Exploring Floating-Point Trade-Offs in Machine Learning

The goal of the analysis is to study what is the impact of floating point precision on the accuracy of well known machine learning predictors: Perceptron (and Average) and SVM. The analysis is built upon numerical libraries: MPFR and SoftFloat.

Overview of the repository

graphs

It contains the graphical representation of the results of the analysis. Each folder reports a different dataset: diabet,fourclass,heart,inosphere,and splice. In each folder you find the execution of Perceptron(P), Average Perceptron(AP), and SVM for the corresponding dataset. Moreover the folder 1.Selected-Graphs contains most interesting graphs selected from the all datasets.

realtime graphs

(Beta) In realtime graphs we rely on the web service https://mybinder.org to reproduce graphs in realtime allowing the user to interact with them. Try it!

src

the source code of the analyzer and useful tools.

wax2018-ML

the work submitted to WAX 2018.

Getting Started

Please follow this instruction to get a copy of the project on your local machine.

Prerequisites

• Python: tested with Python 2.7.12;
• gcc Compiler Tested with gcc (Ubuntu 5.4.0-6ubuntu1~16.04.5);
• MPFR - MPFR library;
• SoftFloat - SoftFloat library (In particular FlexFloat);

Installing

After all libraries have been installed in the machine, clone this repository. To run the analysis, you need the following:

• /src/FlexFloat-ML/* Implementation of the analysis with FlexFloat.
• /src/MPFR-ML/src/* Implementation of the analysis with MPFR.
• /src/execute.py Run the analysis.

the only file you need to modify is src/execute.py. In particular:

• pathDatasets="(insert datasets paths)" ex: pathDatasets="/home/user/project/datasets/";
• mpfrcpp="(path to the project file /src/MPFR-ML/src/mpfrcpp.cpp") ex. mpfrcpp="../src/mpfr.cpp"
• softfloat="(path to SoftFloat library)" ex. softfloat="/home/user/softFloat/"
• flexfloatcpp=(path to the project file /src/FlexFloat-ML/flexfloat.cpp") ex.flexfloatcpp="../src/FlexFloat-ML/flexfloat.cpp"

The directory src/tools/ contains:

• preprocessor.py it converts the dataset from LIBSVM format to our standard.
• surfacePlot.py given in input the results of the analysis it outputs the graphs.

Running the tests

Datasets

The path pathDatasets contains the folder of each dataset we want to analyse. In particular the dataset has to be splitted in four parts with names: part1.txt; part2.txt; part3.txt; part4.txt

The directory tree has to look like the following:

pathDatasets/myDataset/part1.txt
pathDatasets/myDataset/part2.txt
pathDatasets/myDataset/part3.txt
pathDatasets/myDataset/part4.txt

The dataset has to be in the following format:

value value value 1 label

Inside the folder tools exists a file preprocessor.py that performs the following format conversion.

From the LIBSVM format (it can contains missing attributes):

label 1:value 2:value 3:value ... n:value
label 1:value 3:value ... n:value
label 1:value 2:value 3:value ... n:value
label 2:value 3:value ... n:value

To:

value value value ... bias label
value value value ... bias label
value value value ... bias label
value value value ... bias label

At the same time the preprocessor.py splits the dataset in four parts.

Contributing

Versioning

Authors

Acknowledgments