Difference between revisions of "Kyran Adams (Work Log)"

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[[Kyran Adams]] [[Work Logs]] [[Kyran Adams (Work Log)|(log page)]]
 
[[Kyran Adams]] [[Work Logs]] [[Kyran Adams (Work Log)|(log page)]]
  
2018-02-22: Subtracting the means helped very marginally. I'm going to try coming up with some new ideas for features to improve accuracy.
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2018-02-22: Subtracting the means helped very marginally. I'm going to try coming up with some new ideas for features to improve accuracy. Started working on a program web_demo_features.py that will parse URLs and HTML files and count word hits from a file words.txt. This will be a feature for the ML model.
  
 
2018-02-21: A lot of the inaccuracy was probably due to mismatched data (for some reason two entries, Mergelane & Dreamit, were missing from the hand-classified data, and some of the entries were alphabetized differently), cleaned up and matched full datasets. However, now, it seems like we might be overfitting the data. I think it might be necessary to input a few different features. Found the KeyTerms.py file (translated it to python3) to see if I could augment it with some other features. Yang also suggested normalizing the features by subtracting their means, so I will start implementing that.
 
2018-02-21: A lot of the inaccuracy was probably due to mismatched data (for some reason two entries, Mergelane & Dreamit, were missing from the hand-classified data, and some of the entries were alphabetized differently), cleaned up and matched full datasets. However, now, it seems like we might be overfitting the data. I think it might be necessary to input a few different features. Found the KeyTerms.py file (translated it to python3) to see if I could augment it with some other features. Yang also suggested normalizing the features by subtracting their means, so I will start implementing that.

Revision as of 17:43, 22 February 2018

Spring 2018

Kyran Adams Work Logs (log page)

2018-02-22: Subtracting the means helped very marginally. I'm going to try coming up with some new ideas for features to improve accuracy. Started working on a program web_demo_features.py that will parse URLs and HTML files and count word hits from a file words.txt. This will be a feature for the ML model.

2018-02-21: A lot of the inaccuracy was probably due to mismatched data (for some reason two entries, Mergelane & Dreamit, were missing from the hand-classified data, and some of the entries were alphabetized differently), cleaned up and matched full datasets. However, now, it seems like we might be overfitting the data. I think it might be necessary to input a few different features. Found the KeyTerms.py file (translated it to python3) to see if I could augment it with some other features. Yang also suggested normalizing the features by subtracting their means, so I will start implementing that.

2018-02-19: Finally got the classifier to work, but it has pretty low accuracy (70% training, 60% testing). I used code from an extremely similar tutorial. Will work on improving the accuracy.

2018-02-14: Kept working on the classifier. Fixed bug that shows 100% accuracy of classifier. Gradient descent isn't working, for some reason.

2018-02-12: Talked with Ed regarding the classifier. Collected data that will be needed for the classifier in "E:\McNair\Projects\Accelerators\Spring 2018\google_classifier". Took a simple neural network from online that I tried to adapt for this data.

2018-02-07: Got tensorflow working on my computer. It runs SO slowly from Komodo. Talked with Ed about the matlab page and the demo day project. Peter's word counter is supposedly called DemoDayHits.py, I need to find that.

2018-02-05: Added comments to the code about what I ~think~ the code is doing. Researched a bit about word2vec. Created Demo Day Page Google Classifier page.

2018-02-01: Tried to document and clean up/refactor the code a bit. Had a meeting about the accelerator data project.

2018-01-31: Found a slightly better solution to the bug (documented on matlab page). Verified that the code won't crash for all types of tasks. Also ran data for the first time, took about 81 min.

2018-01-29: Solved matrix dimension bug (sort of). The bug and solution is documented on the matlab page.

