Model Building with BoFire

This notebooks shows how to setup and analyze models trained with BoFire. It is still WIP.

Imports

from pydantic import TypeAdapter

import bofire.surrogates.api as surrogates
from bofire.benchmarks.multi import CrossCoupling
from bofire.benchmarks.single import Himmelblau
from bofire.data_models.domain.api import Outputs
from bofire.data_models.enum import CategoricalEncodingEnum
from bofire.data_models.surrogates.api import (
    AnySurrogate,
    EmpiricalSurrogate,
    MixedSingleTaskGPSurrogate,
    RandomForestSurrogate,
    RegressionMLPEnsemble,
    SingleTaskGPSurrogate,
)

Problem Setup

For didactic purposes, we sample data from a Himmelblau benchmark function and use them to train a SingleTaskGP.

benchmark = Himmelblau()
samples = benchmark.domain.inputs.sample(n=50)
experiments = benchmark.f(samples, return_complete=True)

experiments.head(10)

	x_1	x_2	y	valid_y
0	5.773168	-3.625911	491.918799	1
1	0.544645	-0.188584	159.848196	1
2	1.720686	1.917465	40.044607	1
3	4.779693	4.320757	531.904053	1
4	2.976078	3.175476	37.786404	1
5	-0.121115	-1.488390	179.660694	1
6	-0.661445	-5.786917	934.335045	1
7	1.216471	5.707941	732.615968	1
8	5.200258	-4.944909	636.291144	1
9	-4.368319	-3.295914	23.163960	1

Model Fitting

input_features = benchmark.domain.inputs
output_features = benchmark.domain.outputs

input_features.model_dump_json()

'{"type":"Inputs","features":[{"type":"ContinuousInput","key":"x_1","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false},{"type":"ContinuousInput","key":"x_2","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false}]}'

output_features.model_dump_json()

'{"type":"Outputs","features":[{"type":"ContinuousOutput","key":"y","unit":null,"objective":{"type":"MinimizeObjective","w":1.0,"bounds":[0.0,1.0]}}]}'

Single Task GP

Generate the json spec

# we setup the data model, here a Single Task GP
surrogate_data = SingleTaskGPSurrogate(inputs=input_features, outputs=output_features)

# we generate the json spec
jspec = surrogate_data.model_dump_json()

jspec

'{"hyperconfig":{"type":"SingleTaskGPHyperconfig","hyperstrategy":"FractionalFactorialStrategy","inputs":{"type":"Inputs","features":[{"type":"CategoricalInput","key":"kernel","categories":["rbf","matern_1.5","matern_2.5"],"allowed":[true,true,true]},{"type":"CategoricalInput","key":"prior","categories":["mbo","threesix","hvarfner"],"allowed":[true,true,true]},{"type":"CategoricalInput","key":"scalekernel","categories":["True","False"],"allowed":[true,true]},{"type":"CategoricalInput","key":"ard","categories":["True","False"],"allowed":[true,true]}]},"n_iterations":null,"target_metric":"MAE","lengthscale_constraint":null,"outputscale_constraint":null},"aggregations":null,"type":"SingleTaskGPSurrogate","inputs":{"type":"Inputs","features":[{"type":"ContinuousInput","key":"x_1","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false},{"type":"ContinuousInput","key":"x_2","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false}]},"outputs":{"type":"Outputs","features":[{"type":"ContinuousOutput","key":"y","unit":null,"objective":{"type":"MinimizeObjective","w":1.0,"bounds":[0.0,1.0]}}]},"input_preprocessing_specs":{},"dump":null,"categorical_encodings":{},"scaler":"NORMALIZE","output_scaler":"STANDARDIZE","kernel":{"type":"RBFKernel","features":null,"ard":true,"lengthscale_prior":{"type":"DimensionalityScaledLogNormalPrior","loc":1.4142135623730951,"loc_scaling":0.5,"scale":1.7320508075688772,"scale_scaling":0.0},"lengthscale_constraint":null},"noise_prior":{"type":"LogNormalPrior","loc":-4.0,"scale":1.0}}'

Load it from the spec

surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)

Map it

surrogate = surrogates.map(surrogate_data)

Fit it. This is not 100% finished. In the future we will call here hyperfit which will return the CV results etc. This has to be finished. So ignore this for now and just call fit.

surrogate.fit(experiments=experiments)

Dump it.

