Example for using exclusion constraints in discrete searchspaces¶
This examples shows how an exclusion constraint can be created for a discrete searchspace. This can be used if some parameter values are incompatible with values of another parameter.
This example assumes some basic familiarity with using BayBE.
We thus refer to campaign for a basic example.
Necessary imports for this example¶
import numpy as np
from baybe import Campaign
from baybe.constraints import (
DiscreteExcludeConstraint,
SubSelectionCondition,
ThresholdCondition,
)
from baybe.objectives import SingleTargetObjective
from baybe.parameters import (
CategoricalParameter,
NumericalDiscreteParameter,
SubstanceParameter,
)
from baybe.searchspace import SearchSpace
from baybe.targets import NumericalTarget
from baybe.utils.dataframe import add_fake_measurements
Experiment setup¶
We begin by setting up some parameters for our experiments.
dict_solvent = {
"water": "O",
"C1": "C",
"C2": "CC",
"C3": "CCC",
"C4": "CCCC",
"C5": "CCCCC",
"c6": "c1ccccc1",
"C6": "CCCCCC",
}
solvent = SubstanceParameter(name="Solv", data=dict_solvent, encoding="RDKIT")
speed = CategoricalParameter(
name="Speed",
values=["very slow", "slow", "normal", "fast", "very fast"],
encoding="INT",
)
temperature = NumericalDiscreteParameter(
name="Temp", values=list(np.linspace(100, 200, 15)), tolerance=0.4
)
pressure = NumericalDiscreteParameter(
name="Pressure", values=[1, 2, 5, 10], tolerance=0.4
)
parameters = [solvent, speed, temperature, pressure]
Creating the constraint¶
This constraint simulates a situation where solvents C2 and C4 are not
compatible with temperatures larger than 151 and should thus be excluded.
constraint_1 = DiscreteExcludeConstraint(
parameters=["Temp", "Solv"],
combiner="AND",
conditions=[
ThresholdCondition(threshold=151, operator=">"),
SubSelectionCondition(selection=["C4", "C2"]),
],
)
This constraint simulates a situation where solvents C5 and C6 are not
compatible with pressures larger than 5 and should thus be excluded.
constraint_2 = DiscreteExcludeConstraint(
parameters=["Pressure", "Solv"],
combiner="AND",
conditions=[
ThresholdCondition(threshold=5, operator=">"),
SubSelectionCondition(selection=["C5", "C6"]),
],
)
This constraint simulates a situation where pressures below 3 should never be combined with temperatures above 120.
constraint_3 = DiscreteExcludeConstraint(
parameters=["Pressure", "Temp"],
combiner="AND",
conditions=[
ThresholdCondition(threshold=3.0, operator="<"),
ThresholdCondition(threshold=120.0, operator=">"),
],
)
Creating the searchspace and the objective¶
We now create the searchspace using the previously defined constraints.
searchspace = SearchSpace.from_product(
parameters=parameters, constraints=[constraint_1, constraint_2, constraint_3]
)
/home/runner/work/baybe/baybe/.tox/docs-py310/lib/python3.10/site-
packages/baybe/utils/chemistry.py:126: DeprecationWarning: Substance encoding ‘RDKIT’ is deprecated and will be disabled in a future version. Use ‘RDKIT2DDESCRIPTORS’ instead. warnings.warn(
objective = SingleTargetObjective(target=NumericalTarget(name="Target_1", mode="MAX"))
### Creating and printing the campaign
campaign = Campaign(searchspace=searchspace, objective=objective)
print(campaign)
Campaign
Meta Data
Batches done: 0
Fits done: 0
Discrete Subspace Meta Data
Recommended: 0/1150
Measured: 0/1150
Excluded: 0/1150
SearchSpace
Search Space Type: DISCRETE
SubspaceDiscrete
Discrete Parameters
Name Type ... Encoding
nActiveValues 0 Pressure NumericalDiscreteParameter … None NaN 1 Solv SubstanceParameter … SubstanceEncoding.RDKIT 8.0 2 Speed CategoricalParameter … CategoricalEncoding.INT 5.0 3 Temp NumericalDiscreteParameter … None NaN
[4 rows x 5 columns]
Experimental Representation
Solv Speed Temp Pressure
0 C1 fast 100.000 1.0
1 C1 fast 100.000 2.0
2 C1 fast 100.000 5.0
... ... ... ... ...
