Example for using dependency constraints in discrete searchspaces¶
This example shows how a dependency constraint can be created for a discrete searchspace. For instance, some parameters might only be relevant when another parameter has a certain value. All dependencies have to be declared in a single constraint.
This example assumes some basic familiarity with using BayBE.
We thus refer to campaign for a basic example.
Necessary imports for this example¶
import os
import numpy as np
from baybe import Campaign
from baybe.constraints import DiscreteDependenciesConstraint, SubSelectionCondition
from baybe.objective import Objective
from baybe.parameters import (
CategoricalParameter,
NumericalDiscreteParameter,
SubstanceParameter,
)
from baybe.searchspace import SearchSpace
from baybe.targets import NumericalTarget
from baybe.utils.dataframe import add_fake_results
Experiment setup¶
SMOKE_TEST = "SMOKE_TEST" in os.environ
FRAC_RESOLUTION = 3 if SMOKE_TEST else 7
dict_solvent = {
"water": "O",
"C1": "C",
}
solvent = SubstanceParameter(name="Solv", data=dict_solvent, encoding="MORDRED")
switch1 = CategoricalParameter(name="Switch1", values=["on", "off"])
switch2 = CategoricalParameter(name="Switch2", values=["left", "right"])
fraction1 = NumericalDiscreteParameter(
name="Frac1", values=list(np.linspace(0, 100, FRAC_RESOLUTION)), tolerance=0.2
)
frame1 = CategoricalParameter(name="FrameA", values=["A", "B"])
frame2 = CategoricalParameter(name="FrameB", values=["A", "B"])
parameters = [solvent, switch1, switch2, fraction1, frame1, frame2]
Creating the constraints¶
The constraints are handled when creating the searchspace object. It is thus necessary to define it before the searchspace creation. Note that multiple dependencies have to be included in a single constraint object.
constraint = DiscreteDependenciesConstraint(
parameters=["Switch1", "Switch2"],
conditions=[
SubSelectionCondition(selection=["on"]),
SubSelectionCondition(selection=["right"]),
],
affected_parameters=[["Solv", "Frac1"], ["FrameA", "FrameB"]],
)
Creating the searchspace and the objective¶
searchspace = SearchSpace.from_product(parameters=parameters, constraints=[constraint])
objective = Objective(
mode="SINGLE", targets=[NumericalTarget(name="Target_1", mode="MAX")]
)
Creating and printing the campaign¶
campaign = Campaign(searchspace=searchspace, objective=objective)
print(campaign)
[1mCampaign[0m
[1mMeta Data[0m
Batches Done: 0
Fits Done: 0
[1mSearch Space[0m
[1mSearch Space Type: [0mDISCRETE
[1mDiscrete Search Space[0m
[1mDiscrete Parameters[0m
Name Type Num_Values Encoding
0 Solv SubstanceParameter 2 SubstanceEncoding.MORDRED
1 Switch1 CategoricalParameter 2 CategoricalEncoding.OHE
2 Switch2 CategoricalParameter 2 CategoricalEncoding.OHE
3 Frac1 NumericalDiscreteParameter 3 None
4 FrameA CategoricalParameter 2 CategoricalEncoding.OHE
5 FrameB CategoricalParameter 2 CategoricalEncoding.OHE
[1mExperimental Representation[0m
Solv Switch1 ... FrameA FrameB
0 water on ... A A
1 water on ... A A
2 water on ... A A
.. ... ... ... ... ...
32 C1 on ... A B
33 C1 on ... B A
34 C1 on ... B B
[35 rows x 6 columns]
[1mMetadata:[0m
was_recommended: 0/35
was_measured: 0/35
dont_recommend: 0/35
[1mConstraints[0m
Type Affected_Parameters
0 DiscreteDependenciesConstraint [Switch1, Switch2]
[1mComputational Representation[0m
Solv_MORDRED_nAtom Switch1_on ... FrameB_A FrameB_B
0 3.0 1 ... 1 0
1 3.0 1 ... 1 0
2 3.0 1 ... 1 0
.. ... ... ... ... ...
32 5.0 1 ... 0 1
33 5.0 1 ... 1 0
34 5.0 1 ... 0 1
[35 rows x 10 columns]
[1mObjective[0m
[1mMode: [0mSINGLE
[1mTargets [0m
Type Name Mode Lower_Bound Upper_Bound Transformation \
0 NumericalTarget Target_1 MAX -inf inf None
Weight
0 100.0
[1mCombine Function: [0mGEOM_MEAN
TwoPhaseMetaRecommender(allow_repeated_recommendations=None,
allow_recommending_already_measured=None, initial_recommender=RandomRecommender(allow_repeated_recommendations=False, allow_recommending_already_measured=True), recommender=SequentialGreedyRecommender(allow_repeated_recommendations=False, allow_recommending_already_measured=True, surrogate_model=GaussianProcessSurrogate(model_params={}, _model=None), acquisition_function_cls=’qEI’, _acquisition_function=None, hybrid_sampler=’None’, sampling_percentage=1.0), switch_after=1)
Manual verification of the constraints¶
The following loop performs some recommendations and manually verifies the given constraints.
N_ITERATIONS = 2 if SMOKE_TEST else 5
for kIter in range(N_ITERATIONS):
print(f"\n#### ITERATION {kIter+1} ####")
print("## ASSERTS ##")
print(
f"Number entries with both switches on "
f"(expected {7*len(dict_solvent)*2*2}): ",
(
(campaign.searchspace.discrete.exp_rep["Switch1"] == "on")
& (campaign.searchspace.discrete.exp_rep["Switch2"] == "right")
).sum(),
)
print(
f"Number entries with Switch1 off " f"(expected {2*2}): ",
(
(campaign.searchspace.discrete.exp_rep["Switch1"] == "off")
& (campaign.searchspace.discrete.exp_rep["Switch2"] == "right")
).sum(),
)
print(
f"Number entries with Switch2 off "
f"(expected {7*len(dict_solvent)}):"
f" ",
(
(campaign.searchspace.discrete.exp_rep["Switch1"] == "on")
& (campaign.searchspace.discrete.exp_rep["Switch2"] == "left")
).sum(),
)
print(
"Number entries with both switches off (expected 1): ",
(
(campaign.searchspace.discrete.exp_rep["Switch1"] == "off")
& (campaign.searchspace.discrete.exp_rep["Switch2"] == "left")
).sum(),
)
rec = campaign.recommend(batch_size=5)
add_fake_results(rec, campaign)
campaign.add_measurements(rec)
#### ITERATION 1 ####
## ASSERTS ##
Number entries with both switches on (expected 56): 24
Number entries with Switch1 off (expected 4): 4
Number entries with Switch2 off (expected 14): 6
Number entries with both switches off (expected 1): 1
#### ITERATION 2 ####
## ASSERTS ##
Number entries with both switches on (expected 56): 24
Number entries with Switch1 off (expected 4): 4
Number entries with Switch2 off (expected 14): 6
Number entries with both switches off (expected 1): 1