# dump it
dump = surrogate.dumps()

Make predictions.

# predict with it
df_predictions = surrogate.predict(experiments)
# transform to spec
predictions = surrogate.to_predictions(predictions=df_predictions)

Load again from spec and dump and make predictions.

surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
surrogate = surrogates.map(surrogate_data)
surrogate.loads(dump)

# predict with it
df_predictions2 = surrogate.predict(experiments)
# transform to spec
predictions2 = surrogate.to_predictions(predictions=df_predictions2)

# check for equality
predictions == predictions2

True

Random Forest

Generate the json spec

# we setup the data model, here a Single Task GP
surrogate_data = RandomForestSurrogate(
    inputs=input_features,
    outputs=output_features,
    random_state=42,
)

# we generate the json spec
jspec = surrogate_data.model_dump_json()

jspec

'{"hyperconfig":null,"aggregations":null,"type":"RandomForestSurrogate","inputs":{"type":"Inputs","features":[{"type":"ContinuousInput","key":"x_1","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false},{"type":"ContinuousInput","key":"x_2","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false}]},"outputs":{"type":"Outputs","features":[{"type":"ContinuousOutput","key":"y","unit":null,"objective":{"type":"MinimizeObjective","w":1.0,"bounds":[0.0,1.0]}}]},"input_preprocessing_specs":{},"dump":null,"categorical_encodings":{},"scaler":"NORMALIZE","output_scaler":"STANDARDIZE","n_estimators":100,"criterion":"squared_error","max_depth":null,"min_samples_split":2,"min_samples_leaf":1,"min_weight_fraction_leaf":0.0,"max_features":1.0,"max_leaf_nodes":null,"min_impurity_decrease":0.0,"bootstrap":true,"oob_score":false,"random_state":42,"ccp_alpha":0.0,"max_samples":null}'

# Load it from the spec
surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
# Map it
surrogate = surrogates.map(surrogate_data)
# Fit it
surrogate.fit(experiments=experiments)
# dump it
dump = surrogate.dumps()
# predict with it
df_predictions = surrogate.predict(experiments)
# transform to spec
predictions = surrogate.to_predictions(predictions=df_predictions)

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/botorch/models/ensemble.py:82: RuntimeWarning:

Could not update `train_inputs` with transformed inputs since _RandomForest does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/torch/nn/modules/module.py:2910: RuntimeWarning:

Could not update `train_inputs` with transformed inputs since _RandomForest does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/botorch/models/ensemble.py:82: RuntimeWarning:

Could not update `train_inputs` with transformed inputs since _RandomForest does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/torch/nn/modules/module.py:2910: RuntimeWarning:

Could not update `train_inputs` with transformed inputs since _RandomForest does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.

surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
surrogate = surrogates.map(surrogate_data)
surrogate.loads(dump)

# predict with it
df_predictions2 = surrogate.predict(experiments)
# transform to spec
predictions2 = surrogate.to_predictions(predictions=df_predictions2)

# check for equality
predictions == predictions2

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/botorch/models/ensemble.py:82: RuntimeWarning:

Could not update `train_inputs` with transformed inputs since _RandomForest does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/torch/nn/modules/module.py:2910: RuntimeWarning:

Could not update `train_inputs` with transformed inputs since _RandomForest does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/botorch/models/ensemble.py:82: RuntimeWarning:

Could not update `train_inputs` with transformed inputs since _RandomForest does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/torch/nn/modules/module.py:2910: RuntimeWarning:

Could not update `train_inputs` with transformed inputs since _RandomForest does not have a `train_inputs` attribute. Make sure that the `input_transform` is applied to both the train inputs and test inputs.

True

MLP Ensemble

Generate the json spec

# we setup the data model, here a Single Task GP
surrogate_data = RegressionMLPEnsemble(
    inputs=input_features,
    outputs=output_features,
    n_estimators=2,
)

# we generate the json spec
jspec = surrogate_data.model_dump_json()

jspec

'{"hyperconfig":null,"aggregations":null,"type":"RegressionMLPEnsemble","inputs":{"type":"Inputs","features":[{"type":"ContinuousInput","key":"x_1","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false},{"type":"ContinuousInput","key":"x_2","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false}]},"outputs":{"type":"Outputs","features":[{"type":"ContinuousOutput","key":"y","unit":null,"objective":{"type":"MinimizeObjective","w":1.0,"bounds":[0.0,1.0]}}]},"input_preprocessing_specs":{},"dump":null,"categorical_encodings":{},"scaler":"IDENTITY","output_scaler":"IDENTITY","n_estimators":2,"hidden_layer_sizes":[100],"activation":"relu","dropout":0.0,"batch_size":10,"n_epochs":200,"lr":0.0001,"weight_decay":0.0,"subsample_fraction":1.0,"shuffle":true,"final_activation":"identity"}'

