GP-Bayes-Rules-Experiments

Experiments in the empirical evaluation section of the article [OPT2020].

Installation

These experiments require that the library scikit-fda and its dependencies are installed. The stable version can be installed via PyPI:

pip install scikit-fda

The experiments require also the package Sacred to collect the results. This package is also available in PyPI:

pip install sacred

Sacred requires an observer to store the results. The functions used to query the results and plot the output assume that the observer used is a MongoDB database. In that case, MongoDB should be installed and the packages incense and pymongo should also be installed to retrieve the results:

pip install incense
pip install pymongo

Launching a experiment

In order to launch an experiment, we will execute the main files in the project folder. As these experiments use Sacred to run, the configuration options can be set to match the ones used in the article (or to test a different configuration). An example of this is shown below:

python main_brownian_bridge.py with train_n_samples=50 -m localhost:27017:GPBayes

The with keyword is used to change the options of the experiment. The -m localhost:27017:GPBayes adds a MongoDB observer, storing the results in the GPBayes database. It is important to use this name as it is currently harcoded in the retrieval functions.

Plotting a experiment

In order to plot the results, there are functions called plot_experiments in each of the submodules corresponding to the plotting part of each experiment. These functions can create the matplotlib figure of the results, as shown below:

from experiments.brownian_bridge.plot import plot_experiments
import matplotlib.pyplot as plt

plot_experiments([1, 2, 3])
plt.show()

Here we assume that the sacred experiments with ids 1, 2 and 3 contain, respectively, the results of the Brownian bridge experiment with train sizes 50, 200 and 1000, in order to replicate the results of the paper.

List of experiments

The list of the experiments with synthetic data and their configuration parameters is shown below. A comprehensive description of each experiment is in the original article.

The common configuration parameters are the following:

• max_pow = 10: The maximum power of the resolution used in the discretization.
• n_tests = 100: The number of independent replications.
• train_n_samples = 1000: The number of observations in the train set. It must be set to 50, 200 and 1000 in separate runs, to replicate the results of the paper.
• test_n_samples = 1000: The number of observations in the test set.
• random_state_train_seed = 0: A random seed to initialize the RNG that is used in the train set generation.
• random_state_test_seed = 1: A random seed to initialize the RNG that is used in the test set generation.

Brownian processes with different means

This experiment presents a classification problem in which the classes are two Brownian processes with different means. One of the means is 0 and the other is a step function. The experiment folder for this experiment is brownian_step.

The additional configuration parameters for this experiment are:

• step_height = 0.3: The height of the function after the step.

Brownian motion versus Brownian bridge

This experiment presents a classification problem in which the classes are a standard Brownian process and a standard Brownian bridge process. The experiment folder for this experiment is brownian_bridge.

The additional configuration parameters for this experiment are:

• end_position = 0.5: The end of the interval in which the functions are evaluated. Set it to 0.95 to match the results in the article.

Brownian processes with different variances

This experiment presents a classification problem in which the classes are two Brownian processes with different variances. The experiment folder for this experiment is brownian_variances.

The additional configuration parameters for this experiment are:

• class0_var = 1: The variance of class 0.
• class1_var = 1.3: The variance of class 1. Set it to 1.5 to match the results in the article.

The real data example and the simulated data example, available in cars and cars_synthetic are similar to this one. The data for the real data example cannot be publicly posted, as it came from Google Finance. Contact the maintainer for more info.

OPT2020

Torrecilla, J. L., Ramos-Carreño, C., Sánchez-Montañés, M., & Suárez, A. (2020). Optimal classification of Gaussian processes in homo-and heteroscedastic settings. Statistics and Computing, 30(4), 1091-1111. https://doi.org/10.1007/s11222-020-09937-7