Example for using custom constraints in discrete searchspaces¶
This examples shows how a custom constraint can be created for a discrete searchspace. That is, it shows how the user can define a constraint restricting the searchspace.
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
import pandas as pd
from baybe import Campaign
from baybe.constraints import DiscreteCustomConstraint
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¶
We begin by setting up some parameters for our experiments.
TEMPERATURE_RESOLUTION describes the number of different temperatures used.
SMOKE_TEST = "SMOKE_TEST" in os.environ
TEMPERATURE_RESOLUTION = 3 if SMOKE_TEST else 10
dict_solvent = {
"water": "O",
"C1": "C",
"C2": "CC",
"C3": "CCC",
"C4": "CCCC",
"C5": "CCCCC",
"c6": "c1ccccc1",
"C6": "CCCCCC",
}
solvent = SubstanceParameter("Solvent", data=dict_solvent, encoding="RDKIT")
speed = CategoricalParameter(
"Speed", values=["very slow", "slow", "normal", "fast", "very fast"], encoding="INT"
)
temperature = NumericalDiscreteParameter(
"Temperature",
values=list(np.linspace(100, 200, TEMPERATURE_RESOLUTION)),
tolerance=0.5,
)
concentration = NumericalDiscreteParameter(
"Concentration", values=[1, 2, 5, 10], tolerance=0.4
)
parameters = [solvent, speed, temperature, concentration]
Creating the constraint¶
The constraints are handled when creating the searchspace object. We thus need to define our constraint first as follows.
def custom_function(df: pd.DataFrame) -> pd.Series:
"""This constraint implements a custom user-defined filter/validation
functionality.""" # noqa: D401
# Situation 1: We only want entries where the solvent water is used with
# temperatures <= 120 and concentrations <= 5
mask_bad1 = (
(df["Solvent"] == "water")
& (df["Temperature"] > 120)
& (df["Concentration"] > 5)
)
# Situation 2: We only want entries where the solvent C2 is used with
# temperatures <= 180 and concentrations <= 3
mask_bad2 = (
(df["Solvent"] == "C2") & (df["Temperature"] > 180) & (df["Concentration"] > 3)
)
# Situation 3: We only want entries where the solvent C3 is used with
# temperatures <= 150 and concentrations <= 3
mask_bad3 = (
(df["Solvent"] == "C3") & (df["Temperature"] > 150) & (df["Concentration"] > 3)
)
# Combine all situations
mask_good = ~(mask_bad1 | mask_bad2 | mask_bad3)
return mask_good
We now initialize the CustomConstraint with all parameters this function should have
access to.
constraint = DiscreteCustomConstraint(
parameters=["Concentration", "Solvent", "Temperature"], validator=custom_function
)
Creating the searchspace and the objective¶
searchspace = SearchSpace.from_product(parameters=parameters, constraints=[constraint])
objective = Objective(
mode="SINGLE", targets=[NumericalTarget(name="yield", 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 Solvent SubstanceParameter 8 SubstanceEncoding.RDKIT
1 Speed CategoricalParameter 5 CategoricalEncoding.INT
2 Temperature NumericalDiscreteParameter 3 None
3 Concentration NumericalDiscreteParameter 4 None
[1mExperimental Representation[0m
Solvent Speed Temperature Concentration
0 water very slow 100.0 1.0
1 water very slow 100.0 2.0
2 water very slow 100.0 5.0
.. ... ... ... ...
447 C6 very fast 200.0 2.0
448 C6 very fast 200.0 5.0
449 C6 very fast 200.0 10.0
[450 rows x 4 columns]
[1mMetadata:[0m
was_recommended: 0/450
was_measured: 0/450
dont_recommend: 0/450
[1mConstraints[0m
Type Affected_Parameters
0 DiscreteCustomConstraint [Concentration, Solvent, Temperature]
[1mComputational Representation[0m
Solvent_RDKIT_MaxAbsEStateIndex Solvent_RDKIT_MaxPartialCharge ...
Temperature Concentration 0 0.000000 -0.411510 … 100.0 1.0 1 0.000000 -0.411510 … 100.0 2.0 2 0.000000 -0.411510 … 100.0 5.0 .. … … … … … 447 2.231806 -0.053579 … 200.0 2.0 448 2.231806 -0.053579 … 200.0 5.0 449 2.231806 -0.053579 … 200.0 10.0
[450 rows x 10 columns]
[1mObjective[0m
[1mMode: [0mSINGLE
[1mTargets [0m
Type Name Mode Lower_Bound Upper_Bound Transformation \
0 NumericalTarget yield 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 constraint¶
The following loop performs some recommendations 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 water, temp > 120 and concentration > 5: ",
(
campaign.searchspace.discrete.exp_rep["Concentration"].apply(
lambda x: x > 5
)
& campaign.searchspace.discrete.exp_rep["Temperature"].apply(
lambda x: x > 120
)
& campaign.searchspace.discrete.exp_rep["Solvent"].eq("water")
).sum(),
)
print(
"Number of entries with C2, temp > 180 and concentration > 3: ",
(
campaign.searchspace.discrete.exp_rep["Concentration"].apply(
lambda x: x > 3
)
& campaign.searchspace.discrete.exp_rep["Temperature"].apply(
lambda x: x > 180
)
& campaign.searchspace.discrete.exp_rep["Solvent"].eq("C2")
).sum(),
)
print(
"Number of entries with C3, temp > 150 and concentration > 3: ",
(
campaign.searchspace.discrete.exp_rep["Concentration"].apply(
lambda x: x > 3
)
& campaign.searchspace.discrete.exp_rep["Temperature"].apply(
lambda x: x > 150
)
& campaign.searchspace.discrete.exp_rep["Solvent"].eq("C3")
).sum(),
)
rec = campaign.recommend(batch_size=5)
add_fake_results(rec, campaign)
campaign.add_measurements(rec)
#### ITERATION 1 ####
## ASSERTS ##
Number of entries with water, temp > 120 and concentration > 5: 0
Number of entries with C2, temp > 180 and concentration > 3: 0
Number of entries with C3, temp > 150 and concentration > 3: 0
#### ITERATION 2 ####
## ASSERTS ##
Number of entries with water, temp > 120 and concentration > 5: 0
Number of entries with C2, temp > 180 and concentration > 3: 0
Number of entries with C3, temp > 150 and concentration > 3: 0
#### ITERATION 3 ####
## ASSERTS ##
Number of entries with water, temp > 120 and concentration > 5: 0
Number of entries with C2, temp > 180 and concentration > 3: 0
Number of entries with C3, temp > 150 and concentration > 3: 0