vai-lab 0.0.dev1

AI assisted Virtual Laboratory framework.

Virtual Artificially Intelligent Laboratories (VAI Labs)

VAIL is a modular, easy-to-use framework for Virtual Laboraties for science and design, where Artifical Intelligence assists the user in their goals.

Warning This project is currently a work in progress and is intended for wider use when a full release is made.

Users are welcome to use the software in its current state, but should expect to heavily alter source code until full testing has been done.

Consult the development and release schedule for the intended timeline for this project.

Any contributions, forks, or pull requests are very welcome. Feel free to get in touch

How to Contribute

The aim of this framework is to be a community effort that will benefit the science, engineering and more.

We are actively seeking contribution in the form of users, testers, developers, and anyone else who would like to contribute.

• If you have methods which can be added to the framework, get in touch!
• If you think this framework will be useful to your research, get in touch!
• If want to get invovled in development, get in touch!
• Noticed a bug or other issue? get in touch!

How it Works

The VAIL framework uses a modular, plugin-based architecture.

stateDiagram-v2

    VAIL --> Module1
    VAIL --> Module2
    VAIL --> Module_n
    Module1 --> plugin_1.1
    Module1 --> plugin_1.2
    Module1 --> plugin_1.m
    Module2 --> plugin_2.1
    Module2 --> plugin_2.2
    Module2 --> plugin_2.m
    Module_n --> plugin_m.1
    Module_n --> plugin_m.2
    Module_n --> plugin_m.n

Each module represents a process (e.g. Modelling) and each plugin is a specific implementation of that process (e.g. linear regression).

Modules can be chained, looped and modified in real-time to create a highly customisable framework for the user's requirements.

Installation

Clone this repository via HTTPS:

git clone https://github.com/AaltoPML/VAI-labs.git

OR SSH:

git clone [e-mail]:AaltoPML/VAI-labs.git

Change directory

cd VAI-labs

Create a virtual environment and activate it using venv

python3 -m venv venv && source venv/bin/activate

or using conda

conda create --name aidesign python=3.8 && conda activate aidesign

Upgrade pip and install flit

python3 -m pip install -U pip flit

Install the package

flit install

Launching and Examples

To launch the framework with the GUI:

import aidesign as ai

core = ai.Core()
core.run()

or to execute an existing config file:

import aidesign as ai

core = ai.Core()
core.load_config_file("<path_to_config_file>")
core.run()

Examples

Pre-made examples show the syntax and form of the config files and pipeline as a whole, and are the best way to get started.

Some basic use-cases are provided among many others:

• user_feedback_demo.xml Demonstrates manual image classification of chest X-rays
• canvas_demo.xml Launches the canvas state-action pair visualiser and editor
• regression_demo.xml Demonstrates simple linear regression on a small set of sample data

Launching examples:

To demonstrate the syntax for launching examples using user_feedback_demo.xml:

import aidesign as ai

core = ai.Core()
core.load_config_file(("./examples","xml_files",'user_feedback_demo.xml'))
core.run()

Config file paths can be passed as path stings, a list or tuple of directory paths.

Absolute paths, as well paths relative to the library's base directory can be used. For library-relative paths, starting a path definition with "./" defaults to the directory of <path-to-this-repo>/ai-assisted-framework-design/aidesign/

Defining Pipelines in GUI

The VAIL module allows to define a pipeline and the relations within by drawing a flowchart on a canvas. This canvas always starts with an initialiser module and an output module and allows to define any number of modules between these two. To do so, the user needs to define the modules and the relations between them.

Modules

At this moment, there are 7 possible modules for VAIL. initialiser and output are compulsory for the pipeline, the rest of them can be freely placed in the canvas. These are:

• Data processing.
• Modelling.
• Decision making.
• User Feedback Adaptation.
• Input data.

If you click on a module and drag it you can modify its position on the canvas. Finally, they can be deleted by clicking on the desired module and then clicking on the Delete selection button.

Module joining

Each module object has a number of circles that can be used to join two modules. The initially clicked circle will be identified as the parent and the second one as the child (the output of the father is fed to the input of the child). There can be only one connection from each circle. As of this version, if you need to edit an existing connection you need to delete one of the connected modules.