# Load it from the spec
surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
# Map it
surrogate = surrogates.map(surrogate_data)
# Fit it
surrogate.fit(experiments=experiments)
# dump it
dump = surrogate.dumps()
# predict with it
df_predictions = surrogate.predict(experiments)
# transform to spec
predictions = surrogate.to_predictions(predictions=df_predictions)

surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
surrogate = surrogates.map(surrogate_data)
surrogate.loads(dump)

# predict with it
df_predictions2 = surrogate.predict(experiments)
# transform to spec
predictions2 = surrogate.to_predictions(predictions=df_predictions2)

# check for equality
predictions == predictions2

True

Empirical Surrogate

The empirical model is special as it has per default no fit and you need cloudpickle. There can be empirical models which implement a fit, but for this they also have to inherit from Trainable. The current example is the default without any fit functionality.

from botorch.models.deterministic import DeterministicModel
from torch import Tensor


class HimmelblauModel(DeterministicModel):
    def __init__(self):
        super().__init__()
        self._num_outputs = 1

    def forward(self, X: Tensor) -> Tensor:
        return (
            (X[..., 0] ** 2 + X[..., 1] - 11.0) ** 2
            + (X[..., 0] + X[..., 1] ** 2 - 7.0) ** 2
        ).unsqueeze(-1)

surrogate_data = EmpiricalSurrogate(

    inputs=input_features,
    outputs=output_features,
)

# we generate the json spec
jspec = surrogate_data.model_dump_json()

jspec

'{"type":"EmpiricalSurrogate","inputs":{"type":"Inputs","features":[{"type":"ContinuousInput","key":"x_1","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false},{"type":"ContinuousInput","key":"x_2","unit":null,"bounds":[-6.0,6.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false}]},"outputs":{"type":"Outputs","features":[{"type":"ContinuousOutput","key":"y","unit":null,"objective":{"type":"MinimizeObjective","w":1.0,"bounds":[0.0,1.0]}}]},"input_preprocessing_specs":{},"dump":null,"categorical_encodings":{}}'

# Load it from the spec
surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
# Map it
surrogate = surrogates.map(surrogate_data)
# attach the actual model to it
surrogate.model = HimmelblauModel()
# dump it
dump = surrogate.dumps()
# predict with it
df_predictions = surrogate.predict(experiments)
# transform to spec
predictions = surrogate.to_predictions(predictions=df_predictions)

surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
surrogate = surrogates.map(surrogate_data)
surrogate.loads(dump)

# predict with it
df_predictions2 = surrogate.predict(experiments)
# transform to spec
predictions2 = surrogate.to_predictions(predictions=df_predictions2)

# check for equality
predictions == predictions2

True

Mixed GP

Generate data for a mixed problem.

benchmark = CrossCoupling()
samples = benchmark.domain.inputs.sample(n=50)
experiments = benchmark.f(samples, return_complete=True)

experiments.head(10)

	base_eq	t_res	temperature	base	catalyst	yield	cost	valid_cost	valid_yield
0	1.236787	1437.595126	47.184987	DBU	tBuXPhos	0.256631	0.249590	1	1
1	1.838983	321.692734	89.408313	DBU	tBuBrettPhos	1.066157	0.280279	1	1
2	1.766789	1472.181497	39.624336	DBU	AlPhos	0.822108	0.420940	1	1
3	2.038354	784.056921	79.279631	TEA	tBuBrettPhos	0.040317	0.279012	1	1
4	2.027689	923.918891	77.135541	DBU	tBuBrettPhos	1.042584	0.280485	1	1
5	2.229764	982.483194	64.692115	TEA	tBuXPhos	0.072866	0.249051	1	1
6	1.581610	1631.641658	76.996299	TMG	tBuXPhos	0.493793	0.248297	1	1
7	1.597810	1127.063886	71.345228	DBU	AlPhos	0.928765	0.420756	1	1
8	1.732373	104.315896	64.933385	BTMG	tBuBrettPhos	0.968121	0.353940	1	1
9	2.331068	589.274834	57.111092	TMG	tBuBrettPhos	0.121330	0.278355	1	1

