Example for using the multi target mode for the objective

Example for using the multi target mode for the objective. It uses a desirability value to handle several targets.

This example assumes some basic familiarity with using BayBE. We thus refer to campaign for a basic example.

Necessary imports for this example

import pandas as pd
from baybe import Campaign
from baybe.objectives import DesirabilityObjective
from baybe.parameters import CategoricalParameter, NumericalDiscreteParameter
from baybe.searchspace import SearchSpace
from baybe.targets import NumericalTarget
from baybe.utils.dataframe import add_fake_measurements

Experiment setup and creating the searchspace

Categorical_1 = CategoricalParameter("Cat_1", values=["22", "33"], encoding="OHE")
Categorical_2 = CategoricalParameter(
    "Cat_2",
    values=["very bad", "bad", "OK", "good", "very good"],
    encoding="INT",
)
Num_disc_1 = NumericalDiscreteParameter(
    "Num_disc_1", values=[1, 2, 3, 4, 6, 8, 10], tolerance=0.3
)
Num_disc_2 = NumericalDiscreteParameter(
    "Num_disc_2", values=[-1, -3, -6, -9], tolerance=0.3
)
parameters = [Categorical_1, Categorical_2, Num_disc_1, Num_disc_2]
searchspace = SearchSpace.from_product(parameters=parameters)

Defining the targets

The multi target mode is handled when creating the objective object. Thus we first need to define the different targets.

This examples has different targets with different modes. The first target is maximized and while the second one is minimized. Note that in this multi target mode, the user must specify bounds for each target.

Target_1 = NumericalTarget(
    name="Target_1", mode="MAX", bounds=(0, 100), transformation="LINEAR"
)
Target_2 = NumericalTarget(
    name="Target_2", mode="MIN", bounds=(0, 100), transformation="LINEAR"
)

For each target it is also possible to specify a target_transform function. A detailed discussion of this functionality can be found at the end of this example.

In this example, define a third target working with the mode MATCH. We furthermore use target_transform="BELL".

Target_3 = NumericalTarget(
    name="Target_3", mode="MATCH", bounds=(45, 55), transformation="BELL"
)

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]

As the recommender requires a single function, the different targets need to be combined. Thus, a scalarizer is used to create a single target out of the several targets given. The combine function can either be the mean MEAN or the geometric mean GEOM_MEAN. Per default, GEOM_MEAN is used. Weights for each target can also be specified as a list of floats in the arguments Per default, weights are equally distributed between all targets and are normalized internally. It is thus not necessary to handle normalization or scaling.

objective = DesirabilityObjective(
    targets=targets,
    weights=[20, 20, 60],
    scalarizer="MEAN",
)
print(objective)
Objective
   Type: DesirabilityObjective
   Targets
                    Type      Name  ... Transformation  Weight
      0  NumericalTarget  Target_1  ...         LINEAR    20.0
      1  NumericalTarget  Target_2  ...         LINEAR    20.0
      2  NumericalTarget  Target_3  ...           BELL    60.0
      
      [3 rows x 7 columns]
   Scalarizer: MEAN

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: 0/280
         Measured: 0/280
         Excluded: 0/280
   SearchSpace
      Search Space Type: DISCRETE
      SubspaceDiscrete
         Discrete Parameters
                     Name                        Type  ...                 Encoding

nActiveValues 0 Cat_1 CategoricalParameter … CategoricalEncoding.OHE 2.0 1 Cat_2 CategoricalParameter … CategoricalEncoding.INT 5.0 2 Num_disc_1 NumericalDiscreteParameter … None NaN 3 Num_disc_2 NumericalDiscreteParameter … None NaN

            [4 rows x 5 columns]
         Experimental Representation
                Cat_1      Cat_2  Num_disc_1  Num_disc_2
            0      22         OK         1.0        -9.0
            1      22         OK         1.0        -6.0
            2      22         OK         1.0        -3.0
            ..    ...        ...         ...         ...
            277    33  very good        10.0        -6.0
            278    33  very good        10.0        -3.0
            279    33  very good        10.0        -1.0
            
            [280 rows x 4 columns]
         Constraints
            Empty DataFrame
            Columns: []
            Index: []
         Computational Representation
                 Cat_1_22  Cat_1_33  ...  Num_disc_1  Num_disc_2
            0         1.0       0.0  ...         1.0        -9.0
            1         1.0       0.0  ...         1.0        -6.0
            2         1.0       0.0  ...         1.0        -3.0
            ..        ...       ...  ...         ...         ...
            277       0.0       1.0  ...        10.0        -6.0
            278       0.0       1.0  ...        10.0        -3.0
            279       0.0       1.0  ...        10.0        -1.0
            
            [280 rows x 5 columns]
   Objective
      Type: DesirabilityObjective
      Targets
                       Type      Name  ... Transformation  Weight
         0  NumericalTarget  Target_1  ...         LINEAR    20.0
         1  NumericalTarget  Target_2  ...         LINEAR    20.0
         2  NumericalTarget  Target_3  ...           BELL    60.0
         
         [3 rows x 7 columns]
      Scalarizer: MEAN
   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

Performing some iterations

The following loop performs some recommendations and adds fake results. It also prints what happens to internal data.