Loops

If you click on the canvas and drag, you can draw a rectangle that defines which modules are inside the loop. Upon releasing the button you are requested to input what type of loop you want and what condition should be fulfilled to end the loop.

Pipeline uploading via XML file

The pipeline can also be defined uploading an existing XML file. The structure of the XML file is described in the Back-end section.

Plugin Examples

manual_input

Requires the user to indicate to which class the specified data corresponds to. In the current example, the model needs to classify images and the model requires expert knowledge for specific images. The user needs to indicate which classes correspond to the image and save the results to send them to the model.

canvas_input

Requires the user to give feedback to state-action pairs. It opens a tab for each interactable object in the model and either requires adding new state-action samples or to modify the existing ones. In the current example, the model has two interactable objects that require feedback in two forms: (1) an angle for the state and for the action or (2) a tuple of Cartesian coordinates for the state and for the action. It has been adapted to be able to give feedback to any number of objects. These, at the same time, can be either sliding or rotating objects. Specifically, sliding refers to objects that need Cartesian feedback in a two-dimensional space, while rotating refers to objects that require an angle. In order to give feedback, you can choose to either move the corresponding state-action pairs on the canvas or directly edit the tree view display. This last option results in an automatic update on the canvas of the state-action location.

Defining a pipeline in XML

The pipeline structure is defined between the pipeline tags:

<pipeline>
    ...
</pipeline>

Initialise

The Initialise tag is the dedicated entry point to the pipeline. No other entry points can be declared.

Current options:

• name: attribute for user defined name
• initial_data: element for declaring directory for initial data
• relationships: User defined names of modules this one is connected to

Example from canvas_demo.xml

<Initialiser name="Init">
        <inputdata>
            <X file="./examples/state-action/X_data.csv" />
        </inputdata>
        <relationships>
            <child name="My First UserFeedback Module" />
        </relationships>
    </Initialiser>

Loops

Loop tags are used to iterate over a given set of modules until a condition is met. Loops can be nested and named.

See basic_operation.py for full example. Current options:

• type: what variety of loop will this be: for, while, manual(user defined stopping condition on-the-fly)
• condition: Termination condition for the loop. I'm not sure how to deal with the criteria for while loops
• name: User defined name for loop

<loop type="for" condition="10" name="For Loop 1">
    ...
</loop>

Modules

Modules are declared by tags matching their names, e.g. the GUI module is loaded with the GUI tag:

Required:

• name: Unique user defined name for module, so can be referenced later
• plugin: The type of plugin to be loaded into the module, along with associated options.
• relationships: User-defined names of the parent modules which this module receives data from and child modules that this module passes data to.

Example from ridge_regression_demo.xml:

     <Modelling name="Modelling">
        <relationships>
            <parent name="Initialiser" />
            <child name="Output" />
        </relationships>
        <plugin type="RidgeRegression">
            <alpha>
                 0.02
            </alpha>
        </plugin>
    </Modelling>

Data Definition

Data is loaded from existing files in either the Initialiser or Input Data modules and is specified using the inputdata tags.

Example from ridge_regression_demo.xml:

<inputdata>
    <X file="./examples/supervised_regression/X_tr.csv" />
    <Y file="./examples/supervised_regression/Y_tr.csv" />
    <X_test file="./examples/supervised_regression/X_tst.csv" />
    <Y_test file="./examples/supervised_regression/Y_tst.csv" />
</inputdata>

Writing Data

Two methods are given to add data to the XML file. One for modules (append_pipeline_module_to_file) and one for data structures (append_data_structure_field_to_file).

Feature and Release Schedule :calendar:

• October/ November 2022: Public repo, API fixing, Testing
• January 2023: Representative use case release
• Spring 2023: Initial full release of manual pipeline
• Fall 2023: Initial release of preliminary AI-Assistance

Get in Touch

If you would like contribute, test, give feedback, or ask questions about this framework, we'd like to hear from you! Email us at:

• Chris McGreavy, chris.mcgreavy [at] aalto.fi
• Carlos Sevilla-Salcedo, carlos.sevillasalcedo [at] aalto.fi