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.
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.
- pandas
- numpy
- scikit-learn
- matplotlib
- plotly
- PyOpenGL (optional:
pyopengl-accelerate,wheel)
- tkinter
- argparse
- subprocess
- webbrowser
menu.py: Provides a Tkinter-based graphical user interface as the main menu for launching visualization scripts.
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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).
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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.
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plotly_demo.py: Utilizes Plotly for data visualization.- Presents data in draggable axis parallel coordinates plot.
- Classes are distinctly displayed with a heatmap legend.
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parallel_gl.py: Renders parallel coordinates in OpenGL using GPU pipelines.- Includes zoom functionality with the mouse wheel; panning is a work in progress.
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Parallel Andrew's Curves using matplotlib.
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parallel_hb.py: Focuses on the visualization of pure hyper-blocks.
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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.
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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.
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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.
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3D GLC-L Rotation.
- Introduces an additional z-axis using the tan function for GLC-L.
- Features an SVM-determined boundary border.
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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.
- CWU Visual Knowledge Discovery and Imaging Lab at https://github.com/CWU-VKD-LAB