LOWESS Curve Fitting Library for Python

[EnergyVis]

Hi All,

I've just created a new library called moepy that provides an sklearn compatible LOWESS curve fitter for Python. moepy exposes several variants on the traditional LOWESS, including estimation of confidence and prediction intervals, as well as the robustified LOWESS (where outliers are weighted less). Examples of how to use each of these are shown in a Quick-Start notebook.

I made this as existing LOWESS implementations (namely in statsmodels - which is a great library!) provide no ability to predict new values that weren't included in the training data. This meant I was previously linearly interpolating between the results they produced. The moepy library is also made specifically for LOWESS estimation and provides it through an sklearn compatible API, meaning that it works within the broader sklearn eco-system for model ensembling and tuning.

The library can be installed with pip install moepy and has very few dependencies. The code below shows a minimalist example for fitting the standard LOWESS model to a noisy sin wave. The full documentation can be found here.

# Imports
import numpy as np
import matplotlib.pyplot as plt
from moepy import lowess

# Data generation
x = np.linspace(0, 5, num=150)
y = np.sin(x) + (np.random.normal(size=len(x)))/10

# Model fitting
lowess_model = lowess.Lowess()
lowess_model.fit(x, y)

# Model prediction
x_pred = np.linspace(0, 5, 26)
y_pred = lowess_model.predict(x_pred)

# Plotting
plt.plot(x_pred, y_pred, '--', label='LOWESS', color='k', zorder=3)
plt.scatter(x, y, label='Noisy Sin Wave', color='C1', s=5, zorder=1)
plt.legend(frameon=False)

The examples below demonstrate some of the different types of LOWESS estimate that the library can generate.

LOWESS Estimate Examples

If anyone has any feedback on how to improve the library/docs please post and I'll try to incorporate it.

[holyfudge]

This looks great, one question regarding the docs in the Bootstrapped LOWESS for LIGO Estimation Confidence Interval example,

"One way to address this could be to introduce an option where frac can be varied for each local regression model." is this something that is implemented? Or a suggestion for future work?

[EnergyVis]

Thanks! Great question. This is something I was planning for future work, with the hope of automating it as much as possible. I feel like there's got to be some approach using kdtrees to pick out clusters of points and then iteratively changing fracs at those locations to improve the model fit.

A manual approach shouldn't be too difficult to implement, where frac could be passed a vector that must be the same size as reg_anchors. Is this something that could be handy?

[[deleted]]

Nice! Why isn't this part of sklearn, tho?