Base utils¶
Base tools to compute diverse analysis¶
- orion.analysis.base.average(trials, group_by='order', key='best', return_var=False)[source]¶
Compute the average of some trial attribute.
By default it will compute the average objective at each time step across multiple experiments.
- Parameters
- trials: DataFrame
A dataframe of trials containing, at least, the columns ‘best’ and ‘order’.
- group_by: str, optional
The attribute to use to group trials for the average. By default it group trials by order (ex: all first trials across experiments.)
- key: str, optional
One attribute or a list of attributes split by ‘,’ to average. Defaults to ‘best’ as returned by
orion.analysis.regret.- return_var: bool, optional
If True, and a column ‘{key}_var’ where ‘{key}’ is the value of the argument key. Defaults to False.
- Returns
- A dataframe with columns ‘order’, ‘{key}_mean’ and ‘{key}_var’.
- orion.analysis.base.flatten_params(space, params=None)[source]¶
Return the params of the corresponding flat space
If no params are passed, returns all flattened params. If params are passed, returns the corresponding flattened params.
- Parameters
- space: Space object
A space object from an experiment.
- params: list of str, optional
The parameters to select from the search space. If the flattened search space contains flattened params such as (‘y’ -> ‘y[0]’, ‘y[1]’), passing ‘y’ in the list of params will returned the flattened version [‘y[0]’, ‘y[1]’]
- Raises
- ValueError
If one of the parameter names passed is not in the flattened space.
Examples
If space has x~uniform(0, 1) and y~uniform(0, 1, shape=(1, 2)). >>> flatten_params(space) [‘x’, ‘y[0,0]’, ‘y[0,1]’] >>> flatten_params(space, params=[‘x’]) [‘x’] >>> flatten_params(space, params=[‘x’, ‘y’]) [‘x’, ‘y[0,0]’, ‘y[0,1]’] >>> flatten_params(space, params=[‘x’, ‘y[0,1]’]) [‘x’, ‘y[0,1]’] >>> flatten_params(space, params=[‘y[0,1]’, ‘x’]) [‘x’, ‘y[0,1]’]
- orion.analysis.base.ranking(trials, group_by='order', key='best')[source]¶
Compute the ranking of some trial attribute.
By default it will compute the ranking with respect to objectives at each time step across multiple experiments.
- Parameters
- trials: DataFrame
A dataframe of trials containing, at least, the columns ‘best’ and ‘order’.
- group_by: str, optional
The attribute to use to group trials for the ranking. By default it group trials by order (ex: all first trials across experiments.)
- key: str, optional
The attribute to use for the ranking. Defaults to ‘best’ as returned by
orion.analysis.regret.
- Returns
- A copy of the original dataframe with a new column ‘rank’ for the rankings.
- orion.analysis.base.to_numpy(trials, space)[source]¶
Convert trials in DataFrame to Numpy array of (params + objective)
- orion.analysis.base.train_regressor(regressor_name, data, **kwargs)[source]¶
Train regressor model
- Parameters
- model: str
Name of the regression model to use. Can be one of - AdaBoostRegressor - BaggingRegressor - ExtraTreesRegressor - GradientBoostingRegressor - RandomForestRegressor (Default)
- trials: DataFrame or dict
A dataframe of trials containing, at least, the columns ‘objective’ and ‘id’. Or a dict equivalent.
- **kwargs
Arguments for the regressor model.