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README
AGPL-3.0

LearningML

Introduction

LearningML is an educative platform intended to teach and learn Machine Learning (ML) fundamentals through hand-on activities. The full platform is composed of four parts:

The ML editor allows to build ML models from labeled datasets of texts or images. The programming editor allows to code applications able to use these ML models. And the server application is intended to register users and allows them to save and share their projects.

The Machine Learning editor is independent and can be run alone. However if you want to code an application that uses the ML models, you have to use the programming editor. Also, if you want to allow user registration and project saving and sharing, you will have to use the server application.

The main design principle followed when developing LearningML has been simplicity. The main goal has been to build a tool very easy to start with as well as getting some results quickly and easily. Hence, it is a platform specially appropriate to learn and teach ML fundamentals to children.

LearningML is also a computing education research project aiming to discover effective ways to learn and teach artificial intelligence and Machine Learning fundamentals at school.

And, finally, LearningML is an open source project wishing to grow with the help of everyone interested in contributing.

You can install the ML editor, the programming editor and the api server in your computer, study the code and modify it in the terms gathered in their licenses (Affero GNU for the ML editor and BSD-3 for the programming editor).

There is also a cloud instance of the platform freely (as in gratis) available to everybody who wants to use it. You can get access by pointing your browser to https://learnigml.org.

You can find more information about LearningML project in the web site (https://learnigml.org).

Research papers on LearningML

Some papers have been written around LearningML:

The first paper shows an experimental study supporting that LearningML helps to learn AI fundamentals to children in the age 10-16. The second one gives a brief description of the platform. And the last one presents LearningML in depth.

Features

  • Gather image/text training datasets.
  • Build Machine Learning image/text recognition models (with neural network algorithms).
  • Evaluation of built models.
  • Exporting ML models to Scratch.
  • Saving locally the training dataset.
  • Loading training datasets from your computer.
  • Create user accounts (with the API server application).
  • Saving training datasets in the cloud (with the API server application).
  • Sharing training datasets in the cloud (with the API server application).
  • (planned) sound recognition.
  • (planned) tabular datasets management.
  • (planned) Machine Learning algorithm selection.
  • (planned) Configuring relevant parameters of the selected Machine Learning
    algorithm.
  • (planned) Confusion matrix to enrich evaluation phase.
  • (planned) Showing learning evolution trough plots.
  • (planned) Saving and loading ML models.
  • (planned) Desktop (offline) version.

Requirements

The LearningML editor is a cross platform web application, Therefore, it can be deployed on a modern computer running: - Linux, - MacOS or, - Windows OS.

Also, you will need:

Getting started

In order to install a local copy of Learning editor, follow the following steps.

  1. Install node.js runtime environment.
  2. Install Angular CLI.
  3. Open a terminal and clone this repo:
    # git clone https://gitlab.com/learningml/learningml-editor.git # cd learningml-editor
  4. Install dependencies:
    # npm install
  5. Run development server # ng serve
  6. Open a web browser pointing to http://localhost:4200 to access the learning editor application.

You can test the application following these steps (we assume that you have chosen the English language):

  1. Click on "recognize images" button.
  2. Click on "Add new class of images" button and enter the label "face".
  3. Click on "Add new class of images" button again and enter de label "hand"
  4. Click in the button located in "hand" label with a camera image and add some pictures of your face. When you finish, close the camera by clicking on the button with an "X".
  5. Click in the button located in "face" label with a camera image and add some pictures of your face. When you finish, close the camera by clicking on the button with an "X".
  6. Click on "Learning to recognize images" button, and wait until the learning phase is finished.
  7. Click on the button with a camera located in the third column ("Try") and take a picture of your face or your hand. The model will be give as output the probability of being a hand or a face.

User manual

The LearningML editor is very intuitive and building ML models is straightforward: you create some classes and add example data to them. Afterwards, the Machine Learning algorithm is run to build the ML model. Finally you can evaluate this model by giving new data as input and checking the classification thrown as output.

You can find more details in the user manual [https://web.learningml.org/wp-content/uploads/2020/05/Manual_LearningML.pdf]

The manual is written in Spanish, but its translation to English is planned as part of the future development.

Build

Run ng build to build the project. The build artifacts will be stored in the dist/ directory. Use the --prod flag for a production build.

If you are going to deploy the code in a web server under a route distinct to /, you must specify it with the option --base-href. For instance, if the route is /editor/, you can build a deployment directory as follows:

# ng build --base-href /editor/ --configuration production

Docker

LearningML-editor together with lml-scratch can be run in a docker container.

# docker run -d -p 8080:80 juandalibaba/learningml

Running unit tests

Run ng test to execute the unit tests via [Karma](https://karma-runner.github.io).

Running end-to-end tests

Run ng e2e to execute the end-to-end tests via [Protractor](http://www.protractortest.org/).

Appendix A: Reverse proxy with nginx to run the ML editor together with the programming editor

LearningML editor uses tensorflow.js and brain.js to build the ML models, which are saved locally in the browser once they have been built by using of indexeddb. Afterwards, our version of Scratch can read the model from indexeddb. But indexeddb can only be shared among applications running in the same domain. The problem is that in some environments this is not the case. For instance in a development environment each application runs on a different port, and they are considered as different domains.

This problem can be fixed by means of a reverse proxy with nginx which allows to access to both applications from the same domain. For instance, in a development environment where LearningML editor runs in port 4200 and Scratch runs in 8601 port, the following configuration of nginx does the trick:

server {

listen 8080; server_name localhost;

proxy_set_header Host $host; proxy_set_header X-Forwarded-For $remote_addr; proxy_buffering off;

location / {
proxy_pass http://localhost:4200;

} location /scratch/ {

proxy_pass http://localhost:8601/;

}

}

Once the nginx service is started you can access to LearningML editor from:

http://localhost:8080/learningml

And to Scratch from:

http://localhost:8080/scratch

If you choose to serve LearningML editor from a route different to /, as in the example, you must start the development server like this:

# ng serve --base-href /learningml/ --live-reload=false

Affero GPL License

LearningML, the easiest way to learn Machine Learning fundamentals

Copyright (C) 2020 Juan David Rodríguez García & KGB-L3

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU Affero General Public License is a free, copyleft license for software and other kinds of works, specifically designed to ensure cooperation with the community in the case of network server software. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, our General Public Licenses are intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. When we speak of free software, we are referring to freedom, not price. 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EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 16. Limitation of Liability. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. <one line to give the program's name and a brief idea of what it does.> Copyright (C) <year> <name of author> This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see <https://www.gnu.org/licenses/>. Also add information on how to contact you by electronic and paper mail. If your software can interact with users remotely through a computer network, you should also make sure that it provides a way for users to get its source. For example, if your program is a web application, its interface could display a "Source" link that leads users to an archive of the code. There are many ways you could offer source, and different solutions will be better for different programs; see section 13 for the specific requirements. You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU AGPL, see <https://www.gnu.org/licenses/>.

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