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KDEpy

About

This Python 3.8+ package implements various kernel density estimators (KDE). Three algorithms are implemented through the same API: NaiveKDE, TreeKDE and FFTKDE. The class FFTKDE outperforms other popular implementations, see the comparison page. The code is stable and in widespread use by practitioners and in other packages.

Plot

The code generating the above graph is found in examples.py.

Installation

KDEpy is available through PyPI, and may be installed using pip:

pip install KDEpy

If you have trouble on Ubuntu, try running sudo apt install libpython3.X-dev, where 3.X is your Python version.

Example code and documentation

Below is an example showing an unweighted and weighted kernel density. From the code below, it should be clear how to set the kernel, bandwidth (variance of the kernel) and weights. See the documentation for more examples.

from KDEpy import FFTKDE
import matplotlib.pyplot as plt

customer_ages = [40, 56, 20, 35, 27, 24, 29, 37, 39, 46]

# Distribution of customers
x, y = FFTKDE(kernel="gaussian", bw="silverman").fit(customer_ages).evaluate()
plt.plot(x, y)

# Distribution of customer income (weight each customer by their income)
customer_income = [152, 64, 24, 140, 88, 64, 103, 148, 150, 132]

# The `bw` parameter can be manually set, e.g. `bw=5`
x, y = FFTKDE(bw="silverman").fit(customer_ages, weights=customer_income).evaluate()
plt.plot(x, y)

Plot

The package consists of three algorithms. Here's a brief explanation:

Issues and contributing

Issues

If you are having trouble using the package, please let me know by creating an Issue on GitHub and I'll get back to you.

Contributing

Whatever your mathematical and Python background is, you are very welcome to contribute to KDEpy. To contribute, fork the project, create a branch and submit and Pull Request. Please follow these guidelines: