To use the library, either install it via
pip install <todo>or clone the GitHub repository and run
pip install .Note that the package only supports tensorflow versions between 1.4 and 1.7.
See a documentation here. If you're interested in more implementations for conditional density estimation, see our other package including many data generating processes and evaluation methods here.
The following code snipped holds an easy example that demonstrates how to use the cde package.
from cde.density_simulation import SkewNormal
from cde.density_estimator import KernelMixtureNetwork
import numpy as np
""" simulate some data """
density_simulator = SkewNormal(random_seed=22)
X, Y = density_simulator.simulate(n_samples=3000)
""" fit density model """
model = KernelMixtureNetwork("KDE_demo", ndim_x=1, ndim_y=1, n_centers=50,
x_noise_std=0.2, y_noise_std=0.1, random_seed=22)
model.fit(X, Y)
""" query the conditional pdf and cdf """
x_cond = np.zeros((1, 1))
y_query = np.ones((1, 1)) * 0.1
prob = model.pdf(x_cond, y_query)
cum_prob = model.cdf(x_cond, y_query)
""" compute conditional moments & VaR """
mean = model.mean_(x_cond)[0][0]
std = model.std_(x_cond)[0][0]
skewness = model.skewness(x_cond)[0]If you use this package in your research, you can cite it as follows:
@misc{cde2019,
author = {Jonas Rothfuss, Fabio Ferreira},
title = {Conditional Density Estimation},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ferreira-fabio/Conditional_Density_Estimation}},
}