This repository contains examples and pretrained models for our UAI 2021 paper called "GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data". You can explore examples interactively in the examples.ipynb (in the same folder as this README) Jupyter notebook and also re-run our experiments. Further instructions are below.

If you just want to look at the examples, it's enough to install the package in this repository via

pip install path/to/this/folder

which will create a package called neuralprocess in your current Python environment. The package includes our GP-ConvCNP implementation, but also implementations of other major Neural Process variants (ConvCNP, NP, ANP), hence the name. We recommend you create a new virtualenv or conda environment before installing. If you also want to be able to run the experiments yourself, you need to install with the experiment option

pip install path/to/this/folder[experiment]

Alternatively you can run

pip install -r requirements_experiment.txt

after the regular installation. You might receive an error that trixi requires a specific version of scikit-learn, which you can safely ignore.

The notebook examples.ipynb allows you to create plots similar to the ones in the paper. Simply run

jupyter notebook

from this folder (or above), open the notebook and follow the instructions inside.

Our experiment script is part of the neuralprocess package and can be found at neuralprocess/experiment/neuralprocessexperiment.py. You will find that our experiment uses the PytochExperiment class from trixi for logging and configuration. This gives us a great deal of flexibility, but we will only list the relevant options to reproduce the experiments from the paper. You basically only need to run the following command

python neuralprocessexperiment.py LOG_DIR -m MODS

The modifications are defined at the top of the file. The default configuration will be a Neural Process, trained on samples from a GP with an RBF kernel. You can apply the following modifications:

• DETERMINISTICENCODER adds a deterministic path to Neural Process. Required for Attentive Neural Processes.
• ATTENTION will use an ANP instead of a NP. Requires the DETERMINISTICENCODER mod.
• CONVCNP will use a ConvCNP instead of a NP.
• GPCONVCNP will use a GP-ConvCNP instead of a NP. Requires the CONVCNP mod to be set.
• LEARNNOISE makes sigma^2 in the GP learnable. This was used in the experiments in the paper.
• MATERNKERNEL will train on functions from GP with a Matern-5/2 kernel.
• WEAKLYPERIODICKERNEL will train on functions from a GP with a weakly periodic kernel as defined in the ConvCNP paper.
• STEP will train on step functions
• FOURIER will train on random Fourier series.
• LOTKAVOLTERRA will train on population dynamics generated from Lotka-Volterra equations.
• TEMPERATURE will train on temperature measurements taken from here.
• LONG will double the number of training epochs.

Beyond that you can modify any value in the configuration directly, including deeper levels. For example, if you have the ATTENTION option activated, but you only want to use 4 heads in the attention mechanism, you could add the flag --modules_kwargs.attention.num_heads 4. Other useful flags are

• -v will log to Visdom. You need to start a Visdom server beforehand with python -m visdom.server --port 8080
• -ad will generate a description for the experiment by looking at the difference to the default configuration. The description will be saved as part of the config in the logging directory you specify.

To give a more illustrative example, let's assume you want to run GP-ConvCNP on step functions with twice the default amount of epochs, but with a larger initial learning rate. You also want to run the tests at the end, but for some reason only those for prediction and reconstruction ability. The training should be logged to Visdom and you want an automatic description generated. Your command would look like this:

python neuralprocessexperiment.py LOG_DIR -v -ad -m STEP CONVCNP GPCONVCNP LONG --optimizer_kwargs.lr 1e-2 --test --test_diversity false