Machine Learning Quantized classifier in GO

(Last Updated On: January 18, 2017)

Machine Learning Quantized classifier in GO

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ML Quantized classifier in GO
A general purpose, high performance machine learning classifier. Tests are:
ASL Sign language Gesture recognition
Classify breast cancer from 9 features. 96% accuracy at 100% recall first pass with no optimization. Using 623 training rows.
Predict death or survival of Titanic passengers.
Predict Diabetes
Please send me data sets you would like to add to the test.
We Offer Consulting services see: http://BayesAnalytic.com/contact
Thisrepository includes TensorFlow Deep Learning implementation of classifiers using the same data to compare the run-time performance combined with classification recall and accuracy.
Quantized Classifier
The design for Quantized classifiers was inspired by design elements in KNN, Bayesian and SVM engines. A key design goal was a faster mechanism to identify similarity for a given feature while providing very fast classification using moderate hardware resources.
In KNN we find similar records for a given feature by finding those with the most similar value. This works but consumes a lot of space and run-time. In Quantized approach we look at the range of data and attempt to group data based on similar values. EG: if a given feature has a value from 0.0 to 1.0 then a 10 bucket system could consider all records that have a value from 0 to 0.1 as similar. Those from 0.1 to 0.2 are similar, etc. Rather than keeping all the training records we only need to keep the statistics for have many of the records in a given bucket belong to each class which we can then use to compute a base probability by feature by bucket by class. Applying this across active features gives us a set of probabilities that can be combined using ensemble techniques into the probability a given row would belong to any of the classes.
Quantizing the data allows a small memory foot print with fast training without the need to keep all the training records in memory. Retaining only the statistics allows very large training sets with moderate memory use. The trade off is loosing some of KNN ability to adjust the number of closest neighbors considered quick at runtime. The offset is that training is so fast that the quanta size can be adjusted quickly. The memory use is so much smaller that we can afford to keep multiple models with different quanta sizes loaded and updated simultaneously.
ASP (American Sign Language) Gesture classifier
This engine started as a classifier designed to classify Static Gestures for VR with the idea we may be able to produce a useful tool for classifying ASL using VR input devices. That is still a primary focus but the core algorithms can be more broadly applied.
See Overview.pdf in this repository for conceptual overview of the approach when using this kind of classifier for gesture recognition.
This repository includes code written to test ideas for static gesture recognition.
It also includes samples of the classifiers in python that cope well with smaller training data sets and demonstrate using the Quantized classifier approach. They also handle massive training data sets with minimal memory.
Version: 0.1
License: MIT
We do sell consulting services http://BayesAnalytic.com/contact

GO Code is cross platform and will run Linux. This softwar was built using version 1.7.3 windows/amd 64

Python code: Was tested with Python 3.5.2 64 bit
TensorFlow: Lots of crazy dependencies See: tlearn/tensflowReadme.docx
How to Use
Install python. We tested 3.5.2 but should work with newer versions. only needed if you want to run TensorFlow or Python samples we supplied.

Install GO

Install TensorFlow, TFLearn and run their basic tests to ensure they run correctly. This may also require installing CUDA depending on whether you want to use the GPU version of TensorFlow. TFLearn requires Python we tested ours with python 3.5.2. Not needed if you only want to run our GO based classier engines.

setGoEvn.bat – will set the GOHOME directory to current working directory in a command prompt. This is required for the GO compiler to find the source code. Tested on windows 10 but should be similar on linux.

makeGO.bat – First install GO and ensure it has been added to PATH. Open a command line at the base directory containing makeGO.bat and run it. It will build the executables based on GO that are needed to run the tests. Tested on windows 10 but should be similar on linux.

splitData.bat – Creates sub .train.csv and test.csv files for the files used in the classifier tests. Uses splitCSVFile.exe which is built by makeGo. Run this before attempting to run the classifier to ensure proper data is present.

go build src/classifyFiles.go builds executable classifyFiles from GO source. this is actualy done automatically by makeGO.bat replicated here to show how to do it manually

classifyFiles data/breast-cancer-wisconsin.adj.data.train.csv data/breast-cancer-wisconsin.adj.data.test.csv 10 will run the GO based classifier built in GO using the first named file for training and the second named file for testing will print out results of how well classification matches actual source data class.

classifyFiles data/titanic.train.csv data/titanic.test.csv 6 will run the GO based classifier against the two input files this test attempts to predict mortality and will print out quality of predictions from classifier compared to known result.

python quant_filt.py – Runs test on gesture classification data. Shows how quantized concept can be used to implement splay like search trees. It acts something like a decision tree and something like a multi layer CNN. The more quant buckets used the more precise. This is an alternative to the probability model and can provide superior results in some instances.

python quant_prob.py – Runs a test on gesture classification data demonstrates quantized probability theory in smallest possible piece of python code. A more complete version is implemented in classify.go

Basic Contents
Not all files are listed here. The intent is to help you find those files that are most likley to be helpful. when learning the sysem.
todo.md – list of actions and enhancements roughly prioritized top down.
GO Based Classifier
Idea Test Sample Code
quant_filt.py – Machine learning Quantized filter classifier. This system can provide
fast classification with moderate memory use and is easy to see how likely the match is to be accurate.

quant_prob.py – Machine learning Quantized probability classifier. Not quite as precise under some conditions and quant_filt.py but it can cope with greater amounts of training noise while still delivering good results with moderate amounts of training data.

data/data-sources.txt – Explains sources for the included data files some data files are not included and will have to be donwloaded from those sources if the usage license was unclear or restrictive.

data/train/gest_train_ratio2.csv – Input training data used for these tests. We need thousands additional training samples feel free to volunteer after your read overview.pdf in this repository.

Contribution guidelines
todo.md – list of actions and enhancements roughly prioritized top down.

**design-notes.md Engineering Design Notes and design thoughts.


**go-notes.html Notes and helpful links about GO that I recorded while working on the classifer.go

Writing tests

Code review
Other guidelines
Who do I talk to?
Repo owner Joseph Ellsworth
I sell consulting services for Search, Machine Learning, High performance High availability distriuted architecture. http://BayesAnalytic.com/contact

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