| <a id="nets.opti.blackbox"></a> | |
| # :orange[Hyper Paramaters Optimization class] | |
| ## nets.opti.blackbox | |
| <a id="nets.opti.blackbox.Hyper"></a> | |
| ### Hyper Objects | |
| ```python | |
| class Hyper(SCI) | |
| ``` | |
| Hyper parameter tunning class. Allows to generate best NN architecture for task. Inputs are column indexes. idx[-1] is targeted value. | |
| <a id="nets.opti.blackbox.Hyper.start_study"></a> | |
| #### start\_study | |
| ```python | |
| def start_study(n_trials: int = 100, | |
| neptune_project: str = None, | |
| neptune_api: str = None) | |
| ``` | |
| Starts study. Optionally provide your neptune repo and token for report generation. | |
| **Arguments**: | |
| - `n_trials` _int, optional_ - Number of iterations. Defaults to 100. | |
| - `neptune_project` _str, optional_ - None | |
| - neptune_api (str, optional):. Defaults to None. | |
| **Returns**: | |
| - `dict` - quick report of results | |