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BO_hackaton_example.py

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+
+from baybe import Campaign
+from baybe.objective import Objective
+from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter
+from baybe.searchspace import SearchSpace
+from baybe.targets import NumericalTarget
+from baybe.utils.dataframe import add_fake_results
+
+
+
+### Setup
+
+# defining ranges of volumes v_i for each precursor
+NUM_PRECURSORS = 6
+
+parameters = [
+    NumericalContinuousParameter(
+        name=f"v_{k+1}",
+        bounds=(0, 60),
+    )
+    for k in range(NUM_PRECURSORS)
+]
+
+# defining the constraint that the sum of all volumes wont exceed 60uL
+
+constraints = [ContinuousLinearEqualityConstraint(
+        parameters= [f"v_{k+1}" for k in range(NUM_PRECURSORS)], coefficients=[1.0 for k in range(NUM_PRECURSORS)], rhs=60.0
+    )]
+
+
+searchspace = SearchSpace.from_product(parameters=parameters, constraints=constraints)
+
+
+### Defining the targets
+
+Target_1 = NumericalTarget(
+    name="G1", mode="MATCH", bounds=(900, 1100), transformation="LINEAR"
+)
+Target_2 = NumericalTarget(
+    name="G2", mode="MATCH", bounds=(400, 600), transformation="LINEAR"
+)
+
+Target_3 = NumericalTarget(
+    name="relaxation_time", mode=TargetMode.MAX,  
+)
+
+# Note that the `MATCH` mode seeks to have the target at the mean between the two bounds.
+# For example, choosing 95 and 105 will lead the algorithm seeking 100 as the optimal value.
+# Thus, using the bounds, it is possible to control both the match target and
+# the range around this target that is considered viable.
+
+
+### Creating the objective
+
+# Now to work with these three targets the objective object must be properly created.
+# The mode is set to `DESIRABILITY` and the targets are described in a list.
+
+targets = [Target_1, Target_2, Target_3]
+
+objective = Objective(
+    mode="DESIRABILITY",
+    targets=targets,
+    combine_func="MEAN",
+)
+
+### Construct the campaign and run some iterations
+campaign = Campaign(
+    searchspace=searchspace,
+    objective=objective,
+)
+
+
+### Load the existing data
+
+#data = pd.read_csv("./data.csv")
+#campaign.add_measurements(data)
+
+### Generation of recommendations
+batch_size=96
+rec = campaign.recommend(batch_size = batch_size)
+
+
+### Example with fake data
+N_ITERATIONS = 3
+for kIter in range(N_ITERATIONS):
+    print(f"\n\n#### ITERATION {kIter+1} ####")
+
+    rec = campaign.recommend(batch_size = batch_size)
+    print("\nRecommended measurements:\n", rec)
+
+    add_fake_results(rec, campaign)
+    print("\nRecommended measurements with fake measured results:\n", rec)
+
+    campaign.add_measurements(rec)
+
+    print("\n\nInternal measurement dataframe computational representation Y:\n")
+    print(campaign._measurements_targets_comp)