Feat(models): Implemented three models for license similarity

[Kaushl2208]

Description

Implementation of Logistic Regression, Multinomial Naive Bayes and Linear SVC on license dataset licenseList.csv. The main purpose of implementing this idea was to plan for a model which can make atarashi faster and more accurate.

Files

• train.py (Training the models and saving in binary)
• test.py ( For the testing purpose)
• lr_model.pkl (Binary file for logistic regression)
• nb_model.pkl(Binary file for Multinomial Naive Bayes)
• svc_model.pkl(Binary file for Linear SVC)
• vectorizer.pkl (Binary file for storing vocabulary)

How to use?

• Test the models

  • atarashi -a lr_classifier path/to/file (Logistic Regression)
  • atarashi -a nb_classifier path/to/file (Multinomial Naive Bayes)
  • atarashi -a svc_classifier path/to/file (Linear SVC)

• Train the models (Optional)

  • From the base folder run : python3 atarashi/agents/models/train.py

ToDo

• [x] Test working and accuracy of the algorithms using evaluator.py

• [x] proper integration with atarashii.py

Accuracy Score

Model Name	Accuracy Score in %	Time taken on 100 files in (sec)
Logistic Regression	31	88.6
Linear SVC	36	79.4
Multinomial Naive Bayes	30	83.72

Future Scope

• The well-defined dataset will increase the similarity accuracy even more. By well-defined dataset I mean with newly updated licenses also ( 1 class to n License) style license file will do the work.

CC: @hastagAB @GMishx @ag4ums

Signed off by: Kaushlendra Pratap Singh [email protected]

[Kaushl2208]

@hastagAB @GMishx , I implemented the models command into atarashii.py but it seems like I am missing something to update somewhere in code.

[Kaushl2208]

@GMishx @ag4ums I have run all three models on the Test files and I am attaching the screenshot of the results.

SVC

NB

Logistic Regression