deepquantiles 0.0.0

Deep Continuous Quantile Regression

Deep Continuous Quantile Regression

This package explores different approaches to learning the uncertainty, and, more generally, the conditional distribution of the target variable.

This is particularily importent when

• the mean of the target variable is not sufficient for the use case
• the errors are heteroscedastic, i.e. vary depending on input features
• the errors are skewed, making a single descriptor such as variance inadequate.

We explore two main approches:

1. fitting a mixture density model
2. learning the location of conditional qunatiles, q, of the distribution.

Our mixture density network exploits an implementation trick to achieve negative-log-likelihood minimisation in keras.

Same trick is useed to optimize the "pinball" loss in quantile regression networks.

Within the quantile-based approach, we further explore: a. fitting a separate model to predict each quantile b. fitting a multi-output network to predict multiple quantiles simultaneously c. learning a regression on X and q simultanesously, thus effectively learning the complete (conditional) cumulative density function.

Installation

Install package from source:

pip install git+https://github.com/ig248/deepquantiles

Or from PyPi:

pip install deepquantiles

Content

• deepqunatiles.regressors: implementation of core algorithms
• deepquantiles.presets: a collection of pre-configured estimators and settings used in experiments
• deepquantiles.datasets: functions used for generating test data
• deepquantiles.nb_utils: helper functions used in notebooks
• notebooks: Jupyter notebooks with examples and experiments

Tests

Run

make dev-install
make lint
make test

References