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Machine Learning - Voronoi Plots

General

vastorbit’s Voronoi plot functionality offers a powerful visualization tool for understanding spatial distribution and proximity within your data. By dividing a space into cells based on the closest data point, Voronoi plots reveal the geometric relationships and patterns among data points in two-dimensional space. This visualization technique is especially valuable in spatial analysis, clustering, and nearest neighbor applications, providing a clear and intuitive representation of data point relationships and spatial structures. With vastorbit’s Voronoi plots, data analysts can gain deeper insights into their datasets and make informed decisions based on spatial patterns and distributions.

Let’s begin by importing vastorbit.

import vastorbit as vo

Let’s also import numpy to create a random dataset.

import numpy as np

Let’s generate a dataset using the following data.

N = 100 # Number of Records
k = 10 # step

# Normal Distributions
x = np.random.normal(5, 1, round(N / 2))
y = np.random.normal(3, 1, round(N / 2))

# Creating a VastFrame with two clusters
data = vo.VastFrame({
    "x": np.concatenate([x, x + k]),
    "y": np.concatenate([y, y + k]),
})

Let’s proceed by fitting a KMeans model.

# Importing the VAST ML module
import vastorbit.machine_learning.vast as vml

# Defining the KMEANS Model
model = vml.KMeans()

# Fitting the KMEANS model
model.fit(data, ["x", "y"])

In the context of data visualization, we have the flexibility to harness multiple plotting libraries to craft a wide range of graphical representations. vastorbit, as a versatile tool, provides support for several graphic libraries, such as Matplotlib and Plotly. Each of these libraries offers unique features and capabilities, allowing us to choose the most suitable one for our specific data visualization needs.

_images/plotting_libs.png

Note

To select the desired plotting library, we simply need to use the set_option() function. vastorbit offers the flexibility to smoothly transition between different plotting libraries. In instances where a particular graphic is not supported by the chosen library or is not supported within the vastorbit framework, the tool will automatically generate a warning and then switch to an alternative library where the graphic can be created.

Please click on the tabs to view the various graphics generated by the different plotting libraries.

We can switch to using the plotly module.

vo.set_option("plotting_lib", "plotly")

model.plot_voronoi()

We can switch to using the matplotlib module.

vo.set_option("plotting_lib", "matplotlib")

model.plot_voronoi()

Chart Customization

vastorbit empowers users with a high degree of flexibility when it comes to tailoring the visual aspects of their plots. This customization extends to essential elements such as color schemes, text labels, and plot sizes, as well as a wide range of other attributes that can be fine-tuned to align with specific design preferences and analytical requirements. Whether you want to make your visualizations more visually appealing or need to convey specific insights with precision, vastorbit’s customization options enable you to craft graphics that suit your exact needs.

Important

Different customization parameters are available for Plotly and Matplotlib. For a comprehensive list of customization features, please consult the documentation of the respective libraries: plotly, matplotlib.

Colors

Custom colors

model.plot_voronoi(colors = ["red", "blue"])

Custom colors

model.plot_voronoi(colors = ["red", "blue"])

Size

Custom Width and Height

model.plot_voronoi(width = 300, height = 300)

Custom Width and Height

model.plot_voronoi(width = 6, height = 3)

Text

Custom Title

model.plot_voronoi().update_layout(title_text = "Custom Title")

Custom Axis Titles

model.plot_voronoi(yaxis_title = "Custom Y-Axis Title")