Example for the serialization of a campaign¶

This example shows how to serialize and also de-serialize a campaign. It demonstrates and shows that the “original” and “new” objects behave the same.

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

Necessary imports¶

import numpy as np

from baybe import Campaign
from baybe.objective import Objective
from baybe.parameters import (
    CategoricalParameter,
    NumericalDiscreteParameter,
)
from baybe.recommenders import (
    FPSRecommender,
    SequentialGreedyRecommender,
    TwoPhaseMetaRecommender,
)
from baybe.searchspace import SearchSpace
from baybe.targets import NumericalTarget

Experiment setup¶

parameters = [
    CategoricalParameter(
        name="Granularity",
        values=["coarse", "medium", "fine"],
        encoding="OHE",
    ),
    NumericalDiscreteParameter(
        name="Pressure[bar]",
        values=[1, 5, 10],
        tolerance=0.2,
    ),
    NumericalDiscreteParameter(
        name="Temperature[degree_C]",
        values=np.linspace(100, 200, 10),
    ),
]

Creating the campaign¶

campaign = Campaign(
    searchspace=SearchSpace.from_product(parameters=parameters, constraints=None),
    objective=Objective(
        mode="SINGLE", targets=[NumericalTarget(name="Yield", mode="MAX")]
    ),
    recommender=TwoPhaseMetaRecommender(
        recommender=SequentialGreedyRecommender(),
        initial_recommender=FPSRecommender(),
    ),
)

Serialization and de-serialization¶

We begin by printing the original campaign

print("Original object")
print(campaign, end="\n" * 3)

Original object
[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 Granularity CategoricalParameter 3 CategoricalEncoding.OHE 1 Pressure[bar] NumericalDiscreteParameter 3 None 2 Temperature[degree_C] NumericalDiscreteParameter 10 None

   [1mExperimental Representation[0m
               
  Granularity  Pressure[bar]  Temperature[degree_C]
   0       coarse            1.0             100.000000
   1       coarse            1.0             111.111111
   2       coarse            1.0             122.222222
   ..         ...            ...                    ...
   87        fine           10.0             177.777778
   88        fine           10.0             188.888889
   89        fine           10.0             200.000000
   
   [90 rows x 3 columns]
   
   [1mMetadata:[0m

was_recommended: 0/90

was_measured: 0/90

dont_recommend: 0/90

   [1mConstraints[0m
   Empty DataFrame
   Columns: []
   Index: []
               
   [1mComputational Representation[0m
               
   Granularity_coarse  Granularity_medium  ...  Pressure[bar]  Temperature[degree_C]
   0                    1                   0  ...            1.0

100.000000 1 1 0 … 1.0 111.111111 2 1 0 … 1.0 122.222222 .. … … … … … 87 0 0 … 10.0 177.777778 88 0 0 … 10.0 188.888889 89 0 0 … 10.0 200.000000

   [90 rows x 5 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=FPSRecommender(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)

We next serialize the campaign to JSON. This yields a JSON representation in string format. Since it is rather complex, we do not print this string here. Note: Dataframes are binary-encoded and are hence not human-readable.

string = campaign.to_json()

Deserialize the JSON string back to an object.

print("Deserialized object")
campaign_recreate = Campaign.from_json(string)
print(campaign_recreate, end="\n" * 3)

Deserialized object
[1mCampaign[0m
         
 [1mMeta Data[0m
 Batches Done: 0