DCVis: Dynamic Coordinate Visualisation System

DCVis is a Visual Knowledge Discovery (VKD) Artificial Intelligence and Machine Learning (AI/ML) research project.
This project allows for interactive building of visual ML models using multidimensional (n-D) data in visualizations.
With interactive classification tools and synthetic data generation features supporting novel classifier improvement techniques.

Project contributors: Alice Williams, James Battistoni IV, Charles Recaido, and Dr. Boris Kovalerchuk at Central Washington University Visual Knowledge Discovery Lab, project during years 2022 to 2024.

Software developed for the paper:

• Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates by Alice Williams and Dr. Boris Kovalerchuk

Available under the MIT License, allowing for both personal and commercial use.

This software is a rewrite of DSCVis with new visualizations and tooling features.

Requirements

• Python 3.x: Recommended Python 3.6 or higher for compatibility.
• Libraries installed from requirements.txt: Use pip to install the required libraries. Run the following command in the terminal: pip install -r requirements.txt

Running

Execute DCVIS_MAIN.py script to launch the DCVis application: python DCVIS_MAIN.py

Software Features

• Visualize multi-dimensional numerical data (.csv or .txt file) in 2D visualizations using OpenGL.
• Plot can be interacted with through panning, zooming, color and transparency selection, and interactive point selection.
• Data may be highlighted with dual-right click clipping, analyzed, and exported or hidden for further analysis.
• Classification rules can be built from clipped cases.
• Axes can be reordered or inverted to further find n-D data patterns.
• For building visual machine learning models using rules-based classification. Combining and chaining rules to produce classifiers for future data.
• Background, class, and axes colors are each configurable.
• Axes, points, and connecting lines can be shown or hidden.
• Trace mode alternates colors for visual tracing.

Controls

Mouse Interactions

• Pan Plot: Click and drag with the scroll-wheel to pan the plot.
• Zoom: Scroll the mouse wheel to zoom in or out.
• Box Clipping: Right-click twice to create a clipping rectangle (first click sets the upper right corner, second click sets the bottom left corner).
• Select Point: Left-click to select a single point or multiple sample points.

Keyboard Shortcuts

• Cycle Selections: Use the Q and E keys to cycle through the currently selected sample(s).
• Adjust Vertical Position: The W and S keys move the selected cases up or down proportionally.
• Delete Samples: Press the D key to delete the selected samples from the dataset.
• Print Samples: Press the P key to print the details of the selected samples.
• Clone Samples: Press the C key to create a synthetic copy of the selected samples.

UI Elements

• Highlight Clipped Cases: Clipped cases are highlighted on the plot. Use the 'Add Classification Rule' button to convert current clipped rectangles into a new rule.
• Rule Visibility: Toggle the visibility of cases contained within a rule using the checkbox next to the rule.
• Reorder Tables: Drag and drop cells within the class and attribute tables to reorder them.
• Adjust Transparency: A slider below the attribute table adjusts the transparency of attribute markers not selected in the highlight column.

Additional Information

• Hotkeys are provided next to relevant user interface elements for quick reference.

Datasets included

• Fisher Iris of flower measurements -- 150 cases, 4 features, 3 classes
• Breast Cancer Wisconsin diagnostics -- 569 cases, 30 features, 2 classes
• WBC -- 696 cases, 9 features, 2 classes
• Diabetes diagnostics -- 768 cases, 8 features, 2 classes
• Heart Disease diagnostics -- 1,025 cases, 13 features, 2 classes
• Ionosphere atmosphere readings -- 351 cases, 34 features, 2 classes (some empty cells)
• MNIST Capital Letters handwriting -- 20,000 cases, 16 features, 26 classes
• Sinusoidal wave samplings -- 200 cases, 2 features, 2 classes
• Wheat Seed variety measurements -- 210 cases, 7 features, 3 classes
• Wine chemical analysis measurements -- 4,898 cases, 11 features, 7 classes

Dataset requirements

• Format of .txt or .csv
• Header row required
• One label column denoted 'class' in header
• Any number of feature columns attribute name in header
• Features must be numerical data

Visualizations Supported

Generalized vertex class included for adding visualization methods.
This visualization tool features multiple visualisation methods:

• Parallel Coordinates (PC)
• Shifted Paired Coordinates (SPC)
• Dynamic Scaffold Coordinates 1 (DSC1)
• Dynamic Scaffold Coordinates 2 (DSC2)
• Static Circular Coordinates (SCC)
• Dynamic Circular Coordinates (DCC)