From obriencj at gmail.com Mon Oct 2 10:53:28 2017 From: obriencj at gmail.com (Christopher O'Brien) Date: Mon, 2 Oct 2017 10:53:28 -0400 Subject: [TriPython] Two micro-projects that might be interesting Message-ID: Hello! I was originally going to try and show these off tomorrow, but since Raleigh project night is going to be skipping October, I figured I'd just hand them off via list and see what people thought. A friend had approached me about an interesting idea -- an extension of the syntax for structured assignment. He wanted a way to grab a bunch of values from a mapping into the local scope of a function without having to manually write out every single assignment, and ideally without having to represent the same naming information multiple times. I had been messing around with the Python bytecode anyway, and it occurred to me that this was totally possible to do today with just a little bit of introspection! I threw together the following tiny package: https://github.com/obriencj/python-mapbind mapbind in essence looks into the future of function by inspecting the bytecode, and determines how the result of the call would be assigned. I've actually been able to confirm this works across a fairly wide range of versions without having to get into writing any native extensions (2.6, 2.7, 3.4, 3.5, 3.6, pypy, pypy3) An alternative possibility would have been if the locals() call in Python actually returned a more living view from a function scope. As you're all probably well aware, the frame locals() impl in CPython is just a snapshot (when not in the module scope, anyway). I wondered just how difficult it would be to make a living version of that view, and it turns out it's actually pretty easy, but it does require some native extensions. https://github.com/obriencj/python-livelocals I'm not sure how much any of the rest of you enjoy playing around with the internals of the language, but maybe these will be an amusing exercise! - Chris -------------- next part -------------- Hello! I was originally going to try and show these off tomorrow, but since Raleigh project night is going to be skipping October, I figured I'd just hand them off via list and see what people thought. A friend had approached me about an interesting idea -- an extension of the syntax for structured assignment. He wanted a way to grab a bunch of values from a mapping into the local scope of a function without having to manually write out every single assignment, and ideally without having to represent the same naming information multiple times. I had been messing around with the Python bytecode anyway, and it occurred** to me that this was totally possible to do today with just a little bit of introspection! I threw together the following tiny package: [1]https://github.com/obriencj/python-mapbind mapbind in essence looks into the future of function by inspecting the bytecode, and determines how the result of the call would be assigned. I've actually been able to confirm this works across a fairly wide range of versions without having to get into writing any native extensions (2.6, 2.7, 3.4, 3.5, 3.6, pypy, pypy3) An alternative possibility would have been if the locals() call in Python actually returned a more living view from a function scope. As you're all probably well aware, the frame locals() impl in CPython is just a snapshot (when not in the module scope, anyway). I wondered just how difficult it would be to make a living version of that view, and it turns out it's actually pretty easy, but it does require some native extensions. [2]https://github.com/obriencj/python-livelocals I'm not sure how much any of the rest of you enjoy playing around with the internals of the language, but maybe these will be an amusing exercise! - Chris References Visible links 1. https://github.com/obriencj/python-mapbind 2. https://github.com/obriencj/python-livelocals From obriencj at gmail.com Mon Oct 2 14:29:56 2017 From: obriencj at gmail.com (Christopher O'Brien) Date: Mon, 2 Oct 2017 14:29:56 -0400 Subject: [TriPython] Red Hat Annex Message-ID: Hey! I just got confirmation that the Triangle Python Users Group is welcome in the Dodgeball room in the Red Hat Annex tomorrow! I had filed a request last week, but hadn't heard anything back until just now. Are you guys interested in meeting at the annex? - Chris -------------- next part -------------- Hey! I just got confirmation that the Triangle Python Users Group is welcome in the Dodgeball room in the Red Hat Annex tomorrow! I had filed a request last week, but hadn't heard anything back until just now. Are you guys interested in meeting at the annex? - Chris From aikimark at aol.com Mon Oct 2 15:26:46 2017 From: aikimark at aol.com (Mark Hutchinson) Date: Mon, 2 Oct 2017 15:26:46 -0400 Subject: [TriPython] map-bind and livelocals Message-ID: <15ede8e1350-c03-18c2@webjasstg-vab14.srv.aolmail.net> I want to see you do a full presentation on these, Chris. They look interesting. -------------- next part -------------- I want to see you do a full presentation on these, Chris. They look interesting. From cbc at unc.edu Mon Oct 2 16:38:12 2017 From: cbc at unc.edu (Calloway, Chris) Date: Mon, 2 Oct 2017 20:38:12 +0000 Subject: [TriPython] Red Hat Annex In-Reply-To: References: Message-ID: <0FCB343C-9D62-4F46-9200-1EF5C7A0F321@unc.edu> Given that I?ve been in training this afternoon and didn?t see this until just now, and given that the meeting was cancelled and notices to that effect went out on meetup.com, and given that if I did anything about it now most people wouldn?t see it until tomorrow, I don?t know that anyone would show up for it. However, TriPython is a do-acracy. If you want to hold a project night tomorrow at your space, just announce it here and if it?s soon enough in the day I?ll see if I can get it added to meetup.com and tripython.org. Thanks for trying. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 On 10/2/17, 2:29 PM, "TriZPUG on behalf of Christopher O'Brien" wrote: Hey! I just got confirmation that the Triangle Python Users Group is welcome in the Dodgeball room in the Red Hat Annex tomorrow! I had filed a request last week, but hadn't heard anything back until just now. Are you guys interested in meeting at the annex? - Chris From obriencj at gmail.com Mon Oct 2 17:19:47 2017 From: obriencj at gmail.com (Christopher O'Brien) Date: Mon, 2 Oct 2017 17:19:47 -0400 Subject: [TriPython] Red Hat Annex In-Reply-To: <0FCB343C-9D62-4F46-9200-1EF5C7A0F321@unc.edu> References: <0FCB343C-9D62-4F46-9200-1EF5C7A0F321@unc.edu> Message-ID: Yeah it's pretty late. I was really hoping I'd get a "sure, come on over" before Friday. I'll just let it drop and will hack at the coffee shop like normal :) On Mon, Oct 2, 2017 at 4:38 PM, Calloway, Chris wrote: > Given that I?ve been in training this afternoon and didn?t see this until > just now, and given that the meeting was cancelled and notices to that > effect went out on meetup.com, and given that if I did anything about it > now most people wouldn?t see it until tomorrow, I don?t know that anyone > would show up for it. However, TriPython is a do-acracy. If you want to > hold a project night tomorrow at your space, just announce it here and if > it?s soon enough in the day I?ll see if I can get it added to meetup.com > and tripython.org. > > Thanks for trying. > > -- > Sincerely, > > Chris Calloway > Applications Analyst > University of North Carolina > Renaissance Computing Institute > (919) 599-3530 > > > On 10/2/17, 2:29 PM, "TriZPUG on behalf of Christopher O'Brien" > > wrote: > > Hey! > > I just got confirmation that the Triangle Python Users Group is > welcome in > the Dodgeball room in the Red Hat Annex tomorrow! > > I had filed a request last week, but hadn't heard anything back until > just > now. Are you guys interested in meeting at the annex? > > - Chris > > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > -------------- next part -------------- Yeah it's pretty late. I was really hoping I'd get a "sure, come on over" before Friday. I'll just let it drop and will hack at the coffee shop like normal :) On Mon, Oct 2, 2017 at 4:38 PM, Calloway, Chris <[1]cbc at unc.edu> wrote: Given that I***ve been in training this afternoon and didn***t see this until just now, and given that the meeting was cancelled and notices to that effect went out on [2]meetup.com, and given that if I did anything about it now most people wouldn***t see it until tomorrow, I don***t know that anyone would show up for it. However,** TriPython is a do-acracy. If you want to hold a project night tomorrow at your space, just announce it here and if it***s soon enough in the day I***ll see if I can get it added to [3]meetup.com and [4]tripython.org. Thanks for trying. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute [5](919) 599-3530 On 10/2/17, 2:29 PM, "TriZPUG on behalf of Christopher O'Brien" wrote: ** ** Hey! ** ** I just got confirmation that the Triangle Python Users Group is welcome in ** ** the Dodgeball room in the Red Hat Annex tomorrow! ** ** I had filed a request last week, but hadn't heard anything back until just ** ** now. Are you guys interested in meeting at the annex? ** ** - Chris _______________________________________________ TriZPUG mailing list [8]TriZPUG at python.org [9]https://mail.python.org/mailman/listinfo/trizpug [10]http://tripython.org is the Triangle Python Users Group References Visible links 1. mailto:cbc at unc.edu 2. http://meetup.com/ 3. http://meetup.com/ 4. http://tripython.org/ 5. file:///tmp/tel:%28919%29%20599-3530 6. mailto:unc.edu at python.org 7. mailto:obriencj at gmail.com 8. mailto:TriZPUG at python.org 9. https://mail.python.org/mailman/listinfo/trizpug 10. http://tripython.org/ From cbc at unc.edu Mon Oct 2 16:33:27 2017 From: cbc at unc.edu (Calloway, Chris) Date: Mon, 2 Oct 2017 20:33:27 +0000 Subject: [TriPython] Two micro-projects that might be interesting In-Reply-To: References: Message-ID: We are having to move out meeting this month (at RENCI in Chapel Hill) to Thursday, October 19. Would you like to be the featured speaker and show this off there. I think there would be a lot of interest in seeing some bytecode hacking. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 On 10/2/17, 10:53 AM, "TriZPUG on behalf of Christopher O'Brien" wrote: Hello! I was originally going to try and show these off tomorrow, but since Raleigh project night is going to be skipping October, I figured I'd just hand them off via list and see what people thought. A friend had approached me about an interesting idea -- an extension of the syntax for structured assignment. He wanted a way to grab a bunch of values from a mapping into the local scope of a function without having to manually write out every single assignment, and ideally without having to represent the same naming information multiple times. I had been messing around with the Python bytecode anyway, and it occurred to me that this was totally possible to do today with just a little bit of introspection! I threw together the following tiny package: https://github.com/obriencj/python-mapbind mapbind in essence looks into the future of function by inspecting the bytecode, and determines how the result of the call would be assigned. I've actually been able to confirm this works across a fairly wide range of versions without having to get into writing any native extensions (2.6, 2.7, 3.4, 3.5, 3.6, pypy, pypy3) An alternative possibility would have been if the locals() call in Python actually returned a more living view from a function scope. As you're all probably well aware, the frame locals() impl in CPython is just a snapshot (when not in the module scope, anyway). I wondered just how difficult it would be to make a living version of that view, and it turns out it's actually pretty easy, but it does require some native extensions. https://github.com/obriencj/python-livelocals I'm not sure how much any of the rest of you enjoy playing around with the internals of the language, but maybe these will be an amusing exercise! - Chris From scott at hallcomm-inc.com Tue Oct 3 01:22:36 2017 From: scott at hallcomm-inc.com (Scott G. Hall) Date: Tue, 3 Oct 2017 01:22:36 -0400 Subject: [TriPython] PyParsing In-Reply-To: <507F20A2-027E-4E46-993E-3E02557200D8@unc.edu> References: <46f33086-67f2-ff38-c9ee-ede4a73be5c3@pirategrunt.com> <507F20A2-027E-4E46-993E-3E02557200D8@unc.edu> Message-ID: <1e162621-19c1-c85f-1080-646c0968602f@hallcomm-inc.com> I downloaded the ipython notebook, and I get an error which makes no sense (ie. shouldn't be an error): scott at pts/3 $ ipython PyParsing.ipynb --------------------------------------------------------------------------- NameError???????????????????????????????? Traceback (most recent call last) /usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in execfile(fname, *where) ??? 202???????????? else: ??? 203???????????????? filename = fname --> 204???????????? __builtin__.execfile(filename, *where) /home/scott/src/pyparsing/PyParsing.ipynb in () ???? 58??? "execution_count": 20, ???? 59??? "metadata": { ---> 60???? "collapsed": true, ???? 61???? "slideshow": { ???? 62????? "slide_type": "fragment" NameError: name 'true' is not defined You would think 'true' and 'false' would be defined, so this error seems to be a misnomer for a different error. - sgh On 09/29/2017 11:22 AM, Calloway, Chris wrote: > Brian, thank you so much for a great talk and food for thought. There were > many compliment on your presentation at the after-meeting. Your notebook repo > has been linked on the TriPython meetings page: http://tripython.org/meetings/ > Also, thanks for Caktus for the great space and snacks. Thanks for Eric Leary > for the video wrangling. And thanks to our lightning talkers. As mentioned at > the meeting, the October featured speaker meeting will have to be moved from > the usual fourth Thursday just for October only. I?ll send announcements when > we know more. Also, remember that there is no Raleigh Project Night for > October unless we get a temporary venue. The next project night will be Chapel > Hill Project Night on October 11. Raleigh Project Night is expected to resume > in November at WebAssign. > -- Scott G. Hall Chief Engineer/Owner Hall Communications Raleigh, NC, USA ph. 919-624-5973 Scott at HallComm-Inc.Com -------------- next part -------------- I downloaded the ipython notebook, and I get an error which makes no sense (ie. shouldn't be an error): scott at pts/3 $ ipython PyParsing.ipynb --------------------------------------------------------------------------- NameError Traceback (most recent call last) /usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in execfile(fname, *where) 202 else: 203 filename = fname --> 204 __builtin__.execfile(filename, *where) /home/scott/src/pyparsing/PyParsing.ipynb in () 58 "execution_count": 20, 59 "metadata": { ---> 60 "collapsed": true, 61 "slideshow": { 62 "slide_type": "fragment" NameError: name 'true' is not defined You would think 'true' and 'false' would be defined, so this error seems to be a misnomer for a different error. - sgh On 09/29/2017 11:22 AM, Calloway, Chris wrote: Brian, thank you so much for a great talk and food for thought. There were many compliment on your presentation at the after-meeting. Your notebook repo has been linked on the TriPython meetings page: [1]http://tripython.org/meetings/ Also, thanks for Caktus for the great space and snacks. Thanks for Eric Leary for the video wrangling. And thanks to our lightning talkers. As mentioned at the meeting, the October featured speaker meeting will have to be moved from the usual fourth Thursday just for October only. I'll send announcements when we know more. Also, remember that there is no Raleigh Project Night for October unless we get a temporary venue. The next project night will be Chapel Hill Project Night on October 11. Raleigh Project Night is expected to resume in November at WebAssign. -- Scott G. Hall Chief Engineer/Owner Hall Communications Raleigh, NC, USA ph. 919-624-5973 [2]Scott at HallComm-Inc.Com References Visible links 1. http://tripython.org/meetings/ 2. mailto:Scott at hallcomm-inc.com From jefferson.r.heard at gmail.com Tue Oct 3 09:12:10 2017 From: jefferson.r.heard at gmail.com (Jeff Heard) Date: Tue, 3 Oct 2017 09:12:10 -0400 Subject: [TriPython] PyParsing In-Reply-To: <1e162621-19c1-c85f-1080-646c0968602f@hallcomm-inc.com> References: <46f33086-67f2-ff38-c9ee-ede4a73be5c3@pirategrunt.com> <507F20A2-027E-4E46-993E-3E02557200D8@unc.edu> <1e162621-19c1-c85f-1080-646c0968602f@hallcomm-inc.com> Message-ID: Capital T, True / False. Python defines constants in general to use capital letters at least at the beginning. On Tue, Oct 3, 2017 at 1:22 AM, Scott G. Hall wrote: > I downloaded the ipython notebook, and I get an error which makes no > sense > (ie. shouldn't be an error): > > scott at pts/3 $ ipython PyParsing.ipynb > ------------------------------------------------------------ > --------------- > NameError Traceback (most recent call > last) > /usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in > execfile(fname, *where) > 202 else: > 203 filename = fname > --> 204 __builtin__.execfile(filename, *where) > > /home/scott/src/pyparsing/PyParsing.ipynb in () > 58 "execution_count": 20, > 59 "metadata": { > ---> 60 "collapsed": true, > 61 "slideshow": { > 62 "slide_type": "fragment" > > NameError: name 'true' is not defined > > You would think 'true' and 'false' would be defined, so this error seems > to be a misnomer for a different error. > > - sgh > > On 09/29/2017 11:22 AM, Calloway, Chris wrote: > > Brian, thank you so much for a great talk and food for thought. There > were many compliment on your presentation at the after-meeting. Your > notebook repo has been linked on the TriPython meetings page: [1] > http://tripython.org/meetings/ > > Also, thanks for Caktus for the great space and snacks. Thanks for Eric > Leary for the video wrangling. And thanks to our lightning talkers. > > As mentioned at the meeting, the October featured speaker meeting will > have to be moved from the usual fourth Thursday just for October only. I'll > send announcements when we know more. > > Also, remember that there is no Raleigh Project Night for October unless > we get a temporary venue. The next project night will be Chapel Hill > Project Night on October 11. Raleigh Project Night is expected to resume in > November at WebAssign. > > > -- > Scott G. Hall > Chief Engineer/Owner > Hall Communications > Raleigh, NC, USA > ph. 919-624-5973 > [2]Scott at HallComm-Inc.Com > > References > > Visible links > 1. http://tripython.org/meetings/ > 2. mailto:Scott at hallcomm-inc.com > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > > -------------- next part -------------- Capital T, True / False.** Python defines constants in general to use capital letters at least at the beginning. On Tue, Oct 3, 2017 at 1:22 AM, Scott G. Hall <[1]scott at hallcomm-inc.com> wrote: ** **I downloaded the ipython notebook, and I get an error which makes no sense ** **(ie. shouldn't be an error): ** ** **scott at pts/3 $ ipython PyParsing.ipynb ** ** **--------------------------------------------------------------------------- ** ** **NameError** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Traceback (most recent call ** ** **last) ** ** **/usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in ** ** **execfile(fname, *where) ** ** ** ** **202** ** ** ** ** ** **else: ** ** ** ** **203** ** ** ** ** ** ** ** **filename = fname ** ** **--> 204** ** ** ** ** ** **__builtin__.execfile(filename, *where) ** ** **/home/scott/src/pyparsing/PyParsing.ipynb in () ** ** ** ** ** 58** ** "execution_count": 20, ** ** ** ** ** 59** ** "metadata": { ** ** **---> 60** ** **"collapsed": true, ** ** ** ** ** 61** ** **"slideshow": { ** ** ** ** ** 62** ** ** "slide_type": "fragment" ** ** **NameError: name 'true' is not defined ** **You would think 'true' and 'false' would be defined, so this error seems ** **to be a misnomer for a different error. ** **- sgh ** **On 09/29/2017 11:22 AM, Calloway, Chris wrote: **Brian, thank you so much for a great talk and food for thought. There were many compliment on your presentation at the after-meeting. Your notebook repo has been linked on the TriPython meetings page: [1][2]http://tripython.org/meetings/ **Also, thanks for Caktus for the great space and snacks. Thanks for Eric Leary for the video wrangling. And thanks to our lightning talkers. **As mentioned at the meeting, the October featured speaker meeting will have to be moved from the usual fourth Thursday just for October only. I'll send announcements when we know more. **Also, remember that there is no Raleigh Project Night for October unless we get a temporary venue. The next project night will be Chapel Hill Project Night on October 11. Raleigh Project Night is expected to resume in November at WebAssign. **-- **Scott G. Hall **Chief Engineer/Owner **Hall Communications **Raleigh, NC, USA **ph. [3]919-624-5973 **[2]Scott at HallComm-Inc.Com References ** **Visible links ** **1. [4]http://tripython.org/meetings/ ** **2. mailto:[5]Scott at hallcomm-inc.com _______________________________________________ TriZPUG mailing list [6]TriZPUG at python.org [7]https://mail.python.org/mailman/listinfo/trizpug [8]http://tripython.org is the Triangle Python Users Group References Visible links 1. mailto:scott at hallcomm-inc.com 2. http://tripython.org/meetings/ 3. file:///tmp/tel:919-624-5973 4. http://tripython.org/meetings/ 5. mailto:Scott at hallcomm-inc.com 6. mailto:TriZPUG at python.org 7. https://mail.python.org/mailman/listinfo/trizpug 8. http://tripython.org/ From aikimark at aol.com Thu Oct 5 14:45:28 2017 From: aikimark at aol.com (Mark Hutchinson) Date: Thu, 5 Oct 2017 14:45:28 -0400 Subject: [TriPython] Invitation to NC DataPalooza Message-ID: <15eeddb54e9-c0a-1430@webjasstg-vaa08.srv.aolmail.net> My fellow Pythonistas and civic hackers, We just kicked off the 5th annual NC DataPalooza competition at City Camp this past Saturday! We?ve opened the call for teams to pitch at All Things Open for one of the three finalists spots. The finalists will compete for the $5,000 Grand Prize at the NC DataPalooza Finale on November 13th at HQ Raleigh?s Canon Ballroom. It?s an event that brings together coders, designers, entrepreneurs, the public and government to create positive change in the community through the use of open data. The NC DataPalooza page includes lists of past winners and the current set of teams that started at City Camp. Would you be willing to help us promote the event and encourage teams to sign for the competition? This is a link to the marketing template that has bullets about the event and sample email & social media posts. Thanks for your help with this and I hope you?ll be able to join us! Mark Hutchinson -------------- next part -------------- My fellow Pythonistas and civic hackers, We just kicked off the 5th annual [1]NC DataPalooza competition at City Camp this past Saturday! We***ve opened the call for teams to pitch at [2]All Things Open for one of the three finalists spots. The finalists will compete for the $5,000 Grand Prize at the NC DataPalooza Finale on [3]November 13th at HQ Raleigh***s Canon Ballroom. It***s an event that brings together coders, designers, entrepreneurs, the public and government to create positive change in the community through the use of open data. The [4]NC DataPalooza page includes lists of past winners and the current set of teams that started at City Camp. Would you be willing to help us promote the event and encourage teams to sign for the competition? This is a link to the [5]marketing template that has bullets about the event and sample email & social media posts. Thanks for your help with this and I hope you***ll be able to join us! Mark Hutchinson References Visible links 1. http://www.ncdatapalooza.com/ 2. https://allthingsopen.org/ 3. https://www.eventbrite.com/e/nc-datapalooza-2017-tickets-38259132094 4. http://www.ncdatapalooza.com/ 5. https://drive.google.com/open?id=1XzSX3361aSKZp-QDh2kuOOFQzZwjJBMlQ6A9k3W22U0 From cbc at unc.edu Thu Oct 5 15:21:21 2017 From: cbc at unc.edu (Calloway, Chris) Date: Thu, 5 Oct 2017 19:21:21 +0000 Subject: [TriPython] Data+Women+Triangle Message-ID: <013A094D-154E-4BB9-A61A-2BF9B3DD80F6@unc.edu> From Anna Smith (a member of Research Triangle Analysts): Hi Triangle Python, we're Data+Women+Triangle, and this month, we have an event where those seeking training can come hear from many area programs at once. Please consider promoting our event (https://www.meetup.com/DataPlusWomenPlusTriangle/events/242966501/) to your group. Thanks, Anna Smith, neighbor27893 at gmail.com From the event page: THE FUTURE FAIR: Find Data Science Training or Education Right for Women * Monday, October 30, 2017 6:00 PM to 8:30 PM * The Frontier at RTP 800 Park Office Drive, Durham, NC (map) * Price: $10.00 /per person * Are you considering investing in your career with training and education from the area? Or do you advise and support women in the area moving forward in their careers in data science? If so, you want to come to ONE event where you can hear from SIX programs at once (5 in the Triangle; one in Boones)! ? Data Matters training and seminars ? Duke?s Master in Interdisciplinary Data Science ? icimo training ? NC State?s Master of Science in Analytics ? WakeTech?s Associate in Applied Science degree in Business Analytics ? Applachian State University's Master of Science in Applied Data Analytics We are asking these programs questions: Are women applying to your programs and workshops? Are they being accepted? In the program, do they maintain their performance? Do they complete the program? Is the program making a remarkable difference in their careers, meeting both their goals and yours? Which women? ***ALL CAREER TYPES AND LEVELS: BRING AT LEAST 3 COPIES OF YOUR RESUME*** The data shows that annual events from top women's networking groups are likely to offer a splash of wine and lots of interactive conversation. All the more reason to identify the path forward for yourself or a woman near you! Registration, refreshment, and networking begins at 6:00; agenda begins promptly at 6:30. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------- next part -------------- From Anna Smith (a member of Research Triangle Analysts): Hi Triangle Python, we're Data+Women+Triangle, and this month, we have an event where those seeking training can come hear from many area programs at once. Please consider promoting our event ([1]https://www.meetup.com/DataPlusWomenPlusTriangle/events/242966501/) to your group. Thanks, Anna Smith, neighbor27893 at gmail.com From the event page: THE FUTURE FAIR: Find Data Science Training or Education Right for Women o Monday, October 30, 2017 6:00 PM to 8:30 PM o [2]The Frontier at RTP 800 Park Office Drive, Durham, NC ([3]map) o Price: $10.00 /per person o Are you considering investing in your career with training and education from the area? Or do you advise and support women in the area moving forward in their careers in data science? If so, you want to come to ONE event where you can hear from SIX programs at once (5 in the Triangle; one in Boones)! o [4]Data Matters training and seminars o Duke's [5]Master in Interdisciplinary Data Science o [6]icimo training o NC State's [7]Master of Science in Analytics o WakeTech's [8]Associate in Applied Science degree in Business Analytics o Applachian State University's [9]Master of Science in Applied Data Analytics We are asking these programs questions: Are women applying to your programs and workshops? Are they being accepted? In the program, do they maintain their performance? Do they complete the program? Is the program making a remarkable difference in their careers, meeting both their goals and yours? Which women? ***ALL CAREER TYPES AND LEVELS: BRING AT LEAST 3 COPIES OF YOUR RESUME*** The data shows that annual events from top women's networking groups are likely to offer a splash of wine and lots of interactive conversation. All the more reason to identify the path forward for yourself or a woman near you! Registration, refreshment, and networking begins at 6:00; agenda begins promptly at 6:30. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 References Visible links 1. https://www.meetup.com/DataPlusWomenPlusTriangle/events/242966501/ https://www.meetup.com/DataPlusWomenPlusTriangle/events/242966501/ 2. https://maps.google.com/maps?f=q&hl=en&q=800+Park+Office+Drive%2C+Durham%2C+NC%2C+us 3. https://maps.google.com/maps?f=q&hl=en&q=800+Park+Office+Drive%2C+Durham%2C+NC%2C+us 4. http://renci.org/ 5. https://ssri.duke.edu/education/master-interdisciplinary-data-science-mids 6. http://icimo.com/about/training/ 7. http://analytics.ncsu.edu/ 8. https://www.waketech.edu/programs-courses/credit/business-analytics 9. https://business.appstate.edu/academics/ms-applied-data-analytics From cbc at unc.edu Tue Oct 10 10:44:12 2017 From: cbc at unc.edu (Calloway, Chris) Date: Tue, 10 Oct 2017 14:44:12 +0000 Subject: [TriPython] Reminder: Chapel Hill Project Night on Wednesday Message-ID: http://tripython.org/Members/cbc/oct-17-chpn When: Wednesday, October 11, 2017 Where: Renaissance Computing Institute (RENCI) Biltmore Conference Room 5th Floor, Europa Center 100 Europa Drive, Suite 590 Chapel Hill What: Chapel Hill Project Night meets on second Wednesdays. Have a project you want to show off, share, seek help with, or just get some work done surrounded by like-minded Python lovers? Join us for our monthly project night and do just that! Don't have something to work on? Just need some help with Python? Show up and enjoy the energy, sprint on an open source project, find something interesting to contribute to or be inspired by! The setting is informal and there is no schedule, so don't worry if you show up past the start time. Whether you are a Python newbie needing help or have an open source project you want to share, come hang out and hack. We will be joined again this month by the Triangle Deep Learning Study Group. Plenty of free after hours parking is available in the RENCI parking deck. Bring your laptop. