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Scilab-gsoc

Contribution to scilab for google summer of code 2018

Sub-directories

Community bonding

Work done during the community bonding period

Development

Standalone Scilab Machine Learning Toolbox. Sub directories

Algorithms

This directory contains macros for machine learning algorithms with their usage

  • Linear regression

    • linearRegressionTrain(x, y) : Parameters x (feature matrix) and y (target vector) and returns [a, b] where they represent the line fit for a*x + b.

    • linearRegressionPredict(x, a, b) : Parameters x (test feature matrix) and [a, b] from linearRegressionTrain() to get predicted vector.

  • Polynomial regression

    • PolynomialRegressionTrain(x, y, degree) : Parameters x (feature matrix), y (target vector) and degree of the curve to be fit to return model coefficient cf.

    • PolynomialRegressionPredict(x, cf) : Parameters x (test feature matrix) and cf from PolynomialRegressionTrain() to get predicted vector.

  • Logistic regression

    • logisticRegressionTrain(x, y, rate, iter) : Parameters x (feature matrix), y (target vector), rate of learning and number of iterations to return model parameter theta

    • logisticRegressionPredict(x, theta) : Parameters x (test feature matrix) and theta from logisticRegressionTrain() to get predicted vector.

  • Kmeans clustering

    • kmeansCluster(x, centres) : Parameters x (feature matrix) and number of centres (clusters) and returns the flag for the category of each data point

  • Naive Bayes

    • naiveBayes(x, y) : Parameters x (feature matrix) and y (target vector) and returns the matrix of prior probabilities (of size of unique entries in y) as probMat

    • naiveBayesGaussian(x, y, probMat, xtest) : Parameters x (feature matrix), y (target vector), probMat (from naiveBayes) and xtest (the test matrix) and returns the predicted vector

  • Decision Tree

    • decisionTreeFit(x, y) : Parameters x (feature matrix) and y (target vector) and returns [questions, flags] the set of questions (splitting criterion) and flags (leaf node have models)

    • decisionTreePredict(x, questions, flag) : Parameters x (feature matrix), questions and flag from decisionTreeFit() and get the predicted vector.

Preprocessing

This directory contains macros for preprocessing datasets which include the following :

  • Scale (Mean 0 and variance 1)
  • Normalize (0-1)
  • Train test split

Visualisation

This directory contains scripts for plotting different models. The usage can be described as follows :

  • visualizeFit(x, y, pred) : visualize the data points and predicted
  • visualizeSplit(x, y, flags) : visualize the data points with the colour defined by flags

Experimentation

Jupyter integration for python libraries availability within Scilab. The flow is explained as (from the scripts in Automated) :

  • Script <algorithm.sci> is called from a directory on the local server. This script calls python_local.py.
  • Script <python_local.py> clears all already running jupyter kernels and starts a new kernel in a nohup fashion, thus appending the output to nohup.out. After the kernel starts, the script copies the dataset and python script to the server to directory /home/username/Shared/
  • Then the script runs the <python_server.py> with the parameters of the running kernel obtained from <python_local.py>.
  • The script <python_server.py> then starts a connection to the kernel and runs the training script with the results stored in <attributes.p>
  • Then our process returns back to <python_local.py> which then copies back the <attributes.p> pickle file in protocol 2.
  • Then <algorithm.sci> reads the pickle file and then uses it as and so required.

Server backup

Files on the GCP server to ensure smooth running of the experimentation scripts