nl2ml

ramazyant ab4411f9de Add files via upload 2 weeks ago
.dvc 7c97b2a9a3 dvc data fix 2 months ago
.ipynb_checkpoints 82b810b974 last checkpoints added 2 months ago
code2vec 97c5b91e60 code2vec folder added 2 months ago
data
graph c1c30291d2 fix: delimeter in JSON; 3 weeks ago
model_interpretation_results ee690b0621 SVM conf matrix graph 3 and 3.1, validation chunk_size == 10 1 month ago
models
open_data
.gitattributes bdb584c714 Get rid of csv in Git LFS 2 months ago
.gitignore 08a39d0906 open_data folder (with Python150k) added 2 months ago
Comments vs commented code.ipynb a4b1957697 in-code comments classification added 2 months ago
Makefile 3e3cfc582f Makefile 1 month ago
README.md 7cc55383b5 Update README.md 1 month ago
RNN.ipynb ed844fb89f Add files via upload 1 month ago
bert_classifier.ipynb 45a0bcfcdb changed names 3 months ago
bert_distances.ipynb 45a0bcfcdb changed names 3 months ago
check_results.ipynb 91d79ae683 upd: clear outputs; 1 month ago
data.dvc 999d24c49f added: golden 884 set added; 4 weeks ago
github_dataset.ipynb 3f08f4b1c5 exploring.. 2 months ago
kaggle.sh fc9e9f9c28 ramazyant files added 2 months ago
kaggle_parser.ipynb fc9e9f9c28 ramazyant files added 2 months ago
logreg_classifier.ipynb 11e5ea097e upd: clear outputs; 3 weeks ago
logreg_weights_analysis.ipynb 38ffcf58fa upd: logreg weights analysis moved to separate notebook 1 month ago
metrics.csv d3ae751394 logreg v5 validated (again with the right scoring) on the golden set; 3 weeks ago
models.dvc 0b9b0d2347 upd: graph_v4, graph_v5 models and data 1 month ago
nl2ml.pptx ab4411f9de Add files via upload 2 weeks ago
nl2ml_notebook_parser.py 8ba024a8c5 no message 6 months ago
open_data.dvc 08a39d0906 open_data folder (with Python150k) added 2 months ago
params.yml d3ae751394 logreg v5 validated (again with the right scoring) on the golden set; 3 weeks ago
predict_tag.ipynb 489d3332f4 upd: clear outputs; 3 weeks ago
regex.ipynb 7673348010 upd: clear outputs; 3 weeks ago
svm_classifier.ipynb d703f657c1 getting inference for logreg v3 and v3.1; 1 month ago
svm_train.py eb8484c58c added: to meta, GRAPH_VER; upd: print; 3 weeks ago

Data Pipeline

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README.md

Source Code Classification

This is a repo of the Natural Language to Machine Learning (NL2ML) project of the Laboratory of Methods for Big Data Analysis at Higher School of Economics (HSE LAMBDA).

The project's official repo is stored on GitLab (HSE LAMBDA repository) - https://gitlab.com/lambda-hse/nl2ml \ The project's full description is stored on Notion - https://www.notion.so/NL2ML-Corpus-1ed964c08eb049b383c73b9728c3a231 \ The project's experiments are stored on DAGsHub - https://dagshub.com/levin/source_code_classification

Project Goals

Short-Term Goal

To build a model classifying a source code chunk and to specify where the detected class is exactly in the chunk (tag segmentation).

Long-Term Goal

To build a model generating code by getting a short raw english task in as an input.

Repository Description

This repository contains instruments which the project's team has been using to label source code chunks with Knowledge Graph vertices and to train models to recognize these vertices in future. By the Knowledge Graph vertices we mean an elementary part of ML-pipeline. The current latest version of the Knowledge Graph contains the following high-level vertices: ['import', 'data_import', 'data_export', 'preprocessing', 'visualization', 'model', 'deep_learning_model', 'train' 'predict'].

Data Download

To download the project data and models:

  1. Clone this repository
  2. Install DVC from https://dvc.org/doc/install
  3. Do dvc pull data or dvc pull data. Note: if you are failing on dvc pull [folder_to_pull], try dvc pull [folder_to_pull] --jobs 1

Contents:

The instruments which we have been using to reach the project goals are: notebooks parsing from Kaggle API and Github API, data preparation, regex-labellig, training models, validation models, model weights/coefficients analysis, errors analysis, synonyms analysis.

nl2ml_notebook_parser.py - a script for parsing Kaggle notebooks and process them to JSON/CSV/Pandas.

bert_distances.ipynb - a notebook with BERT expiremints concerning sense of distance between BERT embeddings where input tokens were tokenized source code chunks.

bert_classifier.ipynb - a notebook with preprocessing and training BERT-pipeline.

regex.ipynb - a notebook with creating labels for code chunks with regex

logreg_classifier.ipynb - a notebook with training logistic regression model on the regex labels with tf-idf and analyzing the outputs

Comments vs commented code.ipynb - a notebook with a model distinguishing NL-comments from commented source code

github_dataset.ipynb - a notebook with opening github_dataset

predict_tag.ipynb - a notebook with predicting class label (tag) with any model

svm_classifier.ipynb - a notebook with training SVM (replaced by _svmtrain.py) and analyzing SVM outputs

svm_train.py - a script for training SVM model