DeepSDFStruct.design_of_experiments#
Functions
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Create a new experiment directory and specs.json file, copying defaults and applying overrides. |
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Creates an experiment with overrides, trains the model, and logs everything to MLflow. |
Classes
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A dictionary-like class to hold and update experiment specifications. |
- class DeepSDFStruct.design_of_experiments.ExperimentSpecifications(specs=None)#
Bases:
dictA dictionary-like class to hold and update experiment specifications. Supports recursive updates for nested dictionaries. Can be initialized from a dictionary or loaded from a file (JSON/YAML).
- Parameters:
specs (Dict[str, Any] | str | None)
- copy()#
Return a deep copy of the specifications.
- flatten(parent_key='', sep='.')#
Flatten nested dictionary (including lists of dicts) into a single-level dict with dot-separated keys.
- Return type:
Dict[str,Any]- Parameters:
parent_key (str)
sep (str)
- save(filename)#
Save current specifications to a JSON or YAML file.
- Return type:
None- Parameters:
filename (str)
- update(updates)#
Recursively update the experiment specifications with new values.
- Return type:
None- Parameters:
updates (Dict[str, Any])
- DeepSDFStruct.design_of_experiments.create_experiment(exp_dir, specs)#
Create a new experiment directory and specs.json file, copying defaults and applying overrides.
- Parameters:
exp_dir (str) – Path to new experiment directory.
specs (dict) – Dictionary containing the experiment specifications.
- DeepSDFStruct.design_of_experiments.run_experiment(exp_name, data_dir, batch_split=1, run_name=None, specs=None, device='cpu', tracking_uri='mlruns')#
Creates an experiment with overrides, trains the model, and logs everything to MLflow.
- Parameters:
exp_name (str) – Name of the experiment folder.
data_dir (str) – Path to the dataset.
specs (dict) – Experiment specifications
device (str) – Training device (‘cpu’ or ‘cuda’).
mlflow_experiment_name (str) – Name of the MLflow experiment.
register_model (bool) – If True, register the final model in MLflow Model Registry.