframe \n src=\"http://spark.apache.org/docs/latest/mllib-clustering.html#latent-dirichlet-allocation-lda\"\n width=\"95%\" height=\"800\"\n sandbox>\n  <p>\n    <a href=\"http://spark.apache.org/docs/latest/ml-features.html\">\n      Fallback link for browsers that, unlikely, don't support frames\n    </a>\n  </p>\n</iframe>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459997471366E12,"submitTime":1.459997368777E12,"finishTime":1.459997471439E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":true,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"6aa5a91e-2fed-40ff-8587-5deb83df64c6"},{"version":"CommandV1","origId":95533,"guid":"3c87ebcb-0f30-4b48-b43c-e9d2a75dc4d9","subtype":"command","commandType":"auto","position":24.0,"command":"import org.apache.spark.mllib.clustering.EMLDAOptimizer\n\n// Set LDA parameters\nval em_lda = new LDA()\n.setOptimizer(new EMLDAOptimizer())\n.setK(numTopics)\n.setMaxIterations(100)\n.setDocConcentration(-1) // use default values\n.setTopicConcentration(-1) // use default values","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">import org.apache.spark.mllib.clustering.EMLDAOptimizer\nem_lda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@7176a59a\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:32: error: not found: value numTopics\n       .setK(numTopics)\n             ^\n</div>","error":null,"startTime":1.459997588242E12,"submitTime":1.459997485903E12,"finishTime":1.459997588317E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"d6f1e353-57da-4247-a804-854c0889e0ce"},{"version":"CommandV1","origId":95534,"guid":"b1f12f8f-3a4e-4b9c-a89d-1f58adee7326","subtype":"command","commandType":"auto","position":25.0,"command":"val em_ldaModel = em_lda.run(new_lda_countVector)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">em_ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.DistributedLDAModel@5654a48c\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:32: error: not found: value new_lda_countVector\n       val em_ldaModel = em_lda.run(new_lda_countVector)\n                                    ^\n</div>","error":null,"startTime":1.459997591564E12,"submitTime":1.459997489225E12,"finishTime":1.459997623437E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"df853b97-a8fe-455a-82f6-267123678317"},{"version":"CommandV1","origId":95535,"guid":"9a13988e-f6cb-4a15-8d08-1a335cd8fb5f","subtype":"command","commandType":"auto","position":25.5,"command":"%md\nNote that the EMLDAOptimizer produces a DistributedLDAModel, which stores not only the inferred topics but also the full training corpus and topic distributions for each document in the training corpus.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567753625E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"64224731-4e89-4e36-8fc8-df5860cc7e9d"},{"version":"CommandV1","origId":96755,"guid":"533b7892-3a08-438b-ae75-85cbdc04b493","subtype":"command","commandType":"auto","position":25.75,"command":"val topicIndices = em_ldaModel.describeTopics(maxTermsPerTopic = 5)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(47, 155, 124, 76, 218),Array(0.021731135177920788, 0.016402793489762747, 0.015784827128360605, 0.014236324739281266, 0.01069977995545543)), (Array(174, 376, 31, 325, 0),Array(0.013383837363866821, 0.010292082551734102, 0.008039549389022444, 0.007633357944471386, 0.00762669449038805)), (Array(65, 171, 313, 58, 471),Array(0.014582482929017393, 0.013306640446635917, 0.01319397874544908, 0.01063389199241538, 0.010094965595369557)), (Array(189, 62, 289, 13, 52),Array(0.01396778209475855, 0.012442407662526347, 0.012270129483409602, 0.00971707551075861, 0.008670165535655201)), (Array(131, 112, 114, 205, 0),Array(0.014774249841210198, 0.01472101708352839, 0.012856526169517131, 0.011220748016002442, 0.01121031333916818)), (Array(23, 88, 330, 254, 101),Array(0.032328098098879966, 0.013511791382746467, 0.010624585848750407, 0.010053234548630935, 0.009892303966702721)), (Array(50, 99, 1, 154, 32),Array(0.01933819111424734, 0.016430063960990293, 0.014591212868028463, 0.012831103829368333, 0.011737387642684876)), (Array(12, 11, 25, 17, 328),Array(0.024715554075101145, 0.018860536627015576, 0.01113913102866641, 0.009235060603573717, 0.006169193503301667)), (Array(480, 187, 214, 272, 0),Array(0.008134391093925976, 0.007455491378002872, 0.007338867710850107, 0.0070809265323322065, 0.0066708098070737045)), (Array(241, 380, 445, 534, 113),Array(0.011262371351669808, 0.010524851497111375, 0.008454573424664923, 0.007832608671526454, 0.006390451997634408)), (Array(4, 81, 17, 109, 164),Array(0.04621385962315641, 0.019155126806073106, 0.018199377664440387, 0.017829015780798332, 0.01530658854866115)), (Array(26, 147, 291, 375, 354),Array(0.02523224351920664, 0.015132028053982085, 0.012965326071936832, 0.010420083818033865, 0.010332319509074891)), (Array(178, 0, 446, 295, 359),Array(0.016887594414581104, 0.011422443736481653, 0.010163056159288525, 0.009480763103690159, 0.008273916285900816)), (Array(51, 130, 339, 312, 32),Array(0.012945705379462196, 0.010985725790024931, 0.01074449663349066, 0.010547109259517407, 0.009618825382388466)), (Array(0, 2, 183, 15, 5),Array(0.04103419179492583, 0.021671903969276998, 0.01094554996975329, 0.010517816098190474, 0.009823377474213819)), (Array(60, 19, 125, 123, 346),Array(0.021909895478140564, 0.01857558836132334, 0.01485871615893668, 0.014438994587822065, 0.009279372287470302)), (Array(27, 45, 54, 24, 11),Array(0.022005274037133184, 0.021977302374802322, 0.01625470494831252, 0.016001829993456403, 0.015454736788736897)), (Array(20, 234, 116, 514, 133),Array(0.015988081068201616, 0.013917705930545383, 0.011924417195875689, 0.009867442582785501, 0.00948707682650206)), (Array(30, 128, 126, 314, 186),Array(0.019783953090745134, 0.015921870017092693, 0.011668233576256385, 0.009458499359868132, 0.009298234142155769)), (Array(193, 3, 261, 216, 74),Array(0.013706349885984542, 0.013451206225325123, 0.01171996347950028, 0.011315206371089155, 0.010834407806128689)))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459998009781E12,"submitTime":1.459997907149E12,"finishTime":1.45999801001E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"9f3202ac-aa3c-4614-adcd-db4c9135dd7f"},{"version":"CommandV1","origId":96756,"guid":"ee34183c-9338-4d8e-ac4a-626259bc86a8","subtype":"command","commandType":"auto","position":25.875,"command":"val vocabList = vectorizer.vocabulary","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">vocabList: Array[String] = Array(people, time, good, used, windows, want, work, right, problem, need, really, image, data, information, better, believe, using, software, years, year, mail, sure, point, drive, program, available, space, file, power, help, government, things, question, doesn, number, case, world, look, read, version, come, thing, long, different, best, jpeg, fact, university, real, probably, didn, true, state, course, files, high, possible, actually, list, 1993, game, little, news, group, david, send, wrong, graphics, based, able, support, place, free, called, subject, post, john, reason, color, second, great, card, having, public, email, info, following, start, hard, science, says, example, means, code, evidence, person, note, maybe, general, president, heard, problems, quite, mean, source, life, systems, price, order, window, standard, access, jesus, claim, paul, getting, looking, control, trying, simply, disk, seen, times, team, book, play, chip, local, encryption, idea, truth, church, research, opinions, issue, given, images, wrote, display, large, thought, makes, remember, national, doing, format, away, nasa, change, human, home, small, saying, current, mark, interested, today, area, internet, word, original, agree, left, memory, microsoft, works, machine, instead, kind, hardware, working, request, sort, higher, money, later, programs, questions, israel, mike, pretty, hand, guess, netcom, address, include, technology, cause, matter, uiuc, type, video, speed, wire, days, server, usually, view, earth, package, april, open, stuff, unless, told, christian, important, similar, house, major, size, faith, known, provide, body, phone, ground, rights, michael, health, american, apple, feel, including, center, answer, bible, lines, user, cost, text, check, security, mind, anybody, understand, care, copy, wouldn, live, certainly, started, level, network, women, message, mouse, running, study, clinton, making, position, company, came, board, screen, groups, talking, single, common, white, wiring, monitor, likely, christians, test, special, quality, black, light, effect, nice, medical, members, hope, sources, certain, posted, uucp, fine, hear, canada, cars, write, clear, difference, history, couple, build, police, love, launch, press, books, situation, jewish, specific, sense, particular, words, talk, unix, anti, stop, posting, model, religion, discussion, school, contact, private, built, frank, turkish, keys, cable, taking, simple, service, features, legal, sound, consider, short, night, children, reference, argument, tools, comes, scsi, device, application, comments, jews, applications, clipper, doubt, tried, objective, force, process, theory, self, experience, usenet, games, expect, steve, early, tape, uses, needed, interesting, station, easy, death, exactly, manager, killed, value, turn, correct, according, needs, amiga, ones, response, states, drug, considered, reading, james, language, wanted, shuttle, insurance, personal, term, goes, koresh, strong, past, form, opinion, future, taken, result, sorry, mentioned, rules, especially, religious, hell, happy, society, plus, went, country, design, written, various, author, guns, drivers, asked, results, analysis, haven, latest, longer, gets, advance, aren, parts, cases, york, main, section, laws, previous, accept, input, looks, week, mode, required, christ, weapons, washington, community, robert, numbers, head, fast, disease, option, series, circuit, office, offer, macintosh, driver, andrew, period, venus, israeli, range, exist, clock, players, runs, values, department, toronto, involved, moral, allow, organization, brian, sell, colors, knows, picture, months, choice, half, takes, currently, suggest, dave, armenians, cards, federal, individual, object, wasn, hockey, took, includes, candida, policy, total, title, directly, protect, follow, americans, food, equipment, assume, close, purpose, recently, devices, statement, present, happened, happen, users, media, provides, deal, require, reasons, scientific, christianity, road, shall, dead, lost, action, speak, couldn, bike, save, wants, city, george, goal, lead, obviously, completely, california, mission, useful, details, baseball, voice, engineering, league, complete, uunet, terms, batf, condition, court, easily, rest, responsible, administration, ways, international, sent, clearly, compatible, disclaimer, output, water, algorithm, business, appreciated, freedom, kill, issues, digital, pass, hours, fans, figure, error, story, project, deleted, coming, operating, processing, companies, context, average, trade, appropriate, leave, berkeley, season, face, carry, events, port, difficult, addition, performance, building, basis, final, requires, heart, points, armenian, political, lower, player, king, convert, page, environment, apply, knowledge, resolution, field, willing, volume, actual, stanford, site, sale, suppose, related, limited, entries, advice, commercial, sounds, worth, orbit, lots, basic, defense, anonymous, directory, claims, supposed, designed, explain, handle, included, turkey, reply, enforcement, inside, family, ability, signal, young, neutral, forget, changes, wonder, suspect, radio, necessary, programming, wait, understanding, paper, friend, thinking, shot, month, services, greek, installed, create, printer, land, russian, finally, homosexuality, natural, morality, formats, names, machines, report, peter, setting, eric, homosexual, normal, interface, population, hold, break, comment, rutgers, reasonable, arab, logic, miles, secure, east, supply, product, communications, comp, percent, escrow, avoid, lives, colorado, types, room, allowed, multiple, developed, peace, cancer, million, library, expensive, cheap, gary, soon, recent, cubs, agencies, mention, supported, event, gives, soviet, looked, release, obvious, street, physics, suggestions, doctor, technical, caused, happens, table, return, archive, treatment, bitnet, chance, mass, radar, outside, entire, friends, folks, choose, print, install, generally, development, diet, exists, areas, concept, requests, straight, child, learn, supports, behavior, ideas, morning, member, electrical, illegal, weeks, social, wish, smith, trouble, muslim, jack, meaning, united, remove, stand, bring, thank, unit, reality, engine, worked, practice, drives, motif, west, asking, appear, provided, pick, studies, attempt, possibly, answers, decided, near, henry, trust, existence, changed, modem, bits, errors, false, continue, middle, belief, compound, extra, arguments, proper, particularly, class, congress, guys, contains, yeah, safe, facts, loss, worse, heat, appears, thread, andy, pages, manual, function, attack, fonts, aware, privacy, operations, armenia, constitution, institute, frame, command, drugs, stupid, wide, nature, conference, independent, unfortunately, hands, wall, distribution, approach, speaking, edge, division, shouldn, knew, effective, indiana, shows, compression, safety, crime, serial, added, 1992, 