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------- next part -------------- [1]http://tripython.org/Members/cbc/oct-17-chpn When: Wednesday, October 11, 2017 Where: Renaissance Computing Institute (RENCI) Biltmore Conference Room 5th Floor, Europa Center 100 Europa Drive, Suite 590 Chapel Hill What: Chapel Hill Project Night meets on second Wednesdays. Have a project you want to show off, share, seek help with, or just get some work done surrounded by like-minded Python lovers? Join us for our monthly project night and do just that! Don't have something to work on? Just need some help with Python? Show up and enjoy the energy, sprint on an open source project, find something interesting to contribute to or be inspired by! The setting is informal and there is no schedule, so don't worry if you show up past the start time. Whether you are a Python newbie needing help or have an open source project you want to share, come hang out and hack. We will be joined again this month by the Triangle Deep Learning Study Group. Plenty of free after hours parking is available in the RENCI parking deck. Bring your laptop. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 References Visible links 1. http://tripython.org/Members/cbc/oct-17-chpn From artem.nesterenko at gmail.com Tue Oct 10 15:53:09 2017 From: artem.nesterenko at gmail.com (Art) Date: Tue, 10 Oct 2017 15:53:09 -0400 Subject: [TriPython] Prediction Model. Data Visualization. Message-ID: Good afternoon! I'm reaching out to you guys for a suggestion on the data visualization. I'm wondering if anyone has an experience or an idea of visualizing the volume of multiple model prediction targets. Here is an example of a model with just 2 prediction targets I've recently been working on: 1. This model provides 2 predictions: target_1 and target_2. 2. I also know the actual result to compare against, so I could see the model accuracy. 3. I've built a confusion matrix to calculate true/false pos/neg (see below). So, there are 4 values. 4. I chose donut chart to visualize these values (see below). [image: Inline image 2] My question is what if the model provides more than 2 predictions? Our next model has 7 targets, which is 49 pos and neg values as far as I understand. And I think a donut graph is not going to work in this case. Maybe a bar graph or something else fits better. I'd appreciate any ideas or examples of visualizations that easy to look at and understand. I'm using d3/c3.js for data visualization. Thank you! Art Nestsiarenka email: artem.nesterenko at gmail.com -------------- next part -------------- Good afternoon! I'm reaching out to you guys for a suggestion on the data visualization. I'm wondering if anyone has an experience or an idea of visualizing the volume of multiple model prediction targets. Here is an example of a model with just 2 prediction targets I've recently been working on: 1. This model provides 2 predictions: target_1 and target_2.** 2. I also know the actual result to compare against, so I could see the model accuracy.** 3. I've built a confusion matrix to calculate true/false pos/neg (see below). So, there are 4 values. 4. I chose donut**chart to visualize these values (see below). [1]Inline image 2** My question is what if the model provides more than 2 predictions? Our next model has 7 targets, which is 49 pos and neg values as far as I understand. And I think a**donut**graph is not going to work in this case.**Maybe a bar graph or something else fits better.** I'd appreciate any ideas or examples of visualizations that easy to look at and understand. I'm using d3/c3.js for data visualization. Thank you! Art Nestsiarenka email: [2]artem.nesterenko at gmail.com References Visible links 2. mailto:artem.nesterenko at gmail.com From ken at mack-z.com Tue Oct 10 20:20:44 2017 From: ken at mack-z.com (Ken MacKenzie) Date: Tue, 10 Oct 2017 20:20:44 -0400 Subject: [TriPython] Just getting the feelers going Message-ID: I think I have finally decided it is time for a change of scene. Since I would like to have more of my day job be working in python I though I would put the feeler out here, that I am looking for new opportunities. I am an experienced software developer and I have some Linux system administration experience as well. Thank you. -------------- next part -------------- I think I have finally decided it is time for a change of scene. Since I would like to have more of my day job be working in python I though I would put the feeler out here, that I am looking for new opportunities. I am an experienced software developer and I have some Linux system administration experience as well. Thank you. From jefferson.r.heard at gmail.com Tue Oct 10 20:52:11 2017 From: jefferson.r.heard at gmail.com (Jeff Heard) Date: Tue, 10 Oct 2017 20:52:11 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: Message-ID: I'd suggest you look at Seaborn. If you're dead-set on visualizing data in javascript, it might well give you some inspiration to draw from, otherwise it's a great Python solution for data vis. https://seaborn.pydata.org/ . I'm not 100% sure what I'd recommend given that much information. Also, it looks like the mailer scrubbed the images. If you could host the originals somewhere (GitHub?), it'd help understand what you did better to give a recommendation. Are your model outputs continuous or discrete? I understand that you're looking at "positive/negative" values, but decisions/classifications and actual model output can be different. Plus the choice of a donut chart to represent boolean values doesn't make a lot of sense to me, so I thought that I maybe understood you wrong. On Tue, Oct 10, 2017 at 3:53 PM, Art wrote: > Good afternoon! > I'm reaching out to you guys for a suggestion on the data visualization. > I'm wondering if anyone has an experience or an idea of visualizing the > volume of multiple model prediction targets. > Here is an example of a model with just 2 prediction targets I've > recently > been working on: > 1. This model provides 2 predictions: target_1 and target_2.** > 2. I also know the actual result to compare against, so I could see the > model accuracy.** > 3. I've built a confusion matrix to calculate true/false pos/neg (see > below). So, there are 4 values. > 4. I chose donut**chart to visualize these values (see below). > [1]Inline image 2** > > My question is what if the model provides more than 2 predictions? Our > next model has 7 targets, which is 49 pos and neg values as far as I > understand. And I think a**donut**graph is not going to work in this > case.**Maybe a bar graph or something else fits better.** > > I'd appreciate any ideas or examples of visualizations that easy to look > at and understand. > I'm using d3/c3.js for data visualization. > Thank you! > Art Nestsiarenka > email: [2]artem.nesterenko at gmail.com > > References > > Visible links > 2. mailto:artem.nesterenko at gmail.com > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > > -------------- next part -------------- I'd suggest you look at Seaborn. If you're dead-set on visualizing data in javascript, it might well give you some inspiration to draw from, otherwise it's a great Python solution for data vis.**[1]https://seaborn.pydata.org/ . I'm not 100% sure what I'd recommend given that much information. Also, it looks like the mailer scrubbed the images. If you could host the originals somewhere (GitHub?), it'd help understand what you did better to give a recommendation. Are your model outputs continuous or discrete? I understand that you're looking at "positive/negative" values, but decisions/classifications and actual model output can be different. Plus the choice of a donut chart to represent boolean values doesn't make a lot of sense to me, so I thought that I maybe understood you wrong. ** On Tue, Oct 10, 2017 at 3:53 PM, Art <[2]artem.nesterenko at gmail.com> wrote: ** **Good afternoon! ** **I'm reaching out to you guys for a suggestion on the data visualization. ** **I'm wondering if anyone has an experience or an idea of visualizing the ** **volume of multiple model prediction targets. ** **Here is an example of a model with just 2 prediction targets I've recently ** **been working on: ** **1. This model provides 2 predictions: target_1 and target_2.** ** **2. I also know the actual result to compare against, so I could see the ** **model accuracy.** ** **3. I've built a confusion matrix to calculate true/false pos/neg (see ** **below). So, there are 4 values. ** **4. I chose donut**chart to visualize these values (see below). ** **[1]Inline image 2** ** **My question is what if the model provides more than 2 predictions? Our ** **next model has 7 targets, which is 49 pos and neg values as far as I ** **understand. And I think a**donut**graph is not going to work in this ** **case.**Maybe a bar graph or something else fits better.** ** **I'd appreciate any ideas or examples of visualizations that easy to look ** **at and understand. ** **I'm using d3/c3.js for data visualization. ** **Thank you! ** **Art Nestsiarenka ** **email: [2][3]artem.nesterenko at gmail.com References ** **Visible links ** **2. mailto:[4]artem.nesterenko at gmail.com _______________________________________________ TriZPUG mailing list [5]TriZPUG at python.org [6]https://mail.python.org/mailman/listinfo/trizpug [7]http://tripython.org is the Triangle Python Users Group References Visible links 1. https://seaborn.pydata.org/ 2. mailto:artem.nesterenko at gmail.com 3. mailto:artem.nesterenko at gmail.com 4. mailto:artem.nesterenko at gmail.com 5. mailto:TriZPUG at python.org 6. https://mail.python.org/mailman/listinfo/trizpug 7. http://tripython.org/ From jessebikman at gmail.com Tue Oct 10 21:33:59 2017 From: jessebikman at gmail.com (Jesse Bikman) Date: Tue, 10 Oct 2017 18:33:59 -0700 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: Message-ID: Hard to go wrong with a bar chart if there?s no time series element! Sent from my iPhone > On Oct 10, 2017, at 17:52, Jeff Heard wrote: > > I'd suggest you look at Seaborn. If you're dead-set on visualizing data in > javascript, it might well give you some inspiration to draw from, > otherwise it's a great Python solution for data > vis.**[1]https://seaborn.pydata.org/ . I'm not 100% sure what I'd > recommend given that much information. Also, it looks like the mailer > scrubbed the images. If you could host the originals somewhere (GitHub?), > it'd help understand what you did better to give a recommendation. > Are your model outputs continuous or discrete? I understand that you're > looking at "positive/negative" values, but decisions/classifications and > actual model output can be different. Plus the choice of a donut chart to > represent boolean values doesn't make a lot of sense to me, so I thought > that I maybe understood you wrong. > ** > On Tue, Oct 10, 2017 at 3:53 PM, Art <[2]artem.nesterenko at gmail.com> > wrote: > > ** **Good afternoon! > ** **I'm reaching out to you guys for a suggestion on the data > visualization. > ** **I'm wondering if anyone has an experience or an idea of visualizing > the > ** **volume of multiple model prediction targets. > ** **Here is an example of a model with just 2 prediction targets I've > recently > ** **been working on: > ** **1. This model provides 2 predictions: target_1 and target_2.** > ** **2. I also know the actual result to compare against, so I could see > the > ** **model accuracy.** > ** **3. I've built a confusion matrix to calculate true/false pos/neg > (see > ** **below). So, there are 4 values. > ** **4. I chose donut**chart to visualize these values (see below). > ** **[1]Inline image 2** > > ** **My question is what if the model provides more than 2 predictions? > Our > ** **next model has 7 targets, which is 49 pos and neg values as far as > I > ** **understand. And I think a**donut**graph is not going to work in > this > ** **case.**Maybe a bar graph or something else fits better.** > > ** **I'd appreciate any ideas or examples of visualizations that easy to > look > ** **at and understand. > ** **I'm using d3/c3.js for data visualization. > ** **Thank you! > ** **Art Nestsiarenka > ** **email: [2][3]artem.nesterenko at gmail.com > > References > > ** **Visible links > ** **2. mailto:[4]artem.nesterenko at gmail.com > > _______________________________________________ > TriZPUG mailing list > [5]TriZPUG at python.org > [6]https://mail.python.org/mailman/listinfo/trizpug > [7]http://tripython.org is the Triangle Python Users Group > > References > > Visible links > 1. https://seaborn.pydata.org/ > 2. mailto:artem.nesterenko at gmail.com > 3. mailto:artem.nesterenko at gmail.com > 4. mailto:artem.nesterenko at gmail.com > 5. mailto:TriZPUG at python.org > 6. https://mail.python.org/mailman/listinfo/trizpug > 7. http://tripython.org/ > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group From francois.dion at gmail.com Tue Oct 10 21:41:57 2017 From: francois.dion at gmail.com (Francois Dion) Date: Tue, 10 Oct 2017 21:41:57 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: Message-ID: Without the images, it is a bit hard to see what you are trying to achieve. If what you are trying to do is represent a confusion matrix with a semi graphical output (i'm all for that), then have a look at Yellow Brick. The confusion matrix is here: http://www.scikit-yb.org/en/latest/api/classifier/confusion_matrix.html They also have a few more visualizations for classification: http://www.scikit-yb.org/en/latest/#classification-visualization Seaborn has a heatmap, so that can be used for a CM too. Now, if it has to be on the web, interactive, look at bokeh or plot.ly. I did a plotly presentation at PYPTUG last month, some code and a lot more stuff can be found here: https://github.com/fdion/pyptug_plotly Francois On Tue, Oct 10, 2017 at 3:53 PM, Art wrote: > Good afternoon! > I'm reaching out to you guys for a suggestion on the data visualization. > I'm wondering if anyone has an experience or an idea of visualizing the > volume of multiple model prediction targets. > Here is an example of a model with just 2 prediction targets I've > recently > been working on: > 1. This model provides 2 predictions: target_1 and target_2.** > 2. I also know the actual result to compare against, so I could see the > model accuracy.** > 3. I've built a confusion matrix to calculate true/false pos/neg (see > below). So, there are 4 values. > 4. I chose donut**chart to visualize these values (see below). > [1]Inline image 2** > > My question is what if the model provides more than 2 predictions? Our > next model has 7 targets, which is 49 pos and neg values as far as I > understand. And I think a**donut**graph is not going to work in this > case.**Maybe a bar graph or something else fits better.** > > I'd appreciate any ideas or examples of visualizations that easy to look > at and understand. > I'm using d3/c3.js for data visualization. > Thank you! > Art Nestsiarenka > email: [2]artem.nesterenko at gmail.com > > References > > Visible links > 2. mailto:artem.nesterenko at gmail.com > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > > -- about.me/francois.dion - www.pyptug.org - www.3DFutureTech.info - @f_dion -------------- next part -------------- Without the images, it is a bit hard to see what you are trying to achieve. If what you are trying to do is represent a confusion matrix with a semi graphical output (i'm all for that), then have a look at Yellow Brick. The confusion matrix is here: [1]http://www.scikit-yb.org/en/latest/api/classifier/confusion_matrix.html They also have a few more visualizations for classification: [2]http://www.scikit-yb.org/en/latest/#classification-visualization Seaborn has a heatmap, so that can be used for a CM too. Now, if it has to be on the web, interactive, look at bokeh or [3]plot.ly. I did a plotly presentation at PYPTUG last month, some code and a lot more stuff can be found here: [4]https://github.com/fdion/pyptug_plotly Francois On Tue, Oct 10, 2017 at 3:53 PM, Art <[5]artem.nesterenko at gmail.com> wrote: ** **Good afternoon! ** **I'm reaching out to you guys for a suggestion on the data visualization. ** **I'm wondering if anyone has an experience or an idea of visualizing the ** **volume of multiple model prediction targets. ** **Here is an example of a model with just 2 prediction targets I've recently ** **been working on: ** **1. This model provides 2 predictions: target_1 and target_2.** ** **2. I also know the actual result to compare against, so I could see the ** **model accuracy.** ** **3. I've built a confusion matrix to calculate true/false pos/neg (see ** **below). So, there are 4 values. ** **4. I chose donut**chart to visualize these values (see below). ** **[1]Inline image 2** ** **My question is what if the model provides more than 2 predictions? Our ** **next model has 7 targets, which is 49 pos and neg values as far as I ** **understand. And I think a**donut**graph is not going to work in this ** **case.**Maybe a bar graph or something else fits better.** ** **I'd appreciate any ideas or examples of visualizations that easy to look ** **at and understand. ** **I'm using d3/c3.js for data visualization. ** **Thank you! ** **Art Nestsiarenka ** **email: [2][6]artem.nesterenko at gmail.com References ** **Visible links ** **2. mailto:[7]artem.nesterenko at gmail.com _______________________________________________ TriZPUG mailing list [8]TriZPUG at python.org [9]https://mail.python.org/mailman/listinfo/trizpug [10]http://tripython.org is the Triangle Python Users Group -- [11]about.me/francois.dion - [12]www.pyptug.org - [13]www.3DFutureTech.info - [14]@f_dion References Visible links 1. http://www.scikit-yb.org/en/latest/api/classifier/confusion_matrix.html 2. http://www.scikit-yb.org/en/latest/#classification-visualization 3. http://plot.ly/ 4. https://github.com/fdion/pyptug_plotly 5. mailto:artem.nesterenko at gmail.com 6. mailto:artem.nesterenko at gmail.com 7. mailto:artem.nesterenko at gmail.com 8. mailto:TriZPUG at python.org 9. https://mail.python.org/mailman/listinfo/trizpug 10. http://tripython.org/ 11. http://about.me/francois.dion 12. http://www.pyptug.org/ 13. http://www.3dfuturetech.info/ 14. http://twitter.com/f_dion From artem.nesterenko at gmail.com Wed Oct 11 00:22:32 2017 From: artem.nesterenko at gmail.com (Art) Date: Wed, 11 Oct 2017 00:22:32 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: Message-ID: Thank you for the prompt replies! Sorry for the picture, didn't expect to have this issue. So, basically what I did is I built a confusion matrix with correct and incorrect predictions: For example: *Predicted* target_1 target_2 *Real* target_1 120 23 target_2 37 44 and then I built a donut that represents the *number* of true pos, true neg, false pos, false neg predictions from the confusion matrix. I've attached the image. The attached donut doesn't represent the numbers in "%" from the above matrix. This matrix is just an example. And now I'm thinking of the best graph type to represent the number of pos and neg predictions if the model predicts 7 targets. Art Nestsiarenka email: artem.nesterenko at gmail.com Cell: (919) 455-5055 On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard wrote: > I'd suggest you look at Seaborn. If you're dead-set on visualizing data > in > javascript, it might well give you some inspiration to draw from, > otherwise it's a great Python solution for data > vis.**[1]https://seaborn.pydata.org/ . I'm not 100% sure what I'd > recommend given that much information. Also, it looks like the mailer > scrubbed the images. If you could host the originals somewhere > (GitHub?), > it'd help understand what you did better to give a recommendation. > Are your model outputs continuous or discrete? I understand that you're > looking at "positive/negative" values, but decisions/classifications and > actual model output can be different. Plus the choice of a donut chart > to > represent boolean values doesn't make a lot of sense to me, so I thought > that I maybe understood you wrong. > ** > On Tue, Oct 10, 2017 at 3:53 PM, Art <[2]artem.nesterenko at gmail.com> > wrote: > > ** **Good afternoon! > ** **I'm reaching out to you guys for a suggestion on the data > visualization. > ** **I'm wondering if anyone has an experience or an idea of > visualizing > the > ** **volume of multiple model prediction targets. > ** **Here is an example of a model with just 2 prediction targets I've > recently > ** **been working on: > ** **1. This model provides 2 predictions: target_1 and target_2.** > ** **2. I also know the actual result to compare against, so I could > see > the > ** **model accuracy.** > ** **3. I've built a confusion matrix to calculate true/false pos/neg > (see > ** **below). So, there are 4 values. > ** **4. I chose donut**chart to visualize these values (see below). > ** **[1]Inline image 2** > > ** **My question is what if the model provides more than 2 > predictions? > Our > ** **next model has 7 targets, which is 49 pos and neg values as far > as > I > ** **understand. And I think a**donut**graph is not going to work in > this > ** **case.**Maybe a bar graph or something else fits better.** > > ** **I'd appreciate any ideas or examples of visualizations that easy > to > look > ** **at and understand. > ** **I'm using d3/c3.js for data visualization. > ** **Thank you! > ** **Art Nestsiarenka > ** **email: [2][3]artem.nesterenko at gmail.com > > References > > ** **Visible links > ** **2. mailto:[4]artem.nesterenko at gmail.com > > _______________________________________________ > TriZPUG mailing list > [5]TriZPUG at python.org > [6]https://mail.python.org/mailman/listinfo/trizpug > [7]http://tripython.org is the Triangle Python Users Group > > References > > Visible links > 1. https://seaborn.pydata.org/ > 2. mailto:artem.nesterenko at gmail.com > 3. mailto:artem.nesterenko at gmail.com > 4. mailto:artem.nesterenko at gmail.com > 5. mailto:TriZPUG at python.org > 6. https://mail.python.org/mailman/listinfo/trizpug > 7. http://tripython.org/ > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > > -------------- next part -------------- Thank you for the**prompt replies! Sorry for the picture, didn't expect to have this issue. So, basically what I did is I built a confusion matrix with correct and incorrect predictions: For example: ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 Real ** ** target_1 ** **120 ** ** ** ** ** 23 ** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** and then I built a donut that represents the number of true pos, true neg, false pos, false neg predictions from the confusion matrix. I've attached the image. The attached donut doesn't represent the numbers in "%" from the above matrix. This matrix is just an example. And now I'm thinking of the**best graph type to represent the number of pos and neg predictions if the**model predicts 7 targets. Art Nestsiarenka email: [1]artem.nesterenko at gmail.com Cell: (919) 455-5055 On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard <[2]jefferson.r.heard at gmail.com> wrote: ** **I'd suggest you look at Seaborn. If you're dead-set on visualizing data in ** **javascript, it might well give you some inspiration to draw from, ** **otherwise it's a great Python solution for data ** **vis.**[1][3]https://seaborn.pydata.org/ . I'm not 100% sure what I'd ** **recommend given that much information. Also, it looks like the mailer ** **scrubbed the images. If you could host the originals somewhere (GitHub?), ** **it'd help understand what you did better to give a recommendation. ** **Are your model outputs continuous or discrete? I understand that you're ** **looking at "positive/negative" values, but decisions/classifications and ** **actual model output can be different. Plus the choice of a donut chart to ** **represent boolean values doesn't make a lot of sense to me, so I thought ** **that I maybe understood you wrong. ** **** ** **On Tue, Oct 10, 2017 at 3:53 PM, Art <[2][4]artem.nesterenko at gmail.com> ** **wrote: ** ** **** **Good afternoon! ** ** **** **I'm reaching out to you guys for a suggestion on the data ** ** **visualization. ** ** **** **I'm wondering if anyone has an experience or an idea of visualizing ** ** **the ** ** **** **volume of multiple model prediction targets. ** ** **** **Here is an example of a model with just 2 prediction targets I've ** ** **recently ** ** **** **been working on: ** ** **** **1. This model provides 2 predictions: target_1 and target_2.** ** ** **** **2. I also know the actual result to compare against, so I could see ** ** **the ** ** **** **model accuracy.** ** ** **** **3. I've built a confusion matrix to calculate true/false pos/neg ** ** **(see ** ** **** **below). So, there are 4 values. ** ** **** **4. I chose donut**chart to visualize these values (see below). ** ** **** **[1]Inline image 2** ** ** **** **My question is what if the model provides more than 2 predictions? ** ** **Our ** ** **** **next model has 7 targets, which is 49 pos and neg values as far as ** ** **I ** ** **** **understand. And I think a**donut**graph is not going to work in ** ** **this ** ** **** **case.**Maybe a bar graph or something else fits better.** ** ** **** **I'd appreciate any ideas or examples of visualizations that easy to ** ** **look ** ** **** **at and understand. ** ** **** **I'm using d3/c3.js for data visualization. ** ** **** **Thank you! ** ** **** **Art Nestsiarenka ** ** **** **email: [2][3][5]artem.nesterenko at gmail.com ** ** **References ** ** **** **Visible links ** ** **** **2. mailto:[4][6]artem.nesterenko at gmail.com ** ** **_______________________________________________ ** ** **TriZPUG mailing list ** ** **[5][7]TriZPUG at python.org ** ** **[6][8]https://mail.python.org/mailman/listinfo/trizpug ** ** **[7][9]http://tripython.org is the Triangle Python Users Group References ** **Visible links ** **1. [10]https://seaborn.pydata.org/ ** **2. mailto:[11]artem.nesterenko at gmail.com ** **3. mailto:[12]artem.nesterenko at gmail.com ** **4. mailto:[13]artem.nesterenko at gmail.com ** **5. mailto:[14]TriZPUG at python.org ** **6. [15]https://mail.python.org/mailman/listinfo/trizpug ** **7. [16]http://tripython.org/ _______________________________________________ TriZPUG mailing list [17]TriZPUG at python.org [18]https://mail.python.org/mailman/listinfo/trizpug [19]http://tripython.org is the Triangle Python Users Group References Visible links 1. mailto:artem.nesterenko at gmail.com 2. mailto:jefferson.r.heard at gmail.com 3. https://seaborn.pydata.org/ 4. mailto:artem.nesterenko at gmail.com 5. mailto:artem.nesterenko at gmail.com 6. mailto:artem.nesterenko at gmail.com 7. mailto:TriZPUG at python.org 8. https://mail.python.org/mailman/listinfo/trizpug 9. http://tripython.org/ 10. https://seaborn.pydata.org/ 11. mailto:artem.nesterenko at gmail.com 12. mailto:artem.nesterenko at gmail.com 13. mailto:artem.nesterenko at gmail.com 14. mailto:TriZPUG at python.org 15. https://mail.python.org/mailman/listinfo/trizpug 16. http://tripython.org/ 17. mailto:TriZPUG at python.org 18. https://mail.python.org/mailman/listinfo/trizpug 19. http://tripython.org/ From jessebikman at gmail.com Wed Oct 11 01:35:08 2017 From: jessebikman at gmail.com (Jesse Bikman) Date: Tue, 10 Oct 2017 22:35:08 -0700 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: Message-ID: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Image attachment isn?t coming through. Maybe host on imgur or similar site and pass the link. Sent from my iPad > On Oct 10, 2017, at 9:22 PM, Art wrote: > > Thank you for the**prompt replies! > Sorry for the picture, didn't expect to have this issue. > So, basically what I did is I built a confusion matrix with correct and > incorrect predictions: > For example: > ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted ** ** ** ** > ** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 > Real ** ** target_1 ** **120 ** ** ** ** ** 23 > ** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** > and then I built a donut that represents the number of true pos, true neg, > false pos, false neg predictions from the confusion matrix. I've attached > the image. The attached donut doesn't represent the numbers in "%" from > the above matrix. This matrix is just an example. > And now I'm thinking of the**best graph type to represent the number of > pos and neg predictions if the**model predicts 7 targets. > Art Nestsiarenka > email: [1]artem.nesterenko at gmail.com > Cell: (919) 455-5055 > > On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard > <[2]jefferson.r.heard at gmail.com> wrote: > > ** **I'd suggest you look at Seaborn. If you're dead-set on visualizing > data in > ** **javascript, it might well give you some inspiration to draw from, > ** **otherwise it's a great Python solution for data > ** **vis.**[1][3]https://seaborn.pydata.org/ . I'm not 100% sure what > I'd > ** **recommend given that much information. Also, it looks like the > mailer > ** **scrubbed the images. If you could host the originals somewhere > (GitHub?), > ** **it'd help understand what you did better to give a recommendation. > ** **Are your model outputs continuous or discrete? I understand that > you're > ** **looking at "positive/negative" values, but > decisions/classifications and > ** **actual model output can be different. Plus the choice of a donut > chart to > ** **represent boolean values doesn't make a lot of sense to me, so I > thought > ** **that I maybe understood you wrong. > ** **** > ** **On Tue, Oct 10, 2017 at 3:53 PM, Art > <[2][4]artem.nesterenko at gmail.com> > ** **wrote: > > ** ** **** **Good afternoon! > ** ** **** **I'm reaching out to you guys for a suggestion on the data > ** ** **visualization. > ** ** **** **I'm wondering if anyone has an experience or an idea of > visualizing > ** ** **the > ** ** **** **volume of multiple model prediction targets. > ** ** **** **Here is an example of a model with just 2 prediction > targets I've > ** ** **recently > ** ** **** **been working on: > ** ** **** **1. This model provides 2 predictions: target_1 and > target_2.** > ** ** **** **2. I also know the actual result to compare against, so I > could see > ** ** **the > ** ** **** **model accuracy.** > ** ** **** **3. I've built a confusion matrix to calculate true/false > pos/neg > ** ** **(see > ** ** **** **below). So, there are 4 values. > ** ** **** **4. I chose donut**chart to visualize these values (see > below). > ** ** **** **[1]Inline image 2** > > ** ** **** **My question is what if the model provides more than 2 > predictions? > ** ** **Our > ** ** **** **next model has 7 targets, which is 49 pos and neg values as > far as > ** ** **I > ** ** **** **understand. And I think a**donut**graph is not going to > work in > ** ** **this > ** ** **** **case.**Maybe a bar graph or something else fits better.