# we setup the data model, here a Single Task GP
surrogate_data = MixedSingleTaskGPSurrogate(
    inputs=benchmark.domain.inputs,
    outputs=Outputs(features=[benchmark.domain.outputs.features[0]]),
    categorical_encodings={"catalyst": CategoricalEncodingEnum.ORDINAL},
)

# we generate the json spec
jspec = surrogate_data.model_dump_json()

jspec

'{"hyperconfig":{"type":"MixedSingleTaskGPHyperconfig","hyperstrategy":"FractionalFactorialStrategy","inputs":{"type":"Inputs","features":[{"type":"CategoricalInput","key":"continuous_kernel","categories":["rbf","matern_1.5","matern_2.5"],"allowed":[true,true,true]},{"type":"CategoricalInput","key":"prior","categories":["mbo","threesix","hvarfner"],"allowed":[true,true,true]},{"type":"CategoricalInput","key":"ard","categories":["True","False"],"allowed":[true,true]}]},"n_iterations":null,"target_metric":"MAE"},"aggregations":null,"type":"MixedSingleTaskGPSurrogate","inputs":{"type":"Inputs","features":[{"type":"CategoricalDescriptorInput","key":"catalyst","categories":["tBuXPhos","tBuBrettPhos","AlPhos"],"allowed":[true,true,true],"descriptors":["area_cat","M2_cat"],"values":[[460.7543,67.2057],[518.8408,89.8738],[819.933,129.0808]]},{"type":"CategoricalDescriptorInput","key":"base","categories":["TEA","TMG","BTMG","DBU"],"allowed":[true,true,true,true],"descriptors":["area","M2"],"values":[[162.2992,25.8165],[165.5447,81.4847],[227.3523,30.554],[192.4693,59.8367]]},{"type":"ContinuousInput","key":"base_eq","unit":null,"bounds":[1.0,2.5],"local_relative_bounds":null,"stepsize":null,"allow_zero":false},{"type":"ContinuousInput","key":"temperature","unit":null,"bounds":[30.0,100.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false},{"type":"ContinuousInput","key":"t_res","unit":null,"bounds":[60.0,1800.0],"local_relative_bounds":null,"stepsize":null,"allow_zero":false}]},"outputs":{"type":"Outputs","features":[{"type":"ContinuousOutput","key":"yield","unit":null,"objective":{"type":"MaximizeObjective","w":1.0,"bounds":[0.0,1.0]}}]},"input_preprocessing_specs":{"base":"ORDINAL","catalyst":"ORDINAL"},"dump":null,"categorical_encodings":{"catalyst":"ORDINAL","base":"DESCRIPTOR"},"scaler":"NORMALIZE","output_scaler":"STANDARDIZE","continuous_kernel":{"type":"RBFKernel","features":["t_res","base_eq","temperature","base"],"ard":true,"lengthscale_prior":{"type":"DimensionalityScaledLogNormalPrior","loc":1.4142135623730951,"loc_scaling":0.5,"scale":1.7320508075688772,"scale_scaling":0.0},"lengthscale_constraint":{"type":"GreaterThan","lower_bound":0.025}},"categorical_kernel":{"type":"HammingDistanceKernel","features":["catalyst"],"ard":true,"lengthscale_prior":null,"lengthscale_constraint":{"type":"GreaterThan","lower_bound":1e-6}},"noise_prior":{"type":"LogNormalPrior","loc":-4.0,"scale":1.0}}'

# Load it from the spec
surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
# Map it
surrogate = surrogates.map(surrogate_data)
# Fit it
surrogate.fit(experiments=experiments)
# dump it
dump = surrogate.dumps()
# predict with it
df_predictions = surrogate.predict(experiments)
# transform to spec
predictions = surrogate.to_predictions(predictions=df_predictions)

surrogate_data = TypeAdapter(AnySurrogate).validate_json(jspec)
surrogate = surrogates.map(surrogate_data)
surrogate.loads(dump)

# predict with it
df_predictions2 = surrogate.predict(experiments)
# transform to spec
predictions2 = surrogate.to_predictions(predictions=df_predictions2)

# check for equality
predictions == predictions2

True