This repo accompanies Piecewise regression: when one line simply isn’t enough, a blog post about Datadog's approach to piecewise regression. The code included here is intended to be minimal and readable; this is not a Swiss Army knife to solve all variations of piecewise regression problems.
This package was written to work with both Python 2 and Python 3.
To install this package using setup tools, clone this repo and run python setup.py install from within the piecewise root directory.
The package's core piecewise() function for regression requires only numpy. The use of piecewise_plot() for plotting depends also on matplotlib.
Start by preparing your data as list-likes of timestamps (independent variables) and values (dependent variables).
import numpy as np
t = np.arange(10)
v = np.array(
[2*i for i in range(5)] +
[10-i for i in range(5, 10)]
) + np.random.normal(0, 1, 10)
Now, you're ready to import the piecewise() function and fit a piecewise linear regression.
from piecewise import piecewise
model = piecewise(t, v)
model if a FittedModel object. If you are at a shell, you can print the object to see the fitted segments domains and regression coefficients.
>>> model
FittedModel with segments:
* FittedSegment(start_t=0, end_t=5, coeffs=(-0.8576123780622642, 2.224791099812951))
* FittedSegment(start_t=5, end_t=9, coeffs=(10.975487672814133, -1.0722348284390741))
Alternatively, you can use the FittedModel's segments attribute to get at values.
>>> len(model.segments)
2
>>> model.segments[0].coeffs
(-0.8576123780622642, 2.224791099812951)
If you want to interpolate or extrapolate, you can use the FittedModel's predict() function.
>>> model.predict(t_new=[3.5, 100])
array([ 6.92915647, -96.24799517])
To see a plot, instead of getting a FittedModel, use piecewise_plot(). You may also use an existing FittedModel.
from piecewise import piecewise_plot
# using an existing FittedModel
piecewise_plot(t, v, model=model)
# fitting a model on the fly
piecewise_plot(t, v)
