scvi.autotune.AutotuneExperiment

scvi.autotune.AutotuneExperiment#

class scvi.autotune.AutotuneExperiment(model_cls, data, metrics, mode, search_space, num_samples, scheduler='asha', searcher='hyperopt', seed=None, resources=None, name=None, logging_dir=None, scheduler_kwargs=None, searcher_kwargs=None)[source]#

BETA Track hyperparameter tuning experiments.

Parameters:

model_cls (BaseModelClass) – Model class on which to tune hyperparameters. Must implement a constructor and a train method.
data (AnnData | MuData | LightningDataModule) – AnnData or MuData that has been setup with model_cls or a LightningDataModule (EXPERIMENTAL).
metrics (str | list[str]) – Either a single metric or a list of metrics to track during the experiment. If a list is provided, the primary metric will be the first element in the list.
mode (Literal['min', 'max']) – Optimization mode for the primary metric. One of "min" or "max".
search_space (dict[str, dict[Literal['model_params', 'train_params'], dict[str, Any]]]) –
Dictionary of hyperparameter names and their respective search spaces. See the API for available search specifications. Must only contain the following top-level keys:
- "model_params": parameters to pass to the model constructor.
- "train_params": parameters to pass to the model’s train method.
Passed into Tuner as param_space.
num_samples (int) – Total number of hyperparameter configurations to sample from the search space. Passed into TuneConfig.
scheduler (Literal['asha', 'hyperband', 'median', 'fifo'] (default: 'asha')) –
Ray Tune scheduler to use. One of the following:
- "asha": AsyncHyperBandScheduler
- "hyperband": HyperBandScheduler
- "median": MedianStoppingRule
- "fifo": FIFOScheduler
Configured with reasonable defaults, which can be overridden with scheduler_kwargs.
searcher (Literal['hyperopt', 'random'] (default: 'hyperopt')) –
Ray Tune search algorithm to use. One of the following:
- "hyperopt": HyperOptSearch
- "random": BasicVariantGenerator
Configured with reasonable defaults, which can be overridden with searcher_kwargs.
seed (int | None (default: None)) – Random seed to use for the experiment. Propagated to seed and search algorithms. If not provided, defaults to seed.
resources (dict[Literal['cpu', 'gpu', 'memory'], float] | None (default: None)) –
Dictionary of resources to allocate per trial in the experiment. Available keys include:
- "cpu": number of CPU cores
- "gpu": number of GPUs
- "memory": amount of memory
Passed into with_resources().
name (str | None (default: None)) – Name of the experiment, used for logging purposes. Defaults to a unique ID.
logging_dir (str | None (default: None)) – Base directory to store experiment logs. Defaults to logging_dir.
scheduler_kwargs (dict | None (default: None)) – Additional keyword arguments to pass to the scheduler.
searcher_kwargs (dict | None (default: None)) – Additional keyword arguments to pass to the search algorithm.

Notes

Lifecycle: beta

Attributes table#

`data`	Data on which to tune hyperparameters.
`id`	Unique identifier for the experiment.
`logging_dir`	Base directory to store experiment logs.
`metrics`	Metrics to track during the experiment.
`metrics_callback`
`mode`	Optimization mode for the primary metric.
`model_cls`	Model class on which to tune hyperparameters.
`name`	Name of the experiment.
`num_samples`	Total number of hyperparameter configurations to sample.
`resources`	Resources to allocate per trial in the experiment.
`result_grid`	Result grid for the experiment.
`scheduler`	Ray Tune scheduler to use.
`scheduler_kwargs`	Additional keyword arguments to pass to the scheduler.
`search_space`	Search space for hyperparameters.
`searcher`	Ray Tune search algorithm to use.
`searcher_kwargs`	Additional keyword arguments to pass to the search algorithm.
`seed`	Random seed to use for the experiment.
`setup_method_args`	Keyword arguments for the setup method.
`setup_method_name`	Either `"setup_anndata"` or `"setup_mudata"`.

Methods table#

`get_logger`(trial_name)	Configure TensorBoard logger for a trial in this experiment.
`get_tuner`()	Configure a `Tuner` from this experiment.

Attributes#

AutotuneExperiment.data[source]#: Data on which to tune hyperparameters.

AutotuneExperiment.id[source]#: Unique identifier for the experiment.

AutotuneExperiment.logging_dir[source]#: Base directory to store experiment logs.

AutotuneExperiment.metrics[source]#: Metrics to track during the experiment.

AutotuneExperiment.metrics_callback[source]#

AutotuneExperiment.mode[source]#: Optimization mode for the primary metric.

AutotuneExperiment.model_cls[source]#: Model class on which to tune hyperparameters.

AutotuneExperiment.name[source]#: Name of the experiment.

AutotuneExperiment.num_samples[source]#: Total number of hyperparameter configurations to sample.

AutotuneExperiment.resources[source]#: Resources to allocate per trial in the experiment.

AutotuneExperiment.result_grid[source]#: Result grid for the experiment.

AutotuneExperiment.scheduler[source]#: Ray Tune scheduler to use.

AutotuneExperiment.scheduler_kwargs[source]#: Additional keyword arguments to pass to the scheduler.

AutotuneExperiment.search_space[source]#: Search space for hyperparameters.

AutotuneExperiment.searcher[source]#: Ray Tune search algorithm to use.

AutotuneExperiment.searcher_kwargs[source]#: Additional keyword arguments to pass to the search algorithm.

AutotuneExperiment.seed[source]#: Random seed to use for the experiment.

AutotuneExperiment.setup_method_args[source]#: Keyword arguments for the setup method.

AutotuneExperiment.setup_method_name[source]#: Either "setup_anndata" or "setup_mudata".

Methods#

AutotuneExperiment.get_logger(trial_name)[source]#

Configure TensorBoard logger for a trial in this experiment.

Return type:: TensorBoardLogger

AutotuneExperiment.get_tuner()[source]#

Configure a Tuner from this experiment.

Return type:: Tuner