Partial Dependencies

Tools to compute Partial Dependency

orion.analysis.partial_dependency_utils.make_grid(dim, n_points)[source]

Build a grid of n_points for a dim

orion.analysis.partial_dependency_utils.partial_dependency(trials, space, params=None, model='RandomForestRegressor', n_grid_points=10, n_samples=50, **kwargs)[source]

Calculates the partial dependency of parameters in a collection of orion.core.worker.trial.Trial.

trials: DataFrame or dict

A dataframe of trials containing, at least, the columns ‘objective’ and ‘id’. Or a dict equivalent.

space: Space object

A space object from an experiment.

params: list of str, optional

The parameters to include in the computation. All parameters are included by default.

model: str

Name of the regression model to use. Can be one of - AdaBoostRegressor - BaggingRegressor - ExtraTreesRegressor - GradientBoostingRegressor - RandomForestRegressor (Default)

n_grid_points: int

Number of points in the grid to compute partial dependency. Default is 10.

n_samples: int

Number of samples to randomly generate the grid used to compute the partial dependency. Default is 50.


Arguments for the regressor model.


Dictionary of DataFrames. Each combination of parameters as keys (, and for each parameters individually ( Columns are (,, objective) or (, objective).

orion.analysis.partial_dependency_utils.partial_dependency_grid(space, model, params, samples, n_points=40)[source]

Compute the dependency grid for a given set of params (1 or 2)

orion.analysis.partial_dependency_utils.reverse(transformed_space, grid)[source]

Reverse transformations on the grid to bring back to original space