1147 water very slow 192.857 10.0
1148 water very slow 200.000 5.0
1149 water very slow 200.000 10.0
[1150 rows x 4 columns]
Constraints
Type Affected_Paramet
0 DiscreteExcl... [Temp, Solv]
1 DiscreteExcl... [Pressure, S...
2 DiscreteExcl... [Pressure, T...
Computational Representation
Pressure Solv_RDKIT2DDESC ... Speed Temp
0 1.0 0.0 ... 0.0 100.000
1 2.0 0.0 ... 0.0 100.000
2 5.0 0.0 ... 0.0 100.000
... ... ... ... ... ...
1147 10.0 0.0 ... 4.0 192.857
1148 5.0 0.0 ... 4.0 200.000
1149 10.0 0.0 ... 4.0 200.000
[1150 rows x 8 columns]
Objective
Type: SingleTargetObjective
Targets
Type Name ... Upper_Bound Transformation
0 NumericalTarget Target_1 ... inf None
[1 rows x 6 columns]
TwoPhaseMetaRecommender
Initial recommender
RandomRecommender
Compatibility: SearchSpaceType.HYBRID
Recommender
BotorchRecommender
Surrogate
GaussianProcessSurrogate
Supports Transfer Learning: True
Kernel factory: DefaultKernelFactory()
Acquisition function: None
Compatibility: SearchSpaceType.HYBRID
Sequential continuous: True
Hybrid sampler: None
Sampling percentage: 1.0
Switch after: 1
Remain switched: False
Has switched: False
Manual verification of the constraints¶
The following loop performs some iterations and manually verifies the given constraints.
N_ITERATIONS = 3
for kIter in range(N_ITERATIONS):
print(f"\n\n#### ITERATION {kIter+1} ####")
print("## ASSERTS ##")
print(
"Number of entries with either Solvents C2 or C4 and a temperature above 151: ",
(
campaign.searchspace.discrete.exp_rep["Temp"].apply(lambda x: x > 151)
& campaign.searchspace.discrete.exp_rep["Solv"].apply(
lambda x: x in ["C2", "C4"]
)
).sum(),
)
print(
"Number of entries with either Solvents C5 or C6 and a pressure above 5: ",
(
campaign.searchspace.discrete.exp_rep["Pressure"].apply(lambda x: x > 5)
& campaign.searchspace.discrete.exp_rep["Solv"].apply(
lambda x: x in ["C5", "C6"]
)
).sum(),
)
print(
"Number of entries with pressure below 3 and temperature above 120: ",
(
campaign.searchspace.discrete.exp_rep["Pressure"].apply(lambda x: x < 3)
& campaign.searchspace.discrete.exp_rep["Temp"].apply(lambda x: x > 120)
).sum(),
)
rec = campaign.recommend(batch_size=5)
add_fake_measurements(rec, campaign.targets)
campaign.add_measurements(rec)
#### ITERATION 1 ####
## ASSERTS ##
Number of entries with either Solvents C2 or C4 and a temperature above 151: 0
Number of entries with either Solvents C5 or C6 and a pressure above 5: 0
Number of entries with pressure below 3 and temperature above 120: 0
#### ITERATION 2 ####
## ASSERTS ##
Number of entries with either Solvents C2 or C4 and a temperature above 151: 0
Number of entries with either Solvents C5 or C6 and a pressure above 5: 0
Number of entries with pressure below 3 and temperature above 120: 0
#### ITERATION 3 ####
## ASSERTS ##
Number of entries with either Solvents C2 or C4 and a temperature above 151: 0
Number of entries with either Solvents C5 or C6 and a pressure above 5: 0
Number of entries with pressure below 3 and temperature above 120: 0