1990, modern, ethernet, solution, civil, bought, turks, floppy, appreciate, abortion, cramer, blood, blue, letter, plastic, regular, writing, spend, allows, utility, hello, rate, virginia, wondering, germany, gave, record, faster, simms, operation, arms, internal, playing, step, giving, views, switch, tool, decision, atheism, surface, articles, actions, font, additional, method, described, base, concerned, scott, stated, kids, played, prevent, rule, late, quote, buying, capability, places, products, attitude, costs, patients, controller, fair, connected, north, military, keith, cover, levels, highly, considering, resources, turned, apparently, valid, hate, countries, shell, western, poor, excellent, market, necessarily, wires, created, america, richard, boston, calls, brought, functions, received, account, creation, watch, majority, cwru, forward, student, released, driving, authority, committee, chips, quick, protection, notice, produce, sold, arizona, complex, visual, absolutely, arabs, fairly, murder, keyboard, plan, moon, minutes, boxes, lord, properly, greatly, killing, shareware, larry, islam, vote, panel, rangers, effort, options, review, dangerous, holy, intelligence, acts, tests, nation, property, damage, begin, living, electronics, failed)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459998052367E12,"submitTime":1.459997949734E12,"finishTime":1.459998052463E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"16c2a951-e4d5-4216-825e-811fe2d46911"},{"version":"CommandV1","origId":96757,"guid":"18b8e3ec-7ac5-4f4f-932b-2c534adcbd29","subtype":"command","commandType":"auto","position":25.9375,"command":"vocabList.size","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res49: Int = 6122\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:70: error: missing arguments for method count in trait TraversableOnce;\nfollow this method with `_' if you want to treat it as a partially applied function\n              vocabList.count\n                        ^\n</div>","error":null,"startTime":1.459998100815E12,"submitTime":1.459997998456E12,"finishTime":1.459998100929E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"4ecf1764-2017-49f5-993e-d42c26b2735b"},{"version":"CommandV1","origId":96758,"guid":"e4a4992e-5101-4816-abb2-b7d6c635535c","subtype":"command","commandType":"auto","position":25.96875,"command":"val topics = topicIndices.map { case (terms, termWeights) =>\n  terms.map(vocabList(_)).zip(termWeights)\n}","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">topics: Array[Array[(String, Double)]] = Array(Array((university,0.021731135177920788), (interested,0.016402793489762747), (book,0.015784827128360605), (john,0.014236324739281266), (michael,0.01069977995545543)), Array((money,0.013383837363866821), (insurance,0.010292082551734102), (things,0.008039549389022444), (children,0.007633357944471386), (people,0.00762669449038805)), Array((send,0.014582482929017393), (request,0.013306640446635917), (turkish,0.01319397874544908), (list,0.01063389199241538), (armenians,0.010094965595369557)), Array((uiuc,0.01396778209475855), (news,0.012442407662526347), (police,0.012270129483409602), (information,0.00971707551075861), (state,0.008670165535655201)), Array((church,0.014774249841210198), (jesus,0.01472101708352839), (paul,0.012856526169517131), (christian,0.011220748016002442), (people,0.01121031333916818)), Array((drive,0.032328098098879966), (hard,0.013511791382746467), (scsi,0.010624585848750407), (board,0.010053234548630935), (problems,0.009892303966702721)), Array((didn,0.01933819111424734), (president,0.016430063960990293), (time,0.014591212868028463), (mark,0.012831103829368333), (question,0.011737387642684876)), Array((data,0.024715554075101145), (image,0.018860536627015576), (available,0.01113913102866641), (software,0.009235060603573717), (tools,0.006169193503301667)), Array((candida,0.008134391093925976), (cause,0.007455491378002872), (body,0.007338867710850107), (medical,0.0070809265323322065), (people,0.0066708098070737045)), Array((started,0.011262371351669808), (koresh,0.010524851497111375), (venus,0.008454573424664923), (batf,0.007832608671526454), (claim,0.006390451997634408)), Array((windows,0.04621385962315641), (card,0.019155126806073106), (software,0.018199377664440387), (window,0.017829015780798332), (microsoft,0.01530658854866115)), Array((space,0.02523224351920664), (nasa,0.015132028053982085), (launch,0.012965326071936832), (shuttle,0.010420083818033865), (station,0.010332319509074891)), Array((israel,0.016887594414581104), (people,0.011422443736481653), (israeli,0.010163056159288525), (jewish,0.009480763103690159), (killed,0.008273916285900816)), Array((true,0.012945705379462196), (truth,0.010985725790024931), (objective,0.01074449663349066), (frank,0.010547109259517407), (question,0.009618825382388466)), Array((people,0.04103419179492583), (good,0.021671903969276998), (netcom,0.01094554996975329), (believe,0.010517816098190474), (want,0.009823377474213819)), Array((game,0.021909895478140564), (year,0.01857558836132334), (play,0.01485871615893668), (team,0.014438994587822065), (games,0.009279372287470302)), Array((file,0.022005274037133184), (jpeg,0.021977302374802322), (files,0.01625470494831252), (program,0.016001829993456403), (image,0.015454736788736897)), Array((mail,0.015988081068201616), (anybody,0.013917705930545383), (looking,0.011924417195875689), (bike,0.009867442582785501), (opinions,0.00948707682650206)), Array((government,0.019783953090745134), (encryption,0.015921870017092693), (chip,0.011668233576256385), (keys,0.009458499359868132), (technology,0.009298234142155769)), Array((wire,0.013706349885984542), (used,0.013451206225325123), (wiring,0.01171996347950028), (ground,0.011315206371089155), (subject,0.010834407806128689)))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459998163457E12,"submitTime":1.459998060773E12,"finishTime":1.459998163733E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"e9f54743-c965-4de5-af75-6f84eff6779e"},{"version":"CommandV1","origId":96759,"guid":"ce130094-f031-4ce1-a63e-5625d95fb52c","subtype":"command","commandType":"auto","position":25.984375,"command":"vocabList(47) // 47 is the index of the term 'university' or the first term in topics - this may change due to randomness in algorithm","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res50: String = university\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459998275274E12,"submitTime":1.459998172573E12,"finishTime":1.45999827535E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"6131fba2-3ce8-4fb3-b0b2-12fd6715b4f6"},{"version":"CommandV1","origId":96760,"guid":"8f42f6fd-61ab-4420-88dc-175aa42b376c","subtype":"command","commandType":"auto","position":25.9921875,"command":"%md\nThis is just doing it all at once.