N_ITERATIONS = 3
for kIter in range(N_ITERATIONS):
    rec = campaign.recommend(batch_size=3)
    add_fake_measurements(rec, campaign.targets)
    campaign.add_measurements(rec)
    desirability = campaign.objective.transform(campaign.measurements, allow_extra=True)

    print(f"\n\n#### ITERATION {kIter+1} ####")
    print("\nRecommended measurements with fake measured results:\n")
    print(rec)
    print("\nInternal measurement database with desirability values:\n")
    print(pd.concat([campaign.measurements, desirability], axis=1))
#### ITERATION 1 ####

Recommended measurements with fake measured results:

    Cat_1 Cat_2  Num_disc_1  Num_disc_2   Target_1   Target_2   Target_3
29     22   bad         1.0        -6.0  75.770294  14.084796  52.959408
186    33   bad         6.0        -3.0  80.197073  19.623970  54.004200
140    33    OK         1.0        -9.0  24.391262  27.783034  54.028950

Internal measurement database with desirability values:

  Cat_1 Cat_2  Num_disc_1  Num_disc_2   Target_1   Target_2   Target_3  \
0    22   bad         1.0        -6.0  75.770294  14.084796  52.959408   
1    33   bad         6.0        -3.0  80.197073  19.623970  54.004200   
2    33    OK         1.0        -9.0  24.391262  27.783034  54.028950   

   BatchNr  FitNr  Desirability  
0        1    NaN      0.826964  
1        1    NaN      0.756543  
2        1    NaN      0.626885  




#### ITERATION 2 ####

Recommended measurements with fake measured results:

      Cat_1 Cat_2  Num_disc_1  Num_disc_2   Target_1   Target_2   Target_3
index                                                                     
57       22  good         1.0        -6.0  63.471185  92.356097  54.973188
1        22    OK         1.0        -6.0   1.053915  55.318963  52.462658
41       22   bad         4.0        -6.0  83.268433  21.579539  54.130581

Internal measurement database with desirability values:

  Cat_1 Cat_2  Num_disc_1  Num_disc_2   Target_1   Target_2   Target_3  \
0    22   bad         1.0        -6.0  75.770294  14.084796  52.959408   
1    33   bad         6.0        -3.0  80.197073  19.623970  54.004200   
2    33    OK         1.0        -9.0  24.391262  27.783034  54.028950   
3    22  good         1.0        -6.0  63.471185  92.356097  54.973188   
4    22    OK         1.0        -6.0   1.053915  55.318963  52.462658   
5    22   bad         4.0        -6.0  83.268433  21.579539  54.130581   

   BatchNr  FitNr  Desirability  
0        1    1.0      0.826964  
1        1    1.0      0.756543  
2        1    1.0      0.626885  
3        2    NaN      0.508100  
4        2    NaN      0.622934  
5        2    NaN      0.749913  


#### ITERATION 3 ####

Recommended measurements with fake measured results:

      Cat_1 Cat_2  Num_disc_1  Num_disc_2   Target_1   Target_2   Target_3
index                                                                     
187      33   bad         6.0        -1.0  49.502818  53.189357  56.426488
30       22   bad         1.0        -3.0  98.646594  38.452567  51.492841
28       22   bad         1.0        -9.0  44.656707  66.094615  56.716487

Internal measurement database with desirability values:

  Cat_1 Cat_2  Num_disc_1  Num_disc_2   Target_1   Target_2   Target_3  \
0    22   bad         1.0        -6.0  75.770294  14.084796  52.959408   
1    33   bad         6.0        -3.0  80.197073  19.623970  54.004200   
2    33    OK         1.0        -9.0  24.391262  27.783034  54.028950   
3    22  good         1.0        -6.0  63.471185  92.356097  54.973188   
4    22    OK         1.0        -6.0   1.053915  55.318963  52.462658   
5    22   bad         4.0        -6.0  83.268433  21.579539  54.130581   
6    33   bad         6.0        -1.0  49.502818  53.189357  56.426488   
7    22   bad         1.0        -3.0  98.646594  38.452567  51.492841   
8    22   bad         1.0        -9.0  44.656707  66.094615  56.716487   

   BatchNr  FitNr  Desirability  
0        1    1.0      0.826964  
1        1    1.0      0.756543  
2        1    1.0      0.626885  
3        2    2.0      0.508100  
4        2    2.0      0.622934  
5        2    2.0      0.749913  
6        3    NaN      0.455306  
7        3    NaN      0.894232  
8        3    NaN      0.400524  

Addendum: Description of transformation functions

This function is used to transform target values to the interval [0,1] for MAX/MIN mode. An ascending or decreasing LINEAR function is used per default. This function maps input values in a specified interval [lower, upper] to the interval [0,1]. Outside the specified interval, the function remains constant, that is, 0 or 1.

For the match mode, two functions are available TRIANGULAR and BELL. The TRIANGULAR function is 0 outside a specified interval and linearly increases to 1 from both interval ends, reaching the value 1 at the center of the interval. This function is used per default for MATCH mode. The BELL function is a Gaussian bell curve, specified through the boundary values of the sigma interval, reaching the maximum value of 1 at the interval center.