** > > ** ** **** **I'd appreciate any ideas or examples of visualizations that > easy to > ** ** **look > ** ** **** **at and understand. > ** ** **** **I'm using d3/c3.js for data visualization. > ** ** **** **Thank you! > ** ** **** **Art Nestsiarenka > ** ** **** **email: [2][3][5]artem.nesterenko at gmail.com > > ** ** **References > > ** ** **** **Visible links > ** ** **** **2. mailto:[4][6]artem.nesterenko at gmail.com > > ** ** **_______________________________________________ > ** ** **TriZPUG mailing list > ** ** **[5][7]TriZPUG at python.org > ** ** **[6][8]https://mail.python.org/mailman/listinfo/trizpug > ** ** **[7][9]http://tripython.org is the Triangle Python Users Group > > References > > ** **Visible links > ** **1. [10]https://seaborn.pydata.org/ > ** **2. mailto:[11]artem.nesterenko at gmail.com > ** **3. mailto:[12]artem.nesterenko at gmail.com > ** **4. mailto:[13]artem.nesterenko at gmail.com > ** **5. mailto:[14]TriZPUG at python.org > ** **6. [15]https://mail.python.org/mailman/listinfo/trizpug > ** **7. [16]http://tripython.org/ > > _______________________________________________ > TriZPUG mailing list > [17]TriZPUG at python.org > [18]https://mail.python.org/mailman/listinfo/trizpug > [19]http://tripython.org is the Triangle Python Users Group > > References > > Visible links > 1. mailto:artem.nesterenko at gmail.com > 2. mailto:jefferson.r.heard at gmail.com > 3. https://seaborn.pydata.org/ > 4. mailto:artem.nesterenko at gmail.com > 5. mailto:artem.nesterenko at gmail.com > 6. mailto:artem.nesterenko at gmail.com > 7. mailto:TriZPUG at python.org > 8. https://mail.python.org/mailman/listinfo/trizpug > 9. http://tripython.org/ > 10. https://seaborn.pydata.org/ > 11. mailto:artem.nesterenko at gmail.com > 12. mailto:artem.nesterenko at gmail.com > 13. mailto:artem.nesterenko at gmail.com > 14. mailto:TriZPUG at python.org > 15. https://mail.python.org/mailman/listinfo/trizpug > 16. http://tripython.org/ > 17. mailto:TriZPUG at python.org > 18. https://mail.python.org/mailman/listinfo/trizpug > 19. http://tripython.org/ > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group From artem.nesterenko at gmail.com Wed Oct 11 08:52:50 2017 From: artem.nesterenko at gmail.com (Art) Date: Wed, 11 Oct 2017 08:52:50 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: Donut graph: https://imgur.com/a/C7r8x You should be able to see it now. Art Nestsiarenka email: artem.nesterenko at gmail.com Cell: (919) 455-5055 On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman wrote: > Image attachment isn?t coming through. Maybe host on imgur or similar site > and pass the link. > > Sent from my iPad > > > On Oct 10, 2017, at 9:22 PM, Art wrote: > > > > Thank you for the**prompt replies! > > Sorry for the picture, didn't expect to have this issue. > > So, basically what I did is I built a confusion matrix with correct and > > incorrect predictions: > > For example: > > ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted ** ** > ** ** > > ** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 > > Real ** ** target_1 ** **120 ** ** ** ** ** 23 > > ** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** > > and then I built a donut that represents the number of true pos, true > neg, > > false pos, false neg predictions from the confusion matrix. I've > attached > > the image. The attached donut doesn't represent the numbers in "%" from > > the above matrix. This matrix is just an example. > > And now I'm thinking of the**best graph type to represent the number of > > pos and neg predictions if the**model predicts 7 targets. > > Art Nestsiarenka > > email: [1]artem.nesterenko at gmail.com > > Cell: (919) 455-5055 > > > > On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard > > <[2]jefferson.r.heard at gmail.com> wrote: > > > > ** **I'd suggest you look at Seaborn. If you're dead-set on > visualizing > > data in > > ** **javascript, it might well give you some inspiration to draw > from, > > ** **otherwise it's a great Python solution for data > > ** **vis.**[1][3]https://seaborn.pydata.org/ . I'm not 100% sure > what > > I'd > > ** **recommend given that much information. Also, it looks like the > > mailer > > ** **scrubbed the images. If you could host the originals somewhere > > (GitHub?), > > ** **it'd help understand what you did better to give a > recommendation. > > ** **Are your model outputs continuous or discrete? I understand that > > you're > > ** **looking at "positive/negative" values, but > > decisions/classifications and > > ** **actual model output can be different. Plus the choice of a donut > > chart to > > ** **represent boolean values doesn't make a lot of sense to me, so I > > thought > > ** **that I maybe understood you wrong. > > ** **** > > ** **On Tue, Oct 10, 2017 at 3:53 PM, Art > > <[2][4]artem.nesterenko at gmail.com> > > ** **wrote: > > > > ** ** **** **Good afternoon! > > ** ** **** **I'm reaching out to you guys for a suggestion on the > data > > ** ** **visualization. > > ** ** **** **I'm wondering if anyone has an experience or an idea of > > visualizing > > ** ** **the > > ** ** **** **volume of multiple model prediction targets. > > ** ** **** **Here is an example of a model with just 2 prediction > > targets I've > > ** ** **recently > > ** ** **** **been working on: > > ** ** **** **1. This model provides 2 predictions: target_1 and > > target_2.** > > ** ** **** **2. I also know the actual result to compare against, so > I > > could see > > ** ** **the > > ** ** **** **model accuracy.** > > ** ** **** **3. I've built a confusion matrix to calculate true/false > > pos/neg > > ** ** **(see > > ** ** **** **below). So, there are 4 values. > > ** ** **** **4. I chose donut**chart to visualize these values (see > > below). > > ** ** **** **[1]Inline image 2** > > > > ** ** **** **My question is what if the model provides more than 2 > > predictions? > > ** ** **Our > > ** ** **** **next model has 7 targets, which is 49 pos and neg > values as > > far as > > ** ** **I > > ** ** **** **understand. And I think a**donut**graph is not going to > > work in > > ** ** **this > > ** ** **** **case.**Maybe a bar graph or something else fits > better.** > > > > ** ** **** **I'd appreciate any ideas or examples of visualizations > that > > easy to > > ** ** **look > > ** ** **** **at and understand. > > ** ** **** **I'm using d3/c3.js for data visualization. > > ** ** **** **Thank you! > > ** ** **** **Art Nestsiarenka > > ** ** **** **email: [2][3][5]artem.nesterenko at gmail.com > > > > ** ** **References > > > > ** ** **** **Visible links > > ** ** **** **2. mailto:[4][6]artem.nesterenko at gmail.com > > > > ** ** **_______________________________________________ > > ** ** **TriZPUG mailing list > > ** ** **[5][7]TriZPUG at python.org > > ** ** **[6][8]https://mail.python.org/mailman/listinfo/trizpug > > ** ** **[7][9]http://tripython.org is the Triangle Python Users > Group > > > > References > > > > ** **Visible links > > ** **1. [10]https://seaborn.pydata.org/ > > ** **2. mailto:[11]artem.nesterenko at gmail.com > > ** **3. mailto:[12]artem.nesterenko at gmail.com > > ** **4. mailto:[13]artem.nesterenko at gmail.com > > ** **5. mailto:[14]TriZPUG at python.org > > ** **6. [15]https://mail.python.org/mailman/listinfo/trizpug > > ** **7. [16]http://tripython.org/ > > > > _______________________________________________ > > TriZPUG mailing list > > [17]TriZPUG at python.org > > [18]https://mail.python.org/mailman/listinfo/trizpug > > [19]http://tripython.org is the Triangle Python Users Group > > > > References > > > > Visible links > > 1. mailto:artem.nesterenko at gmail.com > > 2. mailto:jefferson.r.heard at gmail.com > > 3. https://seaborn.pydata.org/ > > 4. mailto:artem.nesterenko at gmail.com > > 5. mailto:artem.nesterenko at gmail.com > > 6. mailto:artem.nesterenko at gmail.com > > 7. mailto:TriZPUG at python.org > > 8. https://mail.python.org/mailman/listinfo/trizpug > > 9. http://tripython.org/ > > 10. https://seaborn.pydata.org/ > > 11. mailto:artem.nesterenko at gmail.com > > 12. mailto:artem.nesterenko at gmail.com > > 13. mailto:artem.nesterenko at gmail.com > > 14. mailto:TriZPUG at python.org > > 15. https://mail.python.org/mailman/listinfo/trizpug > > 16. http://tripython.org/ > > 17. mailto:TriZPUG at python.org > > 18. https://mail.python.org/mailman/listinfo/trizpug > > 19. http://tripython.org/ > > _______________________________________________ > > TriZPUG mailing list > > TriZPUG at python.org > > https://mail.python.org/mailman/listinfo/trizpug > > http://tripython.org is the Triangle Python Users Group > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > -------------- next part -------------- Donut graph: [1]https://imgur.com/a/C7r8x You should be able to see it now. Art Nestsiarenka email: [2]artem.nesterenko at gmail.com Cell: (919) 455-5055 On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman <[3]jessebikman at gmail.com> wrote: Image attachment isn***t coming through. Maybe host on imgur or similar site and pass the link. Sent from my iPad > On Oct 10, 2017, at 9:22 PM, Art <[4]artem.nesterenko at gmail.com> wrote: > >** **Thank you for the**prompt replies! >** **Sorry for the picture, didn't expect to have this issue. >** **So, basically what I did is I built a confusion matrix with correct and >** **incorrect predictions: >** **For example: >** **** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted ** ** ** ** >** **** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 >** **Real ** ** target_1 ** **120 ** ** ** ** ** 23 >** **** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** >** **and then I built a donut that represents the number of true pos, true neg, >** **false pos, false neg predictions from the confusion matrix. I've attached >** **the image. The attached donut doesn't represent the numbers in "%" from >** **the above matrix. This matrix is just an example. >** **And now I'm thinking of the**best graph type to represent the number of >** **pos and neg predictions if the**model predicts 7 targets. >** **Art Nestsiarenka >** **email: [1][5]artem.nesterenko at gmail.com >** **Cell: [6](919) 455-5055 > >** **On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard >** **<[2][7]jefferson.r.heard at gmail.com> wrote: > >** ** **** **I'd suggest you look at Seaborn. If you're dead-set on visualizing >** ** **data in >** ** **** **javascript, it might well give you some inspiration to draw from, >** ** **** **otherwise it's a great Python solution for data >** ** **** **vis.**[1][3][8]https://seaborn.pydata.org/ . I'm not 100% sure what >** ** **I'd >** ** **** **recommend given that much information. Also, it looks like the >** ** **mailer >** ** **** **scrubbed the images. If you could host the originals somewhere >** ** **(GitHub?), >** ** **** **it'd help understand what you did better to give a recommendation. >** ** **** **Are your model outputs continuous or discrete? I understand that >** ** **you're >** ** **** **looking at "positive/negative" values, but >** ** **decisions/classifications and >** ** **** **actual model output can be different. Plus the choice of a donut >** ** **chart to >** ** **** **represent boolean values doesn't make a lot of sense to me, so I >** ** **thought >** ** **** **that I maybe understood you wrong. >** ** **** **** >** ** **** **On Tue, Oct 10, 2017 at 3:53 PM, Art >** ** **<[2][4][9]artem.nesterenko at gmail.com> >** ** **** **wrote: > >** ** **** ** **** **Good afternoon! >** ** **** ** **** **I'm reaching out to you guys for a suggestion on the data >** ** **** ** **visualization. >** ** **** ** **** **I'm wondering if anyone has an experience or an idea of >** ** **visualizing >** ** **** ** **the >** ** **** ** **** **volume of multiple model prediction targets. >** ** **** ** **** **Here is an example of a model with just 2 prediction >** ** **targets I've >** ** **** ** **recently >** ** **** ** **** **been working on: >** ** **** ** **** **1. This model provides 2 predictions: target_1 and >** ** **target_2.** >** ** **** ** **** **2. I also know the actual result to compare against, so I >** ** **could see >** ** **** ** **the >** ** **** ** **** **model accuracy.** >** ** **** ** **** **3. I've built a confusion matrix to calculate true/false >** ** **pos/neg >** ** **** ** **(see >** ** **** ** **** **below). So, there are 4 values. >** ** **** ** **** **4. I chose donut**chart to visualize these values (see >** ** **below). >** ** **** ** **** **[1]Inline image 2** > >** ** **** ** **** **My question is what if the model provides more than 2 >** ** **predictions? >** ** **** ** **Our >** ** **** ** **** **next model has 7 targets, which is 49 pos and neg values as >** ** **far as >** ** **** ** **I >** ** **** ** **** **understand. And I think a**donut**graph is not going to >** ** **work in >** ** **** ** **this >** ** **** ** **** **case.**Maybe a bar graph or something else fits better.** > >** ** **** ** **** **I'd appreciate any ideas or examples of visualizations that >** ** **easy to >** ** **** ** **look >** ** **** ** **** **at and understand. >** ** **** ** **** **I'm using d3/c3.js for data visualization. >** ** **** ** **** **Thank you! >** ** **** ** **** **Art Nestsiarenka >** ** **** ** **** **email: [2][3][5][10]artem.nesterenko at gmail.com > >** ** **** ** **References > >** ** **** ** **** **Visible links >** ** **** ** **** **2. mailto:[4][6][11]artem.nesterenko at gmail.com > >** ** **** ** **_______________________________________________ >** ** **** ** **TriZPUG mailing list >** ** **** ** **[5][7][12]TriZPUG at python.org >** ** **** ** **[6][8][13]https://mail.python.org/mailman/listinfo/trizpug >** ** **** ** **[7][9][14]http://tripython.org is the Triangle Python Users Group > >** ** **References > >** ** **** **Visible links >** ** **** **1. [10][15]https://seaborn.pydata.org/ >** ** **** **2. mailto:[11][16]artem.nesterenko at gmail.com >** ** **** **3. mailto:[12][17]artem.nesterenko at gmail.com >** ** **** **4. mailto:[13][18]artem.nesterenko at gmail.com >** ** **** **5. mailto:[14][19]TriZPUG at python.org >** ** **** **6. [15][20]https://mail.python.org/mailman/listinfo/trizpug >** ** **** **7. [16][21]http://tripython.org/ > >** ** **_______________________________________________ >** ** **TriZPUG mailing list >** ** **[17][22]TriZPUG at python.org >** ** **[18][23]https://mail.python.org/mailman/listinfo/trizpug >** ** **[19][24]http://tripython.org is the Triangle Python Users Group > > References > >** **Visible links >** **1. mailto:[25]artem.nesterenko at gmail.com >** **2. mailto:[26]jefferson.r.heard at gmail.com >** **3. [27]https://seaborn.pydata.org/ >** **4. mailto:[28]artem.nesterenko at gmail.com >** **5. mailto:[29]artem.nesterenko at gmail.com >** **6. mailto:[30]artem.nesterenko at gmail.com >** **7. mailto:[31]TriZPUG at python.org >** **8. [32]https://mail.python.org/mailman/listinfo/trizpug >** **9. [33]http://tripython.org/ >** 10. [34]https://seaborn.pydata.org/ >** 11. mailto:[35]artem.nesterenko at gmail.com >** 12. mailto:[36]artem.nesterenko at gmail.com >** 13. mailto:[37]artem.nesterenko at gmail.com >** 14. mailto:[38]TriZPUG at python.org >** 15. [39]https://mail.python.org/mailman/listinfo/trizpug >** 16. [40]http://tripython.org/ >** 17. mailto:[41]TriZPUG at python.org >** 18. [42]https://mail.python.org/mailman/listinfo/trizpug >** 19. [43]http://tripython.org/ > _______________________________________________ > TriZPUG mailing list > [44]TriZPUG at python.org > [45]https://mail.python.org/mailman/listinfo/trizpug > [46]http://tripython.org is the Triangle Python Users Group _______________________________________________ TriZPUG mailing list [47]TriZPUG at python.org [48]https://mail.python.org/mailman/listinfo/trizpug [49]http://tripython.org is the Triangle Python Users Group References Visible links 1. https://imgur.com/a/C7r8x 2. mailto:artem.nesterenko at gmail.com 3. mailto:jessebikman at gmail.com 4. mailto:artem.nesterenko at gmail.com 5. mailto:artem.nesterenko at gmail.com 6. file:///tmp/tel:%28919%29%20455-5055 7. mailto:jefferson.r.heard at gmail.com 8. https://seaborn.pydata.org/ 9. mailto:artem.nesterenko at gmail.com 10. mailto:artem.nesterenko at gmail.com 11. mailto:artem.nesterenko at gmail.com 12. mailto:TriZPUG at python.org 13. https://mail.python.org/mailman/listinfo/trizpug 14. http://tripython.org/ 15. https://seaborn.pydata.org/ 16. mailto:artem.nesterenko at gmail.com 17. mailto:artem.nesterenko at gmail.com 18. mailto:artem.nesterenko at gmail.com 19. mailto:TriZPUG at python.org 20. https://mail.python.org/mailman/listinfo/trizpug 21. http://tripython.org/ 22. mailto:TriZPUG at python.org 23. https://mail.python.org/mailman/listinfo/trizpug 24. http://tripython.org/ 25. mailto:artem.nesterenko at gmail.com 26. mailto:jefferson.r.heard at gmail.com 27. https://seaborn.pydata.org/ 28. mailto:artem.nesterenko at gmail.com 29. mailto:artem.nesterenko at gmail.com 30. mailto:artem.nesterenko at gmail.com 31. mailto:TriZPUG at python.org 32. https://mail.python.org/mailman/listinfo/trizpug 33. http://tripython.org/ 34. https://seaborn.pydata.org/ 35. mailto:artem.nesterenko at gmail.com 36. mailto:artem.nesterenko at gmail.com 37. mailto:artem.nesterenko at gmail.com 38. mailto:TriZPUG at python.org 39. https://mail.python.org/mailman/listinfo/trizpug 40. http://tripython.org/ 41. mailto:TriZPUG at python.org 42. https://mail.python.org/mailman/listinfo/trizpug 43. http://tripython.org/ 44. mailto:TriZPUG at python.org 45. https://mail.python.org/mailman/listinfo/trizpug 46. http://tripython.org/ 47. mailto:TriZPUG at python.org 48. https://mail.python.org/mailman/listinfo/trizpug 49. http://tripython.org/ From jdavep at gmail.com Wed Oct 11 10:11:51 2017 From: jdavep at gmail.com (Dave Powell) Date: Wed, 11 Oct 2017 10:11:51 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: I am not a mathematician or statistician, but in my work for Tableau, a donut is most useful for progress to goal. In your case, perhaps that is a success rate. (Which may not be 100%) In that scenario, having 2 cases makes the viz confusing to read. If you want to do the donut, I'd stick to one case per. Also, who is your audience for the viz? Are you interpreting this for the layman, or is it an expert who expects the complexity? Finally, what is your objective with the viz? Is it to display that case 1 is larger than case 2? If so, other methods may do that more clearly. (Pies/donuts are notoriously bad for conveying true proportions for multiple segments. As for the output tool, take your pick, but keep in mind the audience and objective, and you'll be happy. Good luck! Dave Powell On Oct 11, 2017 7:53 AM, "Art" wrote: > Donut graph: > [1]https://imgur.com/a/C7r8x > You should be able to see it now. > Art Nestsiarenka > email: [2]artem.nesterenko at gmail.com > Cell: (919) 455-5055 > > On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman <[3]jessebikman at gmail.com > > > wrote: > > Image attachment isn***t coming through. Maybe host on imgur or > similar > site and pass the link. > > Sent from my iPad > > > On Oct 10, 2017, at 9:22 PM, Art <[4]artem.nesterenko at gmail.com> > wrote: > > > >** **Thank you for the**prompt replies! > >** **Sorry for the picture, didn't expect to have this issue. > >** **So, basically what I did is I built a confusion matrix with > correct and > >** **incorrect predictions: > >** **For example: > >** **** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted > ** > ** ** ** > >** **** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 > >** **Real ** ** target_1 ** **120 ** ** ** ** ** 23 > >** **** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** > >** **and then I built a donut that represents the number of true pos, > true neg, > >** **false pos, false neg predictions from the confusion matrix. I've > attached > >** **the image. The attached donut doesn't represent the numbers in > "%" > from > >** **the above matrix. This matrix is just an example. > >** **And now I'm thinking of the**best graph type to represent the > number of > >** **pos and neg predictions if the**model predicts 7 targets. > >** **Art Nestsiarenka > >** **email: [1][5]artem.nesterenko at gmail.com > >** **Cell: [6](919) 455-5055 > > > >** **On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard > >** **<[2][7]jefferson.r.heard at gmail.com> wrote: > > > >** ** **** **I'd suggest you look at Seaborn. If you're dead-set on > visualizing > >** ** **data in > >** ** **** **javascript, it might well give you some inspiration to > draw from, > >** ** **** **otherwise it's a great Python solution for data > >** ** **** **vis.**[1][3][8]https://seaborn.pydata.org/ . I'm not > 100% > sure what > >** ** **I'd > >** ** **** **recommend given that much information. Also, it looks > like > the > >** ** **mailer > >** ** **** **scrubbed the images. If you could host the originals > somewhere > >** ** **(GitHub?), > >** ** **** **it'd help understand what you did better to give a > recommendation. > >** ** **** **Are your model outputs continuous or discrete? I > understand that > >** ** **you're > >** ** **** **looking at "positive/negative" values, but > >** ** **decisions/classifications and > >** ** **** **actual model output can be different. Plus the choice > of a > donut > >** ** **chart to > >** ** **** **represent boolean values doesn't make a lot of sense to > me, so I > >** ** **thought > >** ** **** **that I maybe understood you wrong. > >** ** **** **** > >** ** **** **On Tue, Oct 10, 2017 at 3:53 PM, Art > >** ** **<[2][4][9]artem.nesterenko at gmail.com> > >** ** **** **wrote: > > > >** ** **** ** **** **Good afternoon! > >** ** **** ** **** **I'm reaching out to you guys for a suggestion on > the data > >** ** **** ** **visualization. > >** ** **** ** **** **I'm wondering if anyone has an experience or an > idea of > >** ** **visualizing > >** ** **** ** **the > >** ** **** ** **** **volume of multiple model prediction targets. > >** ** **** ** **** **Here is an example of a model with just 2 > prediction > >** ** **targets I've > >** ** **** ** **recently > >** ** **** ** **** **been working on: > >** ** **** ** **** **1. This model provides 2 predictions: target_1 > and > >** ** **target_2.** > >** ** **** ** **** **2. I also know the actual result to compare > against, so I > >** ** **could see > >** ** **** ** **the > >** ** **** ** **** **model accuracy.** > >** ** **** ** **** **3. I've built a confusion matrix to calculate > true/false > >** ** **pos/neg > >** ** **** ** **(see > >** ** **** ** **** **below). So, there are 4 values. > >** ** **** ** **** **4. I chose donut**chart to visualize these > values > (see > >** ** **below). > >** ** **** ** **** **[1]Inline image 2** > > > >** ** **** ** **** **My question is what if the model provides more > than 2 > >** ** **predictions? > >** ** **** ** **Our > >** ** **** ** **** **next model has 7 targets, which is 49 pos and > neg > values as > >** ** **far as > >** ** **** ** **I > >** ** **** ** **** **understand. And I think a**donut**graph is not > going to > >** ** **work in > >** ** **** ** **this > >** ** **** ** **** **case.**Maybe a bar graph or something else fits > better.** > > > >** ** **** ** **** **I'd appreciate any ideas or examples of > visualizations that > >** ** **easy to > >** ** **** ** **look > >** ** **** ** **** **at and understand. > >** ** **** ** **** **I'm using d3/c3.js for data visualization. > >** ** **** ** **** **Thank you! > >** ** **** ** **** **Art Nestsiarenka > >** ** **** ** **** **email: [2][3][5][10]artem.nesterenko at gmail.com > > > >** ** **** ** **References > > > >** ** **** ** **** **Visible links > >** ** **** ** **** **2. mailto:[4][6][11]artem.nesterenko at gmail.com > > > >** ** **** ** **_______________________________________________ > >** ** **** ** **TriZPUG mailing list > >** ** **** ** **[5][7][12]TriZPUG at python.org > >** ** **** ** > **[6][8][13]https://mail.python.org/mailman/listinfo/trizpug > >** ** **** ** **[7][9][14]http://tripython.org is the Triangle > Python > Users Group > > > >** ** **References > > > >** ** **** **Visible links > >** ** **** **1. [10][15]https://seaborn.pydata.org/ > >** ** **** **2. mailto:[11][16]artem.nesterenko at gmail.com > >** ** **** **3. mailto:[12][17]artem.nesterenko at gmail.com > >** ** **** **4. mailto:[13][18]artem.nesterenko at gmail.com > >** ** **** **5. mailto:[14][19]TriZPUG at python.org > >** ** **** **6. > [15][20]https://mail.python.org/mailman/listinfo/trizpug > >** ** **** **7. [16][21]http://tripython.org/ > > > >** ** **_______________________________________________ > >** ** **TriZPUG mailing list > >** ** **[17][22]TriZPUG at python.org > >** ** **[18][23]https://mail.python.org/mailman/listinfo/trizpug > >** ** **[19][24]http://tripython.org is the Triangle Python Users > Group > > > > References > > > >** **Visible links > >** **1. mailto:[25]artem.nesterenko at gmail.com > >** **2. mailto:[26]jefferson.r.heard at gmail.com > >** **3. [27]https://seaborn.pydata.org/ > >** **4. mailto:[28]artem.nesterenko at gmail.com > >** **5. mailto:[29]artem.nesterenko at gmail.com > >** **6. mailto:[30]artem.nesterenko at gmail.com > >** **7. mailto:[31]TriZPUG at python.org > >** **8. [32]https://mail.python.org/mailman/listinfo/trizpug > >** **9. [33]http://tripython.org/ > >** 10. [34]https://seaborn.pydata.org/ > >** 11. mailto:[35]artem.nesterenko at gmail.com > >** 12. mailto:[36]artem.nesterenko at gmail.com > >** 13. mailto:[37]artem.nesterenko at gmail.com > >** 14. mailto:[38]TriZPUG at python.org > >** 15. [39]https://mail.python.org/mailman/listinfo/trizpug > >** 16. [40]http://tripython.org/ > >** 17. mailto:[41]TriZPUG at python.org > >** 18. [42]https://mail.python.org/mailman/listinfo/trizpug > >** 19. [43]http://tripython.org/ > > _______________________________________________ > > TriZPUG mailing list > > [44]TriZPUG at python.org > > [45]https://mail.python.org/mailman/listinfo/trizpug > > [46]http://tripython.org is the Triangle Python Users Group > _______________________________________________ > TriZPUG mailing list > [47]TriZPUG at python.org > [48]https://mail.python.org/mailman/listinfo/trizpug > [49]http://tripython.org is the Triangle Python Users Group > > References > > Visible links > 1. https://imgur.com/a/C7r8x > 2. mailto:artem.nesterenko at gmail.com > 3. mailto:jessebikman at gmail.com > 4. mailto:artem.nesterenko at gmail.com > 5. mailto:artem.nesterenko at gmail.com > 6. file:///tmp/tel:%28919%29%20455-5055 > 7. mailto:jefferson.r.heard at gmail.com > 8. https://seaborn.pydata.org/ > 9. mailto:artem.nesterenko at gmail.com > 10. mailto:artem.nesterenko at gmail.com > 11. mailto:artem.nesterenko at gmail.com > 12. mailto:TriZPUG at python.org > 13. https://mail.python.org/mailman/listinfo/trizpug > 14. http://tripython.org/ > 15. https://seaborn.pydata.org/ > 16. mailto:artem.nesterenko at gmail.com > 17. mailto:artem.nesterenko at gmail.com > 18. mailto:artem.nesterenko at gmail.com > 19. mailto:TriZPUG at python.org > 20. https://mail.python.org/mailman/listinfo/trizpug > 21. http://tripython.org/ > 22. mailto:TriZPUG at python.org > 23. https://mail.python.org/mailman/listinfo/trizpug > 24. http://tripython.org/ > 25. mailto:artem.nesterenko at gmail.com > 26. mailto:jefferson.r.heard at gmail.com > 27. https://seaborn.pydata.org/ > 28. mailto:artem.nesterenko at gmail.com > 29. mailto:artem.nesterenko at gmail.com > 30. mailto:artem.nesterenko at gmail.com > 31. mailto:TriZPUG at python.org > 32. https://mail.python.org/mailman/listinfo/trizpug > 33. http://tripython.org/ > 34. https://seaborn.pydata.org/ > 35. mailto:artem.nesterenko at gmail.com > 36. mailto:artem.nesterenko at gmail.com > 37. mailto:artem.nesterenko at gmail.com > 38. mailto:TriZPUG at python.org > 39. https://mail.python.org/mailman/listinfo/trizpug > 40. http://tripython.org/ > 41. mailto:TriZPUG at python.org > 42. https://mail.python.org/mailman/listinfo/trizpug > 43. http://tripython.org/ > 44. mailto:TriZPUG at python.org > 45. https://mail.python.org/mailman/listinfo/trizpug > 46. http://tripython.org/ > 47. mailto:TriZPUG at python.org > 48. https://mail.python.org/mailman/listinfo/trizpug > 49. http://tripython.org/ > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > > -------------- next part -------------- I am not a mathematician or statistician, but in my work for Tableau, a donut is most useful for progress to goal.** In your case, perhaps that is a success rate. (Which may not be 100%) In that scenario, having 2 cases makes the viz confusing to read.** If you want to do the donut, I'd stick to one case per. Also, who is your audience for the viz?** Are you interpreting this for the layman, or is it an expert who expects the complexity?**** Finally, what is your objective with the viz?** Is it to display that case 1 is larger than case 2?** If so, other methods may do that more clearly. (Pies/donuts are notoriously bad for conveying true proportions for multiple segments. As for the output tool, take your pick, but keep in mind the audience and objective, and you'll be happy. Good luck! Dave Powell** On Oct 11, 2017 7:53 AM, "Art" <[1]artem.nesterenko at gmail.com> wrote: ** **Donut graph: ** **[1][2]https://imgur.com/a/C7r8x ** **You should be able to see it now. ** **Art Nestsiarenka ** **email: [2][3]artem.nesterenko at gmail.com ** **Cell: [4](919) 455-5055 ** **On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman <[3][5]jessebikman at gmail.com> ** **wrote: ** ** **Image attachment isn***t coming through. Maybe host on imgur or similar ** ** **site and pass the link. ** ** **Sent from my iPad ** ** **> On Oct 10, 2017, at 9:22 PM, Art <[4][6]artem.nesterenko at gmail.com> ** ** **wrote: ** ** **> ** ** **>** **Thank you for the**prompt replies! ** ** **>** **Sorry for the picture, didn't expect to have this issue. ** ** **>** **So, basically what I did is I built a confusion matrix with ** ** **correct and ** ** **>** **incorrect predictions: ** ** **>** **For example: ** ** **>** **** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted ** ** ** **** ** ** ** ** **>** **** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 ** ** **>** **Real ** ** target_1 ** **120 ** ** ** ** ** 23 ** ** **>** **** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** ** ** **>** **and then I built a donut that represents the number of true pos, ** ** **true neg, ** ** **>** **false pos, false neg predictions from the confusion matrix. I've ** ** **attached ** ** **>** **the image. The attached donut doesn't represent the numbers in "%" ** ** **from ** ** **>** **the above matrix. This matrix is just an example. ** ** **>** **And now I'm thinking of the**best graph type to represent the ** ** **number of ** ** **>** **pos and neg predictions if the**model predicts 7 targets. ** ** **>** **Art Nestsiarenka ** ** **>** **email: [1][5][7]artem.nesterenko at gmail.com ** ** **>** **Cell: [6][8](919) 455-5055 ** ** **> ** ** **>** **On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard ** ** **>** **<[2][7][9]jefferson.r.heard at gmail.com> wrote: ** ** **> ** ** **>** ** **** **I'd suggest you look at Seaborn. If you're dead-set on ** ** **visualizing ** ** **>** ** **data in ** ** **>** ** **** **javascript, it might well give you some inspiration to ** ** **draw from, ** ** **>** ** **** **otherwise it's a great Python solution for data ** ** **>** ** **** **vis.