DISCLAIMER: NOT MAINTAINED. While this is the code we used to run our experiments in the paper. We recommend our newer, maintained codebase https://github.com/automl/PFNs if you want to work on PFNs.

We train Transformers to do Bayesian Prediction on novel datasets for a large variety of priors. For more info read our paper. You can play with our model in an interactive demo with a GP prior and compare it to the ground truth GP posterior, as described in the paper's section 5.1.

For insights into experiments, please see our notebooks folder. From where most experiments, besides some baselines are started.

Training the transformers can be quickly done for all tasks considered, but we still provide models for the tabular tasks as convenience to be able solve new tabular tasks out-of-the-box.

Getting Started

This is a python project, we used Python 3.9 in development and recommend to use a virtualenv or conda. To use our code, clone the project with

git clone git@github.com:automl/TransformersCanDoBayesianInference.git

install all dependencies with

pip install -r requirements.txt

Reproducing the GP results

You can have a look at notebooks/SetupForGPFittingExperiments.ipynb. The hyper-paramters are chosen to reproduce figure 3 a). If you want to consider smaller datasets reduce bptt and the max number of training samples provided in utils.get_weighted_single_eval_pos_sampler.

Training a model with a custom prior

notebooks/BayesianModels_And_Custom_Pyro_Modules.ipynb provides a workflow to train and evaluate a PFN model with a custom prior. A prior is defined by providing a sampling procedure as a PyroModule. A prior template can be found in this notebook.

Below we show an overview of training a PFN for a custom prior. A full example can be found in BayesianModels_And_Custom_Pyro_Modules.ipynb.

class CustomModel(PyroModule):
    def __init__(self, device='cuda'):
        super().__init__()

        self.model = model_spec()

    def forward(self, seq_len=1):
        with pyro.plate("x_plate", seq_len):
            d_ = dist.Normal(torch.tensor([0.0]).to(self.device), torch.tensor([1.0]).to(self.device)).expand(
                [self.num_features]).to_event(1)
            x = pyro.sample("x", d_)

        out = self.model(x)
        
        return x, out

# Function which generates a model from the prior
model_sampler = lambda : BayesianModel(model_spec, device = device)

config = {'lr': 2.006434218345026e-05, 'epochs': 160}

transformer_model = get_model(model_sampler, config, should_train = True)

Evaluating Tabular Models

notebooks/TabularEvalSimple.ipynb provides a workflow to evaluate baselines and the transformer on the balanced subset of the AutoML Benchmark (filtered by Nans, number of features).

Cite

@inproceedings{
    muller2022transformers,
    title={Transformers Can Do Bayesian Inference},
    author={Samuel M{\"u}ller and Noah Hollmann and Sebastian Pineda Arango and Josif Grabocka and Frank Hutter},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=KSugKcbNf9}
}