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

This example shows a simulation for a direct arylation where all combinations have been measured. This allows us to access information about previously conducted experiments from .xlsx- files.

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 pandas as pd
import seaborn as sns

from baybe import Campaign
from baybe.objectives import SingleTargetObjective
from baybe.parameters import (
    CategoricalParameter,
    NumericalDiscreteParameter,
    SubstanceParameter,
)
from baybe.recommenders import RandomRecommender
from baybe.searchspace import SearchSpace
from baybe.simulation import simulate_scenarios
from baybe.targets import NumericalTarget
from baybe.utils.plotting import create_example_plots

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_DOE_ITERATIONS = 2 if SMOKE_TEST else 20
N_MC_ITERATIONS = 2 if SMOKE_TEST else 200
BATCH_SIZE = 1 if SMOKE_TEST else 2

Lookup functionality and data creation

We read the information about the conducted experiments from a .xlsx-file. This data set was obtained from Shields, B.J., Stevens et al. Nature 590, 89–96 (2021) and contains measurements of a reaction yield, varying typical reaction conditions. Depending on your system and settings, you might need to slightly adjust the following paths. The reason is that it depends on the folder in which you execute the python call. This code assumes that you call python either from the repository root folder or this folder.

try:
    lookup = pd.read_excel("./lookup.xlsx")
except FileNotFoundError:
    try:
        lookup = pd.read_excel("examples/Backtesting/lookup.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 Objective

objective = SingleTargetObjective(target=NumericalTarget(name="yield", mode="MAX"))

Constructing campaigns for the simulation loop

In this example, we create several campaigns. First let us create three campaigns that each use a different chemical encoding to treat substances.

substance_encodings = ["MORDRED", "RDKIT", "MORGAN_FP"]
scenarios = {
    encoding: Campaign(
        searchspace=SearchSpace.from_product(
            parameters=[
                SubstanceParameter(
                    name="Solvent", data=dict_solvent, encoding=encoding
                ),
                SubstanceParameter(name="Base", data=dict_base, encoding=encoding),
                SubstanceParameter(name="Ligand", data=dict_ligand, encoding=encoding),
                NumericalDiscreteParameter(
                    name="Temp_C", values=[90, 105, 120], tolerance=2
                ),
                NumericalDiscreteParameter(
                    name="Concentration", values=[0.057, 0.1, 0.153]
                ),
            ]
        ),
        objective=objective,
    )
    for encoding in substance_encodings
}

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Now we create another campaign that treats the substances as simple one-hot encoded categories.

parameters = [
    CategoricalParameter(name="Solvent", values=dict_solvent.keys(), encoding="OHE"),
    CategoricalParameter(name="Base", values=dict_base.keys(), encoding="OHE"),
    CategoricalParameter(name="Ligand", values=dict_ligand.keys(), encoding="OHE"),
    NumericalDiscreteParameter(name="Temp_C", values=[90, 105, 120], tolerance=2),
    NumericalDiscreteParameter(
        name="Concentration", values=[0.057, 0.1, 0.153], tolerance=0.005
    ),
]
campaign_ohe = Campaign(
    searchspace=SearchSpace.from_product(parameters=parameters),
    objective=objective,
)

Finally, as baseline, we specify a campaign which provides recommendations randomly.

campaign_rand = Campaign(
    searchspace=SearchSpace.from_product(parameters=parameters),
    recommender=RandomRecommender(),
    objective=objective,
)

Update the scenarios:

scenarios.update({"OneHot": campaign_ohe, "Random Baseline": campaign_rand})

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

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

Let’s visualize the results. As you can see, the type of encoding has a tremendous impact on the outcome, with chemical encodings performing much better than traditional ones at almost no extra cost.

results.rename(columns={"Scenario": "Substance Encoding"}, inplace=True)
ax = sns.lineplot(
    data=results,
    marker="o",
    markersize=10,
    x="Num_Experiments",
    y="yield_CumBest",
    hue="Substance Encoding",
)
create_example_plots(ax=ax, base_name="full_lookup")
../../_images/full_lookup_light.svg ../../_images/full_lookup_dark.svg