API Reference¶
- class gmmvi.gmmvi_runner.GmmviRunner(config, log_metrics_interval)[source]¶
This class runs
GMMVI
, but also evaluates learning metrics and provides logging functionality.- Parameters:
config – dict A dictionary containing the hyperparameters and environment specifications.
log_metrics_interval – int metrics that take non-negligible overhead are evaluated ever log_metrics_interval iterations.
- static build_from_config(config: dict)[source]¶
Create a
GMMVI
instance from a configuration dictionary.This static method provides a convenient way to create a
GMMVI
instance, based on a dictionary containing the types and parameters of theGMMVI modules
.- Parameters:
config – dict The dictionary should contain for each
GMMVI module
an entry of the form XXX_type (a string) and XXX_config (a dict) for specifying the type of each module, and the module-specific hyperparameters. For example, the dictionary could contain sample_selector_type={“component-based”} and sample_selector_config={“desired_samples_per_component”: 100, “ratio_reused_samples_to_desired”: 2.}. Refer to the example yml-configs, or to the individual GMMVI module for the expected parameters, and type-strings.
- finalize()[source]¶
Can be called after learning. Saves the final model parameters to the hard drive.
- get_cheap_metrics()[source]¶
Returns a dictionary of ‘cheap’ metrics, e.g. the current number of components, that we can obtain after every iteration without adding computational overhead.
- Returns:
A dictionary containing cheap metrics.
- Return type:
- get_expensive_metrics()[source]¶
Computes ‘expensive’ metrics, such as plots, test-set evaluations, etc. Some of these metrics can be task-specific (see
LNPDF.expensive_metrics()
).- Returns:
A dictionary containing expensive metrics.
- Return type:
- get_samples_and_entropy(num_samples)[source]¶
Draws num_samples from the model and uses them to estimate the model’s entropy.