py-tbfm

Implementation of the Temporal Basis Function Model (TBFM)

Quick start

If you are looking to use the TBFM implementation, simply install this module; e.g.:

pip install .

then use it as follows:

model = tbfm.TBFM(NUM_CHANNELS,              # Dimensionality of time series
                  STIM_DESC_DIM,             # Dimensionality of stimulation descriptor
                  RUNWAY_LENGTH,             # Length of runway, in time steps
                  NUM_BASES,                 # Number of bases we will learn
                  FORECAST_HORIZON,          # Length of forecast, in time steps
                  batchy=y_train,            # A training dataset, for estimating means/stdevs
                  latent_dim=LATENT_DIM,     # Latent dimension of basis generator network
                  basis_depth=BASIS_DEPTH,   # Depth of basis generator network
                  device=DEVICE)             # Choice of device, e.g. "cpu" or "cuda:0"
optim = model.get_optim(lr=2e-4)             # Optimizer for use in training

A forward pass looks like this:

FORECAST_HORIZON = TRIAL_LENGTH - RUNWAY_LENGTH

yhat = model(
             runways,            # tensor shaped (batch_size, RUNWAY_LENGTH, NUM_CHANNELS)
             stim_descriptor,    # tensor shaped (batch_size, FORECAST_HORIZON, STIM_DESC_DIM)
       )

# yhat is a tensor shaped (batch_size, FORECAST_HORIZON, NUM_CHANNELS)

Walkthrough and demo

TBFM Demo.ipynb provides a detailed walkthrough which uses some synthetic data. TBFM FSAM Demo.ipynb provides an additional demo where we build the TBFM using forward stagewise additive modeling. It's recommended to go through this one after the first.

Architecture