**[1][3][8][10]https://seaborn.pydata.org/ . I'm not 100% ** ** **sure what ** ** **>** ** **I'd ** ** **>** ** **** **recommend given that much information. Also, it looks like ** ** **the ** ** **>** ** **mailer ** ** **>** ** **** **scrubbed the images. If you could host the originals ** ** **somewhere ** ** **>** ** **(GitHub?), ** ** **>** ** **** **it'd help understand what you did better to give a ** ** **recommendation. ** ** **>** ** **** **Are your model outputs continuous or discrete? I ** ** **understand that ** ** **>** ** **you're ** ** **>** ** **** **looking at "positive/negative" values, but ** ** **>** ** **decisions/classifications and ** ** **>** ** **** **actual model output can be different. Plus the choice of a ** ** **donut ** ** **>** ** **chart to ** ** **>** ** **** **represent boolean values doesn't make a lot of sense to ** ** **me, so I ** ** **>** ** **thought ** ** **>** ** **** **that I maybe understood you wrong. ** ** **>** ** **** **** ** ** **>** ** **** **On Tue, Oct 10, 2017 at 3:53 PM, Art ** ** **>** ** **<[2][4][9][11]artem.nesterenko at gmail.com> ** ** **>** ** **** **wrote: ** ** **> ** ** **>** ** **** ** **** **Good afternoon! ** ** **>** ** **** ** **** **I'm reaching out to you guys for a suggestion on ** ** **the data ** ** **>** ** **** ** **visualization. ** ** **>** ** **** ** **** **I'm wondering if anyone has an experience or an ** ** **idea of ** ** **>** ** **visualizing ** ** **>** ** **** ** **the ** ** **>** ** **** ** **** **volume of multiple model prediction targets. ** ** **>** ** **** ** **** **Here is an example of a model with just 2 ** ** **prediction ** ** **>** ** **targets I've ** ** **>** ** **** ** **recently ** ** **>** ** **** ** **** **been working on: ** ** **>** ** **** ** **** **1. This model provides 2 predictions: target_1 and ** ** **>** ** **target_2.** ** ** **>** ** **** ** **** **2. I also know the actual result to compare ** ** **against, so I ** ** **>** ** **could see ** ** **>** ** **** ** **the ** ** **>** ** **** ** **** **model accuracy.** ** ** **>** ** **** ** **** **3. I've built a confusion matrix to calculate ** ** **true/false ** ** **>** ** **pos/neg ** ** **>** ** **** ** **(see ** ** **>** ** **** ** **** **below). So, there are 4 values. ** ** **>** ** **** ** **** **4. I chose donut**chart to visualize these values ** ** **(see ** ** **>** ** **below). ** ** **>** ** **** ** **** **[1]Inline image 2** ** ** **> ** ** **>** ** **** ** **** **My question is what if the model provides more ** ** **than 2 ** ** **>** ** **predictions? ** ** **>** ** **** ** **Our ** ** **>** ** **** ** **** **next model has 7 targets, which is 49 pos and neg ** ** **values as ** ** **>** ** **far as ** ** **>** ** **** ** **I ** ** **>** ** **** ** **** **understand. And I think a**donut**graph is not ** ** **going to ** ** **>** ** **work in ** ** **>** ** **** ** **this ** ** **>** ** **** ** **** **case.**Maybe a bar graph or something else fits ** ** **better.** ** ** **> ** ** **>** ** **** ** **** **I'd appreciate any ideas or examples of ** ** **visualizations that ** ** **>** ** **easy to ** ** **>** ** **** ** **look ** ** **>** ** **** ** **** **at and understand. ** ** **>** ** **** ** **** **I'm using d3/c3.js for data visualization. ** ** **>** ** **** ** **** **Thank you! ** ** **>** ** **** ** **** **Art Nestsiarenka ** ** **>** ** **** ** **** **email: [2][3][5][10][12]artem.nesterenko at gmail.com ** ** **> ** ** **>** ** **** ** **References ** ** **> ** ** **>** ** **** ** **** **Visible links ** ** **>** ** **** ** **** **2. mailto:[4][6][11][13]artem.nesterenko at gmail.com ** ** **> ** ** **>** ** **** ** **_______________________________________________ ** ** **>** ** **** ** **TriZPUG mailing list ** ** **>** ** **** ** **[5][7][12][14]TriZPUG at python.org ** ** **>** ** **** ** ** ** ****[6][8][13][15]https://mail.python.org/mailman/listinfo/trizpug ** ** **>** ** **** ** **[7][9][14][16]http://tripython.org is the Triangle Python ** ** **Users Group ** ** **> ** ** **>** ** **References ** ** **> ** ** **>** ** **** **Visible links ** ** **>** ** **** **1. [10][15][17]https://seaborn.pydata.org/ ** ** **>** ** **** **2. mailto:[11][16][18]artem.nesterenko at gmail.com ** ** **>** ** **** **3. mailto:[12][17][19]artem.nesterenko at gmail.com ** ** **>** ** **** **4. mailto:[13][18][20]artem.nesterenko at gmail.com ** ** **>** ** **** **5. mailto:[14][19][21]TriZPUG at python.org ** ** **>** ** **** **6. ** ** **[15][20][22]https://mail.python.org/mailman/listinfo/trizpug ** ** **>** ** **** **7. [16][21][23]http://tripython.org/ ** ** **> ** ** **>** ** **_______________________________________________ ** ** **>** ** **TriZPUG mailing list ** ** **>** ** **[17][22][24]TriZPUG at python.org ** ** **>** ** **[18][23][25]https://mail.python.org/mailman/listinfo/trizpug ** ** **>** ** **[19][24][26]http://tripython.org is the Triangle Python Users Group ** ** **> ** ** **> References ** ** **> ** ** **>** **Visible links ** ** **>** **1. mailto:[25][27]artem.nesterenko at gmail.com ** ** **>** **2. mailto:[26][28]jefferson.r.heard at gmail.com ** ** **>** **3. [27][29]https://seaborn.pydata.org/ ** ** **>** **4. mailto:[28][30]artem.nesterenko at gmail.com ** ** **>** **5. mailto:[29][31]artem.nesterenko at gmail.com ** ** **>** **6. mailto:[30][32]artem.nesterenko at gmail.com ** ** **>** **7. mailto:[31][33]TriZPUG at python.org ** ** **>** **8. [32][34]https://mail.python.org/mailman/listinfo/trizpug ** ** **>** **9. [33][35]http://tripython.org/ ** ** **>** 10. [34][36]https://seaborn.pydata.org/ ** ** **>** 11. mailto:[35][37]artem.nesterenko at gmail.com ** ** **>** 12. mailto:[36][38]artem.nesterenko at gmail.com ** ** **>** 13. mailto:[37][39]artem.nesterenko at gmail.com ** ** **>** 14. mailto:[38][40]TriZPUG at python.org ** ** **>** 15. [39][41]https://mail.python.org/mailman/listinfo/trizpug ** ** **>** 16. [40][42]http://tripython.org/ ** ** **>** 17. mailto:[41][43]TriZPUG at python.org ** ** **>** 18. [42][44]https://mail.python.org/mailman/listinfo/trizpug ** ** **>** 19. [43][45]http://tripython.org/ ** ** **> _______________________________________________ ** ** **> TriZPUG mailing list ** ** **> [44][46]TriZPUG at python.org ** ** **> [45][47]https://mail.python.org/mailman/listinfo/trizpug ** ** **> [46][48]http://tripython.org is the Triangle Python Users Group ** ** **_______________________________________________ ** ** **TriZPUG mailing list ** ** **[47][49]TriZPUG at python.org ** ** **[48][50]https://mail.python.org/mailman/listinfo/trizpug ** ** **[49][51]http://tripython.org is the Triangle Python Users Group References ** **Visible links ** **1. [52]https://imgur.com/a/C7r8x ** **2. mailto:[53]artem.nesterenko at gmail.com ** **3. mailto:[54]jessebikman at gmail.com ** **4. mailto:[55]artem.nesterenko at gmail.com ** **5. mailto:[56]artem.nesterenko at gmail.com ** **6. file:///tmp/tel:%28919%29%20455-5055 ** **7. mailto:[57]jefferson.r.heard at gmail.com ** **8. [58]https://seaborn.pydata.org/ ** **9. mailto:[59]artem.nesterenko at gmail.com ** 10. mailto:[60]artem.nesterenko at gmail.com ** 11. mailto:[61]artem.nesterenko at gmail.com ** 12. mailto:[62]TriZPUG at python.org ** 13. [63]https://mail.python.org/mailman/listinfo/trizpug ** 14. [64]http://tripython.org/ ** 15. [65]https://seaborn.pydata.org/ ** 16. mailto:[66]artem.nesterenko at gmail.com ** 17. mailto:[67]artem.nesterenko at gmail.com ** 18. mailto:[68]artem.nesterenko at gmail.com ** 19. mailto:[69]TriZPUG at python.org ** 20. [70]https://mail.python.org/mailman/listinfo/trizpug ** 21. [71]http://tripython.org/ ** 22. mailto:[72]TriZPUG at python.org ** 23. [73]https://mail.python.org/mailman/listinfo/trizpug ** 24. [74]http://tripython.org/ ** 25. mailto:[75]artem.nesterenko at gmail.com ** 26. mailto:[76]jefferson.r.heard at gmail.com ** 27. [77]https://seaborn.pydata.org/ ** 28. mailto:[78]artem.nesterenko at gmail.com ** 29. mailto:[79]artem.nesterenko at gmail.com ** 30. mailto:[80]artem.nesterenko at gmail.com ** 31. mailto:[81]TriZPUG at python.org ** 32. [82]https://mail.python.org/mailman/listinfo/trizpug ** 33. [83]http://tripython.org/ ** 34. [84]https://seaborn.pydata.org/ ** 35. mailto:[85]artem.nesterenko at gmail.com ** 36. mailto:[86]artem.nesterenko at gmail.com ** 37. mailto:[87]artem.nesterenko at gmail.com ** 38. mailto:[88]TriZPUG at python.org ** 39. [89]https://mail.python.org/mailman/listinfo/trizpug ** 40. [90]http://tripython.org/ ** 41. mailto:[91]TriZPUG at python.org ** 42. [92]https://mail.python.org/mailman/listinfo/trizpug ** 43. [93]http://tripython.org/ ** 44. mailto:[94]TriZPUG at python.org ** 45. [95]https://mail.python.org/mailman/listinfo/trizpug ** 46. [96]http://tripython.org/ ** 47. mailto:[97]TriZPUG at python.org ** 48. [98]https://mail.python.org/mailman/listinfo/trizpug ** 49. [99]http://tripython.org/ _______________________________________________ TriZPUG mailing list [100]TriZPUG at python.org [101]https://mail.python.org/mailman/listinfo/trizpug [102]http://tripython.org is the Triangle Python Users Group References Visible links 1. mailto:artem.nesterenko at gmail.com 2. https://imgur.com/a/C7r8x 3. mailto:artem.nesterenko at gmail.com 4. file:///tmp/tel:%28919%29%20455-5055 5. mailto:jessebikman at gmail.com 6. mailto:artem.nesterenko at gmail.com 7. mailto:artem.nesterenko at gmail.com 8. file:///tmp/tel:%28919%29%20455-5055 9. mailto:jefferson.r.heard at gmail.com 10. https://seaborn.pydata.org/ 11. mailto:artem.nesterenko at gmail.com 12. mailto:artem.nesterenko at gmail.com 13. mailto:artem.nesterenko at gmail.com 14. mailto:TriZPUG at python.org 15. https://mail.python.org/mailman/listinfo/trizpug 16. http://tripython.org/ 17. https://seaborn.pydata.org/ 18. mailto:artem.nesterenko at gmail.com 19. mailto:artem.nesterenko at gmail.com 20. mailto:artem.nesterenko at gmail.com 21. mailto:TriZPUG at python.org 22. https://mail.python.org/mailman/listinfo/trizpug 23. http://tripython.org/ 24. mailto:TriZPUG at python.org 25. https://mail.python.org/mailman/listinfo/trizpug 26. http://tripython.org/ 27. mailto:artem.nesterenko at gmail.com 28. mailto:jefferson.r.heard at gmail.com 29. https://seaborn.pydata.org/ 30. mailto:artem.nesterenko at gmail.com 31. mailto:artem.nesterenko at gmail.com 32. mailto:artem.nesterenko at gmail.com 33. mailto:TriZPUG at python.org 34. https://mail.python.org/mailman/listinfo/trizpug 35. http://tripython.org/ 36. https://seaborn.pydata.org/ 37. mailto:artem.nesterenko at gmail.com 38. mailto:artem.nesterenko at gmail.com 39. mailto:artem.nesterenko at gmail.com 40. mailto:TriZPUG at python.org 41. https://mail.python.org/mailman/listinfo/trizpug 42. http://tripython.org/ 43. mailto:TriZPUG at python.org 44. https://mail.python.org/mailman/listinfo/trizpug 45. http://tripython.org/ 46. mailto:TriZPUG at python.org 47. https://mail.python.org/mailman/listinfo/trizpug 48. http://tripython.org/ 49. mailto:TriZPUG at python.org 50. https://mail.python.org/mailman/listinfo/trizpug 51. http://tripython.org/ 52. https://imgur.com/a/C7r8x 53. mailto:artem.nesterenko at gmail.com 54. mailto:jessebikman at gmail.com 55. mailto:artem.nesterenko at gmail.com 56. mailto:artem.nesterenko at gmail.com 57. mailto:jefferson.r.heard at gmail.com 58. https://seaborn.pydata.org/ 59. mailto:artem.nesterenko at gmail.com 60. mailto:artem.nesterenko at gmail.com 61. mailto:artem.nesterenko at gmail.com 62. mailto:TriZPUG at python.org 63. https://mail.python.org/mailman/listinfo/trizpug 64. http://tripython.org/ 65. https://seaborn.pydata.org/ 66. mailto:artem.nesterenko at gmail.com 67. mailto:artem.nesterenko at gmail.com 68. mailto:artem.nesterenko at gmail.com 69. mailto:TriZPUG at python.org 70. https://mail.python.org/mailman/listinfo/trizpug 71. http://tripython.org/ 72. mailto:TriZPUG at python.org 73. https://mail.python.org/mailman/listinfo/trizpug 74. http://tripython.org/ 75. mailto:artem.nesterenko at gmail.com 76. mailto:jefferson.r.heard at gmail.com 77. https://seaborn.pydata.org/ 78. mailto:artem.nesterenko at gmail.com 79. mailto:artem.nesterenko at gmail.com 80. mailto:artem.nesterenko at gmail.com 81. mailto:TriZPUG at python.org 82. https://mail.python.org/mailman/listinfo/trizpug 83. http://tripython.org/ 84. https://seaborn.pydata.org/ 85. mailto:artem.nesterenko at gmail.com 86. mailto:artem.nesterenko at gmail.com 87. mailto:artem.nesterenko at gmail.com 88. mailto:TriZPUG at python.org 89. https://mail.python.org/mailman/listinfo/trizpug 90. http://tripython.org/ 91. mailto:TriZPUG at python.org 92. https://mail.python.org/mailman/listinfo/trizpug 93. http://tripython.org/ 94. mailto:TriZPUG at python.org 95. https://mail.python.org/mailman/listinfo/trizpug 96. http://tripython.org/ 97. mailto:TriZPUG at python.org 98. https://mail.python.org/mailman/listinfo/trizpug 99. http://tripython.org/ 100. mailto:TriZPUG at python.org 101. https://mail.python.org/mailman/listinfo/trizpug 102. http://tripython.org/ From ginnyghezzo at gmail.com Wed Oct 11 10:48:52 2017 From: ginnyghezzo at gmail.com (Ginny Ghezzo) Date: Wed, 11 Oct 2017 10:48:52 -0400 Subject: [TriPython] Join PyLadies on October 18 at Genesys Message-ID: All, The next meetup of PyLadies RDU is on October 18th at 6pm at Genesys. We will be discussing Python Tribal knowledge based on a recent presentation by Cathy Deng. Please spread the word, especially to women who are using or want to pick up Python. Everyone is welcome! RSVP so we can plan for dinner: https://www.meetup.com/pyladies-rdu/events/243371745/ Thanks, Ginny Ghezzo -------------- next part -------------- All,** The next meetup of PyLadies RDU is on October 18th at 6pm at Genesys. We will be discussing Python Tribal knowledge based on a recent presentation by Cathy Deng.** Please spread the word, especially to women who are using or want to pick up Python. Everyone is welcome!** RSVP so we can plan for dinner: [1]https://www.meetup.com/pyladies-rdu/events/243371745/** Thanks,** Ginny Ghezzo** References Visible links 1. https://www.meetup.com/pyladies-rdu/events/243371745/ From thomson at neuro.duke.edu Wed Oct 11 10:55:07 2017 From: thomson at neuro.duke.edu (Eric Thomson) Date: Wed, 11 Oct 2017 14:55:07 +0000 Subject: [TriPython] Prediction Model. Data Visualization In-Reply-To: References: Message-ID: For visualizing confusion matrices, converting to donut plot (or its horrible fraternal twin, the pie graph) and the bar plot is very hard to quickly see the results. I would just leave it in confusion matrix format and color-code each cell by performance (hot colors good, cool colors bad performance). This naturally scales to N categories. Just show the colorbar on the side. People use confusion matrices because they are so easy to interpret, I wouldn't mess with that too much. Best, Eric ________________________________________ From: TriZPUG [trizpug-bounces+thomson=neuro.duke.edu at python.org] on behalf of trizpug-request at python.org [trizpug-request at python.org] Sent: Wednesday, October 11, 2017 10:12 AM To: trizpug at python.org Subject: TriZPUG Digest, Vol 114, Issue 9 Send TriZPUG mailing list submissions to trizpug at python.org To subscribe or unsubscribe via the World Wide Web, visit https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= or, via email, send a message with subject or body 'help' to trizpug-request at python.org You can reach the person managing the list at trizpug-owner at python.org When replying, please edit your Subject line so it is more specific than "Re: Contents of TriZPUG digest..." Today's Topics: 1. Re: Prediction Model. Data Visualization. (Art) 2. Re: Prediction Model. Data Visualization. (Dave Powell) ---------------------------------------------------------------------- Message: 1 Date: Wed, 11 Oct 2017 08:52:50 -0400 From: Art To: "Triangle (North Carolina) Python Users Group (formerly TriZPUG)" Subject: Re: [TriPython] Prediction Model. Data Visualization. Message-ID: Content-Type: text/plain; charset="utf-8" Donut graph: https://urldefense.proofpoint.com/v2/url?u=https-3A__imgur.com_a_C7r8x&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=qRSfQlbbQCl7rdpGcv4s4s4EfRCCxyH1nK4CUH081NM&e= You should be able to see it now. Art Nestsiarenka email: artem.nesterenko at gmail.com Cell: (919) 455-5055 On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman wrote: > Image attachment isn?t coming through. Maybe host on imgur or similar site > and pass the link. > > Sent from my iPad > > > On Oct 10, 2017, at 9:22 PM, Art wrote: > > > > Thank you for the**prompt replies! > > Sorry for the picture, didn't expect to have this issue. > > So, basically what I did is I built a confusion matrix with correct and > > incorrect predictions: > > For example: > > ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted ** ** > ** ** > > ** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 > > Real ** ** target_1 ** **120 ** ** ** ** ** 23 > > ** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** > > and then I built a donut that represents the number of true pos, true > neg, > > false pos, false neg predictions from the confusion matrix. I've > attached > > the image. The attached donut doesn't represent the numbers in "%" from > > the above matrix. This matrix is just an example. > > And now I'm thinking of the**best graph type to represent the number of > > pos and neg predictions if the**model predicts 7 targets. > > Art Nestsiarenka > > email: [1]artem.nesterenko at gmail.com > > Cell: (919) 455-5055 > > > > On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard > > <[2]jefferson.r.heard at gmail.com> wrote: > > > > ** **I'd suggest you look at Seaborn. If you're dead-set on > visualizing > > data in > > ** **javascript, it might well give you some inspiration to draw > from, > > ** **otherwise it's a great Python solution for data > > ** **vis.**[1][3]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= . I'm not 100% sure > what > > I'd > > ** **recommend given that much information. Also, it looks like the > > mailer > > ** **scrubbed the images. If you could host the originals somewhere > > (GitHub?), > > ** **it'd help understand what you did better to give a > recommendation. > > ** **Are your model outputs continuous or discrete? I understand that > > you're > > ** **looking at "positive/negative" values, but > > decisions/classifications and > > ** **actual model output can be different. Plus the choice of a donut > > chart to > > ** **represent boolean values doesn't make a lot of sense to me, so I > > thought > > ** **that I maybe understood you wrong. > > ** **** > > ** **On Tue, Oct 10, 2017 at 3:53 PM, Art > > <[2][4]artem.nesterenko at gmail.com> > > ** **wrote: > > > > ** ** **** **Good afternoon! > > ** ** **** **I'm reaching out to you guys for a suggestion on the > data > > ** ** **visualization. > > ** ** **** **I'm wondering if anyone has an experience or an idea of > > visualizing > > ** ** **the > > ** ** **** **volume of multiple model prediction targets. > > ** ** **** **Here is an example of a model with just 2 prediction > > targets I've > > ** ** **recently > > ** ** **** **been working on: > > ** ** **** **1. This model provides 2 predictions: target_1 and > > target_2.** > > ** ** **** **2. I also know the actual result to compare against, so > I > > could see > > ** ** **the > > ** ** **** **model accuracy.** > > ** ** **** **3. I've built a confusion matrix to calculate true/false > > pos/neg > > ** ** **(see > > ** ** **** **below). So, there are 4 values. > > ** ** **** **4. I chose donut**chart to visualize these values (see > > below). > > ** ** **** **[1]Inline image 2** > > > > ** ** **** **My question is what if the model provides more than 2 > > predictions? > > ** ** **Our > > ** ** **** **next model has 7 targets, which is 49 pos and neg > values as > > far as > > ** ** **I > > ** ** **** **understand. And I think a**donut**graph is not going to > > work in > > ** ** **this > > ** ** **** **case.**Maybe a bar graph or something else fits > better.** > > > > ** ** **** **I'd appreciate any ideas or examples of visualizations > that > > easy to > > ** ** **look > > ** ** **** **at and understand. > > ** ** **** **I'm using d3/c3.js for data visualization. > > ** ** **** **Thank you! > > ** ** **** **Art Nestsiarenka > > ** ** **** **email: [2][3][5]artem.nesterenko at gmail.com > > > > ** ** **References > > > > ** ** **** **Visible links > > ** ** **** **2. mailto:[4][6]artem.nesterenko at gmail.com > > > > ** ** **_______________________________________________ > > ** ** **TriZPUG mailing list > > ** ** **[5][7]TriZPUG at python.org > > ** ** **[6][8]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > > ** ** **[7][9]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users > Group > > > > References > > > > ** **Visible links > > ** **1. [10]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= > > ** **2. mailto:[11]artem.nesterenko at gmail.com > > ** **3. mailto:[12]artem.nesterenko at gmail.com > > ** **4. mailto:[13]artem.nesterenko at gmail.com > > ** **5. mailto:[14]TriZPUG at python.org > > ** **6. [15]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > > ** **7. [16]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > > > > _______________________________________________ > > TriZPUG mailing list > > [17]TriZPUG at python.org > > [18]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > > [19]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group > > > > References > > > > Visible links > > 1. mailto:artem.nesterenko at gmail.com > > 2. mailto:jefferson.r.heard at gmail.com > > 3. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= > > 4. mailto:artem.nesterenko at gmail.com > > 5. mailto:artem.nesterenko at gmail.com > > 6. mailto:artem.nesterenko at gmail.com > > 7. mailto:TriZPUG at python.org > > 8. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > > 9. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > > 10. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= > > 11. mailto:artem.nesterenko at gmail.com > > 12. mailto:artem.nesterenko at gmail.com > > 13. mailto:artem.nesterenko at gmail.com > > 14. mailto:TriZPUG at python.org > > 15. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > > 16. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > > 17. mailto:TriZPUG at python.org > > 18. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > > 19. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > > _______________________________________________ > > TriZPUG mailing list > > TriZPUG at python.org > > https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > > https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group > -------------- next part -------------- Donut graph: [1]https://urldefense.proofpoint.com/v2/url?u=https-3A__imgur.com_a_C7r8x&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=qRSfQlbbQCl7rdpGcv4s4s4EfRCCxyH1nK4CUH081NM&e= You should be able to see it now. Art Nestsiarenka email: [2]artem.nesterenko at gmail.com Cell: (919) 455-5055 On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman <[3]jessebikman at gmail.com> wrote: Image attachment isn***t coming through. Maybe host on imgur or similar site and pass the link. Sent from my iPad > On Oct 10, 2017, at 9:22 PM, Art <[4]artem.nesterenko at gmail.com> wrote: > >** **Thank you for the**prompt replies! >** **Sorry for the picture, didn't expect to have this issue. >** **So, basically what I did is I built a confusion matrix with correct and >** **incorrect predictions: >** **For example: >** **** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted ** ** ** ** >** **** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 >** **Real ** ** target_1 ** **120 ** ** ** ** ** 23 >** **** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** >** **and then I built a donut that represents the number of true pos, true neg, >** **false pos, false neg predictions from the confusion matrix. I've attached >** **the image. The attached donut doesn't represent the numbers in "%" from >** **the above matrix. This matrix is just an example. >** **And now I'm thinking of the**best graph type to represent the number of >** **pos and neg predictions if the**model predicts 7 targets. >** **Art Nestsiarenka >** **email: [1][5]artem.nesterenko at gmail.com >** **Cell: [6](919) 455-5055 > >** **On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard >** **<[2][7]jefferson.r.heard at gmail.com> wrote: > >** ** **** **I'd suggest you look at Seaborn. If you're dead-set on visualizing >** ** **data in >** ** **** **javascript, it might well give you some inspiration to draw from, >** ** **** **otherwise it's a great Python solution for data >** ** **** **vis.**[1][3][8]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= . I'm not 100% sure what >** ** **I'd >** ** **** **recommend given that much information. Also, it looks like the >** ** **mailer >** ** **** **scrubbed the images. If you could host the originals somewhere >** ** **(GitHub?), >** ** **** **it'd help understand what you did better to give a recommendation. >** ** **** **Are your model outputs continuous or discrete? I understand that >** ** **you're >** ** **** **looking at "positive/negative" values, but >** ** **decisions/classifications and >** ** **** **actual model output can be different. Plus the choice of a donut >** ** **chart to >** ** **** **represent boolean values doesn't make a lot of sense to me, so I >** ** **thought >** ** **** **that I maybe understood you wrong. >** ** **** **** >** ** **** **On Tue, Oct 10, 2017 at 3:53 PM, Art >** ** **<[2][4][9]artem.nesterenko at gmail.com> >** ** **** **wrote: > >** ** **** ** **** **Good afternoon! >** ** **** ** **** **I'm reaching out to you guys for a suggestion on the data >** ** **** ** **visualization. >** ** **** ** **** **I'm wondering if anyone has an experience or an idea of >** ** **visualizing >** ** **** ** **the >** ** **** ** **** **volume of multiple model prediction targets. >** ** **** ** **** **Here is an example of a model with just 2 prediction >** ** **targets I've >** ** **** ** **recently >** ** **** ** **** **been working on: >** ** **** ** **** **1. This model provides 2 predictions: target_1 and >** ** **target_2.** >** ** **** ** **** **2. I also know the actual result to compare against, so I >** ** **could see >** ** **** ** **the >** ** **** ** **** **model accuracy.** >** ** **** ** **** **3. I've built a confusion matrix to calculate true/false >** ** **pos/neg >** ** **** ** **(see >** ** **** ** **** **below). So, there are 4 values. >** ** **** ** **** **4. I chose donut**chart to visualize these values (see >** ** **below). >** ** **** ** **** **[1]Inline image 2** > >** ** **** ** **** **My question is what if the model provides more than 2 >** ** **predictions? >** ** **** ** **Our >** ** **** ** **** **next model has 7 targets, which is 49 pos and neg values as >** ** **far as >** ** **** ** **I >** ** **** ** **** **understand. And I think a**donut**graph is not going to >** ** **work in >** ** **** ** **this >** ** **** ** **** **case.**Maybe a bar graph or something else fits better.** > >** ** **** ** **** **I'd appreciate any ideas or examples of visualizations that >** ** **easy to >** ** **** ** **look >** ** **** ** **** **at and understand. >** ** **** ** **** **I'm using d3/c3.js for data visualization. >** ** **** ** **** **Thank you! >** ** **** ** **** **Art Nestsiarenka >** ** **** ** **** **email: [2][3][5][10]artem.nesterenko at gmail.com > >** ** **** ** **References > >** ** **** ** **** **Visible links >** ** **** ** **** **2. mailto:[4][6][11]artem.nesterenko at gmail.com > >** ** **** ** **_______________________________________________ >** ** **** ** **TriZPUG mailing list >** ** **** ** **[5][7][12]TriZPUG at python.org >** ** **** ** **[6][8][13]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= >** ** **** ** **[7][9][14]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group > >** ** **References > >** ** **** **Visible links >** ** **** **1. [10][15]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= >** ** **** **2. mailto:[11][16]artem.nesterenko at gmail.com >** ** **** **3. mailto:[12][17]artem.nesterenko at gmail.com >** ** **** **4. mailto:[13][18]artem.nesterenko at gmail.com >** ** **** **5. mailto:[14][19]TriZPUG at python.org >** ** **** **6. [15][20]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= >** ** **** **7. [16][21]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > >** ** **_______________________________________________ >** ** **TriZPUG mailing list >** ** **[17][22]TriZPUG at python.org >** ** **[18][23]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= >** ** **[19][24]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group > > References > >** **Visible links >** **1. mailto:[25]artem.nesterenko at gmail.com >** **2. mailto:[26]jefferson.r.heard at gmail.com >** **3. 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mailto:artem.nesterenko at gmail.com 3. mailto:jessebikman at gmail.com 4. mailto:artem.nesterenko at gmail.com 5. mailto:artem.nesterenko at gmail.com 6. file:///tmp/tel:%28919%29%20455-5055 7. mailto:jefferson.r.heard at gmail.com 8. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 9. mailto:artem.nesterenko at gmail.com 10. mailto:artem.nesterenko at gmail.com 11. mailto:artem.nesterenko at gmail.com 12. mailto:TriZPUG at python.org 13. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 14. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 15. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 16. mailto:artem.nesterenko at gmail.com 17. mailto:artem.nesterenko at gmail.com 18. mailto:artem.nesterenko at gmail.com 19. mailto:TriZPUG at python.org 20. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 21. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 22. mailto:TriZPUG at python.org 23. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 24. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 25. mailto:artem.nesterenko at gmail.com 26. mailto:jefferson.r.heard at gmail.com 27. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 28. mailto:artem.nesterenko at gmail.com 29. mailto:artem.nesterenko at gmail.com 30. mailto:artem.nesterenko at gmail.com 31. mailto:TriZPUG at python.org 32. