v0.7.3

• Ensemble deep kernel learning (DKL) as an 'approximation' to the fully Bayesian DKL
• Thompson sampler for active learning now comes as a built-in method in the DKL class
• Option to select between correlated and independent outputs for vector-valued function in DKL

Example of using an ensemble of DKL models:

# Initialize and train ensemble of models
dklgp = aoi.models.dklGPR(indim=X_train.shape[-1], embedim=2)
dklgp.fit_ensemble(X_train, y_train, n_models=5, training_cycles=1500, lr=0.01)
# Make a prediction
y_samples = dklgp.sample_from_posterior(X_test, num_samples=1000) # n_models x n_samples x n_data
y_pred = y_samples.mean(axis=(0,1)) # average over model and sample dimensions
y_var = y_samples.var(axis=(0,1))

Example of using a built-in Thompson sampler for active learning:

for e in range(exploration_steps):
    # obtain/update DKL-GP posterior
    dklgp = aoi.models.dklGPR(data_dim, embedim=2, precision="single")
    dklgp.fit(X_train, y_train, training_cycles=50)
    # Thompson sampling for selecting the next measurement/evaluation point
    obj, next_point = dklgp.thompson(X_cand)
    # Perform a 'measurement'
    y_measured = measure(next_point)
    # Update measured and candidate points, etc...