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b2c6f0d2-834a-4bcf-86cf-d23b06a18d2a"},{"version":"CommandV1","origId":95536,"guid":"2fbdf7ee-4b39-4d21-869c-37bf9cef239e","subtype":"command","commandType":"auto","position":26.0,"command":"val topicIndices = em_ldaModel.describeTopics(maxTermsPerTopic = 5)\nval vocabList = vectorizer.vocabulary\nval topics = topicIndices.map { case (terms, termWeights) =>\n  terms.map(vocabList(_)).zip(termWeights)\n}\nprintln(s\"$numTopics topics:\")\ntopics.zipWithIndex.foreach { case (topic, i) =>\n  println(s\"TOPIC $i\")\n  topic.foreach { case (term, weight) => println(s\"$term\\t$weight\") }\n  println(s\"==========\")\n}","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">20 topics:\nTOPIC 0\nuniversity\t0.021731135177920788\ninterested\t0.016402793489762747\nbook\t0.015784827128360605\njohn\t0.014236324739281266\nmichael\t0.01069977995545543\n==========\nTOPIC 1\nmoney\t0.013383837363866821\ninsurance\t0.010292082551734102\nthings\t0.008039549389022444\nchildren\t0.007633357944471386\npeople\t0.00762669449038805\n==========\nTOPIC 2\nsend\t0.014582482929017393\nrequest\t0.013306640446635917\nturkish\t0.01319397874544908\nlist\t0.01063389199241538\narmenians\t0.010094965595369557\n==========\nTOPIC 3\nuiuc\t0.01396778209475855\nnews\t0.012442407662526347\npolice\t0.012270129483409602\ninformation\t0.00971707551075861\nstate\t0.008670165535655201\n==========\nTOPIC 4\nchurch\t0.014774249841210198\njesus\t0.01472101708352839\npaul\t0.012856526169517131\nchristian\t0.011220748016002442\npeople\t0.01121031333916818\n==========\nTOPIC 5\ndrive\t0.032328098098879966\nhard\t0.013511791382746467\nscsi\t0.010624585848750407\nboard\t0.010053234548630935\nproblems\t0.009892303966702721\n==========\nTOPIC 6\ndidn\t0.01933819111424734\npresident\t0.016430063960990293\ntime\t0.014591212868028463\nmark\t0.012831103829368333\nquestion\t0.011737387642684876\n==========\nTOPIC 7\ndata\t0.024715554075101145\nimage\t0.018860536627015576\navailable\t0.01113913102866641\nsoftware\t0.009235060603573717\ntools\t0.006169193503301667\n==========\nTOPIC 8\ncandida\t0.008134391093925976\ncause\t0.007455491378002872\nbody\t0.007338867710850107\nmedical\t0.0070809265323322065\npeople\t0.0066708098070737045\n==========\nTOPIC 9\nstarted\t0.011262371351669808\nkoresh\t0.010524851497111375\nvenus\t0.008454573424664923\nbatf\t0.007832608671526454\nclaim\t0.006390451997634408\n==========\nTOPIC 10\nwindows\t0.04621385962315641\ncard\t0.019155126806073106\nsoftware\t0.018199377664440387\nwindow\t0.017829015780798332\nmicrosoft\t0.01530658854866115\n==========\nTOPIC 11\nspace\t0.02523224351920664\nnasa\t0.015132028053982085\nlaunch\t0.012965326071936832\nshuttle\t0.010420083818033865\nstation\t0.010332319509074891\n==========\nTOPIC 12\nisrael\t0.016887594414581104\npeople\t0.011422443736481653\nisraeli\t0.010163056159288525\njewish\t0.009480763103690159\nkilled\t0.008273916285900816\n==========\nTOPIC 13\ntrue\t0.012945705379462196\ntruth\t0.010985725790024931\nobjective\t0.01074449663349066\nfrank\t0.010547109259517407\nquestion\t0.009618825382388466\n==========\nTOPIC 14\npeople\t0.04103419179492583\ngood\t0.021671903969276998\nnetcom\t0.01094554996975329\nbelieve\t0.010517816098190474\nwant\t0.009823377474213819\n==========\nTOPIC 15\ngame\t0.021909895478140564\nyear\t0.01857558836132334\nplay\t0.01485871615893668\nteam\t0.014438994587822065\ngames\t0.009279372287470302\n==========\nTOPIC 16\nfile\t0.022005274037133184\njpeg\t0.021977302374802322\nfiles\t0.01625470494831252\nprogram\t0.016001829993456403\nimage\t0.015454736788736897\n==========\nTOPIC 17\nmail\t0.015988081068201616\nanybody\t0.013917705930545383\nlooking\t0.011924417195875689\nbike\t0.009867442582785501\nopinions\t0.00948707682650206\n==========\nTOPIC 18\ngovernment\t0.019783953090745134\nencryption\t0.015921870017092693\nchip\t0.011668233576256385\nkeys\t0.009458499359868132\ntechnology\t0.009298234142155769\n==========\nTOPIC 19\nwire\t0.013706349885984542\nused\t0.013451206225325123\nwiring\t0.01171996347950028\nground\t0.011315206371089155\nsubject\t0.010834407806128689\n==========\ntopicIndices: Array[(Array[Int], Array[Double])] = Array((Array(47, 155, 124, 76, 218),Array(0.021731135177920788, 0.016402793489762747, 0.015784827128360605, 0.014236324739281266, 0.01069977995545543)), (Array(174, 376, 31, 325, 0),Array(0.013383837363866821, 0.010292082551734102, 0.008039549389022444, 0.007633357944471386, 0.00762669449038805)), (Array(65, 171, 313, 58, 471),Array(0.014582482929017393, 0.013306640446635917, 0.01319397874544908, 0.01063389199241538, 0.010094965595369557)), (Array(189, 62, 289, 13, 52),Array(0.01396778209475855, 0.012442407662526347, 0.012270129483409602, 0.00971707551075861, 0.008670165535655201)), (Array(131, 112, 114, 205, 0),Array(0.014774249841210198, 0.01472101708352839, 0.012856526169517131, 0.011220748016002442, 0.01121031333916818)), (Array(23, 88, 330, 254, 101),Array(0.032328098098879966, 0.013511791382746467, 0.010624585848750407, 0.010053234548630935, 0.009892303966702721)), (Array(50, 99, 1, 154, 32),Array(0.01933819111424734, 0.016430063960990293, 0.014591212868028463, 0.012831103829368333, 0.011737387642684876)), (Array(12, 11, 25, 17, 328),Array(0.024715554075101145, 0.018860536627015576, 0.01113913102866641, 0.009235060603573717, 0.006169193503301667)), (Array(480, 187, 214, 272, 0),Array(0.008134391093925976, 0.007455491378002872, 0.007338867710850107, 0.0070809265323322065, 0.0066708098070737045)), (Array(241, 380, 445, 534, 113),Array(0.011262371351669808, 0.010524851497111375, 0.008454573424664923, 0.007832608671526454, 0.006390451997634408)), (Array(4, 81, 17, 109, 164),Array(0.04621385962315641, 0.019155126806073106, 0.018199377664440387, 0.017829015780798332, 0.01530658854866115)), (Array(26, 147, 291, 375, 354),Array(0.02523224351920664, 0.015132028053982085, 0.012965326071936832, 0.010420083818033865, 0.010332319509074891)), (Array(178, 0, 446, 295, 359),Array(0.016887594414581104, 0.011422443736481653, 0.010163056159288525, 0.009480763103690159, 0.008273916285900816)), (Array(51, 130, 339, 312, 32),Array(0.012945705379462196, 0.010985725790024931, 0.01074449663349066, 0.010547109259517407, 0.009618825382388466)), (Array(0, 2, 183, 15, 5),Array(0.04103419179492583, 0.021671903969276998, 0.01094554996975329, 0.010517816098190474, 0.009823377474213819)), (Array(60, 19, 125, 123, 346),Array(0.021909895478140564, 0.01857558836132334, 0.01485871615893668, 0.014438994587822065, 0.009279372287470302)), (Array(27, 45, 54, 24, 11),Array(0.022005274037133184, 0.021977302374802322, 0.01625470494831252, 0.016001829993456403, 0.015454736788736897)), (Array(20, 234, 116, 514, 133),Array(0.015988081068201616, 0.013917705930545383, 0.011924417195875689, 0.009867442582785501, 0.00948707682650206)), (Array(30, 128, 126, 314, 186),Array(0.019783953090745134, 0.015921870017092693, 0.011668233576256385, 0.009458499359868132, 0.009298234142155769)), (Array(193, 3, 261, 216, 74),Array(0.013706349885984542, 0.013451206225325123, 0.01171996347950028, 0.011315206371089155, 0.010834407806128689)))\nvocabList: Array[String] = Array(people, time, good, used, windows, want, work, right, problem, need, really, image, data, information, better, believe, using, software, years, year, mail, sure, point, drive, program, available, space, file, power, help, government, things, question, doesn, number, case, world, look, read, version, come, thing, long, different, best, jpeg, fact, university, real, probably, didn, true, state, course, files, high, possible, actually, list, 