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 33. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 34. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 35. mailto:artem.nesterenko at gmail.com 36. mailto:artem.nesterenko at gmail.com 37. mailto:artem.nesterenko at gmail.com 38. mailto:TriZPUG at python.org 39. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 40. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 41. mailto:TriZPUG at python.org 42. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 43. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 44. mailto:TriZPUG at python.org 45. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 46. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 47. mailto:TriZPUG at python.org 48. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 49. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= ------------------------------ Message: 2 Date: Wed, 11 Oct 2017 10:11:51 -0400 From: Dave Powell To: "Triangle (North Carolina) Python Users Group (formerly TriZPUG)" Subject: Re: [TriPython] Prediction Model. Data Visualization. Message-ID: Content-Type: text/plain; charset="utf-8" I am not a mathematician or statistician, but in my work for Tableau, a donut is most useful for progress to goal. In your case, perhaps that is a success rate. (Which may not be 100%) In that scenario, having 2 cases makes the viz confusing to read. If you want to do the donut, I'd stick to one case per. Also, who is your audience for the viz? Are you interpreting this for the layman, or is it an expert who expects the complexity? Finally, what is your objective with the viz? Is it to display that case 1 is larger than case 2? If so, other methods may do that more clearly. (Pies/donuts are notoriously bad for conveying true proportions for multiple segments. As for the output tool, take your pick, but keep in mind the audience and objective, and you'll be happy. Good luck! Dave Powell On Oct 11, 2017 7:53 AM, "Art" wrote: > Donut graph: > [1]https://urldefense.proofpoint.com/v2/url?u=https-3A__imgur.com_a_C7r8x&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=qRSfQlbbQCl7rdpGcv4s4s4EfRCCxyH1nK4CUH081NM&e= > You should be able to see it now. > Art Nestsiarenka > email: [2]artem.nesterenko at gmail.com > Cell: (919) 455-5055 > > On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman <[3]jessebikman at gmail.com > > > wrote: > > Image attachment isn***t coming through. Maybe host on imgur or > similar > site and pass the link. > > Sent from my iPad > > > On Oct 10, 2017, at 9:22 PM, Art <[4]artem.nesterenko at gmail.com> > wrote: > > > >** **Thank you for the**prompt replies! > >** **Sorry for the picture, didn't expect to have this issue. > >** **So, basically what I did is I built a confusion matrix with > correct and > >** **incorrect predictions: > >** **For example: > >** **** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted > ** > ** ** ** > >** **** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 > >** **Real ** ** target_1 ** **120 ** ** ** ** ** 23 > >** **** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** > >** **and then I built a donut that represents the number of true pos, > true neg, > >** **false pos, false neg predictions from the confusion matrix. I've > attached > >** **the image. The attached donut doesn't represent the numbers in > "%" > from > >** **the above matrix. This matrix is just an example. > >** **And now I'm thinking of the**best graph type to represent the > number of > >** **pos and neg predictions if the**model predicts 7 targets. > >** **Art Nestsiarenka > >** **email: [1][5]artem.nesterenko at gmail.com > >** **Cell: [6](919) 455-5055 > > > >** **On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard > >** **<[2][7]jefferson.r.heard at gmail.com> wrote: > > > >** ** **** **I'd suggest you look at Seaborn. If you're dead-set on > visualizing > >** ** **data in > >** ** **** **javascript, it might well give you some inspiration to > draw from, > >** ** **** **otherwise it's a great Python solution for data > >** ** **** **vis.**[1][3][8]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= . I'm not > 100% > sure what > >** ** **I'd > >** ** **** **recommend given that much information. Also, it looks > like > the > >** ** **mailer > >** ** **** **scrubbed the images. If you could host the originals > somewhere > >** ** **(GitHub?), > >** ** **** **it'd help understand what you did better to give a > recommendation. > >** ** **** **Are your model outputs continuous or discrete? I > understand that > >** ** **you're > >** ** **** **looking at "positive/negative" values, but > >** ** **decisions/classifications and > >** ** **** **actual model output can be different. Plus the choice > of a > donut > >** ** **chart to > >** ** **** **represent boolean values doesn't make a lot of sense to > me, so I > >** ** **thought > >** ** **** **that I maybe understood you wrong. > >** ** **** **** > >** ** **** **On Tue, Oct 10, 2017 at 3:53 PM, Art > >** ** **<[2][4][9]artem.nesterenko at gmail.com> > >** ** **** **wrote: > > > >** ** **** ** **** **Good afternoon! > >** ** **** ** **** **I'm reaching out to you guys for a suggestion on > the data > >** ** **** ** **visualization. > >** ** **** ** **** **I'm wondering if anyone has an experience or an > idea of > >** ** **visualizing > >** ** **** ** **the > >** ** **** ** **** **volume of multiple model prediction targets. > >** ** **** ** **** **Here is an example of a model with just 2 > prediction > >** ** **targets I've > >** ** **** ** **recently > >** ** **** ** **** **been working on: > >** ** **** ** **** **1. This model provides 2 predictions: target_1 > and > >** ** **target_2.** > >** ** **** ** **** **2. I also know the actual result to compare > against, so I > >** ** **could see > >** ** **** ** **the > >** ** **** ** **** **model accuracy.** > >** ** **** ** **** **3. I've built a confusion matrix to calculate > true/false > >** ** **pos/neg > >** ** **** ** **(see > >** ** **** ** **** **below). So, there are 4 values. > >** ** **** ** **** **4. I chose donut**chart to visualize these > values > (see > >** ** **below). > >** ** **** ** **** **[1]Inline image 2** > > > >** ** **** ** **** **My question is what if the model provides more > than 2 > >** ** **predictions? > >** ** **** ** **Our > >** ** **** ** **** **next model has 7 targets, which is 49 pos and > neg > values as > >** ** **far as > >** ** **** ** **I > >** ** **** ** **** **understand. And I think a**donut**graph is not > going to > >** ** **work in > >** ** **** ** **this > >** ** **** ** **** **case.**Maybe a bar graph or something else fits > better.** > > > >** ** **** ** **** **I'd appreciate any ideas or examples of > visualizations that > >** ** **easy to > >** ** **** ** **look > >** ** **** ** **** **at and understand. > >** ** **** ** **** **I'm using d3/c3.js for data visualization. > >** ** **** ** **** **Thank you! > >** ** **** ** **** **Art Nestsiarenka > >** ** **** ** **** **email: [2][3][5][10]artem.nesterenko at gmail.com > > > >** ** **** ** **References > > > >** ** **** ** **** **Visible links > >** ** **** ** **** **2. mailto:[4][6][11]artem.nesterenko at gmail.com > > > >** ** **** ** **_______________________________________________ > >** ** **** ** **TriZPUG mailing list > >** ** **** ** **[5][7][12]TriZPUG at python.org > >** ** **** ** > **[6][8][13]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > >** ** **** ** **[7][9][14]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle > Python > Users Group > > > >** ** **References > > > >** ** **** **Visible links > >** ** **** **1. [10][15]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= > >** ** **** **2. mailto:[11][16]artem.nesterenko at gmail.com > >** ** **** **3. mailto:[12][17]artem.nesterenko at gmail.com > >** ** **** **4. mailto:[13][18]artem.nesterenko at gmail.com > >** ** **** **5. mailto:[14][19]TriZPUG at python.org > >** ** **** **6. > [15][20]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > >** ** **** **7. [16][21]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > > > >** ** **_______________________________________________ > >** ** **TriZPUG mailing list > >** ** **[17][22]TriZPUG at python.org > >** ** **[18][23]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > >** ** **[19][24]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users > Group > > > > References > > > >** **Visible links > >** **1. mailto:[25]artem.nesterenko at gmail.com > >** **2. mailto:[26]jefferson.r.heard at gmail.com > >** **3. 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[43]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > > _______________________________________________ > > TriZPUG mailing list > > [44]TriZPUG at python.org > > [45]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > > [46]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group > _______________________________________________ > TriZPUG mailing list > [47]TriZPUG at python.org > [48]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > [49]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group > > References > > Visible links > 1. https://urldefense.proofpoint.com/v2/url?u=https-3A__imgur.com_a_C7r8x&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=qRSfQlbbQCl7rdpGcv4s4s4EfRCCxyH1nK4CUH081NM&e= > 2. mailto:artem.nesterenko at gmail.com > 3. mailto:jessebikman at gmail.com > 4. mailto:artem.nesterenko at gmail.com > 5. mailto:artem.nesterenko at gmail.com > 6. file:///tmp/tel:%28919%29%20455-5055 > 7. mailto:jefferson.r.heard at gmail.com > 8. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= > 9. mailto:artem.nesterenko at gmail.com > 10. mailto:artem.nesterenko at gmail.com > 11. mailto:artem.nesterenko at gmail.com > 12. mailto:TriZPUG at python.org > 13. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > 14. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > 15. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= > 16. mailto:artem.nesterenko at gmail.com > 17. mailto:artem.nesterenko at gmail.com > 18. mailto:artem.nesterenko at gmail.com > 19. mailto:TriZPUG at python.org > 20. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > 21. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > 22. mailto:TriZPUG at python.org > 23. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > 24. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > 25. mailto:artem.nesterenko at gmail.com > 26. mailto:jefferson.r.heard at gmail.com > 27. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= > 28. mailto:artem.nesterenko at gmail.com > 29. mailto:artem.nesterenko at gmail.com > 30. mailto:artem.nesterenko at gmail.com > 31. mailto:TriZPUG at python.org > 32. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > 33. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > 34. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= > 35. mailto:artem.nesterenko at gmail.com > 36. mailto:artem.nesterenko at gmail.com > 37. mailto:artem.nesterenko at gmail.com > 38. mailto:TriZPUG at python.org > 39. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > 40. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > 41. mailto:TriZPUG at python.org > 42. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > 43. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > 44. mailto:TriZPUG at python.org > 45. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > 46. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > 47. mailto:TriZPUG at python.org > 48. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > 49. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= > https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group > > -------------- next part -------------- I am not a mathematician or statistician, but in my work for Tableau, a donut is most useful for progress to goal.** In your case, perhaps that is a success rate. (Which may not be 100%) In that scenario, having 2 cases makes the viz confusing to read.** If you want to do the donut, I'd stick to one case per. Also, who is your audience for the viz?** Are you interpreting this for the layman, or is it an expert who expects the complexity?**** Finally, what is your objective with the viz?** Is it to display that case 1 is larger than case 2?** If so, other methods may do that more clearly. (Pies/donuts are notoriously bad for conveying true proportions for multiple segments. As for the output tool, take your pick, but keep in mind the audience and objective, and you'll be happy. Good luck! Dave Powell** On Oct 11, 2017 7:53 AM, "Art" <[1]artem.nesterenko at gmail.com> wrote: ** **Donut graph: ** **[1][2]https://urldefense.proofpoint.com/v2/url?u=https-3A__imgur.com_a_C7r8x&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=qRSfQlbbQCl7rdpGcv4s4s4EfRCCxyH1nK4CUH081NM&e= ** **You should be able to see it now. ** **Art Nestsiarenka ** **email: [2][3]artem.nesterenko at gmail.com ** **Cell: [4](919) 455-5055 ** **On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman <[3][5]jessebikman at gmail.com> ** **wrote: ** ** **Image attachment isn***t coming through. Maybe host on imgur or similar ** ** **site and pass the link. ** ** **Sent from my iPad ** ** **> On Oct 10, 2017, at 9:22 PM, Art <[4][6]artem.nesterenko at gmail.com> ** ** **wrote: ** ** **> ** ** **>** **Thank you for the**prompt replies! ** ** **>** **Sorry for the picture, didn't expect to have this issue. ** ** **>** **So, basically what I did is I built a confusion matrix with ** ** **correct and ** ** **>** **incorrect predictions: ** ** **>** **For example: ** ** **>** **** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **Predicted ** ** ** **** ** ** ** ** **>** **** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** target_2 ** ** **>** **Real ** ** target_1 ** **120 ** ** ** ** ** 23 ** ** **>** **** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** ** ** **>** **and then I built a donut that represents the number of true pos, ** ** **true neg, ** ** **>** **false pos, false neg predictions from the confusion matrix. I've ** ** **attached ** ** **>** **the image. The attached donut doesn't represent the numbers in "%" ** ** **from ** ** **>** **the above matrix. This matrix is just an example. ** ** **>** **And now I'm thinking of the**best graph type to represent the ** ** **number of ** ** **>** **pos and neg predictions if the**model predicts 7 targets. ** ** **>** **Art Nestsiarenka ** ** **>** **email: [1][5][7]artem.nesterenko at gmail.com ** ** **>** **Cell: [6][8](919) 455-5055 ** ** **> ** ** **>** **On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard ** ** **>** **<[2][7][9]jefferson.r.heard at gmail.com> wrote: ** ** **> ** ** **>** ** **** **I'd suggest you look at Seaborn. If you're dead-set on ** ** **visualizing ** ** **>** ** **data in ** ** **>** ** **** **javascript, it might well give you some inspiration to ** ** **draw from, ** ** **>** ** **** **otherwise it's a great Python solution for data ** ** **>** ** **** **vis.**[1][3][8][10]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= . I'm not 100% ** ** **sure what ** ** **>** ** **I'd ** ** **>** ** **** **recommend given that much information. Also, it looks like ** ** **the ** ** **>** ** **mailer ** ** **>** ** **** **scrubbed the images. If you could host the originals ** ** **somewhere ** ** **>** ** **(GitHub?), ** ** **>** ** **** **it'd help understand what you did better to give a ** ** **recommendation. ** ** **>** ** **** **Are your model outputs continuous or discrete? I ** ** **understand that ** ** **>** ** **you're ** ** **>** ** **** **looking at "positive/negative" values, but ** ** **>** ** **decisions/classifications and ** ** **>** ** **** **actual model output can be different. Plus the choice of a ** ** **donut ** ** **>** ** **chart to ** ** **>** ** **** **represent boolean values doesn't make a lot of sense to ** ** **me, so I ** ** **>** ** **thought ** ** **>** ** **** **that I maybe understood you wrong. ** ** **>** ** **** **** ** ** **>** ** **** **On Tue, Oct 10, 2017 at 3:53 PM, Art ** ** **>** ** **<[2][4][9][11]artem.nesterenko at gmail.com> ** ** **>** ** **** **wrote: ** ** **> ** ** **>** ** **** ** **** **Good afternoon! ** ** **>** ** **** ** **** **I'm reaching out to you guys for a suggestion on ** ** **the data ** ** **>** ** **** ** **visualization. ** ** **>** ** **** ** **** **I'm wondering if anyone has an experience or an ** ** **idea of ** ** **>** ** **visualizing ** ** **>** ** **** ** **the ** ** **>** ** **** ** **** **volume of multiple model prediction targets. ** ** **>** ** **** ** **** **Here is an example of a model with just 2 ** ** **prediction ** ** **>** ** **targets I've ** ** **>** ** **** ** **recently ** ** **>** ** **** ** **** **been working on: ** ** **>** ** **** ** **** **1. This model provides 2 predictions: target_1 and ** ** **>** ** **target_2.** ** ** **>** ** **** ** **** **2. I also know the actual result to compare ** ** **against, so I ** ** **>** ** **could see ** ** **>** ** **** ** **the ** ** **>** ** **** ** **** **model accuracy.** ** ** **>** ** **** ** **** **3. I've built a confusion matrix to calculate ** ** **true/false ** ** **>** ** **pos/neg ** ** **>** ** **** ** **(see ** ** **>** ** **** ** **** **below). So, there are 4 values. ** ** **>** ** **** ** **** **4. I chose donut**chart to visualize these values ** ** **(see ** ** **>** ** **below). ** ** **>** ** **** ** **** **[1]Inline image 2** ** ** **> ** ** **>** ** **** ** **** **My question is what if the model provides more ** ** **than 2 ** ** **>** ** **predictions? ** ** **>** ** **** ** **Our ** ** **>** ** **** ** **** **next model has 7 targets, which is 49 pos and neg ** ** **values as ** ** **>** ** **far as ** ** **>** ** **** ** **I ** ** **>** ** **** ** **** **understand. And I think a**donut**graph is not ** ** **going to ** ** **>** ** **work in ** ** **>** ** **** ** **this ** ** **>** ** **** ** **** **case.**Maybe a bar graph or something else fits ** ** **better.** ** ** **> ** ** **>** ** **** ** **** **I'd appreciate any ideas or examples of ** ** **visualizations that ** ** **>** ** **easy to ** ** **>** ** **** ** **look ** ** **>** ** **** ** **** **at and understand. ** ** **>** ** **** ** **** **I'm using d3/c3.js for data visualization. ** ** **>** ** **** ** **** **Thank you! ** ** **>** ** **** ** **** **Art Nestsiarenka ** ** **>** ** **** ** **** **email: [2][3][5][10][12]artem.nesterenko at gmail.com ** ** **> ** ** **>** ** **** ** **References ** ** **> ** ** **>** ** **** ** **** **Visible links ** ** **>** ** **** ** **** **2. mailto:[4][6][11][13]artem.nesterenko at gmail.com ** ** **> ** ** **>** ** **** ** **_______________________________________________ ** ** **>** ** **** ** **TriZPUG mailing list ** ** **>** ** **** ** **[5][7][12][14]TriZPUG at python.org ** ** **>** ** **** ** ** ** ****[6][8][13][15]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= ** ** **>** ** **** ** **[7][9][14][16]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python ** ** **Users Group ** ** **> ** ** **>** ** **References ** ** **> ** ** **>** ** **** **Visible links ** ** **>** ** **** **1. [10][15][17]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= ** ** **>** ** **** **2. mailto:[11][16][18]artem.nesterenko at gmail.com ** ** **>** ** **** **3. mailto:[12][17][19]artem.nesterenko at gmail.com ** ** **>** ** **** **4. mailto:[13][18][20]artem.nesterenko at gmail.com ** ** **>** ** **** **5. mailto:[14][19][21]TriZPUG at python.org ** ** **>** ** **** **6. ** ** **[15][20][22]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= ** ** **>** ** **** **7. [16][21][23]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= ** ** **> ** ** **>** ** **_______________________________________________ ** ** **>** ** **TriZPUG mailing list ** ** **>** ** **[17][22][24]TriZPUG at python.org ** ** **>** ** **[18][23][25]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= ** ** **>** ** **[19][24][26]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group ** ** **> ** ** **> References ** ** **> ** ** **>** **Visible links ** ** **>** **1. mailto:[25][27]artem.nesterenko at gmail.com ** ** **>** **2. mailto:[26][28]jefferson.r.heard at gmail.com ** ** **>** **3. [27][29]https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= ** ** **>** **4. mailto:[28][30]artem.nesterenko at gmail.com ** ** **>** **5. mailto:[29][31]artem.nesterenko at gmail.com ** ** **>** **6. mailto:[30][32]artem.nesterenko at gmail.com ** ** **>** **7. mailto:[31][33]TriZPUG at python.org ** ** **>** **8. [32][34]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= ** ** **>** **9. 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[90]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= ** 41. mailto:[91]TriZPUG at python.org ** 42. [92]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= ** 43. [93]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= ** 44. mailto:[94]TriZPUG at python.org ** 45. [95]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= ** 46. [96]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= ** 47. mailto:[97]TriZPUG at python.org ** 48. [98]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= ** 49. [99]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= _______________________________________________ TriZPUG mailing list [100]TriZPUG at python.org [101]https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= [102]https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group References Visible links 1. mailto:artem.nesterenko at gmail.com 2. https://urldefense.proofpoint.com/v2/url?u=https-3A__imgur.com_a_C7r8x&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=qRSfQlbbQCl7rdpGcv4s4s4EfRCCxyH1nK4CUH081NM&e= 3. mailto:artem.nesterenko at gmail.com 4. file:///tmp/tel:%28919%29%20455-5055 5. mailto:jessebikman at gmail.com 6. mailto:artem.nesterenko at gmail.com 7. mailto:artem.nesterenko at gmail.com 8. file:///tmp/tel:%28919%29%20455-5055 9. mailto:jefferson.r.heard at gmail.com 10. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 11. mailto:artem.nesterenko at gmail.com 12. mailto:artem.nesterenko at gmail.com 13. mailto:artem.nesterenko at gmail.com 14. mailto:TriZPUG at python.org 15. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 16. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 17. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 18. mailto:artem.nesterenko at gmail.com 19. mailto:artem.nesterenko at gmail.com 20. mailto:artem.nesterenko at gmail.com 21. mailto:TriZPUG at python.org 22. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 23. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 24. mailto:TriZPUG at python.org 25. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 26. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 27. mailto:artem.nesterenko at gmail.com 28. mailto:jefferson.r.heard at gmail.com 29. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 30. mailto:artem.nesterenko at gmail.com 31. mailto:artem.nesterenko at gmail.com 32. mailto:artem.nesterenko at gmail.com 33. mailto:TriZPUG at python.org 34. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 35. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 36. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 37. mailto:artem.nesterenko at gmail.com 38. mailto:artem.nesterenko at gmail.com 39. mailto:artem.nesterenko at gmail.com 40. mailto:TriZPUG at python.org 41. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 42. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 43. mailto:TriZPUG at python.org 44. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 45. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 46. mailto:TriZPUG at python.org 47. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 48. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 49. mailto:TriZPUG at python.org 50. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 51. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 52. https://urldefense.proofpoint.com/v2/url?u=https-3A__imgur.com_a_C7r8x&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=qRSfQlbbQCl7rdpGcv4s4s4EfRCCxyH1nK4CUH081NM&e= 53. mailto:artem.nesterenko at gmail.com 54. mailto:jessebikman at gmail.com 55. mailto:artem.nesterenko at gmail.com 56. mailto:artem.nesterenko at gmail.com 57. mailto:jefferson.r.heard at gmail.com 58. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 59. mailto:artem.nesterenko at gmail.com 60. mailto:artem.nesterenko at gmail.com 61. mailto:artem.nesterenko at gmail.com 62. mailto:TriZPUG at python.org 63. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 64. https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=a8UybtGtZcB0ZSJQx0Gli_ri21TZKWnqL9E7qalKXEY&e= 65. https://urldefense.proofpoint.com/v2/url?u=https-3A__seaborn.pydata.org_&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=00RLAj4EBqnxjj9hu1cAHcAiAtmWocoTD60zVxvwOCs&e= 66. mailto:artem.nesterenko at gmail.com 67. mailto:artem.nesterenko at gmail.com 68. mailto:artem.nesterenko at gmail.com 69. mailto:TriZPUG at python.org 70. https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_trizpug&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=XUfTqPVii_Q60fIRtqmp12jUzDFHyENCR_VtNqKgNKw&e= 71. 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https://urldefense.proofpoint.com/v2/url?u=http-3A__tripython.org&d=DwICAg&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=xLfTt4GWo52-xrBaYVQFv_rd751H30S-dxwZoh1Ocu8&m=9VGq8AYGQZkMPgpykfMNkUrekrrWByNbT-BE4sW1x6w&s=1x_9wsuebxiVZGBs8lTZCs0nQEbcqRugaj0QTOsIxEo&e= is the Triangle Python Users Group ------------------------------ End of TriZPUG Digest, Vol 114, Issue 9 *************************************** From ncdave4life at gmail.com Tue Oct 10 23:13:35 2017 From: ncdave4life at gmail.com (David Burton) Date: Tue, 10 Oct 2017 23:13:35 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: Message-ID: Hi Art, If you don't have a web site/server of your own to use, there are quite a few image hosting services available. Here's a good article: https://www.bleepingcomputer.com/forums/t/650637/photobucket-alternatives/ Here's another article: https://www.ghacks.net/2017/07/02/photobucket-alternatives-for-third-party/ For visualization, on my sealevel.info site I use Google Charts (javascript). It's really quite nice. Here are some examples: https://www.sealevel.info/boxcar_weights_graph.html http://www.sealevel.info/co2_and_ch4.html https://www.sealevel.info/MSL_graph.php?id=Honolulu http://www.sealevel.info/MSL_graph.php?id=Honolulu&xtraseg= 2&g_date=1900/1-2099/12&x_date=2017/10-2099/12&x1=0.041& x2=3.089&xslope=2.000 It's all done in javascript, within your web browser. You can hover the mouse cursor over the graphs to see values, and pick options to change the graphs, all without hitting the server. In fact, just to verify that, I brought up the Honolulu graph page and then disconnected my Internet, and tried changing various options in the graphs. It worked fine. in fact, if you bookmark the "downloadable, bookmarkable image" ink in those Honolulu graphs (in Chrome or Firefox) you're actually saving an entire copy of the graph within the bookmark, in "data:image/png;base64" form, so you can open that bookmark with no Internet connection. The thing that was hardest to do on my pages was the "watermark." Google Charts doesn't have an option to set the graph background. So my watermark is really a background image *behind* the chart, which is initially covered up by a solid white image. When Google Charts fills in the graph, it deletes the solid white image which was in the DIV, and replaces it with the graph. I gave the graph a transparent background, so when the white image disappears that makes the watermark beneath it appear simultaneously with the graph. There are, of course, many other visualization packages out there. When I was trying to decide which package to use, not quite two years ago, the other one which was frequently recommended was D3. But it sounds like it has a steeper learning curve than Google Charts. This is an excerpt from a Jan 3, 2016 email that I wrote, when I was trying to decide what package to use: plot.ly turns out to be very not-free (though the javascript foundation for > it is). But there are lots of alternatives... > > "gnu plot" vs "google charts" vs CanvasJS vs D3.js vs Matplotlib vs flot > or Flotr2 vs amcharts vs "highcharts js" > Etc. > http://stackoverflow.com/questions/52652/pretty-graphs- > and-charts-in-python > http://swizec.com/blog/flotr2-my-favorite-javascript-graph- > library/swizec/4558 > https://www.reddit.com/r/programming/comments/c8h5a/ > have_you_used_the_google_charts_api_yet_it_is/ > http://sealevel.info/peterschlamp/drawcharttest.html (was > http://peterschlamp.com/js/drawcharttest.html > > ) > http://sami.salkosuo.net/out-google-charts-in-amcharts/ > https://en.wikipedia.org/wiki/Comparison_of_JavaScript_charting_frameworks > https://plot.ly/javascript/ > A few weeks later I attended an Analytics / Data Science meetup featuring a distinguished panel of data science experts. There were a *lot* of people there! At the end of the event I raised my hand and asked the panel for a recommendation for data visualization. They all just kind of looked at each other. I don't know whether they were thinking, "what a dumb question," or "gee, I don't know." Finally one of them said she uses D3. I asked if D3 is a better choice than Google Charts, and, again, they all looked kind of befuddled. Finally, one of them said that either should work fine. But I remember thinking that D3 seemed to be the most common choice, among that crowd. In fact, it might have been the *only* thing that most of them had used. My suspicion is that hardly anyone really knows the answer to questions like mine, because hardly anyone uses *several* of these packages intensely enough to really compare them. Dave On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard wrote: > I'd suggest you look at Seaborn. If you're dead-set on visualizing data > in > javascript, it might well give you some inspiration to draw from, > otherwise it's a great Python solution for data > vis.**[1]https://seaborn.pydata.org/ . I'm not 100% sure what I'd > recommend given that much information. Also, it looks like the mailer > scrubbed the images. If you could host the originals somewhere > (GitHub?), > it'd help understand what you did better to give a recommendation. > Are your model outputs continuous or discrete? I understand that you're > looking at "positive/negative" values, but decisions/classifications and > actual model output can be different. Plus the choice of a donut chart > to > represent boolean values doesn't make a lot of sense to me, so I thought > that I maybe understood you wrong. > ** > On Tue, Oct 10, 2017 at 3:53 PM, Art <[2]artem.nesterenko at gmail.com> > wrote: > > ** **Good afternoon! > ** **I'm reaching out to you guys for a suggestion on the data > visualization. > *...