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VKD_Tools

Overview

VKD_Tools are designed for the visual knowledge discovery of multidimensional classification data. These tools facilitate the visualization, exploration, and identification of complex n-D data patterns through lossless reversible visualizations.

To get started, launch the menu.py script and load a dataset. Datasets can be added to the datasets folder, adhering to the following requirements:

  • A column with the header 'class' must be present for labeling purposes.
  • Other columns are assumed to be feature columns.
  • The top header row must label the 'class' and feature columns.

Select a visualization to explore from the descriptions provided below.

Libraries

The following Python libraries are required to run the scripts:

To install: python -m pip install -r requirements.txt, the requirements.txt file contains all libraries and versions used for pip to install from.

Data Manipulation and Analysis

  • pandas
  • numpy
  • scikit-learn

Data Visualization

  • matplotlib
  • plotly
  • PyOpenGL (optional: pyopengl-accelerate, wheel)

User Interface and System Interaction

  • tkinter
  • argparse
  • subprocess
  • webbrowser

Main Menu Script

  • menu.py: Provides a Tkinter-based graphical user interface as the main menu for launching visualization scripts.

menu screenshot

Visualization Scripts

  1. classifier_tuner.py: Tunes the hyperparameters of the selected classifier with a search through common options in 5-fold cross-validation.

    • Results are displayed as pair-wise scatterplots in an attribute pairing matrix (bottom-half).

    Tuner options Tuner demo

  2. envelope_plotter.py: Creates an interactive application for plotting envelope-like structures.

    • Utilizes PyQt6 for the graphical user interface.
    • Employs OpenGL for rendering.
    • Features drag and drop searchable hyper-rectangle with WASD resizing; right-click to clear.

    Envelope Demo

  3. plotly_demo.py: Utilizes Plotly for data visualization.

    • Presents data in draggable axis parallel coordinates plot.
    • Classes are distinctly displayed with a heatmap legend.

    Plotly Demo

  4. parallel_gl.py: Renders parallel coordinates in OpenGL using GPU pipelines.

    • Includes zoom functionality with the mouse wheel; panning is a work in progress.

    PC GL Demo

  5. Parallel Andrew's Curves using matplotlib.

    PC Curves Demo

  6. parallel_hb.py: Focuses on the visualization of pure hyper-blocks.

    PC HB Demo

  7. shifted_paired.py: Generates a sequence of shifted paired coordinates subplots.

    • Plots all attributes of feature vectors as normalized paired axes.
    • Connects feature vector samples with a line across subplots.
    • Duplicates the last attribute when the feature vector length is odd.
    • Allows scrolling through permutations of the feature vector with the mouse wheel.
    • Displays a Linear Discriminant Analysis (LDA) resultant coefficient-determined permutation first.

    Collocated Paired Coordinates Demo

  8. tree_glyph_plotter.py: Generates high-dimensional data visualizations using tree-like glyphs.

    • Offers lossless visualization of high-dimensional data.
    • Plots a permutation of the feature vector in tree glyphs.
    • Permits cycling through plotted permutations with the mouse wheel.
    • Prioritizes displaying an LDA resultant coefficient-determined permutation first.

    Tree Glyph Output Demo

  9. glc_line_plotter.py: Produces GLC linear plots.

    • Displays the first class in the top subplot, with other classes below.
    • Projects the last glyph per class onto the x-axis.
    • Processes data with LDA and sorts by the coefficient array.
    • Plots the LDA boundary with a yellow dotted line on the x and y axes.
    • Utilizes the GLC-AL algorithm for a 100-epoch search to maximize accuracy of coefficients.

    GLC Lines Demo

  10. 3D GLC-L Rotation.

    • Introduces an additional z-axis using the tan function for GLC-L.
    • Features an SVM-determined boundary border.

    Demo example

  11. circular_plotter.py: Generates circular plots using Matplotlib and scikit-learn.

    • Processes data with LDA and plots the discriminant line.
    • Displays a classification confusion matrix.
    • Manages data preprocessing with Pandas and NumPy.
    • Includes a draggable LDA discriminant line.

    Circular Demo


Acknowledgements