2018-01-26: Started to try to fix a bug that occurs both in the Adjusted and Readjusted code. The bug is that on the second stage of gmm_2stage_estimation, there is a matrix dimension mismatch. Also, I'm not really sure of the point of the solver at all. The genetic algorithm only runs a few iterations (< 5), and it is actually gurobi that does most of the work. Useful finding: this runs astronomically faster with gurobi display turned off (about 20x lol).

2018-01-25: Continued working on matlab page. Deleted unused smle and cmaes estimators. Not sure why we have so many estimators (that apparently don't work) in the first place. Deleted unused example.m. Profiled program and created an option to plot the GA's progress. Put up signs with Dr. Dayton for event tomorrow.

2018-01-24: Kept working through the codebase and editing the matlab page. Deleted "strip_" files because they only work with task="monte_data". Deleted JCtest files because they were unnecessary. Deleted gurobi_gmm... because it was unused.

2018-01-22: Kept working on the Matlab page. Read reference paper in Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists. Talked with Ed about what I'm supposed to do; I'm going to refactor the code and hopefully simplify it a bit and remove some redundancies, and figure out what each file does, what the data files mean, what the program outputs, and the general control flow.

Possibly useful info:

  • only 'ga' and 'msm' work apparently, I have to verify this
  • Christy and Abhijit both worked on this
  • This program is supposed to solve for the distribution of "unobserved complementarities" between VC and firms?

2018-01-19: Wrote page Using R in PostgreSQL. Also started wiki page Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists Matlab Code. Tried to understand even a little of what's going on in this codebase

2018-01-18: Started work on running R functions from PostgreSQL queries following this tutorial. First installed R 3.4.3, then realized we needed R 3.3.0 for PL/R to work with PostgreSQL 9.5. Installed R 3.3.0. Link from tutorial for PL/R doesn't work, I used this instead. To use R from pgAdmin III, follow the instructions in the tutorial: choose the database, click SQL, and run "CREATE EXTENSION plr;". This was run on databases tigertest and template1. You should be able to run the given examples. Another possibly useful presentation on PL/R. Keep in mind, if the version of PostgreSQL is updated, both the R version and PL/R version will have to match it.


Fall 2017

2017-11-22: Fixed outjoiner.py python 3 compatibility bugs, color coded plot so errors are easier to see

2017-11-20: Continued debugging circles.py and changed outjoiner.py so it generates an error file, which contains all files with errors.

2017-11-17: Finished duplicated points code, but it still gives errors... Wrote a plotter so that I can debug it more.

2017-11-15: Worked on making duplicated points work with circles.py.

2017-11-13: Put some examples of nonworking files into OliverLovesCircles/problemdata. The files are not just St. Louis, but a lot of different files, meaning the bug is pretty widespread. Found possible solution; removing duplicate points before running program fixes it?

2017-11-10:

  1. Wrote some docs for ecircalg.py. Narrowed the St. Louis problem to the algorithm itself; it's returning circles with a radius of 0.0 for some reason. Wrote some testing code that prints errors if there are unenclosed points or circles with too few points.
  2. Also created a new parts list for the GPU build using server parts. Did some research on NVLink.

2017-11-08: Commented and wrote documentation for vc_circles, circles, and outjoiner.

2017-11-03: Fixed bug in circles.py that output place names in files as lists instead of strings (['S','t','L','o','u','i','s'] instead of "St. Louis"). Changed vc_circles.py to call outjoiner.py automatically. Refactored vc_circles.py, circles.py, and outjoiner.py to put all of the configuration in vc_circles.py. Wrote out method to plot circles using ArcMap in here.

2017-11-01: Familiarized with Enclosing Circle Algorithm to find St. Louis bug

2017-10-30: Rechecked parts compatibility, switched PSU and case

2017-10-27: Decided on dual GPU system, switched motherboard and CPU

2017-10-25: Worked on the partpicker for the dual GPU build.

2017-10-23: Started researching GPU Build. Researched the practical differences between single vs. multiple GPUs.

2017-10-20: Set up my wiki page :)