[](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation) [](https://pepy.tech/project/cde)
# Conditional Density Estimation (CDE)
## Description
Implementations of various methods for conditional density estimation
* **Parametric neural network based methods**
* Mixture Density Network (MDN)
* Kernel Mixture Network (KMN)
* Normalizing Flows (NF)
* **Nonparametric methods**
* Conditional Kernel Density Estimation (CKDE)
* Neighborhood Kernel Density Estimation (NKDE)
* **Semiparametric methods**
* Least Squares Conditional Density Estimation (LSKDE)
Beyond estimating conditional probability densities, the package features extensive functionality for computing:
* **Centered moments:** mean, covariance, skewness and kurtosis
* **Statistical divergences:** KL-divergence, JS-divergence, Hellinger distance
* **Percentiles and expected shortfall**
## Installation
To use the library, you can directly use the python package index:
```bash
pip install cde
```
or clone the GitHub repository and run
```bash
pip install .
```
Note that the package only supports tensorflow versions between 1.4 and 1.7.
## Documentation and paper
See the documentation [here](https://freelunchtheorem.github.io/Conditional_Density_Estimation). A paper on best practices and benchmarks on conditional density estimation with neural networks that makes extensive use of this library can be found [here](https://arxiv.org/abs/1903.00954).
## Usage
The following code snipped holds an easy example that demonstrates how to use the cde package.
```python
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]
```
## Citing
If you use CDE in your research, you can cite it as follows:
```
@article{rothfuss2019conditional,
title={Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks},
author={Rothfuss, Jonas and Ferreira, Fabio and Walther, Simon and Ulrich, Maxim},
journal={arXiv:1903.00954},
year={2019}
}
```
## Todo
- creating a branch just for our conditional estimators + python package
