fast_posterior_mean

Resources and high-level API for a fast posterior mean inference workflow.

The MuyGPyS.examples subpackage is deprecated and will be streamlined or removed in future versions.

make_fast_regressor() is a high-level API for creating the necessary components for fast posterior mean inference.

do_fast_posterior_mean() is a high-level api for executing a simple, generic fast posterior medan workflow given data. It calls the maker APIs above and fast_posterior_mean_any().

MuyGPyS.examples.fast_posterior_mean.do_fast_posterior_mean(test_features, train_features, train_targets, nn_count=30, batch_count=200, loss_fn=<MuyGPyS.optimize.loss.LossFn object>, opt_fn=<MuyGPyS.optimize.chassis.OptimizeFn object>, k_kwargs={}, nn_kwargs={}, opt_kwargs={}, verbose=False)[source]

Convenience function initializing a model and performing fast posterior mean inference.

Expected parameters include keyword argument dicts specifying kernel parameters and nearest neighbor parameters. See the docstrings of the appropriate functions for specifics.

Example

>>> from MuyGPyS.testing.test_utils import _make_gaussian_data
>>> from MuyGPyS.examples.fast_posterior_mean import do_fast_posterior_mean
>>> from MuyGPyS.gp.deformation import F2, Isotropy
>>> from MuyGPyS.gp.hyperparameter import Parameter
>>> from MuyGPyS.gp.hyperparameter import AnalyticScale
>>> from MuyGPyS.gp.kernels import RBF
>>> from MuyGPyS.gp.noise import HomoscedasticNoise
>>> from MuyGPyS.optimize import Bayes_optimize
>>> from MuyGPyS.optimize.objective import mse_fn
>>> train_features, train_responses = make_train()  # stand-in function
>>> test_features, test_responses = make_test()  # stand-in function
>>> nn_kwargs = {"nn_method": "exact", "algorithm": "ball_tree"}
>>> k_kwargs = {
...     "kernel": RBF(
...         deformation=Isotropy(
...             metric=F2,
...             length_scale=Parameter(1.0, (1e-2, 1e2))
...         )
...     ),
...     "noise": HomoscedasticNoise(1e-5),
...     "scale": AnalyticScale(),
... }
>>> (
...     muygps, nbrs_lookup, predictions, precomputed_coefficients_matrix
... ) = do_fast_posterior_mean(
...         test_features,
...         train_features,
...         train_responses,
...         nn_count=30,
...         batch_count=200,
...         loss_fn=lool_fn,
...         opt_fn=Bayes_optimize,
...         k_kwargs=k_kwargs,
...         nn_kwargs=nn_kwargs,
...         verbose=False,
... )
Parameters:

  • test_features (ndarray) – A matrix of shape (test_count, feature_count) whose rows consist of observation vectors of the test data.

  • train_features (ndarray) – A matrix of shape (train_count, feature_count) whose rows consist of observation vectors of the train data.

  • train_targets (ndarray) – A matrix of shape (train_count, response_count) whose rows consist of response vectors of the train data.

  • nn_count (int) – The number of nearest neighbors to employ.

  • batch_count (int) – The number of elements to sample batch for hyperparameter optimization.

  • loss_fn (LossFn) – The loss functor to use in hyperparameter optimization. Ignored if all of the parameters specified by argument k_kwargs are fixed.

  • opt_fn (OptimizeFn) – The optimization functor to use in hyperparameter optimization. Ignored if all of the parameters specified by argument k_kwargs are fixed.

  • k_kwargs (Dict) – Assume that the elements are keyword arguments to a MuyGPs model (see make_regressor()).

  • nn_kwargs (Dict) – Parameters for the nearest neighbors wrapper. See MuyGPyS.neighbors.NN_Wrapper for the supported methods and their parameters.

  • opt_kwargs (Dict) – Parameters for the wrapped optimizer. See the docs of the corresponding library for supported parameters.

  • verbose (bool) – If True, print summary statistics.

Return type:

Tuple[ndarray, NN_Wrapper, ndarray, ndarray, Dict]

Returns:

  • muygps – A (possibly trained) MuyGPs object.

  • nbrs_lookup – A data structure supporting nearest neighbor queries into train_features.

  • predictions – The predicted response associated with each test observation.

  • precomputed_coefficients_matrix – A matrix of shape (train_count, nn_count) whose rows list the precomputed coefficients for each nearest neighbors set in the training data.

  • timing – A dictionary containing timings for the training, precomputation, nearest neighbor computation, and prediction.

MuyGPyS.examples.fast_posterior_mean.fast_posterior_mean_any(muygps, test_features, train_features, nbrs_lookup, train_targets)[source]

Convenience function performing fast posterior mean inference using a pre-trained model.

Parameters:

  • muygps (MuyGPS) – A (possibly trained) MuyGPS object.

  • test_features (ndarray) – A matrix of shape (test_count, feature_count) whose rows consist of observation vectors of the test data.

  • train_features (ndarray) – A matrix of shape (train_count, feature_count) whose rows consist of observation vectors of the train data.

  • nbrs_lookup (NN_Wrapper) – A data structure supporting nearest neighbor queries into train_features.

  • train_targets (ndarray) – A matrix of shape (train_count, response_count) whose rows consist of response vectors of the train data.

Return type:

Tuple[ndarray, ndarray, Dict]

Returns:

  • posterior_mean – The predicted response associated with each test observation.

  • precomputed_coefficients_matrix – A matrix of shape (train_count, nn_count) whose rows list the precomputed coefficients for each nearest neighbors set in the training data.

  • timing – A dictionary containing timings for the training, precomputation, nearest neighbor computation, and prediction.

MuyGPyS.examples.fast_posterior_mean.make_fast_regressor(muygps, nbrs_lookup, train_features, train_targets)[source]

Convenience function for creating precomputed coefficient matrix and neighbor lookup data structure.

Parameters:

  • muygps (MuyGPS) – A (possibly trained) MuyGPS object.

  • nbrs_lookup (NN_Wrapper) – A data structure supporting nearest neighbor queries into train_features.

  • train_features (ndarray) – A matrix of shape (train_count, feature_count) whose rows consist of observation vectors of the train data.

  • train_targets (ndarray) – A matrix of shape (train_count, response_count) whose rows consist of response vectors of the train data.

Return type:

Tuple[ndarray, ndarray]

Returns:

  • precomputed_coefficients_matrix – A matrix of shape (train_count, nn_count) whose rows list the precomputed coefficients for each nearest neighbors set in the training data.

  • nn_indices – A numpy.ndarrray supporting nearest neighbor queries.