Example for surrogate model with a custom pretrained model¶

This example shows how to pre-train a model and use it as a surrogate. Please note that the model is not designed to be useful but to demonstrate the workflow.

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

Necessary imports¶

import numpy as np
import torch
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx.operator_converters.linear_regressor import convert_sklearn_bayesian_ridge
from sklearn.linear_model import BayesianRidge

from baybe.campaign import Campaign
from baybe.objective import Objective
from baybe.parameters import NumericalDiscreteParameter
from baybe.recommenders import (
    FPSRecommender,
    SequentialGreedyRecommender,
    TwoPhaseMetaRecommender,
)
from baybe.searchspace import SearchSpace
from baybe.surrogates import CustomONNXSurrogate
from baybe.targets import NumericalTarget
from baybe.utils.dataframe import add_fake_results, to_tensor

Experiment Setup¶

parameters = [
    NumericalDiscreteParameter(
        name="Pressure[bar]",
        values=[1, 5, 10],
        tolerance=0.2,
    ),
    NumericalDiscreteParameter(
        name="Temperature[degree_C]",
        values=np.linspace(100, 200, 10),
    ),
]

“Pre-training” stage¶

Note that this example trains with several helpers built-in to BayBE. This can be done independently (and elsewhere). The only requirement that BayBE needs is that the model is in an onnx format. And The format should return both the mean and standard deviation. This example is based on a BayesianRidge regressor from sklearn. Its native conversion to onnx is supported via skl2onnx. Please also note that this example does not give a useful model. Its purpose is to show the workflow for using pre-trained surrogates in BayBE.

searchspace = SearchSpace.from_product(parameters=parameters, constraints=None)
train_x = to_tensor(searchspace.discrete.comp_rep)
train_y = torch.rand(train_x.size(dim=0))  # train with a random y vector

Define model and fit

model = BayesianRidge()
model.fit(train_x, train_y)

Convert model to onnx¶

Need the option to return standard deviation

options = {type(model): {"return_std": True}}

Specify what the input name is

ONNX_INPUT_NAME = "example_input_name"

input dimensions and input type (should always be a float)

input_dim = train_x.size(dim=1)
initial_type = [(ONNX_INPUT_NAME, FloatTensorType([None, input_dim]))]

Conversion

onnx_str = convert_sklearn(
    model,
    initial_types=initial_type,
    options=options,
    custom_conversion_functions={type(model): convert_sklearn_bayesian_ridge},
).SerializeToString()  # serialize to string to save in file

Create a surrogate model with a pretrained model¶

surrogate_model = CustomONNXSurrogate(
    onnx_str=onnx_str,
    onnx_input_name=ONNX_INPUT_NAME,  # specify input name
)

Create 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(surrogate_model=surrogate_model),
        initial_recommender=FPSRecommender(),
    ),
)

Iterate with recommendations and measurements¶

# Let's do a first round of recommendation
recommendation = campaign.recommend(batch_size=2)

print("Recommendation from campaign:")
print(recommendation)

Recommendation from campaign:
    Pressure[bar]  Temperature[degree_C]
0             1.0                  100.0
29           10.0                  200.0

Add some fake results

add_fake_results(recommendation, campaign)
campaign.add_measurements(recommendation)

Model Outputs¶

# Do another round of recommendations
recommendation = campaign.recommend(batch_size=2)

Print second round of recommendations

print("Recommendation from campaign:")
print(recommendation)

Recommendation from campaign:
       Pressure[bar]  Temperature[degree_C]
index                                      
1                1.0             111.111111
2                1.0             122.222222

Using configuration instead¶

Note that this can be placed inside an overall baybe config Refer to create_from_config for an example

CONFIG = {
    "type": "CustomONNXSurrogate",
    "onnx_str": onnx_str,
    "onnx_input_name": ONNX_INPUT_NAME,
}

### Model creation from dict (or json if string)
model_from_python = CustomONNXSurrogate(
    onnx_str=onnx_str, onnx_input_name=ONNX_INPUT_NAME
)
model_from_configs = CustomONNXSurrogate.from_dict(CONFIG)

This configuration creates the same model

assert model_from_python == model_from_configs

JSON configuration (expects onnx_str to be decoded with ISO-8859-1)

model_json = model_from_python.to_json()
assert model_from_python == CustomONNXSurrogate.from_json(model_json)