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.
Installation & dependencies
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
plot_data_with_regression() for plotting depends also on
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.regressor 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
segments attribute to get at values.
>>> len(model.segments) 2 >>> model.segments.coeffs (-0.8576123780622642, 2.224791099812951)
If you want to interpolate or extrapolate, you can use the
>>> model.predict(t_new=[3.5, 100]) array([ 6.92915647, -96.24799517])
To see a plot, instead of getting a
from piecewise.plotter import plot_data_with_regression plot_data_with_regression(t, v)