Filling the Area of Different Classes in a Scatter Plot with Matplotlib

Introduction

When working with scatter plots created using matplotlib, it’s often desirable to add an additional layer of visualization that helps differentiate between classes. One way to achieve this is by filling the area behind the scatter plot for each class. In this article, we’ll explore how to implement this feature using various techniques and modules in Python.

Background

To begin with, let’s take a look at some of the code snippets provided in the question. The scatter plot created using plt.scatter displays points of different colors based on their corresponding classes. However, the background area behind these points is not filled, making it difficult to visualize relationships between different classes.

Using Contour Plots

One approach to solve this problem is by using contour plots. A contour plot is a type of 2D or 3D graph that displays constant values as contours. In our case, we can use contour plots to fill the area behind the scatter plot for each class.

The example code provided in the question demonstrates how to create a contour plot using ax2.tricontourf. This method takes several parameters:

points[:, 0] and points[:, 1]: These are the x and y coordinates of the points.
group: This is the class label for each point, which determines the color and fill of the contour plot.
levels: This specifies the values at which to draw the contours. In this case, we’re using np.arange(-0.5, 4) as the levels, resulting in four contours (one for each class).

However, there’s a catch! Contour plots can sometimes produce narrow zones between different classes, especially if the contour plot only looks at numeric values.

Using Nearest Neighbor Classification

Another approach to solve this problem is by using nearest neighbor classification. This method involves fitting a classifier to the data points and then using it to predict the class label for any point within a certain range.

In the example code provided in the question, we use sklearn.neighbors.KNeighborsClassifier to create a nearest neighbor classifier. We first fit this classifier to our dataset of points and their corresponding classes.

Then, we generate a meshgrid of x and y values using np.meshgrid. These grids represent all possible combinations of x and y coordinates within the range of our original data points. We then pass these coordinates through the nearest neighbor classifier to predict the class label for each point on the grid.

Finally, we display the predicted class labels as an image using ax2.imshow, which creates a filled contour plot that highlights the differences between different classes.

Implementing Filled Scatter Plots

To create filled scatter plots in matplotlib, you can use the following code snippet:

import numpy as np
import matplotlib.pyplot as plt

# Create a figure with two subplots
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 5), sharey=True, sharex=True)

# Generate some random data points
N = 100
points = np.random.normal(np.tile(np.random.uniform(1, 10, 2 * M), N)).reshape(-1, 2)
groups = np.tile(np.arange(M), N)

# Create scatter plots for both subplots
ax1.scatter(points[:, 0], points[:, 1], c=groups)
ax2.scatter(points[:, 0], points[:, 1], c=groups)

# Fill the area behind the scatter plot for each class
for i in range(M):
    ax2.fill_between(points[groups == i, 0],
                     points[groups == i, 1] - 0.5,
                     points[groups == i, 1] + 0.5,
                     color=plt.cm.tab10(i),
                     alpha=0.3)

plt.show()

This code creates two scatter plots in the same figure but fills the area behind each class with a different colored contour.

Conclusion

In this article, we explored how to fill the area of different classes in scatter plot matplotlib using various techniques and modules. We discussed using contour plots and nearest neighbor classification as potential approaches. Finally, we provided an example code snippet that demonstrates how to create filled scatter plots for different classes.

By following these tips and examples, you can enhance your scatter plot visualizations with additional information about class labels and relationships between them.

Last modified on 2023-06-29