1993, game, little, news, group, david, send, wrong, graphics, based, able, support, place, free, called, subject, post, john, reason, color, second, great, card, having, public, email, info, following, start, hard, science, says, example, means, code, evidence, person, note, maybe, general, president, heard, problems, quite, mean, source, life, systems, price, order, window, standard, access, jesus, claim, paul, getting, looking, control, trying, simply, disk, seen, times, team, book, play, chip, local, encryption, idea, truth, church, research, opinions, issue, given, images, wrote, display, large, thought, makes, remember, national, doing, format, away, nasa, change, human, home, small, saying, current, mark, interested, today, area, internet, word, original, agree, left, memory, microsoft, works, machine, instead, kind, hardware, working, request, sort, higher, money, later, programs, questions, israel, mike, pretty, hand, guess, netcom, address, include, technology, cause, matter, uiuc, type, video, speed, wire, days, server, usually, view, earth, package, april, open, stuff, unless, told, christian, important, similar, house, major, size, faith, known, provide, body, phone, ground, rights, michael, health, american, apple, feel, including, center, answer, bible, lines, user, cost, text, check, security, mind, anybody, understand, care, copy, wouldn, live, certainly, started, level, network, women, message, mouse, running, study, clinton, making, position, company, came, board, screen, groups, talking, single, common, white, wiring, monitor, likely, christians, test, special, quality, black, light, effect, nice, medical, members, hope, sources, certain, posted, uucp, fine, hear, canada, cars, write, clear, difference, history, couple, build, police, love, launch, press, books, situation, jewish, specific, sense, particular, words, talk, unix, anti, stop, posting, model, religion, discussion, school, contact, private, built, frank, turkish, keys, cable, taking, simple, service, features, legal, sound, consider, short, night, children, reference, argument, tools, comes, scsi, device, application, comments, jews, applications, clipper, doubt, tried, objective, force, process, theory, self, experience, usenet, games, expect, steve, early, tape, uses, needed, interesting, station, easy, death, exactly, manager, killed, value, turn, correct, according, needs, amiga, ones, response, states, drug, considered, reading, james, language, wanted, shuttle, insurance, personal, term, goes, koresh, strong, past, form, opinion, future, taken, result, sorry, mentioned, rules, especially, religious, hell, happy, society, plus, went, country, design, written, various, author, guns, drivers, asked, results, analysis, haven, latest, longer, gets, advance, aren, parts, cases, york, main, section, laws, previous, accept, input, looks, week, mode, required, christ, weapons, washington, community, robert, numbers, head, fast, disease, option, series, circuit, office, offer, macintosh, driver, andrew, period, venus, israeli, range, exist, clock, players, runs, values, department, toronto, involved, moral, allow, organization, brian, sell, colors, knows, picture, months, choice, half, takes, currently, suggest, dave, armenians, cards, federal, individual, object, wasn, hockey, took, includes, candida, policy, total, title, directly, protect, follow, americans, food, equipment, assume, close, purpose, recently, devices, statement, present, happened, happen, users, media, provides, deal, require, reasons, scientific, christianity, road, shall, dead, lost, action, speak, couldn, bike, save, wants, city, george, goal, lead, obviously, completely, california, mission, useful, details, baseball, voice, engineering, league, complete, uunet, terms, batf, condition, court, easily, rest, responsible, administration, ways, international, sent, clearly, compatible, disclaimer, output, water, algorithm, business, appreciated, freedom, kill, issues, digital, pass, hours, fans, figure, error, story, project, deleted, coming, operating, processing, companies, context, average, trade, appropriate, leave, berkeley, season, face, carry, events, port, difficult, addition, performance, building, basis, final, requires, heart, points, armenian, political, lower, player, king, convert, page, environment, apply, knowledge, resolution, field, willing, volume, actual, stanford, site, sale, suppose, related, limited, entries, advice, commercial, sounds, worth, orbit, lots, basic, defense, anonymous, directory, claims, supposed, designed, explain, handle, included, turkey, reply, enforcement, inside, family, ability, signal, young, neutral, forget, changes, wonder, suspect, radio, necessary, programming, wait, understanding, paper, friend, thinking, shot, month, services, greek, installed, create, printer, land, russian, finally, homosexuality, natural, morality, formats, names, machines, report, peter, setting, eric, homosexual, normal, interface, population, hold, break, comment, rutgers, reasonable, arab, logic, miles, secure, east, supply, product, communications, comp, percent, escrow, avoid, lives, colorado, types, room, allowed, multiple, developed, peace, cancer, million, library, expensive, cheap, gary, soon, recent, cubs, agencies, mention, supported, event, gives, soviet, looked, release, obvious, street, physics, suggestions, doctor, technical, caused, happens, table, return, archive, treatment, bitnet, chance, mass, radar, outside, entire, friends, folks, choose, print, install, generally, development, diet, exists, areas, concept, requests, straight, child, learn, supports, behavior, ideas, morning, member, electrical, illegal, weeks, social, wish, smith, trouble, muslim, jack, meaning, united, remove, stand, bring, thank, unit, reality, engine, worked, practice, drives, motif, west, asking, appear, provided, pick, studies, attempt, possibly, answers, decided, near, henry, trust, existence, changed, modem, bits, errors, false, continue, middle, belief, compound, extra, arguments, proper, particularly, class, congress, guys, contains, yeah, safe, facts, loss, worse, heat, appears, thread, andy, pages, manual, function, attack, fonts, aware, privacy, operations, armenia, constitution, institute, frame, command, drugs, stupid, wide, nature, conference, independent, unfortunately, hands, wall, distribution, approach, speaking, edge, division, shouldn, knew, effective, indiana, shows, compression, safety, crime, serial, added, 1992, 