[snip]* -------------- next part -------------- Hi Art, If you don't have a web site/server of your own to use, there are quite a few image hosting services available. Here's a good article: [1]https://www.bleepingcomputer.com/forums/t/650637/photobucket-alternatives/ Here's another article: [2]https://www.ghacks.net/2017/07/02/photobucket-alternatives-for-third-party/ For visualization, on my [3]sealevel.info site I use Google Charts (javascript). It's really quite nice. Here are some examples: [4]https://www.sealevel.info/boxcar_weights_graph.html [5]http://www.sealevel.info/co2_and_ch4.html [6]https://www.sealevel.info/MSL_graph.php?id=Honolulu [7]http://www.sealevel.info/MSL_graph.php?id=Honolulu&xtraseg=2&g_date=1900/1-2099/12&x_date=2017/10-2099/12&x1=0.041&x2=3.089&xslope=2.000 It's all done in javascript, within your web browser. You can hover the mouse cursor over the graphs to see values, and pick options to change the graphs, all without hitting the server. In fact, just to verify that, I brought up the Honolulu graph page and then disconnected my Internet, and tried changing various options in the graphs. It worked fine. in fact, if you bookmark the "downloadable, bookmarkable image" ink in those Honolulu graphs (in Chrome or Firefox) you're actually saving an entire copy of the graph within the bookmark, in "data:image/png;base64" form, so you can open that bookmark with no Internet connection. The thing that was hardest to do on my pages was the "watermark." Google Charts doesn't have an option to set the graph background. So my watermark is really a background image**behind**the chart, which is initially covered up by a solid white image. When Google Charts fills in the graph, it deletes the solid white image which was in the DIV, and replaces it with the graph. I gave the graph a transparent background, so when the white image disappears that makes the watermark beneath it appear simultaneously with the graph. There are, of course, many other visualization packages out there. When I was trying to decide which package to use, not quite two years ago, the other one which was frequently recommended was D3. But it sounds like it has a steeper learning curve than Google Charts. This is an excerpt from a Jan 3, 2016 email that I wrote, when I was trying to decide what package to use: [8]plot.ly**turns out to be very not-free (though the javascript foundation for it is).** But there are lots of alternatives... "gnu plot" vs "google charts" vs CanvasJS vs D3.js vs Matplotlib vs flot or Flotr2 vs amcharts vs "highcharts js" Etc. [9]http://stackoverflow.com/questions/52652/pretty-graphs-and-charts-in-python [10]http://swizec.com/blog/flotr2-my-favorite-javascript-graph-library/swizec/4558 [11]https://www.reddit.com/r/programming/comments/c8h5a/have_you_used_the_google_charts_api_yet_it_is/ [12]http://sealevel.info/peterschlamp/drawcharttest.html** (was**[13]http://peterschlamp.com/js/drawcharttest.html) [14]http://sami.salkosuo.net/out-google-charts-in-amcharts/ [15]https://en.wikipedia.org/wiki/Comparison_of_JavaScript_charting_frameworks [16]https://plot.ly/javascript/ A few weeks later I attended an**[17]Analytics / Data Science meetup**featuring a distinguished panel of data science experts. There were a lot of people there! At**the end of the event I raised my hand and asked the panel for a recommendation for data visualization. They all just kind of looked at each other. I don't know whether they were thinking, "what a dumb question," or "gee, I don't know." Finally one of them said she uses D3. I asked if D3 is a better choice than Google Charts, and, again, they all looked kind of befuddled. Finally, one of them said that either should work fine. But I remember thinking that D3 seemed to be the most common choice, among that crowd. In fact, it might have been the only thing that most of them had used. My suspicion is that hardly anyone really knows the answer to questions like mine, because hardly anyone uses**several**of these packages intensely enough to really compare them. Dave On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard <[18]jefferson.r.heard at gmail.com> wrote: ** **I'd suggest you look at Seaborn. If you're dead-set on visualizing data in ** **javascript, it might well give you some inspiration to draw from, ** **otherwise it's a great Python solution for data ** **vis.**[1][19]https://seaborn.pydata.org/ . I'm not 100% sure what I'd ** **recommend given that much information. Also, it looks like the mailer ** **scrubbed the images. If you could host the originals somewhere (GitHub?), ** **it'd help understand what you did better to give a recommendation. ** **Are your model outputs continuous or discrete? I understand that you're ** **looking at "positive/negative" values, but decisions/classifications and ** **actual model output can be different. Plus the choice of a donut chart to ** **represent boolean values doesn't make a lot of sense to me, so I thought ** **that I maybe understood you wrong. ** **** ** **On Tue, Oct 10, 2017 at 3:53 PM, Art <[2][20]artem.nesterenko at gmail.com> ** **wrote: ** ** **** **Good afternoon! ** ** **** **I'm reaching out to you guys for a suggestion on the data ** ** **visualization. ...[snip] References Visible links 1. https://www.bleepingcomputer.com/forums/t/650637/photobucket-alternatives/ 2. https://www.ghacks.net/2017/07/02/photobucket-alternatives-for-third-party/ 3. http://sealevel.info/ 4. https://www.sealevel.info/boxcar_weights_graph.html 5. http://www.sealevel.info/co2_and_ch4.html 6. https://www.sealevel.info/MSL_graph.php?id=Honolulu 7. http://www.sealevel.info/MSL_graph.php?id=Honolulu&xtraseg=2&g_date=1900/1-2099/12&x_date=2017/10-2099/12&x1=0.041&x2=3.089&xslope=2.000 8. http://plot.ly/ 9. http://stackoverflow.com/questions/52652/pretty-graphs-and-charts-in-python 10. http://swizec.com/blog/flotr2-my-favorite-javascript-graph-library/swizec/4558 11. https://www.reddit.com/r/programming/comments/c8h5a/have_you_used_the_google_charts_api_yet_it_is/ 12. http://sealevel.info/peterschlamp/drawcharttest.html 13. http://web.archive.org/web/*/http://peterschlamp.com/js/drawcharttest.html 14. http://sami.salkosuo.net/out-google-charts-in-amcharts/ 15. https://en.wikipedia.org/wiki/Comparison_of_JavaScript_charting_frameworks 16. https://plot.ly/javascript/ 17. http://www.meetup.com/ac-rdu/events/227317630/ 18. mailto:jefferson.r.heard at gmail.com 19. https://seaborn.pydata.org/ 20. mailto:artem.nesterenko at gmail.com From artem.nesterenko at gmail.com Wed Oct 11 11:19:43 2017 From: artem.nesterenko at gmail.com (Art) Date: Wed, 11 Oct 2017 11:19:43 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: Dave, Thank you for your comments! 1. I'd say this visualization is for a layman. I need to create it in a way so it would be easy for upper management to understand the graph. 2. If I understood your 2nd question correctly then case 2 (model with 7 predictions) is totally separate from case 1 (model with 2 predictions). I just need to come up with something other than donut/pie graph. 3. @Francois, this visualization must be on the web and interactive. It'll be like user chooses the date from the drop-down box and graph updates numbers right away. Our team and some other people in the company will have the access to it. 4. @David Burton, thank you for the detailed suggestion. There are a lot of tools out there and I think D3 is not a requirement, it's just that our team wants to use this lib. I personally would go with a package that will allow me to get it done quickly with less code because of the deadline and stuff like that, and maybe then if I would be asked to make it nice, fancy and interactive then I could move to D3 or other tools. Art Nestsiarenka email: artem.nesterenko at gmail.com Cell: (919) 455-5055 On Wed, Oct 11, 2017 at 10:11 AM, Dave Powell wrote: > I am not a mathematician or statistician, but in my work for Tableau, a > donut is most useful for progress to goal.** In your case, perhaps that > is > a success rate. (Which may not be 100%) > In that scenario, having 2 cases makes the viz confusing to read.** If > you > want to do the donut, I'd stick to one case per. > Also, who is your audience for the viz?** Are you interpreting this for > the layman, or is it an expert who expects the complexity?**** > Finally, what is your objective with the viz?** Is it to display that > case > 1 is larger than case 2?** If so, other methods may do that more > clearly. > (Pies/donuts are notoriously bad for conveying true proportions for > multiple segments. > As for the output tool, take your pick, but keep in mind the audience > and > objective, and you'll be happy. > Good luck! > Dave Powell** > On Oct 11, 2017 7:53 AM, "Art" <[1]artem.nesterenko at gmail.com> wrote: > > ** **Donut graph: > ** **[1][2]https://imgur.com/a/C7r8x > ** **You should be able to see it now. > ** **Art Nestsiarenka > ** **email: [2][3]artem.nesterenko at gmail.com > ** **Cell: [4](919) 455-5055 > > ** **On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman > <[3][5]jessebikman at gmail.com> > ** **wrote: > > ** ** **Image attachment isn***t coming through. Maybe host on imgur > or > similar > ** ** **site and pass the link. > > ** ** **Sent from my iPad > > ** ** **> On Oct 10, 2017, at 9:22 PM, Art > <[4][6]artem.nesterenko at gmail.com> > ** ** **wrote: > ** ** **> > ** ** **>** **Thank you for the**prompt replies! > ** ** **>** **Sorry for the picture, didn't expect to have this issue. > ** ** **>** **So, basically what I did is I built a confusion matrix > with > ** ** **correct and > ** ** **>** **incorrect predictions: > ** ** **>** **For example: > ** ** **>** **** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** > **Predicted ** > ** ** **** ** ** > ** ** **>** **** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** > target_2 > ** ** **>** **Real ** ** target_1 ** **120 ** ** ** ** ** 23 > ** ** **>** **** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** > 44** > ** ** **>** **and then I built a donut that represents the number of > true pos, > ** ** **true neg, > ** ** **>** **false pos, false neg predictions from the confusion > matrix. I've > ** ** **attached > ** ** **>** **the image. The attached donut doesn't represent the > numbers in "%" > ** ** **from > ** ** **>** **the above matrix. This matrix is just an example. > ** ** **>** **And now I'm thinking of the**best graph type to > represent > the > ** ** **number of > ** ** **>** **pos and neg predictions if the**model predicts 7 > targets. > ** ** **>** **Art Nestsiarenka > ** ** **>** **email: [1][5][7]artem.nesterenko at gmail.com > ** ** **>** **Cell: [6][8](919) 455-5055 > ** ** **> > ** ** **>** **On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard > ** ** **>** **<[2][7][9]jefferson.r.heard at gmail.com> wrote: > ** ** **> > ** ** **>** ** **** **I'd suggest you look at Seaborn. If you're > dead-set on > ** ** **visualizing > ** ** **>** ** **data in > ** ** **>** ** **** **javascript, it might well give you some > inspiration to > ** ** **draw from, > ** ** **>** ** **** **otherwise it's a great Python solution for data > ** ** **>** ** **** **vis.**[1][3][8][10]https://seaborn.pydata.org/ > . > I'm not 100% > ** ** **sure what > ** ** **>** ** **I'd > ** ** **>** ** **** **recommend given that much information. Also, it > looks like > ** ** **the > ** ** **>** ** **mailer > ** ** **>** ** **** **scrubbed the images. If you could host the > originals > ** ** **somewhere > ** ** **>** ** **(GitHub?), > ** ** **>** ** **** **it'd help understand what you did better to > give a > ** ** **recommendation. > ** ** **>** ** **** **Are your model outputs continuous or discrete? I > ** ** **understand that > ** ** **>** ** **you're > ** ** **>** ** **** **looking at "positive/negative" values, but > ** ** **>** ** **decisions/classifications and > ** ** **>** ** **** **actual model output can be different. Plus the > choice of a > ** ** **donut > ** ** **>** ** **chart to > ** ** **>** ** **** **represent boolean values doesn't make a lot of > sense to > ** ** **me, so I > ** ** **>** ** **thought > ** ** **>** ** **** **that I maybe understood you wrong. > ** ** **>** ** **** **** > ** ** **>** ** **** **On Tue, Oct 10, 2017 at 3:53 PM, Art > ** ** **>** ** **<[2][4][9][11]artem.nesterenko at gmail.com> > ** ** **>** ** **** **wrote: > ** ** **> > ** ** **>** ** **** ** **** **Good afternoon! > ** ** **>** ** **** ** **** **I'm reaching out to you guys for a > suggestion on > ** ** **the data > ** ** **>** ** **** ** **visualization. > ** ** **>** ** **** ** **** **I'm wondering if anyone has an > experience > or an > ** ** **idea of > ** ** **>** ** **visualizing > ** ** **>** ** **** ** **the > ** ** **>** ** **** ** **** **volume of multiple model prediction > targets. > ** ** **>** ** **** ** **** **Here is an example of a model with just > 2 > ** ** **prediction > ** ** **>** ** **targets I've > ** ** **>** ** **** ** **recently > ** ** **>** ** **** ** **** **been working on: > ** ** **>** ** **** ** **** **1. This model provides 2 predictions: > target_1 and > ** ** **>** ** **target_2.** > ** ** **>** ** **** ** **** **2. I also know the actual result to > compare > ** ** **against, so I > ** ** **>** ** **could see > ** ** **>** ** **** ** **the > ** ** **>** ** **** ** **** **model accuracy.** > ** ** **>** ** **** ** **** **3. I've built a confusion matrix to > calculate > ** ** **true/false > ** ** **>** ** **pos/neg > ** ** **>** ** **** ** **(see > ** ** **>** ** **** ** **** **below). So, there are 4 values. > ** ** **>** ** **** ** **** **4. I chose donut**chart to visualize > these > values > ** ** **(see > ** ** **>** ** **below). > ** ** **>** ** **** ** **** **[1]Inline image 2** > ** ** **> > ** ** **>** ** **** ** **** **My question is what if the model > provides > more > ** ** **than 2 > ** ** **>** ** **predictions? > ** ** **>** ** **** ** **Our > ** ** **>** ** **** ** **** **next model has 7 targets, which is 49 > pos > and neg > ** ** **values as > ** ** **>** ** **far as > ** ** **>** ** **** ** **I > ** ** **>** ** **** ** **** **understand. And I think a**donut**graph > is > not > ** ** **going to > ** ** **>** ** **work in > ** ** **>** ** **** ** **this > ** ** **>** ** **** ** **** **case.**Maybe a bar graph or something > else > fits > ** ** **better.** > ** ** **> > ** ** **>** ** **** ** **** **I'd appreciate any ideas or examples of > ** ** **visualizations that > ** ** **>** ** **easy to > ** ** **>** ** **** ** **look > ** ** **>** ** **** ** **** **at and understand. > ** ** **>** ** **** ** **** **I'm using d3/c3.js for data > visualization. > ** ** **>** ** **** ** **** **Thank you! > ** ** **>** ** **** ** **** **Art Nestsiarenka > ** ** **>** ** **** ** **** **email: > [2][3][5][10][12]artem.nesterenko at gmail.com > ** ** **> > ** ** **>** ** **** ** **References > ** ** **> > ** ** **>** ** **** ** **** **Visible links > ** ** **>** ** **** ** **** **2. > mailto:[4][6][11][13]artem.nesterenko at gmail.com > ** ** **> > ** ** **>** ** **** ** **____________________________ > ___________________ > ** ** **>** ** **** ** **TriZPUG mailing list > ** ** **>** ** **** ** **[5][7][12][14]TriZPUG at python.org > ** ** **>** ** **** ** > ** ** ****[6][8][13][15]https://mail.python.org/mailman/ > listinfo/trizpug > ** ** **>** ** **** ** **[7][9][14][16]http://tripython.org is the > Triangle Python > ** ** **Users Group > ** ** **> > ** ** **>** ** **References > ** ** **> > ** ** **>** ** **** **Visible links > ** ** **>** ** **** **1. [10][15][17]https://seaborn.pydata.org/ > ** ** **>** ** **** **2. mailto:[11][16][18]artem. > nesterenko at gmail.com > ** ** **>** ** **** **3. mailto:[12][17][19]artem. > nesterenko at gmail.com > ** ** **>** ** **** **4. mailto:[13][18][20]artem. > nesterenko at gmail.com > ** ** **>** ** **** **5. mailto:[14][19][21]TriZPUG at python.org > ** ** **>** ** **** **6. > ** ** **[15][20][22]https://mail.python.org/mailman/listinfo/trizpug > ** ** **>** ** **** **7. [16][21][23]http://tripython.org/ > ** ** **> > ** ** **>** ** **_______________________________________________ > ** ** **>** ** **TriZPUG mailing list > ** ** **>** ** **[17][22][24]TriZPUG at python.org > ** ** **>** ** > **[18][23][25]https://mail.python.org/mailman/listinfo/trizpug > ** ** **>** ** **[19][24][26]http://tripython.org is the Triangle > Python > Users Group > ** ** **> > ** ** **> References > ** ** **> > ** ** **>** **Visible links > ** ** **>** **1. mailto:[25][27]artem.nesterenko at gmail.com > ** ** **>** **2. mailto:[26][28]jefferson.r.heard at gmail.com > ** ** **>** **3. [27][29]https://seaborn.pydata.org/ > ** ** **>** **4. mailto:[28][30]artem.nesterenko at gmail.com > ** ** **>** **5. mailto:[29][31]artem.nesterenko at gmail.com > ** ** **>** **6. mailto:[30][32]artem.nesterenko at gmail.com > ** ** **>** **7. mailto:[31][33]TriZPUG at python.org > ** ** **>** **8. > [32][34]https://mail.python.org/mailman/listinfo/trizpug > ** ** **>** **9. [33][35]http://tripython.org/ > ** ** **>** 10. [34][36]https://seaborn.pydata.org/ > ** ** **>** 11. mailto:[35][37]artem.nesterenko at gmail.com > ** ** **>** 12. mailto:[36][38]artem.nesterenko at gmail.com > ** ** **>** 13. mailto:[37][39]artem.nesterenko at gmail.com > ** ** **>** 14. mailto:[38][40]TriZPUG at python.org > ** ** **>** 15. [39][41]https://mail.python. > org/mailman/listinfo/trizpug > ** ** **>** 16. [40][42]http://tripython.org/ > ** ** **>** 17. mailto:[41][43]TriZPUG at python.org > ** ** **>** 18. [42][44]https://mail.python. > org/mailman/listinfo/trizpug > ** ** **>** 19. [43][45]http://tripython.org/ > ** ** **> _______________________________________________ > ** ** **> TriZPUG mailing list > ** ** **> [44][46]TriZPUG at python.org > ** ** **> [45][47]https://mail.python.org/mailman/listinfo/trizpug > ** ** **> [46][48]http://tripython.org is the Triangle Python Users > Group > ** ** **_______________________________________________ > ** ** **TriZPUG mailing list > ** ** **[47][49]TriZPUG at python.org > ** ** **[48][50]https://mail.python.org/mailman/listinfo/trizpug > ** ** **[49][51]http://tripython.org is the Triangle Python Users > Group > > References > > ** **Visible links > ** **1. [52]https://imgur.com/a/C7r8x > ** **2. mailto:[53]artem.nesterenko at gmail.com > ** **3. mailto:[54]jessebikman at gmail.com > ** **4. mailto:[55]artem.nesterenko at gmail.com > ** **5. mailto:[56]artem.nesterenko at gmail.com > ** **6. file:///tmp/tel:%28919%29%20455-5055 > ** **7. mailto:[57]jefferson.r.heard at gmail.com > ** **8. [58]https://seaborn.pydata.org/ > ** **9. mailto:[59]artem.nesterenko at gmail.com > ** 10. mailto:[60]artem.nesterenko at gmail.com > ** 11. mailto:[61]artem.nesterenko at gmail.com > ** 12. mailto:[62]TriZPUG at python.org > ** 13. [63]https://mail.python.org/mailman/listinfo/trizpug > ** 14. [64]http://tripython.org/ > ** 15. [65]https://seaborn.pydata.org/ > ** 16. mailto:[66]artem.nesterenko at gmail.com > ** 17. mailto:[67]artem.nesterenko at gmail.com > ** 18. mailto:[68]artem.nesterenko at gmail.com > ** 19. mailto:[69]TriZPUG at python.org > ** 20. [70]https://mail.python.org/mailman/listinfo/trizpug > ** 21. [71]http://tripython.org/ > ** 22. mailto:[72]TriZPUG at python.org > ** 23. [73]https://mail.python.org/mailman/listinfo/trizpug > ** 24. [74]http://tripython.org/ > ** 25. mailto:[75]artem.nesterenko at gmail.com > ** 26. mailto:[76]jefferson.r.heard at gmail.com > ** 27. [77]https://seaborn.pydata.org/ > ** 28. mailto:[78]artem.nesterenko at gmail.com > ** 29. mailto:[79]artem.nesterenko at gmail.com > ** 30. mailto:[80]artem.nesterenko at gmail.com > ** 31. mailto:[81]TriZPUG at python.org > ** 32. [82]https://mail.python.org/mailman/listinfo/trizpug > ** 33. [83]http://tripython.org/ > ** 34. [84]https://seaborn.pydata.org/ > ** 35. mailto:[85]artem.nesterenko at gmail.com > ** 36. mailto:[86]artem.nesterenko at gmail.com > ** 37. mailto:[87]artem.nesterenko at gmail.com > ** 38. mailto:[88]TriZPUG at python.org > ** 39. [89]https://mail.python.org/mailman/listinfo/trizpug > ** 40. [90]http://tripython.org/ > ** 41. mailto:[91]TriZPUG at python.org > ** 42. [92]https://mail.python.org/mailman/listinfo/trizpug > ** 43. [93]http://tripython.org/ > ** 44. mailto:[94]TriZPUG at python.org > ** 45. [95]https://mail.python.org/mailman/listinfo/trizpug > ** 46. [96]http://tripython.org/ > ** 47. mailto:[97]TriZPUG at python.org > ** 48. [98]https://mail.python.org/mailman/listinfo/trizpug > ** 49. [99]http://tripython.org/ > > _______________________________________________ > TriZPUG mailing list > [100]TriZPUG at python.org > [101]https://mail.python.org/mailman/listinfo/trizpug > [102]http://tripython.org is the Triangle Python Users Group > > References > > Visible links > 1. mailto:artem.nesterenko at gmail.com > 2. https://imgur.com/a/C7r8x > 3. mailto:artem.nesterenko at gmail.com > 4. file:///tmp/tel:%28919%29%20455-5055 > 5. mailto:jessebikman at gmail.com > 6. mailto:artem.nesterenko at gmail.com > 7. mailto:artem.nesterenko at gmail.com > 8. file:///tmp/tel:%28919%29%20455-5055 > 9. mailto:jefferson.r.heard at gmail.com > 10. https://seaborn.pydata.org/ > 11. mailto:artem.nesterenko at gmail.com > 12. mailto:artem.nesterenko at gmail.com > 13. mailto:artem.nesterenko at gmail.com > 14. mailto:TriZPUG at python.org > 15. https://mail.python.org/mailman/listinfo/trizpug > 16. http://tripython.org/ > 17. https://seaborn.pydata.org/ > 18. mailto:artem.nesterenko at gmail.com > 19. mailto:artem.nesterenko at gmail.com > 20. mailto:artem.nesterenko at gmail.com > 21. mailto:TriZPUG at python.org > 22. https://mail.python.org/mailman/listinfo/trizpug > 23. http://tripython.org/ > 24. mailto:TriZPUG at python.org > 25. https://mail.python.org/mailman/listinfo/trizpug > 26. http://tripython.org/ > 27. mailto:artem.nesterenko at gmail.com > 28. mailto:jefferson.r.heard at gmail.com > 29. https://seaborn.pydata.org/ > 30. mailto:artem.nesterenko at gmail.com > 31. mailto:artem.nesterenko at gmail.com > 32. mailto:artem.nesterenko at gmail.com > 33. mailto:TriZPUG at python.org > 34. https://mail.python.org/mailman/listinfo/trizpug > 35. http://tripython.org/ > 36. https://seaborn.pydata.org/ > 37. mailto:artem.nesterenko at gmail.com > 38. mailto:artem.nesterenko at gmail.com > 39. mailto:artem.nesterenko at gmail.com > 40. mailto:TriZPUG at python.org > 41. https://mail.python.org/mailman/listinfo/trizpug > 42. http://tripython.org/ > 43. mailto:TriZPUG at python.org > 44. https://mail.python.org/mailman/listinfo/trizpug > 45. http://tripython.org/ > 46. mailto:TriZPUG at python.org > 47. https://mail.python.org/mailman/listinfo/trizpug > 48. http://tripython.org/ > 49. mailto:TriZPUG at python.org > 50. https://mail.python.org/mailman/listinfo/trizpug > 51. http://tripython.org/ > 52. https://imgur.com/a/C7r8x > 53. mailto:artem.nesterenko at gmail.com > 54. mailto:jessebikman at gmail.com > 55. mailto:artem.nesterenko at gmail.com > 56. mailto:artem.nesterenko at gmail.com > 57. mailto:jefferson.r.heard at gmail.com > 58. https://seaborn.pydata.org/ > 59. mailto:artem.nesterenko at gmail.com > 60. mailto:artem.nesterenko at gmail.com > 61. mailto:artem.nesterenko at gmail.com > 62. mailto:TriZPUG at python.org > 63. https://mail.python.org/mailman/listinfo/trizpug > 64. http://tripython.org/ > 65. https://seaborn.pydata.org/ > 66. mailto:artem.nesterenko at gmail.com > 67. mailto:artem.nesterenko at gmail.com > 68. mailto:artem.nesterenko at gmail.com > 69. mailto:TriZPUG at python.org > 70. https://mail.python.org/mailman/listinfo/trizpug > 71. http://tripython.org/ > 72. mailto:TriZPUG at python.org > 73. https://mail.python.org/mailman/listinfo/trizpug > 74. http://tripython.org/ > 75. mailto:artem.nesterenko at gmail.com > 76. mailto:jefferson.r.heard at gmail.com > 77. https://seaborn.pydata.org/ > 78. mailto:artem.nesterenko at gmail.com > 79. mailto:artem.nesterenko at gmail.com > 80. mailto:artem.nesterenko at gmail.com > 81. mailto:TriZPUG at python.org > 82. https://mail.python.org/mailman/listinfo/trizpug > 83. http://tripython.org/ > 84. https://seaborn.pydata.org/ > 85. mailto:artem.nesterenko at gmail.com > 86. mailto:artem.nesterenko at gmail.com > 87. mailto:artem.nesterenko at gmail.com > 88. mailto:TriZPUG at python.org > 89. https://mail.python.org/mailman/listinfo/trizpug > 90. http://tripython.org/ > 91. mailto:TriZPUG at python.org > 92. https://mail.python.org/mailman/listinfo/trizpug > 93. http://tripython.org/ > 94. mailto:TriZPUG at python.org > 95. https://mail.python.org/mailman/listinfo/trizpug > 96. http://tripython.org/ > 97. mailto:TriZPUG at python.org > 98. https://mail.python.org/mailman/listinfo/trizpug > 99. http://tripython.org/ > 100. mailto:TriZPUG at python.org > 101. https://mail.python.org/mailman/listinfo/trizpug > 102. http://tripython.org/ > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > > -------------- next part -------------- Dave, Thank you for your comments! 1. I'd say this visualization is for a layman. I need to create it in a way so it would be easy for upper management to understand the graph. 2. If I understood your 2nd question correctly then case 2 (model with 7 predictions) is totally**separate from case 1 (model with 2 predictions). I just need to come up with something other than donut/pie graph.** **** 3. @Francois, this visualization must be on the web and interactive. It'll be like user chooses the date from the drop-down box and graph updates numbers right away. Our team and some other people in the company will have the access to it.** 4. @David Burton, thank**you for the detailed suggestion. There are a lot of tools out there and I think D3 is not a requirement, it's just that our team wants to use this lib. I personally would go with a package that will allow me to get it done quickly with less code because of the deadline and stuff like that, and maybe then if I would be asked to make it nice, fancy and interactive then I could move to D3 or other tools.** ** Art Nestsiarenka email: [1]artem.nesterenko at gmail.com Cell: (919) 455-5055 On Wed, Oct 11, 2017 at 10:11 AM, Dave Powell <[2]jdavep at gmail.com> wrote: ** **I am not a mathematician or statistician, but in my work for Tableau, a ** **donut is most useful for progress to goal.** In your case, perhaps that is ** **a success rate. (Which may not be 100%) ** **In that scenario, having 2 cases makes the viz confusing to read.** If you ** **want to do the donut, I'd stick to one case per. ** **Also, who is your audience for the viz?** Are you interpreting this for ** **the layman, or is it an expert who expects the complexity?**** ** **Finally, what is your objective with the viz?** Is it to display that case ** **1 is larger than case 2?** If so, other methods may do that more clearly. ** **(Pies/donuts are notoriously bad for conveying true proportions for ** **multiple segments. ** **As for the output tool, take your pick, but keep in mind the audience and ** **objective, and you'll be happy. ** **Good luck! ** **Dave Powell** ** **On Oct 11, 2017 7:53 AM, "Art" <[1][3]artem.nesterenko at gmail.com> wrote: ** ** **** **Donut graph: ** ** **** **[1][2][4]https://imgur.com/a/C7r8x ** ** **** **You should be able to see it now. ** ** **** **Art Nestsiarenka ** ** **** **email: [2][3][5]artem.nesterenko at gmail.com ** ** **** **Cell: [4][6](919) 455-5055 ** ** **** **On Wed, Oct 11, 2017 at 1:35 AM, Jesse Bikman ** ** **<[3][5][7]jessebikman at gmail.com> ** ** **** **wrote: ** ** **** ** **Image attachment isn***t coming through. Maybe host on imgur or ** ** **similar ** ** **** ** **site and pass the link. ** ** **** ** **Sent from my iPad ** ** **** ** **> On Oct 10, 2017, at 9:22 PM, Art ** ** **<[4][6][8]artem.nesterenko at gmail.com> ** ** **** ** **wrote: ** ** **** ** **> ** ** **** ** **>** **Thank you for the**prompt replies! ** ** **** ** **>** **Sorry for the picture, didn't expect to have this issue. ** ** **** ** **>** **So, basically what I did is I built a confusion matrix ** ** **with ** ** **** ** **correct and ** ** **** ** **>** **incorrect predictions: ** ** **** ** **>** **For example: ** ** **** ** **>** **** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ****Predicted ** ** ** **** ** **** ** ** ** ** **** ** **>** **** ** ** ** ** ** ** ** ** ** ** ** ** **target_1 ** ** ** **target_2 ** ** **** ** **>** **Real ** ** target_1 ** **120 ** ** ** ** ** 23 ** ** **** ** **>** **** ** ** ** ** ** target_2 ** ** 37 ** ** ** ** ** ** 44** ** ** **** ** **>** **and then I built a donut that represents the number of ** ** **true pos, ** ** **** ** **true neg, ** ** **** ** **>** **false pos, false neg predictions from the confusion ** ** **matrix. I've ** ** **** ** **attached ** ** **** ** **>** **the image. The attached donut doesn't represent the ** ** **numbers in "%" ** ** **** ** **from ** ** **** ** **>** **the above matrix. This matrix is just an example. ** ** **** ** **>** **And now I'm thinking of the**best graph type to represent ** ** **the ** ** **** ** **number of ** ** **** ** **>** **pos and neg predictions if the**model predicts 7 targets. ** ** **** ** **>** **Art Nestsiarenka ** ** **** ** **>** **email: [1][5][7][9]artem.nesterenko at gmail.com ** ** **** ** **>** **Cell: [6][8][10](919) 455-5055 ** ** **** ** **> ** ** **** ** **>** **On Tue, Oct 10, 2017 at 8:52 PM, Jeff Heard ** ** **** ** **>** **<[2][7][9][11]jefferson.r.heard at gmail.com> wrote: ** ** **** ** **> ** ** **** ** **>** ** **** **I'd suggest you look at Seaborn. If you're ** ** **dead-set on ** ** **** ** **visualizing ** ** **** ** **>** ** **data in ** ** **** ** **>** ** **** **javascript, it might well give you some ** ** **inspiration to ** ** **** ** **draw from, ** ** **** ** **>** ** **** **otherwise it's a great Python solution for data ** ** **** ** **>** ** **** **vis.