def get_experiment_name(self,
setting: SettingABC,
experiment_id: str = None) -> str:
"""Gets a unique name for the experiment where `self` is applied to `setting`.
This experiment name will be passed to `orion` when performing a run of
Hyper-Parameter Optimization.
Parameters
----------
- setting : Setting
The `Setting` onto which this method will be applied. This method will be used when
- experiment_id: str, optional
A custom hash to append to the experiment name. When `None` (default), a
unique hash will be created based on the values of the Setting's fields.
Returns
-------
str
The name for the experiment.
"""
if not experiment_id:
setting_dict = setting.to_dict()
# BUG: Some settings have non-string keys/value or something?
d = flatten_dict(setting_dict)
experiment_id = compute_identity(size=5, **d)
assert isinstance(setting.dataset,
str), "assuming that dataset is a str for now."
return (
f"{self.get_name()}-{setting.get_name()}_{setting.dataset}_{experiment_id}"
)
def __eq__(self, other: object):
if not isinstance(other, tuple):
return NotImplemented
elif not len(other) == 2:
return NotImplemented
# NOTE: This doesn't do any kind
other_hp, other_perf = other
hps_equal = self.hp == other_hp
if not hps_equal and isinstance(other_hp, dict):
other_id = compute_identity(**other_hp)
# this is hairy, but need to check if the dicts would be equal.
if isinstance(self.hp, dict):
# This should ideally never be the case, we would hope that
# people are using HyperParameter objects in the Point tuples.
hp_id = compute_identity(**self.hp)
else:
hp_id = self.hp.id()
hps_equal = hp_id == other_id
return hps_equal and self.perf == other[1]
def id(self):
return compute_identity(**self.to_dict())
def space_id(cls):
return compute_identity(**cls.get_orion_space_dict())