1990, modern, ethernet, solution, civil, bought, turks, floppy, appreciate, abortion, cramer, blood, blue, letter, plastic, regular, writing, spend, allows, utility, hello, rate, virginia, wondering, germany, gave, record, faster, simms, operation, arms, internal, playing, step, giving, views, switch, tool, decision, atheism, surface, articles, actions, font, additional, method, described, base, concerned, scott, stated, kids, played, prevent, rule, late, quote, buying, capability, places, products, attitude, costs, patients, controller, fair, connected, north, military, keith, cover, levels, highly, considering, resources, turned, apparently, valid, hate, countries, shell, western, poor, excellent, market, necessarily, wires, created, america, richard, boston, calls, brought, functions, received, account, creation, watch, majority, cwru, forward, student, released, driving, authority, committee, chips, quick, protection, notice, produce, sold, arizona, complex, visual, absolutely, arabs, fairly, murder, keyboard, plan, moon, minutes, boxes, lord, properly, greatly, killing, shareware, larry, islam, vote, panel, rangers, effort, options, review, dangerous, holy, intelligence, acts, tests, nation, property, damage, begin, living, electronics, failed)\ntopics: Array[Array[(String, Double)]] = Array(Array((university,0.021731135177920788), (interested,0.016402793489762747), (book,0.015784827128360605), (john,0.014236324739281266), (michael,0.01069977995545543)), Array((money,0.013383837363866821), (insurance,0.010292082551734102), (things,0.008039549389022444), (children,0.007633357944471386), (people,0.00762669449038805)), Array((send,0.014582482929017393), (request,0.013306640446635917), (turkish,0.01319397874544908), (list,0.01063389199241538), (armenians,0.010094965595369557)), Array((uiuc,0.01396778209475855), (news,0.012442407662526347), (police,0.012270129483409602), (information,0.00971707551075861), (state,0.008670165535655201)), Array((church,0.014774249841210198), (jesus,0.01472101708352839), (paul,0.012856526169517131), (christian,0.011220748016002442), (people,0.01121031333916818)), Array((drive,0.032328098098879966), (hard,0.013511791382746467), (scsi,0.010624585848750407), (board,0.010053234548630935), (problems,0.009892303966702721)), Array((didn,0.01933819111424734), (president,0.016430063960990293), (time,0.014591212868028463), (mark,0.012831103829368333), (question,0.011737387642684876)), Array((data,0.024715554075101145), (image,0.018860536627015576), (available,0.01113913102866641), (software,0.009235060603573717), (tools,0.006169193503301667)), Array((candida,0.008134391093925976), (cause,0.007455491378002872), (body,0.007338867710850107), (medical,0.0070809265323322065), (people,0.0066708098070737045)), Array((started,0.011262371351669808), (koresh,0.010524851497111375), (venus,0.008454573424664923), (batf,0.007832608671526454), (claim,0.006390451997634408)), Array((windows,0.04621385962315641), (card,0.019155126806073106), (software,0.018199377664440387), (window,0.017829015780798332), (microsoft,0.01530658854866115)), Array((space,0.02523224351920664), (nasa,0.015132028053982085), (launch,0.012965326071936832), (shuttle,0.010420083818033865), (station,0.010332319509074891)), Array((israel,0.016887594414581104), (people,0.011422443736481653), (israeli,0.010163056159288525), (jewish,0.009480763103690159), (killed,0.008273916285900816)), Array((true,0.012945705379462196), (truth,0.010985725790024931), (objective,0.01074449663349066), (frank,0.010547109259517407), (question,0.009618825382388466)), Array((people,0.04103419179492583), (good,0.021671903969276998), (netcom,0.01094554996975329), (believe,0.010517816098190474), (want,0.009823377474213819)), Array((game,0.021909895478140564), (year,0.01857558836132334), (play,0.01485871615893668), (team,0.014438994587822065), (games,0.009279372287470302)), Array((file,0.022005274037133184), (jpeg,0.021977302374802322), (files,0.01625470494831252), (program,0.016001829993456403), (image,0.015454736788736897)), Array((mail,0.015988081068201616), (anybody,0.013917705930545383), (looking,0.011924417195875689), (bike,0.009867442582785501), (opinions,0.00948707682650206)), Array((government,0.019783953090745134), (encryption,0.015921870017092693), (chip,0.011668233576256385), (keys,0.009458499359868132), (technology,0.009298234142155769)), Array((wire,0.013706349885984542), (used,0.013451206225325123), (wiring,0.01171996347950028), (ground,0.011315206371089155), (subject,0.010834407806128689)))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\">&lt;console&gt;:54: error: not found: value em_ldaModel\n              val topicIndices = em_ldaModel.describeTopics(maxTermsPerTopic = 5)\n                                 ^\n</div>","error":null,"startTime":1.459997628017E12,"submitTime":1.459997525677E12,"finishTime":1.459997628589E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"fd421d5d-ad14-426a-9495-4f81373b0928"},{"version":"CommandV1","origId":95537,"guid":"55963bd4-d0aa-45a7-b0c4-eaa7709f4433","subtype":"command","commandType":"auto","position":27.0,"command":"%md \nWe've managed to get some good results here. For example, we can easily infer that Topic 2 is about space, Topic 3 is about israel, etc. \n\n\nWe still get some ambiguous results like Topic 0.\n","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567754004E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"44d0e308-3464-45fc-8852-4dbeecbeb7e2"},{"version":"CommandV1","origId":95538,"guid":"aacde93c-f0f1-46d1-b8fe-59c2c13da215","subtype":"command","commandType":"auto","position":28.0,"command":"%md\n\nTo improve our results further, we could employ some of the below methods:\n- Refilter data for additional data-specific stopwords\n- Use Stemming or Lemmatization to preprocess data\n- Experiment with a smaller number of topics, since some of these topics in the 20 Newsgroups are pretty similar\n- Increase model's MaxIterations","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567754203E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"979c7518-328c-4b9c-a259-18306b3bddb3"},{"version":"CommandV1","origId":95539,"guid":"883e8fe6-36ae-4be4-a5ab-4ebfc0087ece","subtype":"command","commandType":"auto","position":30.0,"command":"%md \n## Visualize Results\n\nWe will try visualizing the results obtained from the EM LDA model with a d3 bubble chart.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567754412E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"eabfa9f8-776c-4ab6-b8ee-46fc69f894fa"},{"version":"CommandV1","origId":95540,"guid":"427b89a4-8a25-4ff4-8d73-814e50ae390c","subtype":"command","commandType":"auto","position":30.5,"command":"// Zip topic terms with topic IDs\nval termArray = topics.zipWithIndex","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">termArray: Array[(Array[(String, Double)], Int)] = Array((Array((university,0.021731135177920788), (interested,0.016402793489762747), (book,0.015784827128360605), (john,0.014236324739281266), (michael,0.01069977995545543)),0), (Array((money,0.013383837363866821), (insurance,0.010292082551734102), (things,0.008039549389022444), (children,0.007633357944471386), (people,0.00762669449038805)),1), (Array((send,0.014582482929017393), (request,0.013306640446635917), (turkish,0.01319397874544908), (list,0.01063389199241538), (armenians,0.010094965595369557)),2), (Array((uiuc,0.01396778209475855), (news,0.012442407662526347), (police,0.012270129483409602), (information,0.00971707551075861), (state,0.008670165535655201)),3), (Array((church,0.014774249841210198), (jesus,0.01472101708352839), (paul,0.012856526169517131), (christian,0.011220748016002442), (people,0.01121031333916818)),4), (Array((drive,0.032328098098879966), (hard,0.013511791382746467), (scsi,0.010624585848750407), (board,0.010053234548630935), (problems,0.009892303966702721)),5), (Array((didn,0.01933819111424734), (president,0.016430063960990293), (time,0.014591212868028463), (mark,0.012831103829368333), (question,0.011737387642684876)),6), (Array((data,0.024715554075101145), (image,0.018860536627015576), (available,0.01113913102866641), (software,0.009235060603573717), (tools,0.006169193503301667)),7), (Array((candida,0.008134391093925976), (cause,0.007455491378002872), (body,0.007338867710850107), (medical,0.0070809265323322065), (people,0.0066708098070737045)),8), (Array((started,0.011262371351669808), (koresh,0.010524851497111375), (venus,0.008454573424664923), (batf,0.007832608671526454), (claim,0.006390451997634408)),9), (Array((windows,0.04621385962315641), (card,0.019155126806073106), (software,0.018199377664440387), (window,0.017829015780798332), (microsoft,0.01530658854866115)),10), (Array((space,0.02523224351920664), (nasa,0.015132028053982085), (launch,0.012965326071936832), (shuttle,0.010420083818033865), (station,0.010332319509074891)),11), (Array((israel,0.016887594414581104), (people,0.011422443736481653), (israeli,0.010163056159288525), (jewish,0.009480763103690159), (killed,0.008273916285900816)),12), (Array((true,0.012945705379462196), (truth,0.010985725790024931), (objective,0.01074449663349066), (frank,0.010547109259517407), (question,0.009618825382388466)),13), (Array((people,0.04103419179492583), (good,0.021671903969276998), (netcom,0.01094554996975329), (believe,0.010517816098190474), (want,0.009823377474213819)),14), (Array((game,0.021909895478140564), (year,0.01857558836132334), (play,0.01485871615893668), (team,0.014438994587822065), (games,0.009279372287470302)),15), (Array((file,0.022005274037133184), (jpeg,0.021977302374802322), (files,0.01625470494831252), (program,0.016001829993456403), (image,0.015454736788736897)),16), (Array((mail,0.015988081068201616), (anybody,0.013917705930545383), (looking,0.011924417195875689), (bike,0.009867442582785501), (opinions,0.00948707682650206)),17), (Array((government,0.019783953090745134), (encryption,0.015921870017092693), (chip,0.011668233576256385), (keys,0.009458499359868132), (technology,0.009298234142155769)),18), (Array((wire,0.013706349885984542), (used,0.013451206225325123), (wiring,0.01171996347950028), (ground,0.011315206371089155), (subject,0.010834407806128689)),19))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459998419446E12,"submitTime":1.459998316615E12,"finishTime":1.45999841962E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"ec128bf7-8532-492c-a67d-5a2bd9cd2618"},{"version":"CommandV1","origId":95541,"guid":"66a990f0-dce2-49e2-99ed-4c868a00b471","subtype":"command","commandType":"auto","position":31.0,"command":"// Transform data into the form (term, probability, topicId)\nval termRDD = sc.parallelize(termArray)\nval termRDD2 =termRDD.flatMap( (x: (Array[(String, Double)], Int)) => {\n  val arrayOfTuple = x._1\n  val topicId = x._2\n  arrayOfTuple.map(el => (el._1, el._2, topicId))\n})","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">termRDD: org.apache.spark.rdd.RDD[(Array[(String, Double)], Int)] = ParallelCollectionRDD[5191] at parallelize at &lt;console&gt;:85\ntermRDD2: org.apache.spark.rdd.RDD[(String, Double, Int)] = MapPartitionsRDD[5192] at flatMap at &lt;console&gt;:86\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command skipped","error":null,"startTime":1.459998437458E12,"submitTime":1.459998335076E12,"finishTime":1.459998437754E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"c96c1475-10c6-45d4-b4e8-9242bbf550bf"},{"version":"CommandV1","origId":95542,"guid":"545e765d-4bcc-4b07-9e79-6969d0a9acb8","subtype":"command","commandType":"auto","position":32.0,"command":"// Create DF with proper column names\nval termDF = termRDD2.toDF.withColumnRenamed(\"_1\", \"term\").withColumnRenamed(\"_2\", \"probability\").withColumnRenamed(\"_3\", \"topicId\")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">termDF: org.apache.spark.sql.DataFrame = [term: string, probability: double, topicId: int]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Command 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\nWe are now ready to use D3 on the rawJson data.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.452567756031E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"30807967-406c-49f6-8105-5ef7387402f9"},{"version":"CommandV1","origId":95547,"guid":"0b1937f1-4a2e-4814-9022-b538a19e6cdd","subtype":"command","commandType":"auto","position":36.0,"command":"displayHTML(s\"\"\"\n<!DOCTYPE html>\n<meta charset=\"utf-8\">\n<style>\n\ncircle {\n  fill: rgb(31, 119, 180);\n  fill-opacity: 0.5;\n  stroke: rgb(31, 119, 180);\n  stroke-width: 1px;\n}\n\n.leaf circle {\n  fill: #ff7f0e;\n  fill-opacity: 1;\n}\n\ntext {\n  font: 14px sans-serif;\n}\n\n</style>\n<body>\n<script src=\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\"><\/script>\n<script>\n\nvar json = {\n \"name\": \"data\",\n \"children\": [\n  {\n     \"name\": \"topics\",\n     \"children\": [\n      ${rawJson}\n     ]\n    }\n   ]\n};\n\nvar r = 1500,\n    format = d3.format(\",d\"),\n    fill = d3.scale.category20c();\n\nvar bubble = d3.layout.pack()\n    .sort(null)\n    .size([r, r])\n    .padding(1.5);\n\nvar vis = d3.select(\"body\").append(\"svg\")\n    .attr(\"width\", r)\n    .attr(\"height\", r)\n    .attr(\"class\", \"bubble\");\n\n  \nvar node = vis.selectAll(\"g.node\")\n    .data(bubble.nodes(classes(json))\n    .filter(function(d) { return !d.children; }))\n    .enter().append(\"g\")\n    .attr(\"class\", \"node\")\n    .attr(\"transform\", function(d) { return 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skipped","error":null,"startTime":1.459998531527E12,"submitTime":1.459998429131E12,"finishTime":1.459998531631E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"See it in D3","showCommandTitle":true,"hideCommandCode":true,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"fc85abbc-4ea6-4473-8af5-2b7dba7be8bb"},{"version":"CommandV1","origId":95548,"guid":"d6435745-8376-4b70-844c-cd7a92c28b04","subtype":"command","commandType":"auto","position":36.5,"command":"%md\n### You try!\n\n**NOW or Later as HOMEWORK** Try to do the same process for the State of the Union Addresses dataset from Week1.\n\nAs a first step, first locate where that data is... Go to week1 and try to see if each SoU can be treated as a document for topic modeling and whether there is temporal clustering of SoU's within the same topic.