**[1][3][8][10][12]https://seaborn.pydata.org/ . ** ** **I'm not 100% ** ** **** ** **sure what ** ** **** ** **>** ** **I'd ** ** **** ** **>** ** **** **recommend given that much information. Also, it ** ** **looks like ** ** **** ** **the ** ** **** ** **>** ** **mailer ** ** **** ** **>** ** **** **scrubbed the images. If you could host the ** ** **originals ** ** **** ** **somewhere ** ** **** ** **>** ** **(GitHub?), ** ** **** ** **>** ** **** **it'd help understand what you did better to give a ** ** **** ** **recommendation. ** ** **** ** **>** ** **** **Are your model outputs continuous or discrete? I ** ** **** ** **understand that ** ** **** ** **>** ** **you're ** ** **** ** **>** ** **** **looking at "positive/negative" values, but ** ** **** ** **>** ** **decisions/classifications and ** ** **** ** **>** ** **** **actual model output can be different. Plus the ** ** **choice of a ** ** **** ** **donut ** ** **** ** **>** ** **chart to ** ** **** ** **>** ** **** **represent boolean values doesn't make a lot of ** ** **sense to ** ** **** ** **me, so I ** ** **** ** **>** ** **thought ** ** **** ** **>** ** **** **that I maybe understood you wrong. ** ** **** ** **>** ** **** **** ** ** **** ** **>** ** **** **On Tue, Oct 10, 2017 at 3:53 PM, Art ** ** **** ** **>** ** **<[2][4][9][11][13]artem.nesterenko at gmail.com> ** ** **** ** **>** ** **** **wrote: ** ** **** ** **> ** ** **** ** **>** ** **** ** **** **Good afternoon! ** ** **** ** **>** ** **** ** **** **I'm reaching out to you guys for a ** ** **suggestion on ** ** **** ** **the data ** ** **** ** **>** ** **** ** **visualization. ** ** **** ** **>** ** **** ** **** **I'm wondering if anyone has an experience ** ** **or an ** ** **** ** **idea of ** ** **** ** **>** ** **visualizing ** ** **** ** **>** ** **** ** **the ** ** **** ** **>** ** **** ** **** **volume of multiple model prediction ** ** **targets. ** ** **** ** **>** ** **** ** **** **Here is an example of a model with just 2 ** ** **** ** **prediction ** ** **** ** **>** ** **targets I've ** ** **** ** **>** ** **** ** **recently ** ** **** ** **>** ** **** ** **** **been working on: ** ** **** ** **>** ** **** ** **** **1. This model provides 2 predictions: ** ** **target_1 and ** ** **** ** **>** ** **target_2.** ** ** **** ** **>** ** **** ** **** **2. I also know the actual result to ** ** **compare ** ** **** ** **against, so I ** ** **** ** **>** ** **could see ** ** **** ** **>** ** **** ** **the ** ** **** ** **>** ** **** ** **** **model accuracy.** ** ** **** ** **>** ** **** ** **** **3. I've built a confusion matrix to ** ** **calculate ** ** **** ** **true/false ** ** **** ** **>** ** **pos/neg ** ** **** ** **>** ** **** ** **(see ** ** **** ** **>** ** **** ** **** **below). So, there are 4 values. ** ** **** ** **>** ** **** ** **** **4. I chose donut**chart to visualize these ** ** **values ** ** **** ** **(see ** ** **** ** **>** ** **below). ** ** **** ** **>** ** **** ** **** **[1]Inline image 2** ** ** **** ** **> ** ** **** ** **>** ** **** ** **** **My question is what if the model provides ** ** **more ** ** **** ** **than 2 ** ** **** ** **>** ** **predictions? ** ** **** ** **>** ** **** ** **Our ** ** **** ** **>** ** **** ** **** **next model has 7 targets, which is 49 pos ** ** **and neg ** ** **** ** **values as ** ** **** ** **>** ** **far as ** ** **** ** **>** ** **** ** **I ** ** **** ** **>** ** **** ** **** **understand. And I think a**donut**graph is ** ** **not ** ** **** ** **going to ** ** **** ** **>** ** **work in ** ** **** ** **>** ** **** ** **this ** ** **** ** **>** ** **** ** **** **case.**Maybe a bar graph or something else ** ** **fits ** ** **** ** **better.** ** ** **** ** **> ** ** **** ** **>** ** **** ** **** **I'd appreciate any ideas or examples of ** ** **** ** **visualizations that ** ** **** ** **>** ** **easy to ** ** **** ** **>** ** **** ** **look ** ** **** ** **>** ** **** ** **** **at and understand. ** ** **** ** **>** ** **** ** **** **I'm using d3/c3.js for data visualization. ** ** **** ** **>** ** **** ** **** **Thank you! ** ** **** ** **>** ** **** ** **** **Art Nestsiarenka ** ** **** ** **>** ** **** ** **** **email: ** ** **[2][3][5][10][12][14]artem.nesterenko at gmail.com ** ** **** ** **> ** ** **** ** **>** ** **** ** **References ** ** **** ** **> ** ** **** ** **>** ** **** ** **** **Visible links ** ** **** ** **>** ** **** ** **** **2. ** ** **mailto:[4][6][11][13][15]artem.nesterenko at gmail.com ** ** **** ** **> ** ** **** ** **>** ** **** ** **_______________________________________________ ** ** **** ** **>** ** **** ** **TriZPUG mailing list ** ** **** ** **>** ** **** ** **[5][7][12][14][16]TriZPUG at python.org ** ** **** ** **>** ** **** ** ** ** **** ** ****[6][8][13][15][17]https://mail.python.org/mailman/listinfo/trizpug ** ** **** ** **>** ** **** ** **[7][9][14][16][18]http://tripython.org is the ** ** **Triangle Python ** ** **** ** **Users Group ** ** **** ** **> ** ** **** ** **>** ** **References ** ** **** ** **> ** ** **** ** **>** ** **** **Visible links ** ** **** ** **>** ** **** **1. 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[204]http://tripython.org/ _______________________________________________ TriZPUG mailing list [205]TriZPUG at python.org [206]https://mail.python.org/mailman/listinfo/trizpug [207]http://tripython.org is the Triangle Python Users Group References Visible links 1. mailto:artem.nesterenko at gmail.com 2. mailto:jdavep at gmail.com 3. mailto:artem.nesterenko at gmail.com 4. https://imgur.com/a/C7r8x 5. mailto:artem.nesterenko at gmail.com 6. file:///tmp/tel:%28919%29%20455-5055 7. mailto:jessebikman at gmail.com 8. mailto:artem.nesterenko at gmail.com 9. mailto:artem.nesterenko at gmail.com 10. file:///tmp/tel:%28919%29%20455-5055 11. mailto:jefferson.r.heard at gmail.com 12. https://seaborn.pydata.org/ 13. mailto:artem.nesterenko at gmail.com 14. mailto:artem.nesterenko at gmail.com 15. mailto:artem.nesterenko at gmail.com 16. mailto:TriZPUG at python.org 17. https://mail.python.org/mailman/listinfo/trizpug 18. http://tripython.org/ 19. https://seaborn.pydata.org/ 20. 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https://mail.python.org/mailman/listinfo/trizpug 173. http://tripython.org/ 174. mailto:TriZPUG at python.org 175. https://mail.python.org/mailman/listinfo/trizpug 176. http://tripython.org/ 177. mailto:artem.nesterenko at gmail.com 178. mailto:jefferson.r.heard at gmail.com 179. https://seaborn.pydata.org/ 180. mailto:artem.nesterenko at gmail.com 181. mailto:artem.nesterenko at gmail.com 182. mailto:artem.nesterenko at gmail.com 183. mailto:TriZPUG at python.org 184. https://mail.python.org/mailman/listinfo/trizpug 185. http://tripython.org/ 186. https://seaborn.pydata.org/ 187. mailto:artem.nesterenko at gmail.com 188. mailto:artem.nesterenko at gmail.com 189. mailto:artem.nesterenko at gmail.com 190. mailto:TriZPUG at python.org 191. https://mail.python.org/mailman/listinfo/trizpug 192. http://tripython.org/ 193. mailto:TriZPUG at python.org 194. https://mail.python.org/mailman/listinfo/trizpug 195. http://tripython.org/ 196. mailto:TriZPUG at python.org 197. https://mail.python.org/mailman/listinfo/trizpug 198. http://tripython.org/ 199. mailto:TriZPUG at python.org 200. https://mail.python.org/mailman/listinfo/trizpug 201. http://tripython.org/ 202. mailto:TriZPUG at python.org 203. https://mail.python.org/mailman/listinfo/trizpug 204. http://tripython.org/ 205. mailto:TriZPUG at python.org 206. https://mail.python.org/mailman/listinfo/trizpug 207. http://tripython.org/ From cbc at unc.edu Wed Oct 11 11:30:54 2017 From: cbc at unc.edu (Calloway, Chris) Date: Wed, 11 Oct 2017 15:30:54 +0000 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: On 10/11/17, 11:19 AM, "TriZPUG on behalf of Art" wrote: There are a lot of tools out there and I think D3 is not a requirement, it's just that our team wants to use this lib. This is where I point at: http://mpld3.github.io/ -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 From francois.dion at gmail.com Wed Oct 11 12:18:36 2017 From: francois.dion at gmail.com (Francois Dion) Date: Wed, 11 Oct 2017 12:18:36 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: Art (and list members interested in visualization), As Dave mentioned, donut charts work best for progress to goal. ie. a percentage. Like a dashboard guage. Or something where the 50% mark is important, say a win/loss indicator of the Carolina Hurricanes against visitor. Similarly, the ancestor of donut chart, the pie chart is best suited for parts of a whole when you have 2 or 3 elements at most. Beyond that, it is almost impossible to figure out the percentages and relative importance. Bar charts do much better when there are more than 2 or 3 values. A confusion matrix, in the simplest binary case, bins 4 possible outcome of a classifier. True positive (you are part of the class and I said so), false positive (you are not part of the class but I said you were), true negative (you are not part of the class and I said so) and false negative (you are part of the class but I said you were not). The expectation of representation of a confusion matrix, is unsurprisingly, as a matrix. The standard way to represent this is in a table format, a matrix (of actual against predicted), hence the name. This has been the case since at least the 1950s (without doing an exhaustive search, just from memory). For example, I just pulled Mike James' "Classification Algorithms" from 1985, page 83, and there it is. He also sums each row and column. But, sure, the plain text table is a bit drab if you are looking for maximum impact. So, that's where I was suggesting a heatmap. Or you can use the python package yellow brick. Here's an example using seaborn's heatmap (and making sure I label the axis, else it is useless). I used cmap="Greens": https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential I've had no problem using this with technical and non technical audiences. Shown CMs like the above (and a variety of other graphical and semigraphical displays) to business folks who then proceeded to green light further phases of fairly large data science projects. Once they've seen one and got it you never have to explain it again. Without the heatmap colors, it was super challenging to have people "get it". Also, you might be interested in this list of books on visualization (from my "ex-libris" series on linkedin): https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/ In particular, Stephen Few's "Show Me the Numbers : Designing Tables and Graphs to Enlighten" should definitely be on everyone's reading list, along with Cairo's "The Functional Art", will get you started, if you can't commit to reading 1 viz book per week for the next 2 years :) Thanks, Francois On Wed, Oct 11, 2017 at 8:52 AM, Art wrote: > Donut graph: > [1]https://imgur.com/a/C7r8x > You should be able to see it now. > Art Nestsiarenka > -- about.me/francois.dion - www.pyptug.org - www.3DFutureTech.info - @f_dion -------------- next part -------------- Art (and list members interested in visualization), As Dave mentioned, donut charts work best for progress to goal. ie. a percentage. Like a dashboard guage. Or something where the 50% mark is important, say a win/loss indicator of the Carolina Hurricanes against visitor. Similarly, the ancestor of donut chart, the pie chart is best suited for parts of a whole when you have 2 or 3 elements at most. Beyond that, it is almost impossible to figure out the percentages and relative importance. Bar charts do much better when there are more than 2 or 3 values. A confusion matrix, in the simplest binary case, bins 4 possible outcome of a classifier. True positive (you are part of the class and I said so), false positive (you are not part of the class but I said you were), true negative (you are not part of the class and I said so) and false negative (you are part of the class but I said you were not). The expectation of representation of a confusion matrix, is unsurprisingly, as a matrix. The standard way to represent this is in a table format, a matrix (of actual against predicted), hence the name. This has been the case since at least the 1950s (without doing an exhaustive search, just from memory). For example, I just pulled Mike James' "Classification Algorithms" from 1985, page 83, and there it is. He also sums each row and column. But, sure, the plain text table is a bit drab if you are looking for maximum impact. So, that's where I was suggesting a heatmap. Or you can use the python package yellow brick. Here's an example using seaborn's heatmap (and making sure I label the axis, else it is useless). I used cmap="Greens": [1]https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential I've had no problem using this with technical and non technical audiences. Shown CMs like the above (and a variety of other graphical and semigraphical displays) to business folks who then proceeded to green light further phases of fairly large data science projects. Once they've seen one and got it you never have to explain it again. Without the heatmap colors, it was super challenging to have people "get it". Also, you might be interested in this list of books on visualization (from my "ex-libris" series on linkedin): [2]https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/ In particular, Stephen Few's "Show Me the Numbers : Designing Tables and Graphs to Enlighten" should definitely be on everyone's reading list, along with Cairo's "The Functional Art", will get you started, if you can't commit to reading 1 viz book per week for the next 2 years :) Thanks, Francois On Wed, Oct 11, 2017 at 8:52 AM, Art <[3]artem.nesterenko at gmail.com> wrote: ** **Donut graph: ** **[1][4]https://imgur.com/a/C7r8x ** **You should be able to see it now. ** **Art Nestsiarenka -- [5]about.me/francois.dion - [6]www.pyptug.org - [7]www.3DFutureTech.info - [8]@f_dion References Visible links 1. https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential 2. https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/ 3. mailto:artem.nesterenko at gmail.com 4. https://imgur.com/a/C7r8x 5. http://about.me/francois.dion 6. http://www.pyptug.org/ 7. http://www.3dfuturetech.info/ 8. http://twitter.com/f_dion From artem.nesterenko at gmail.com Wed Oct 11 13:19:25 2017 From: artem.nesterenko at gmail.com (Art) Date: Wed, 11 Oct 2017 13:19:25 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: Francois, Thank you for your response! I like the idea of building a heatmap or using yellow brick rather than going with the bar graph. Will see if I can make it happen... I'd like to say a big thank you to everyone for your suggestions and links! Now I have plenty of materials to work with. It was a good idea to email this group:) Best, Art. Art Nestsiarenka email: artem.nesterenko at gmail.com Cell: (919) 455-5055 On Wed, Oct 11, 2017 at 12:18 PM, Francois Dion wrote: > Art (and list members interested in visualization), > > As Dave mentioned, donut charts work best for progress to goal. ie. a > percentage. Like a dashboard guage. Or something where the 50% mark is > important, say a win/loss indicator of the Carolina Hurricanes against > visitor. Similarly, the ancestor of donut chart, the pie chart is best > suited for parts of a whole when you have 2 or 3 elements at most. > Beyond > that, it is almost impossible to figure out the percentages and relative > importance. Bar charts do much better when there are more than 2 or 3 > values. > > A confusion matrix, in the simplest binary case, bins 4 possible outcome > of a classifier. True positive (you are part of the class and I said > so), > false positive (you are not part of the class but I said you were), true > negative (you are not part of the class and I said so) and false > negative > (you are part of the class but I said you were not). The expectation of > representation of a confusion matrix, is unsurprisingly, as a matrix. > The > standard way to represent this is in a table format, a matrix (of actual > against predicted), hence the name. This has been the case since at > least > the 1950s (without doing an exhaustive search, just from memory). For > example, I just pulled Mike James' "Classification Algorithms" from > 1985, > page 83, and there it is. He also sums each row and column. > > But, sure, the plain text table is a bit drab if you are looking for > maximum impact. So, that's where I was suggesting a heatmap. Or you can > use the python package yellow brick. > > Here's an example using seaborn's heatmap (and making sure I label the > axis, else it is useless). I used cmap="Greens": > > [1]https://datasciencefrancois.tumblr.com/post/166291770900/ > confusion-matrix-with-a-single-color-sequential > > I've had no problem using this with technical and non technical > audiences. > Shown CMs like the above (and a variety of other graphical and > semigraphical displays) to business folks who then proceeded to green > light further phases of fairly large data science projects. Once they've > seen one and got it you never have to explain it again. Without the > heatmap colors, it was super challenging to have people "get it". > Also, you might be interested in this list of books on visualization > (from > my "ex-libris" series on linkedin): > > [2]https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v- > visualization-francois-dion/ > > In particular, Stephen Few's "Show Me the Numbers : Designing Tables and > Graphs to Enlighten" should definitely be on everyone's reading list, > along with Cairo's "The Functional Art", will get you started, if you > can't commit to reading 1 viz book per week for the next 2 years :) > > Thanks, > Francois > On Wed, Oct 11, 2017 at 8:52 AM, Art <[3]artem.nesterenko at gmail.com> > wrote: > > ** **Donut graph: > ** **[1][4]https://imgur.com/a/C7r8x > ** **You should be able to see it now. > ** **Art Nestsiarenka > > -- > [5]about.me/francois.dion - [6]www.pyptug.org - [7] > www.3DFutureTech.info - > [8]@f_dion > > References > > Visible links > 1. https://datasciencefrancois.tumblr.com/post/166291770900/ > confusion-matrix-with-a-single-color-sequential > 2. https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v- > visualization-francois-dion/ > 3. mailto:artem.nesterenko at gmail.com > 4. https://imgur.com/a/C7r8x > 5. http://about.me/francois.dion > 6. http://www.pyptug.org/ > 7. http://www.3dfuturetech.info/ > 8. http://twitter.com/f_dion > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > > -------------- next part -------------- Francois, Thank you for your response! I like the idea of building a heatmap or using yellow brick rather than going with the bar graph. Will see if I can make it happen... I'd like to say a big thank you to everyone for your suggestions and links! Now I have plenty of materials to work with.**** It was a good idea to email this group:) Best, Art. Art Nestsiarenka email: [1]artem.nesterenko at gmail.com Cell: (919) 455-5055 On Wed, Oct 11, 2017 at 12:18 PM, Francois Dion <[2]francois.dion at gmail.com> wrote: ** **Art (and list members interested in visualization), ** **As Dave mentioned, donut charts work best for progress to goal. ie. a ** **percentage. Like a dashboard guage. Or something where the 50% mark is ** **important, say a win/loss indicator of the Carolina Hurricanes against ** **visitor. Similarly, the ancestor of donut chart, the pie chart is best ** **suited for parts of a whole when you have 2 or 3 elements at most. Beyond ** **that, it is almost impossible to figure out the percentages and relative ** **importance. Bar charts do much better when there are more than 2 or 3 ** **values. ** **A confusion matrix, in the simplest binary case, bins 4 possible outcome ** **of a classifier. True positive (you are part of the class and I said so), ** **false positive (you are not part of the class but I said you were), true ** **negative (you are not part of the class and I said so) and false negative ** **(you are part of the class but I said you were not). The expectation of ** **representation of a confusion matrix, is unsurprisingly, as a matrix. The ** **standard way to represent this is in a table format, a matrix (of actual ** **against predicted), hence the name. This has been the case since at least ** **the 1950s (without doing an exhaustive search, just from memory). For ** **example, I just pulled Mike James' "Classification Algorithms" from 1985, ** **page 83, and there it is. He also sums each row and column. ** **But, sure, the plain text table is a bit drab if you are looking for ** **maximum impact. So, that's where I was suggesting a heatmap. Or you can ** **use the python package yellow brick. ** **Here's an example using seaborn's heatmap (and making sure I label the ** **axis, else it is useless). I used cmap="Greens": ** **[1][3]https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential ** **I've had no problem using this with technical and non technical audiences. ** **Shown CMs like the above (and a variety of other graphical and ** **semigraphical displays) to business folks who then proceeded to green ** **light further phases of fairly large data science projects. Once they've ** **seen one and got it you never have to explain it again. Without the ** **heatmap colors, it was super challenging to have people "get it". ** **Also, you might be interested in this list of books on visualization (from ** **my "ex-libris" series on linkedin): ** **[2][4]https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/ ** **In particular, Stephen Few's "Show Me the Numbers : Designing Tables and ** **Graphs to Enlighten" should definitely be on everyone's reading list, ** **along with Cairo's "The Functional Art", will get you started, if you ** **can't commit to reading 1 viz book per week for the next 2 years :) ** **Thanks, ** **Francois ** **On Wed, Oct 11, 2017 at 8:52 AM, Art <[3][5]artem.nesterenko at gmail.com> ** **wrote: ** ** **** **Donut graph: ** ** **** **[1][4][6]https://imgur.com/a/C7r8x ** ** **** **You should be able to see it now. ** ** **** **Art Nestsiarenka ** **-- ** **[5][7]about.me/francois.dion - [6][8]www.pyptug.org - [7][9]www.3DFutureTech.info - ** **[8]@f_dion References ** **Visible links ** **1. [10]https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential ** **2. [11]https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/ ** **3. mailto:[12]artem.nesterenko at gmail.com ** **4. [13]https://imgur.com/a/C7r8x ** **5. [14]http://about.me/francois.dion ** **6. [15]http://www.pyptug.org/ ** **7. [16]http://www.3dfuturetech.info/ ** **8. [17]http://twitter.com/f_dion _______________________________________________ TriZPUG mailing list [18]TriZPUG at python.org [19]https://mail.python.org/mailman/listinfo/trizpug [20]http://tripython.org is the Triangle Python Users Group References Visible links 1. mailto:artem.nesterenko at gmail.com 2. mailto:francois.dion at gmail.com 3. https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential 4. https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/ 5. mailto:artem.nesterenko at gmail.com 6. https://imgur.com/a/C7r8x 7. http://about.me/francois.dion 8. http://www.pyptug.org/ 9. http://www.3dfuturetech.info/ 10. https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential 11. https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/ 12. mailto:artem.nesterenko at gmail.com 13. https://imgur.com/a/C7r8x 14. http://about.me/francois.dion 15. http://www.pyptug.org/ 16. http://www.3dfuturetech.info/ 17. http://twitter.com/f_dion 18. mailto:TriZPUG at python.org 19. https://mail.python.org/mailman/listinfo/trizpug 20. http://tripython.org/ From cbc at unc.edu Wed Oct 11 11:13:47 2017 From: cbc at unc.edu (Calloway, Chris) Date: Wed, 11 Oct 2017 15:13:47 +0000 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: On 10/11/17, 1:35 AM, "TriZPUG on behalf of Jesse Bikman" wrote: Image attachment isn?t coming through. Maybe host on imgur or similar site and pass the link. Correct. Attachments and HTML on posts to this list are stripped per Mailman best practice. Host images and other binaries elsewhere per open source etiquette. Thank you. Good thread, BTW. And great to hear from Dave Powell. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 From charlotte.ann.mays at gmail.com Wed Oct 11 18:35:11 2017 From: charlotte.ann.mays at gmail.com (Charlotte Mays) Date: Wed, 11 Oct 2017 18:35:11 -0400 Subject: [TriPython] October Presentation Message-ID: I've volunteered to give the October presentation to TriPython (on the 19th, not the usual week) Title: Why You Need An API And How To Build It Using Django Rest Framework Summary: This talk will discuss a few off-the-beaten-path applications of APIs, and make the case for writing them into your web applications. Since the combination of Django and DRF makes it so easy to get a simple API running, it becomes a very powerful, flexible, and expandable tool for a variety of uses. The only thing these applications may have in common is their need to share data with other apps. Some prior knowledge of Django would be useful but not required. -------------- next part -------------- I've volunteered to give the October presentation to TriPython (on the 19th, not the usual week) Title: Why You Need An API And How To Build It Using Django Rest Framework Summary: This talk will discuss a few off-the-beaten-path applications of APIs, and make the case for writing them into your web applications. Since the combination of Django and DRF makes it so easy to get a simple API running, it becomes a very powerful, flexible, and expandable tool for a variety of uses. The only thing these applications may have in common is their need to share data with other apps. Some prior knowledge of Django would be useful but not required. From cbc at unc.edu Wed Oct 11 19:27:39 2017 From: cbc at unc.edu (Calloway, Chris) Date: Wed, 11 Oct 2017 23:27:39 +0000 Subject: [TriPython] TriPython October 2017 Meeting: Why You Need A REST API And How To Build It Message-ID: Our featured speaker meeting this month will take place next week on the third Thursday instead of the fourth Thursday. We are fortunate that Charlotte Mays volunteered to give her celebrated DjangoCon talk to us. http://tripython.org/Members/cbc/oct-17-mtg When: Thursday, October 19, 7-9pm Where: Renaissance Computing Institute (RENCI) Biltmore Conference Room 5th Floor 100 Europa Drive Suite 590 Chapel Hill What: Charlotte Mays will discuss a few off-the-beaten-path applications of APIs, and make the case for writing them into your web applications. Since the combination of Django and DRF makes it so easy to get a simple API running, it becomes a very powerful, flexible, and expandable tool for a variety of uses. The only thing these applications may have in common is their need to share data with other apps. Some prior knowledge of Django would be useful but not required. Charlotte is a developer of fine Django web applications at our sponsor Caktus Group. Extemporaneous "lightning talks" of 5-10 minute duration are also welcome and don't need to be pre-announced. Lightning talks are for you to "show and tell" something you've learned about Python recently, no matter how small. We all use Python, therefore, we are always learning something new about Python that we can tell others. Plenty of free parking is available in the RENCI parking deck. The meeting will be followed by our usual after-meeting at a nearby tavern for food and beverage. Come join us for a fun and informative evening. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------- next part -------------- Our featured speaker meeting this month will take place next week on the third Thursday instead of the fourth Thursday. We are fortunate that Charlotte Mays volunteered to give her celebrated DjangoCon talk to us. [1]http://tripython.org/Members/cbc/oct-17-mtg When: Thursday, October 19, 7-9pm Where: Renaissance Computing Institute (RENCI) Biltmore Conference Room 5th Floor 100 Europa Drive Suite 590 Chapel Hill What: Charlotte Mays will discuss a few off-the-beaten-path applications of APIs, and make the case for writing them into your web applications. Since the combination of Django and DRF makes it so easy to get a simple API running, it becomes a very powerful, flexible, and expandable tool for a variety of uses. The only thing these applications may have in common is their need to share data with other apps. Some prior knowledge of Django would be useful but not required. Charlotte is a developer of fine Django web applications at our sponsor Caktus Group. Extemporaneous "lightning talks" of 5-10 minute duration are also welcome and don't need to be pre-announced. Lightning talks are for you to "show and tell" something you've learned about Python recently, no matter how small. We all use Python, therefore, we are always learning something new about Python that we can tell others. Plenty of free parking is available in the RENCI parking deck. The meeting will be followed by our usual after-meeting at a nearby tavern for food and beverage. Come join us for a fun and informative evening. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 References Visible links 1. http://tripython.org/Members/cbc/oct-17-mtg From ncdave4life at gmail.com Thu Oct 12 14:06:36 2017 From: ncdave4life at gmail.com (David Burton) Date: Thu, 12 Oct 2017 14:06:36 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: Chris, would it be possible to change the mailman configuration so that it forwards messages to *everyone* on the list, instead of to everyone except the sender? The problem is that when mailman mungs a sender's emails the sender may have no idea that it has happened. So we make the same mistakes over and over. Regards, Dave On Wed, Oct 11, 2017 at 11:13 AM, Calloway, Chris wrote: > On 10/11/17, 1:35 AM, "TriZPUG on behalf of Jesse Bikman" > > wrote: > Image attachment isn?t coming through. Maybe host on imgur or similar site > and pass the link. > > Correct. Attachments and HTML on posts to this list are stripped per > Mailman best practice. Host images and other binaries elsewhere per open > source etiquette. Thank you. > *...[snip] * -------------- next part -------------- Chris, would it be possible to change the mailman configuration so that it forwards messages to everyone on the list, instead of to everyone except the sender? The problem is that when mailman mungs a sender's emails the sender may have no idea that it has happened. So we make the same mistakes over and over. Regards, Dave On Wed, Oct 11, 2017 at 11:13 AM, Calloway, Chris <[1]cbc at unc.edu> wrote: On 10/11/17, 1:35 AM, "TriZPUG on behalf of Jesse Bikman" wrote: Image attachment isn***t coming through. Maybe host on imgur or similar site and pass the link. Correct. Attachments and HTML on posts to this list are stripped per Mailman best practice. Host images and other binaries elsewhere per open source etiquette. Thank you. ...