Example for full simulation loop using a table-based lookup mechanism with incomplete

data

This example shows a simulation for a direct arylation where not all combinations were measured. This allows us to access information about previously conducted experiments from .xlsx- files.

This examples assumes some basic familiarity with using BayBE and the lookup mechanism. We refer to campaign for a more basic example resp. to full_lookup for details on the lookup mechanism.

Necessary imports for this example

import os

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

from baybe import Campaign
from baybe.objectives import SingleTargetObjective
from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter
from baybe.recommenders import RandomRecommender, TwoPhaseMetaRecommender
from baybe.searchspace import SearchSpace
from baybe.simulation import simulate_scenarios
from baybe.targets import NumericalTarget

Parameters for a full simulation loop

For the full simulation, we need to define some additional parameters. These are the number of Monte Carlo runs and the number of experiments to be conducted per run.

SMOKE_TEST = "SMOKE_TEST" in os.environ

N_MC_ITERATIONS = 2 if SMOKE_TEST else 5
N_DOE_ITERATIONS = 2 if SMOKE_TEST else 5
BATCH_SIZE = 1 if SMOKE_TEST else 3

Lookup functionality and data creation

See full_lookup for details.

try:
    lookup = pd.read_excel("./lookup_withmissing.xlsx")
except FileNotFoundError:
    try:
        lookup = pd.read_excel("examples/Backtesting/lookup_withmissing.xlsx")
    except FileNotFoundError as e:
        print(e)

As usual, we set up some experiment. Note that we now need to ensure that the names fit the names in the provided .xlsx file!

dict_solvent = {
    "DMAc": r"CC(N(C)C)=O",
    "Butyornitrile": r"CCCC#N",
    "Butyl Ester": r"CCCCOC(C)=O",
    "p-Xylene": r"CC1=CC=C(C)C=C1",
}
dict_base = {
    "Potassium acetate": r"O=C([O-])C.[K+]",
    "Potassium pivalate": r"O=C([O-])C(C)(C)C.[K+]",
    "Cesium acetate": r"O=C([O-])C.[Cs+]",
    "Cesium pivalate": r"O=C([O-])C(C)(C)C.[Cs+]",
}
dict_ligand = {
    "BrettPhos": r"CC(C)C1=CC(C(C)C)=C(C(C(C)C)=C1)C2=C(P(C3CCCCC3)C4CCCCC4)C(OC)="
    "CC=C2OC",
    "Di-tert-butylphenylphosphine": r"CC(C)(C)P(C1=CC=CC=C1)C(C)(C)C",
    "(t-Bu)PhCPhos": r"CN(C)C1=CC=CC(N(C)C)=C1C2=CC=CC=C2P(C(C)(C)C)C3=CC=CC=C3",
    "Tricyclohexylphosphine": r"P(C1CCCCC1)(C2CCCCC2)C3CCCCC3",
    "PPh3": r"P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3",
    "XPhos": r"CC(C1=C(C2=CC=CC=C2P(C3CCCCC3)C4CCCCC4)C(C(C)C)=CC(C(C)C)=C1)C",
    "P(2-furyl)3": r"P(C1=CC=CO1)(C2=CC=CO2)C3=CC=CO3",
    "Methyldiphenylphosphine": r"CP(C1=CC=CC=C1)C2=CC=CC=C2",
    "1268824-69-6": r"CC(OC1=C(P(C2CCCCC2)C3CCCCC3)C(OC(C)C)=CC=C1)C",
    "JackiePhos": r"FC(F)(F)C1=CC(P(C2=C(C3=C(C(C)C)C=C(C(C)C)C=C3C(C)C)C(OC)=CC=C2OC)"
    r"C4=CC(C(F)(F)F)=CC(C(F)(F)F)=C4)=CC(C(F)(F)F)=C1",
    "SCHEMBL15068049": r"C[C@]1(O2)O[C@](C[C@]2(C)P3C4=CC=CC=C4)(C)O[C@]3(C)C1",
    "Me2PPh": r"CP(C)C1=CC=CC=C1",
}

Creating the searchspace and the objective

Here, we create the parameter objects, the searchspace and the objective.

solvent = SubstanceParameter(name="Solvent", data=dict_solvent, encoding="MORDRED")
base = SubstanceParameter(name="Base", data=dict_base, encoding="MORDRED")
ligand = SubstanceParameter(name="Ligand", data=dict_ligand, encoding="MORDRED")
temperature = NumericalDiscreteParameter(
    name="Temp_C", values=[90, 105, 120], tolerance=2
)
concentration = NumericalDiscreteParameter(
    name="Concentration", values=[0.057, 0.1, 0.153], tolerance=0.005
)

parameters = [solvent, base, ligand, temperature, concentration]

searchspace = SearchSpace.from_product(parameters=parameters)
objective = SingleTargetObjective(target=NumericalTarget(name="yield", mode="MAX"))

Constructing campaigns for the simulation loop

In this example, we create two campaigns. One uses the default recommender and the other one makes random recommendations.

campaign = Campaign(searchspace=searchspace, objective=objective)
campaign_rand = Campaign(
    searchspace=searchspace,
    recommender=TwoPhaseMetaRecommender(recommender=RandomRecommender()),
    objective=objective,
)

We can now use the simulate_scenarios function to simulate a full experiment. Note that this function enables to run multiple scenarios by a single function call. For this, it is necessary to define a dictionary mapping scenario names to campaigns.

scenarios = {"Test_Scenario": campaign, "Random": campaign_rand}

The lookup table does not contain data for all possible combination of parameters. Consequently, we need to inform the function how to deal with missing entries. This is done via the impute_mode keyword. The following options are available:

  • "error": an error will be thrown

  • "worst": imputation using the worst available value for each target

  • "best": imputation using the best available value for each target

  • "mean": imputation using mean value for each target

  • "random": a random row will be used as lookup

  • "ignore": the search space is stripped before recommendations are made so that unmeasured experiments will not be recommended

results = simulate_scenarios(
    scenarios,
    lookup,
    batch_size=BATCH_SIZE,
    n_doe_iterations=N_DOE_ITERATIONS,
    n_mc_iterations=N_MC_ITERATIONS,
    impute_mode="best",
)

The following lines plot the results and save the plot in run_impute_mode.png

max_yield = lookup["yield"].max()
sns.lineplot(
    data=results, x="Num_Experiments", y="yield_CumBest", hue="Scenario", marker="x"
)
plt.plot([3, 3 * N_DOE_ITERATIONS], [max_yield, max_yield], "--r")
plt.legend(loc="lower right")
plt.gcf().set_size_inches(20, 8)
plt.savefig("./run_impute_mode.png")