\n\nTry to improve the models (if you want to do a project based on this, perhaps).","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"1b0183b8-bf74-409c-9eae-0f169c7dedb3"},{"version":"CommandV1","origId":96814,"guid":"d5b6d9b2-d29d-44fa-a444-f8b8544ccc84","subtype":"command","commandType":"auto","position":36.75,"command":"%md\n#### Old Bailey, London's Central Criminal Court, 1674 to 1913\n\n* with Full XML Data for another great project. ","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"8bdf3ece-21f0-4cfa-9315-0f6507570d2e"},{"version":"CommandV1","origId":97065,"guid":"09aaf1ae-331d-4be9-9311-58c028ffd03e","subtype":"command","commandType":"auto","position":36.875,"command":"displayHTML(frameIt(\"https://www.oldbaileyonline.org/\", 450))","commandVersion":0,"state":"finished","results":{"type":"htmlSandbox","data":"<iframe \n src=\"https://www.oldbaileyonline.org/\"\n width=\"95%\" height=\"450\"\n sandbox>\n  <p>\n    <a href=\"http://spark.apache.org/docs/latest/ml-features.html\">\n      Fallback link for browsers that, unlikely, don't support frames\n    </a>\n  </p>\n</iframe>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.45999964772E12,"submitTime":1.459999545285E12,"finishTime":1.459999647787E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":true,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"9a1f963a-fd57-4b00-b3b9-a00884ac984c"},{"version":"CommandV1","origId":97064,"guid":"11ed7fe4-67bb-42b2-8e6c-392e796bbcfb","subtype":"command","commandType":"auto","position":36.9375,"command":"%md\n\n#### This exciting dataset is here for a fun project:\n\n* Try the xml-parsing of the dataset already started in [Workspace/scalable-data-science/xtraResources -> OldBaileyOnline -> OBO_LoadExtract](#workspace/scalable-data-science/xtraResources/OldBaileyOnline/OBO_LoadExtract)\n\n* [http://www.math.canterbury.ac.nz/~r.sainudiin/datasets/public/OldBailey/](http://www.math.canterbury.ac.nz/~r.sainudiin/datasets/public/OldBailey/)\n\n* First see [Jasper Mackenzie, Raazesh Sainudiin, James Smithies and Heather Wolffram, A nonparametric view of the civilizing process in London's Old Bailey, Research Report UCDMS2015/1, 32 pages, 2015](http://www.math.canterbury.ac.nz/~r.sainudiin/preprints/20150828_civilizingProcOBO.pdf) (the second revision is in prgress June 2016).\n\n\n***\n***","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"4dddd766-74ca-46fe-830a-7f960eaf48c2"},{"version":"CommandV1","origId":95549,"guid":"5f6c43a6-2055-4d8e-9b2a-ea3011bac6e9","subtype":"command","commandType":"auto","position":37.0,"command":"%md \n## Step 1. Downloading and Loading Data into DBFS\n\nHere are the steps taken for downloading and saving data to the distributed file system.  Uncomment them for repeating this process on your databricks cluster or for downloading a new source of data.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"startTime":0.0,"submitTime":1.45256775644E12,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"raazesh.sainudiin@gmail.com","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"817b9ca0-2440-4782-b5b4-cd6b56260dec"},{"version":"CommandV1","origId":95550,"guid":"671f6cd1-af02-4e5e-aa17-ae373291bb51","subtype":"command","commandType":"auto","position":37.5,"command":"//%sh wget http://kdd.ics.uci.edu/databases/20newsgroups/mini_newsgroups.tar.gz -O /tmp/newsgroups.tar.gz","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">--2016-04-06 22:06:17--  http://kdd.ics.uci.edu/databases/20newsgroups/mini_newsgroups.tar.gz\nResolving kdd.ics.uci.edu (kdd.ics.uci.edu)... 128.195.1.95\nConnecting to kdd.ics.uci.edu (kdd.ics.uci.edu)|128.195.1.95|:80... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 1860687 (1.8M) [application/x-gzip]\nSaving to: &apos;/tmp/newsgroups.tar.gz&apos;\n\n     0K .......... .......... .......... .......... ..........  2%  134K 13s\n    50K .......... .......... .......... .......... ..........  5%  270K 10s\n   100K .......... .......... .......... .......... ..........  8% 15.8M 6s\n   150K .......... .......... .......... .......... .......... 11%  273K 6s\n   200K .......... .......... .......... .......... .......... 13% 25.1M 5s\n   250K .......... .......... .......... .......... .......... 16% 31.0M 4s\n   300K .......... .......... .......... .......... .......... 19%  276K 4s\n   350K .......... .......... .......... .......... .......... 22% 34.5M 3s\n   400K .......... .......... .......... .......... .......... 24% 31.4M 3s\n   450K .......... .......... .......... .......... .......... 27% 42.9M 2s\n   500K .......... .......... .......... .......... .......... 30% 53.9M 2s\n   550K .......... .......... .......... .......... .......... 33% 73.5M 2s\n   600K .......... .......... .......... .......... .......... 35%  275K 2s\n   650K .......... .......... .......... .......... .......... 38% 54.7M 2s\n   700K .......... .......... .......... .......... .......... 41% 46.4M 2s\n   750K .......... .......... .......... .......... .......... 44% 52.4M 1s\n   800K .......... .......... .......... .......... .......... 46% 32.4M 1s\n   850K .......... .......... .......... .......... .......... 49% 52.3M 1s\n   900K .......... .......... .......... .......... .......... 52% 66.7M 1s\n   950K .......... .......... .......... .......... .......... 55%  279K 1s\n  1000K .......... .......... .......... .......... .......... 57% 34.8M 1s\n  1050K .......... .......... .......... .......... .......... 60% 47.8M 1s\n  1100K .......... .......... .......... .......... .......... 63% 22.5M 1s\n  1150K .......... .......... .......... .......... .......... 66% 58.1M 1s\n  1200K .......... .......... .......... .......... .......... 68% 47.8M 1s\n  1250K .......... .......... .......... .......... .......... 71% 51.4M 1s\n  1300K .......... .......... .......... .......... .......... 74% 59.8M 0s\n  1350K .......... .......... .......... .......... .......... 77%  281K 0s\n  1400K .......... .......... .......... .......... .......... 79% 31.7M 0s\n  1450K .......... .......... .......... .......... .......... 82% 32.7M 0s\n  1500K .......... .......... .......... .......... .......... 85% 36.6M 0s\n  1550K .......... .......... .......... .......... .......... 88% 54.1M 0s\n  1600K .......... .......... .......... .......... .......... 90% 44.1M 0s\n  1650K .......... .......... .......... .......... .......... 93% 51.3M 0s\n  1700K .......... .......... .......... .......... .......... 96% 55.8M 0s\n  1750K .......... .......... .......... .......... .......... 99%  281K 0s\n  1800K .......... .......                                    100% 32.3M=1.7s\n\n2016-04-06 22:06:20 (1.06 MB/s) - &apos;/tmp/newsgroups.tar.gz&apos; saved [1860687/1860687]\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.459980377041E12,"submitTime":1.459980274563E12,"finishTime":1.459980380703E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":null,"showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"f092ff12-77ff-48cb-9f1a-060768e21b39"},{"version":"CommandV1","origId":95551,"guid":"52c27c4c-e7b8-4995-b68c-b8a0731dbc62","subtype":"command","commandType":"auto","position":38.0,"command":"%md \nUntar the file into the /tmp/ 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