[snip]** References Visible links 1. mailto:cbc at unc.edu 2. mailto:unc.edu at python.org 3. mailto:jessebikman at gmail.com From cbc at unc.edu Thu Oct 12 15:15:18 2017 From: cbc at unc.edu (Calloway, Chris) Date: Thu, 12 Oct 2017 19:15:18 +0000 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> Message-ID: <71A58300-6786-4BD4-AE05-9F57B34A5562@unc.edu> Dave, I checked and the list settings are already for members to get copies of their own posts. I get copies of mine. I also checked your user profile and your it also is set to send you copies of your own posts. Anyone else not getting copies of their own posts from this list? -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 On 10/12/17, 2:06 PM, "TriZPUG on behalf of David Burton" wrote: Chris, would it be possible to change the mailman configuration so that it forwards messages to *everyone* on the list, instead of to everyone except the sender? The problem is that when mailman mungs a sender's emails the sender may have no idea that it has happened. So we make the same mistakes over and over. Regards, Dave On Wed, Oct 11, 2017 at 11:13 AM, Calloway, Chris wrote: > On 10/11/17, 1:35 AM, "TriZPUG on behalf of Jesse Bikman" > > wrote: > Image attachment isn?t coming through. Maybe host on imgur or similar site > and pass the link. > > Correct. Attachments and HTML on posts to this list are stripped per > Mailman best practice. Host images and other binaries elsewhere per open > source etiquette. Thank you. > *...[snip] * From jadevinson at gmail.com Thu Oct 12 17:04:35 2017 From: jadevinson at gmail.com (Jade Vinson) Date: Thu, 12 Oct 2017 17:04:35 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: <71A58300-6786-4BD4-AE05-9F57B34A5562@unc.edu> References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> <71A58300-6786-4BD4-AE05-9F57B34A5562@unc.edu> Message-ID: Dave and Chris, I have had issues in the past with gmail with other mailing lists where it may not display a message in my inbox if I was the sender. Jade On Thu, Oct 12, 2017 at 3:15 PM, Calloway, Chris wrote: > Dave, > > I checked and the list settings are already for members to get copies of > their own posts. I get copies of mine. > > I also checked your user profile and your it also is set to send you > copies of your own posts. > > Anyone else not getting copies of their own posts from this list? > > -- > Sincerely, > > Chris Calloway > Applications Analyst > University of North Carolina > Renaissance Computing Institute > (919) 599-3530 > > > On 10/12/17, 2:06 PM, "TriZPUG on behalf of David Burton" > > wrote: > > Chris, would it be possible to change the mailman configuration so > that it > forwards messages to *everyone* on the list, instead of to everyone > except > the sender? > > The problem is that when mailman mungs a sender's emails the sender may > have no idea that it has happened. So we make the same mistakes over > and > over. > > Regards, > Dave > > > > On Wed, Oct 11, 2017 at 11:13 AM, Calloway, Chris wrote: > > > On 10/11/17, 1:35 AM, "TriZPUG on behalf of Jesse Bikman" > > jessebikman at gmail.com> > > wrote: > > Image attachment isn?t coming through. Maybe host on imgur or > similar site > > and pass the link. > > > > Correct. Attachments and HTML on posts to this list are stripped per > > Mailman best practice. Host images and other binaries elsewhere per > open > > source etiquette. Thank you. > > > *...[snip] * > > > _______________________________________________ > TriZPUG mailing list > TriZPUG at python.org > https://mail.python.org/mailman/listinfo/trizpug > http://tripython.org is the Triangle Python Users Group > -- Jade Vinson 617-501-9261 jadevinson at gmail.com ---- -------------- next part -------------- Dave and Chris, I have had issues in the past with gmail with other mailing lists where it may not display a message in my inbox if I was the sender. Jade On Thu, Oct 12, 2017 at 3:15 PM, Calloway, Chris <[1]cbc at unc.edu> wrote: Dave, I checked and the list settings are already for members to get copies of their own posts. I get copies of mine. I also checked your user profile and your it also is set to send you copies of your own posts. Anyone else not getting copies of their own posts from this list? -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute [2](919) 599-3530 On 10/12/17, 2:06 PM, "TriZPUG on behalf of David Burton" wrote: ** ** Chris, would it be possible to change the mailman configuration so that it ** ** forwards messages to *everyone* on the list, instead of to everyone except ** ** the sender? ** ** The problem is that when mailman mungs a sender's emails the sender may ** ** have no idea that it has happened. So we make the same mistakes over and ** ** over. ** ** Regards, ** ** Dave ** ** On Wed, Oct 11, 2017 at 11:13 AM, Calloway, Chris <[5]cbc at unc.edu> wrote: ** ** > On 10/11/17, 1:35 AM, "TriZPUG on behalf of Jesse Bikman" ** ** > ** ** > wrote: ** ** > Image attachment isn***t coming through. Maybe host on imgur or similar site ** ** > and pass the link. ** ** > ** ** > Correct. Attachments and HTML on posts to this list are stripped per ** ** > Mailman best practice. Host images and other binaries elsewhere per open ** ** > source etiquette. Thank you. ** ** > ** ** *...[snip] * _______________________________________________ TriZPUG mailing list [8]TriZPUG at python.org [9]https://mail.python.org/mailman/listinfo/trizpug [10]http://tripython.org is the Triangle Python Users Group -- Jade Vinson 617-501-9261 [11]jadevinson at gmail.com ---- References Visible links 1. mailto:cbc at unc.edu 2. file:///tmp/tel:%28919%29%20599-3530 3. mailto:unc.edu at python.org 4. mailto:ncdave4life at gmail.com 5. mailto:cbc at unc.edu 6. mailto:unc.edu at python.org 7. mailto:jessebikman at gmail.com 8. mailto:TriZPUG at python.org 9. https://mail.python.org/mailman/listinfo/trizpug 10. http://tripython.org/ 11. mailto:jadevinson at gmail.com From ncdave4life at gmail.com Thu Oct 12 17:22:39 2017 From: ncdave4life at gmail.com (David Burton) Date: Thu, 12 Oct 2017 17:22:39 -0400 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> <71A58300-6786-4BD4-AE05-9F57B34A5562@unc.edu> Message-ID: Oooooh. And here it is: https://uisapp2.iu.edu/confluence-prd/pages/viewpage.action?pageId=176620087 Excerpt: *Gmail (and the gmail-based IU Umail system) has a rather annoying "feature" whereby it will not show you email you send yourself. This affects email you send via mailing lists, google groups, and email forwards...* *Technically, it will not show you messages you send when they arrive back at gmail with the same Message-ID. This is typically the case for mailing lists and forwards...* I don't suppose there's a mailman option to change the Message-ID of emails sent through the list, is there? Dave On Thu, Oct 12, 2017 at 5:04 PM, Jade Vinson wrote: > Dave and Chris, > I have had issues in the past with gmail with other mailing lists where > it > may not display a message in my inbox if I was the sender. > Jade > On Thu, Oct 12, 2017 at 3:15 PM, Calloway, Chris <[1]cbc at unc.edu> > wrote: > > Dave, > > I checked and the list settings are already for members to get copies > of > their own posts. I get copies of mine. > > I also checked your user profile and your it also is set to send you > copies of your own posts. > > Anyone else not getting copies of their own posts from this list? > > -- > Sincerely, > > Chris Calloway > Applications Analyst > University of North Carolina > Renaissance Computing Institute > [2](919) 599-3530 > > On 10/12/17, 2:06 PM, "TriZPUG on behalf of David Burton" > [4]ncdave4life at gmail.com> wrote: > > ** ** Chris, would it be possible to change the mailman configuration > so > that it > ** ** forwards messages to *everyone* on the list, instead of to > everyone except > ** ** the sender? > > ** ** The problem is that when mailman mungs a sender's emails the > sender may > ** ** have no idea that it has happened. So we make the same mistakes > over and > ** ** over. > > ** ** Regards, > ** ** Dave > *...[snip] * -------------- next part -------------- Oooooh. And here it is: [1]https://uisapp2.iu.edu/confluence-prd/pages/viewpage.action?pageId=176620087 Excerpt: ** Gmail (and the gmail-based IU Umail system) has a rather annoying "feature" whereby it will not show you email you send yourself. This affects email you send via mailing lists, google groups, and email forwards... Technically, it will not show you messages you send when they arrive back at gmail with the same Message-ID. This is typically the case for mailing lists and forwards... I don't suppose there's a mailman option to change the Message-ID of emails sent through the list, is there? Dave On Thu, Oct 12, 2017 at 5:04 PM, Jade Vinson <[2]jadevinson at gmail.com> wrote: ** **Dave and Chris, ** **I have had issues in the past with gmail with other mailing lists where it ** **may not display a message in my inbox if I was the sender. ** **Jade ** **On Thu, Oct 12, 2017 at 3:15 PM, Calloway, Chris <[1][3]cbc at unc.edu> wrote: ** ** **Dave, ** ** **I checked and the list settings are already for members to get copies of ** ** **their own posts. I get copies of mine. ** ** **I also checked your user profile and your it also is set to send you ** ** **copies of your own posts. ** ** **Anyone else not getting copies of their own posts from this list? ** ** **-- ** ** **Sincerely, ** ** **Chris Calloway ** ** **Applications Analyst ** ** **University of North Carolina ** ** **Renaissance Computing Institute ** ** **[2][4](919) 599-3530 ** ** **On 10/12/17, 2:06 PM, "TriZPUG on behalf of David Burton" ** ** ** wrote: ** ** **** ** Chris, would it be possible to change the mailman configuration so ** ** **that it ** ** **** ** forwards messages to *everyone* on the list, instead of to ** ** **everyone except ** ** **** ** the sender? ** ** **** ** The problem is that when mailman mungs a sender's emails the ** ** **sender may ** ** **** ** have no idea that it has happened. So we make the same mistakes ** ** **over and ** ** **** ** over. ** ** **** ** Regards, ** ** **** ** Dave ...[snip]** References Visible links 1. https://uisapp2.iu.edu/confluence-prd/pages/viewpage.action?pageId=176620087 2. mailto:jadevinson at gmail.com 3. mailto:cbc at unc.edu 4. file:///tmp/tel:%28919%29%20599-3530 5. mailto:unc.edu at python.org 6. mailto:ncdave4life at gmail.com From cbc at unc.edu Fri Oct 13 10:52:27 2017 From: cbc at unc.edu (Calloway, Chris) Date: Fri, 13 Oct 2017 14:52:27 +0000 Subject: [TriPython] Prediction Model. Data Visualization. In-Reply-To: References: <26C90273-A669-4C4C-8480-3E84ADCE8BD2@gmail.com> <71A58300-6786-4BD4-AE05-9F57B34A5562@unc.edu> Message-ID: <5403D752-DE95-4F12-9882-600546F7B30C@unc.edu> On 10/12/17, 5:22 PM, "TriZPUG on behalf of David Burton" wrote: I don't suppose there's a mailman option to change the Message-ID of emails sent through the list, is there? Not that I can see. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 From cbc at unc.edu Mon Oct 16 13:05:32 2017 From: cbc at unc.edu (Calloway, Chris) Date: Mon, 16 Oct 2017 17:05:32 +0000 Subject: [TriPython] Reminder: Durham Project Night Message-ID: Another fun project night at Caktus tonight: http://tripython.org/Members/markdlavin/oct-17-dpn When: Monday, October 16, 6-9pm Where: Caktus Group Tech Space, 108 Morris St., Durham What: Have a project you want to show off, share, seek help with, or just get some work done surrounded by like-minded Python lovers? Join us for our monthly project night and do just that! Don't have something to work on? Just need some help with Python? Show up and enjoy the energy, sprint on an open source project, find something interesting to contribute to or be inspired! The setting is informal and there is no schedule, so don't worry if you show up past the start time. Whether you are a Python newbie needing help or have an open source project you want to share, come hang out and hack. Park in the municipal deck on the other side of the Arts Council across W. Morgan St. The entrance to the Caktus Tech Space is on Morris St. Bring your laptop. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------- next part -------------- Another fun project night at Caktus tonight: http://tripython.org/Members/markdlavin/oct-17-dpn When: Monday, October 16, 6-9pm Where: Caktus Group Tech Space, 108 Morris St., Durham What: Have a project you want to show off, share, seek help with, or just get some work done surrounded by like-minded Python lovers? Join us for our monthly project night and do just that! Don't have something to work on? Just need some help with Python? Show up and enjoy the energy, sprint on an open source project, find something interesting to contribute to or be inspired! The setting is informal and there is no schedule, so don't worry if you show up past the start time. Whether you are a Python newbie needing help or have an open source project you want to share, come hang out and hack. Park in the municipal deck on the other side of the Arts Council across W. Morgan St. The entrance to the Caktus Tech Space is on Morris St. Bring your laptop. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 From cbc at unc.edu Thu Oct 19 15:06:58 2017 From: cbc at unc.edu (Calloway, Chris) Date: Thu, 19 Oct 2017 19:06:58 +0000 Subject: [TriPython] Reminder: TriPython October 2017 Meeting: Why You Need A REST API And How To Build It Message-ID: <32ECA4F8-897F-4DCD-83AF-6281C634B846@unc.edu> Please come out tonight to hear Charlotte talk to us about REST APIs in Django. We are going to try something new for the after-meeting tonight. We?re going to try on a new place called bartaco. It has $2.50 - $3/50 tacos, rice bowls, and tapas. It?s open until 2am and is very lively. It has an outdoor patio. It?s right next door to Silverspot Cinema: http://bartaco.com/ -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 On 10/11/17, 7:27 PM, "TriZPUG on behalf of Calloway, Chris" wrote: Our featured speaker meeting this month will take place next week on the third Thursday instead of the fourth Thursday. We are fortunate that Charlotte Mays volunteered to give her celebrated DjangoCon talk to us. http://tripython.org/Members/cbc/oct-17-mtg When: Thursday, October 19, 7-9pm Where: Renaissance Computing Institute (RENCI) Biltmore Conference Room 5th Floor 100 Europa Drive Suite 590 Chapel Hill What: Charlotte Mays will discuss a few off-the-beaten-path applications of APIs, and make the case for writing them into your web applications. Since the combination of Django and DRF makes it so easy to get a simple API running, it becomes a very powerful, flexible, and expandable tool for a variety of uses. The only thing these applications may have in common is their need to share data with other apps. Some prior knowledge of Django would be useful but not required. Charlotte is a developer of fine Django web applications at our sponsor Caktus Group. Extemporaneous "lightning talks" of 5-10 minute duration are also welcome and don't need to be pre-announced. Lightning talks are for you to "show and tell" something you've learned about Python recently, no matter how small. We all use Python, therefore, we are always learning something new about Python that we can tell others. Plenty of free parking is available in the RENCI parking deck. The meeting will be followed by our usual after-meeting at a nearby tavern for food and beverage. Come join us for a fun and informative evening. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 From cbc at unc.edu Fri Oct 20 14:17:00 2017 From: cbc at unc.edu (Calloway, Chris) Date: Fri, 20 Oct 2017 18:17:00 +0000 Subject: [TriPython] Last evening's presentation Message-ID: Thanks to everyone who came out last night. Bartaco turned out to be a winning location for the after-meeting as well. Thank you Charlotte for one again presenting. Last evening?s presentation was excellent. I want to pass along the links to your presentation: http://cakt.us/DjangoCon2017talk (slides) https://www.youtube.com/watch?v=-6tR5TffP0w (video-recording from DjangoCon 2017) Also, so many good lightning talks last night. Thanks to everyone who stepped up. If you gave a lightning talk last night with links, please post them on this list. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------- next part -------------- Thanks to everyone who came out last night. Bartaco turned out to be a winning location for the after-meeting as well. Thank you Charlotte for one again presenting. Last evening's presentation was excellent. I want to pass along the links to your presentation: [1]http://cakt.us/DjangoCon2017talk (slides) [2]https://www.youtube.com/watch?v=-6tR5TffP0w (video-recording from DjangoCon 2017) Also, so many good lightning talks last night. Thanks to everyone who stepped up. If you gave a lightning talk last night with links, please post them on this list. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 References Visible links 1. http://cakt.us/DjangoCon2017talk 2. https://www.youtube.com/watch?v=-6tR5TffP0w From cbc at unc.edu Thu Oct 26 16:22:15 2017 From: cbc at unc.edu (Calloway, Chris) Date: Thu, 26 Oct 2017 20:22:15 +0000 Subject: [TriPython] Raleigh Project Night Resumes Message-ID: <9F1AAEFF-6CE9-498D-84D2-A2E84B8DDAC6@unc.edu> I?m very happy to report to you that Raleigh Project Night resumes in November at WebAssign: http://tripython.org/Members/sgambino/nov-17-rpn/ When: Tuesday, November 7, 6-9pm Where: WebAssign NCSU Centennial Campus 1791 Varsity Drive Suite 200 Raleigh What: Raleigh Project Night meets on first Tuesdays. Have a project you want to show off, share, seek help with, or just get some work done surrounded by like-minded Python lovers? Join us for our monthly project night and do just that! Don't have something to work on? Just need some help with Python? Show up and enjoy the energy, sprint on an open source project, find something interesting to contribute to or be inspired by! The setting is informal and there is no schedule, so don't worry if you show up past the start time. Whether you are a Python newbie needing help or have an open source project you want to share, come hang out and hack. Plenty of free after-hours parking is available in the upper level of the deck behind WebAssign (turn through the median just before the intersection of Varsity and Main Campus Drives). If the door is locked, call the number posted on the door. Bring your laptop. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------- next part -------------- I'm very happy to report to you that Raleigh Project Night resumes in November at WebAssign: [1]http://tripython.org/Members/sgambino/nov-17-rpn/ When: Tuesday, November 7, 6-9pm Where: WebAssign NCSU Centennial Campus 1791 Varsity Drive Suite 200 Raleigh What: Raleigh Project Night meets on first Tuesdays. Have a project you want to show off, share, seek help with, or just get some work done surrounded by like-minded Python lovers? Join us for our monthly project night and do just that! Don't have something to work on? Just need some help with Python? Show up and enjoy the energy, sprint on an open source project, find something interesting to contribute to or be inspired by! The setting is informal and there is no schedule, so don't worry if you show up past the start time. Whether you are a Python newbie needing help or have an open source project you want to share, come hang out and hack. Plenty of free after-hours parking is available in the upper level of the deck behind WebAssign (turn through the median just before the intersection of Varsity and Main Campus Drives). If the door is locked, call the number posted on the door. Bring your laptop. -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 References Visible links 1. http://tripython.org/Members/sgambino/nov-17-rpn/ From cbc at unc.edu Fri Oct 27 16:55:30 2017 From: cbc at unc.edu (Calloway, Chris) Date: Fri, 27 Oct 2017 20:55:30 +0000 Subject: [TriPython] November Meeting Needs a Speaker Message-ID: I would be very pleased to hear from anyone who would like to be our featured speaker at out November meeting at WebAssign: http://tripython.org/Members/sgambino/nov-17-mtg/ -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------- next part -------------- I would be very pleased to hear from anyone who would like to be our featured speaker at out November meeting at WebAssign: http://tripython.org/Members/sgambino/nov-17-mtg/ -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 From omaciel at ogmaciel.com Fri Oct 27 17:14:36 2017 From: omaciel at ogmaciel.com (Og Maciel) Date: Fri, 27 Oct 2017 17:14:36 -0400 Subject: [TriPython] November Meeting Needs a Speaker In-Reply-To: References: Message-ID: I could give a talk about web ui automation with python + selenium. Sent from my phone On Oct 27, 2017, 17:11, at 17:11, "Calloway, Chris" wrote: >I would be very pleased to hear from anyone who would like to be our >featured speaker at out November meeting at WebAssign: > >http://tripython.org/Members/sgambino/nov-17-mtg/ > >-- >Sincerely, > >Chris Calloway >Applications Analyst >University of North Carolina >Renaissance Computing Institute >(919) 599-3530 > > > >------------------------------------------------------------------------ > >_______________________________________________ >TriZPUG mailing list >TriZPUG at python.org >https://mail.python.org/mailman/listinfo/trizpug >http://tripython.org is the Triangle Python Users Group -------------- next part -------------- I could give a talk about web ui automation with python + selenium. Sent from my phone On Oct 27, 2017, at 17:11, "Calloway, Chris" <[1]cbc at unc.edu> wrote: I would be very pleased to hear from anyone who would like to be our featured speaker at out November meeting at WebAssign: [2]http://tripython.org/Members/sgambino/nov-17-mtg/ -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------------------------------------------------------------------- TriZPUG mailing list TriZPUG at python.org [3]https://mail.python.org/mailman/listinfo/trizpug [4]http://tripython.org is the Triangle Python Users Group References Visible links 1. mailto:cbc at unc.edu 2. http://tripython.org/Members/sgambino/nov-17-mtg 3. https://mail.python.org/mailman/listinfo/trizpug 4. http://tripython.org/ From cbc at unc.edu Mon Oct 30 10:45:38 2017 From: cbc at unc.edu (Calloway, Chris) Date: Mon, 30 Oct 2017 14:45:38 +0000 Subject: [TriPython] November Meeting Needs a Speaker Message-ID: Og, that would be a very useful talk. Would you please send a talk title, brief description, and brief bio for the announcement? Yet another TriPythoneer stepping up to give more than one talk in the last year. Thank you! -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 On 10/27/17, 5:14 PM, "TriZPUG on behalf of Og Maciel" wrote: I could give a talk about web ui automation with python + selenium. Sent from my phone On Oct 27, 2017, 17:11, at 17:11, "Calloway, Chris" wrote: >I would be very pleased to hear from anyone who would like to be our >featured speaker at out November meeting at WebAssign: > >http://tripython.org/Members/sgambino/nov-17-mtg/ > >-- >Sincerely, > >Chris Calloway >Applications Analyst >University of North Carolina >Renaissance Computing Institute >(919) 599-3530 > > > >------------------------------------------------------------------------ > >_______________________________________________ >TriZPUG mailing list >TriZPUG at python.org >https://mail.python.org/mailman/listinfo/trizpug >http://tripython.org is the Triangle Python Users Group From omaciel at ogmaciel.com Mon Oct 30 12:20:04 2017 From: omaciel at ogmaciel.com (Og Maciel) Date: Mon, 30 Oct 2017 12:20:04 -0400 Subject: [TriPython] November Meeting Needs a Speaker In-Reply-To: References: Message-ID: <5FBBDE05-3749-45CE-B390-8A97E9AC9C9D@ogmaciel.com> Title: Web UI Automation with Selenium for Beginners Abstract: In this talk I will cover web ui automation using selenium with a focus on the Python programming language. Attendants will learn how to easily gather web ui information, record their actions and play them back via Selenium IDE, then learn how to write Python code to perform the same actions and finally how to use your automation with py.test. Other topic that will be discussed, time allowing, will be how to use SauceLabs to execute automated tests on multiple types of operating systems and web browser combinations. Bio: Og is a Senior Manager of Quality Engineering for the Red Hat Satellite team. He has spent the last 5+ years building a team of black belt quality engineers responsible for the automation of complex systems and delivering quality products through the use of continuous delivery of processes. He is also a podcaster, a dad, and an avid reader. -- Og Maciel - GPG Keys: CD03D583 omaciel at ogmaciel.com https://omaciel.github.io From cbc at unc.edu Mon Oct 30 14:58:09 2017 From: cbc at unc.edu (Calloway, Chris) Date: Mon, 30 Oct 2017 18:58:09 +0000 Subject: [TriPython] TriPython November 2017 Meeting: Web UI Automation with Selenium for Beginners Message-ID: http://tripython.org/Members/sgambino/nov-17-mtg When: Wednesday, November 16, 7-9pm Where: WebAssign NCSU Centennial Campus 1791 Varsity Drive Suite 200 Raleigh What: This talk will cover web user interface automation using Selenium with a focus on the Python programming language. Attendants will learn how to easily gather web UI information, record their actions and play them back via Selenium IDE, then learn how to write Python code to perform the same actions and finally how to use your automation with py.test. Another topic that will be discussed, time allowing, will be how to use SauceLabs to execute automated tests on multiple types of operating systems and web browser combinations. Og Maciel presents. Og is a Senior Manager of Quality Engineering for the Red Hat Satellite team. He has spent the last 5+ years building a team of black belt quality engineers responsible for the automation of complex systems and delivering quality products through the use of continuous delivery of processes. He is also a podcaster, a dad, and an avid reader. The November meeting date is shifted from the normal fourth to the third Thursday in order to work around the holidays. Extemporaneous "lightning talks" of 5-10 minute duration are also welcome and don't need to be pre-announced. Plenty of free after hours parking is available in the upper level of the deck behind WebAssign (turn through the median just before the intersection of Varsity and Main Campus Drives). If the door is locked, call the number posted on the door. An after-meeting location for food and beverage will be decided at the meeting (usually BaDa Wings at Mission Valley). -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 -------------- next part -------------- [1]http://tripython.org/Members/sgambino/nov-17-mtg When: Wednesday, November 16, 7-9pm Where: WebAssign NCSU Centennial Campus 1791 Varsity Drive Suite 200 Raleigh What: This talk will cover web user interface automation using Selenium with a focus on the Python programming language. Attendants will learn how to easily gather web UI information, record their actions and play them back via Selenium IDE, then learn how to write Python code to perform the same actions and finally how to use your automation with py.test. Another topic that will be discussed, time allowing, will be how to use SauceLabs to execute automated tests on multiple types of operating systems and web browser combinations. Og Maciel presents. Og is a Senior Manager of Quality Engineering for the Red Hat Satellite team. He has spent the last 5+ years building a team of black belt quality engineers responsible for the automation of complex systems and delivering quality products through the use of continuous delivery of processes. He is also a podcaster, a dad, and an avid reader. The November meeting date is shifted from the normal fourth to the third Thursday in order to work around the holidays. Extemporaneous "lightning talks" of 5-10 minute duration are also welcome and don't need to be pre-announced. Plenty of free after hours parking is available in the upper level of the deck behind WebAssign (turn through the median just before the intersection of Varsity and Main Campus Drives). If the door is locked, call the number posted on the door. An after-meeting location for food and beverage will be decided at the meeting (usually BaDa Wings at Mission Valley). -- Sincerely, Chris Calloway Applications Analyst University of North Carolina Renaissance Computing Institute (919) 599-3530 References Visible links 1. http://tripython.org/Members/sgambino/nov-17-mtg From phillip at foospidy.com Mon Oct 30 15:44:17 2017 From: phillip at foospidy.com (Px Mx) Date: Mon, 30 Oct 2017 15:44:17 -0400 Subject: [TriPython] Python Tutor Message-ID: <5BFE7C27-556A-4A82-AA5F-B99FF884A996@foospidy.com> Hello, I?m looking for a Python tutor! Specifically with the Python Twisted framework. If there is anyone on this list or that you know in the area that has Python Twisted knowledge/experience and would be interested in tutoring, please let me know. Thanks! ? Px Mx https://keybase.io/foospidy/key.asc -------------- next part -------------- Hello, I***m looking for a Python tutor! Specifically with the Python Twisted framework. If there is anyone on this list or that you know in the area that has Python Twisted knowledge/experience and would be interested in tutoring, please let me know. Thanks! *** Px Mx [1]https://keybase.io/foospidy/key.asc References Visible links 1. https://keybase.io/foospidy/key.asc From devin at nacredata.com Mon Oct 30 14:05:27 2017 From: devin at nacredata.com (Devin Ceartas) Date: Mon, 30 Oct 2017 14:05:27 -0400 Subject: [TriPython] (unpaid) Internship opportunity Message-ID: http://korumindfulness.org/about/careers/#backend-dev-intern devin ????? ?????? -- contact info: https://nacredata.com/devin ? Online Task Management software: https://www.next-task.com ? Amazing Tie-Dye Tee-shirts: https://www.icedyedesigns.com ? Support progressive climate politics: https://www.voteclimatepac.org ? In Solidarity we are strong: http://www.ncsolidarity.net -------------- next part -------------- [1]http://korumindfulness.org/about/careers/#backend-dev-intern devin d+y+f+y+n+ k+y+r+t+a+c+ contact info: [2]https://nacredata.com/devin o Online Task Management software: [3]https://www.next-task.com o Amazing Tie-Dye Tee-shirts: [4]https://www.icedyedesigns.com o Support progressive climate politics: [5]https://www.voteclimatepac.org o In Solidarity we are strong: [6]http://www.ncsolidarity.net References Visible links 1. http://korumindfulness.org/about/careers/#backend-dev-intern 2. https://nacredata.com/devin 3. https://www.next-task.com/ 4. https://www.icedyedesigns.com/ 5. https://www.voteclimatepac.org/ 6. http://www.ncsolidarity.net/