CWGP 2.0.7

CWGP

Compositionally Warped Gaussian Processes

This package is dedicated to realizing methods used in this paper.

TLDR;

A package that transforms anything to a Gaussian distribution.

Tutorial

Visit here

Installation

pip install CWGP

Quick Start

Let's randomly generate 100 numbers following an exponential distribution.

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

exp = np.random.exponential(scale=5, size=50)
idx = np.arange(50)

We now instantiate a CWGP class consisting of 3 Sinh-Arcsinh transformations.

from cwgp.cwgp import CWGP

compgp = CWGP(["sa","box_cox"])

We then fit our data into the model. This minimizes the negative log likelihood function and stores the corresponding parameters for us.

compgp.fit(exp, idx)

To get the parameters, we do

params = compgp.phi.res.x

We then transform the data via

t_exp, d = compgp.phi.comp_phi(params, exp)
sns.distplot(t_exp)
plt.show()

Let's make a QQ-plot and see how Gaussian it is.

from scipy import stats

stats.probplot(t_exp, dist="norm", plot=plt)
plt.show()

The inverse function is also implemented.

inv_t_exp = compgp.phi.inv_comp_phi(params, t_exp)

fig, ax = plt.subplots(1, 2)
sns.distplot(inv_t_exp, ax=ax[0])
sns.distplot(exp, ax=ax[1])
plt.show()

The one on the left is the one being transformed and transformed-back, and the one on the right is the original distribution. They should be exactly the same.

Transformations

Sinh-Arcsinh (sa)

from cwgp.transformations import sa

Arcsinh (asinh)

from cwgp.transformations import asinh

Box-Cox (box_cox)

from cwgp.transformations import box_cox

Sinh-Arcsinh and Affine (SAL)

from cwgp.transformations import sal