Machine Learning Experiments

A simple and experimental project for a bunch of machine learning codes and utils

Download data

Data will downloaded programatically when using download() function of loaders. It will prompt to the user the username and key of kaggle.json.

Execution

Execute: Option 1 - Using Docker

Install Docker

1. run build.bat (Works with sh too)
2. run run.bat (Works with sh too)

Execute: Option 2 - Using local enviroment

Install python, pip and run

1. python -m venv venv
2. Activate venv enviroment, example: .\venv\Scripts\Activate.ps1
3. run pip install -r requirements.txt
4. run python call_test.py
5. or run python call_test_gui.py if you want a gui

Execute: Option 3 - Install as a package

You can install as a package (it doesn't include test/ folder) using pip:

pip install git@https://github.com/erickfmm/ML-experiments.git

Then just import the module you want, for example (see test files):

import mlexperiments.unsupervised.clustering.cluster_sklearn as clustering_sk

labels1 = [int(i/1000) for i in range(4000)]
np.random.seed(844)
clust1 = np.random.normal(5, 2, (1000, 2))
clust2 = np.random.normal(15, 3, (1000, 2))
clust3 = np.random.multivariate_normal([17, 3], [[1, 0], [0, 1]], 1000)
clust4 = np.random.multivariate_normal([2, 16], [[1, 0], [0, 1]], 1000)
simple_dataset = np.concatenate((clust1, clust2, clust3, clust4))

assignments = clustering_sk.dbscan(dataset1)

TODO (ideas):

• More loaders
• Documentation (pydoc or similar)
• Documentation - UML
• Better menu (call_test), maybe using some ncurses or similar
• Documentation inside test/ files
• More tests (more ML models and experiments)

Screenshots

By now I only have 2 interfaces. The main and one experiment:

Main interface:

Segmentator of Butterflies: