# Fit a Gaussian Process surrogate model¶

Here we define a surrogate model using Gaussian Processes. We use the GP model from ScikitLearn - we compared it to other models like GPFlow but observed better speed and better code maintenance in this model.

import warnings

import chart_studio
from besos import eppy_funcs as ef, sampling
from besos.evaluator import EvaluatorEP, EvaluatorGeneric
from besos.problem import EPProblem
from chart_studio import plotly as py
from plotly import graph_objs as go
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, Matern, RationalQuadratic
from sklearn.model_selection import GridSearchCV, train_test_split

from parameter_sets import parameter_set


We begin by: + getting a predefined list of 7 parameters from parameter_sets.py + making these into a problem with electricty use as the objective + and making an evaluator using the default EnergyPlus building.

parameters = parameter_set(7)
problem = EPProblem(parameters, ["Electricity:Facility"])
building = ef.get_building()
evaluator = EvaluatorEP(problem, building)


Then we get 50 samples across this design space and evaluate them.

inputs = sampling.dist_sampler(sampling.lhs, problem, 5)
outputs = evaluator.df_apply(inputs)
inputs

HBox(children=(FloatProgress(value=0.0, description='Executing', max=5.0, style=ProgressStyle(description_widt…

Conductivity Thickness U-Factor Solar Heat Gain Coefficient ElectricEquipment Lights Window to Wall Ratio
0 0.110159 0.191814 3.365800 0.983485 11.383518 13.646694 0.735013
1 0.046016 0.127066 0.356207 0.614671 13.463401 12.900007 0.954627
2 0.186359 0.251974 1.175726 0.368073 12.436931 10.900630 0.338765
3 0.073534 0.170320 4.906628 0.189328 10.613030 11.837185 0.496910
4 0.152748 0.265122 2.522534 0.447570 14.663626 14.946526 0.167042

## Train-test split¶

Next we split the data into a training set (80%) and a testing set (20%).

train_in, test_in, train_out, test_out = train_test_split(
inputs, outputs, test_size=0.2
)


## Hyper-parameters¶

Before fitting the GP model we define the set of hyperparameters we want to optimize. Here we use :raw-latex:\textit{3} folds in the k-fold cross validation scheme. We select a set of Kernel functions, which must fit the characteristics of a problem - details and examples may be found in the Kernel cookbook. Note that the parameters of the Kernel itself are optimized during each model fitting run.

hyperparameters = {
"kernel": [
None,
1.0 * RBF(length_scale=1.0, length_scale_bounds=(1e-1, 10.0)),
# ConstantKernel(0.1, (0.01, 10.0))*(DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0))**2),
1.0 * Matern(length_scale=1.0, length_scale_bounds=(1e-1, 10.0)),
]
}

folds = 3


## Model fitting¶

Here we fit the model using these hyperparameters.

gp = GaussianProcessRegressor(normalize_y=True)

clf = GridSearchCV(gp, hyperparameters, iid=True, cv=folds)

with warnings.catch_warnings():
warnings.simplefilter("ignore", category=FutureWarning)
clf.fit(inputs, outputs)

print(f"Best performing model $R^2$ score on training set: {clf.best_score_}")
print(f"Model $R^2$ parameters: {clf.best_params_}")
print(
f"Best performing model $R^2$ score on a separate test set: {clf.best_estimator_.score(test_in, test_out)}"
)

Best performing model $R^2$ score on training set: nan
Model $R^2$ parameters: {'kernel': None}
Best performing model $R^2$ score on a separate test set: nan

/home/user/.local/lib/python3.7/site-packages/sklearn/metrics/_regression.py:594: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
warnings.warn(msg, UndefinedMetricWarning)
/home/user/.local/lib/python3.7/site-packages/sklearn/metrics/_regression.py:594: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
warnings.warn(msg, UndefinedMetricWarning)
/home/user/.local/lib/python3.7/site-packages/sklearn/metrics/_regression.py:594: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
warnings.warn(msg, UndefinedMetricWarning)
/home/user/.local/lib/python3.7/site-packages/sklearn/metrics/_regression.py:594: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
warnings.warn(msg, UndefinedMetricWarning)
/home/user/.local/lib/python3.7/site-packages/sklearn/metrics/_regression.py:594: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.
warnings.warn(msg, UndefinedMetricWarning)


## Surrogate Modelling Evaluator object¶

We can wrap the fitted model in a BESOS Evaluator.

def evaluation_func(ind):
return (clf.predict([ind]),)

GP_SM = EvaluatorGeneric(evaluation_func, problem)


This has identical behaviour to the original EnergyPlus Evaluator object. In the next cells we generate a single input sample and evaluate it using the surrogate model and EnergyPlus.

sample = sampling.dist_sampler(sampling.lhs, problem, 1)
values = sample.values
print(values)

[ 0.15579269  0.26956427  0.43244217  0.68968071 12.15878908 13.37350072
0.20776428]

GP_SM(values)

2076892603.2093327

evaluator(values)

2096737467.8634224


## Running a large surrogate evaluation¶

inputs = sampling.dist_sampler(sampling.lhs, problem, 5000)
outputs = GP_SM.df_apply(inputs)
results = inputs.join(outputs)

HBox(children=(FloatProgress(value=0.0, description='Executing', max=5000.0, style=ProgressStyle(description_w…

Conductivity Thickness U-Factor Solar Heat Gain Coefficient ElectricEquipment Lights Window to Wall Ratio Electricity:Facility
0 0.174172 0.170220 4.092570 0.139609 12.716906 11.659730 0.121882 2.049882e+09
1 0.153848 0.158066 0.642023 0.544972 12.237855 11.648225 0.294227 1.998581e+09
2 0.107891 0.187473 4.980959 0.211675 13.198086 10.719210 0.888578 2.062038e+09
3 0.052859 0.182125 1.620017 0.533700 11.440617 10.386052 0.340614 2.008998e+09
4 0.171637 0.214592 1.243917 0.685709 10.228580 14.148959 0.352664 2.066421e+09

## Generate an idf/epJSON file with data in dataframe¶

Generate an idf/epJSON file with selected row of data in dataframe and save it in current directory.

# generate_building(dataframe, index, filename)
evaluator.generate_building(results, 2, "output")


## Visualization¶

chart_studio.tools.set_credentials_file(
)
df = inputs.round(3)

# generate list if dictionaries
l = list()
for i in df.columns:
l.extend([dict(label=i, values=df[i])])

l.extend([dict(label=outputs.columns, values=outputs.round(-5))])

data = [
go.Parcoords(
line=dict(
color=outputs["Electricity:Facility"],
colorscale=[[0, "#D7C16B"], [0.5, "#23D8C3"], [1, "#F3F10F"]],
),
dimensions=l,
)
]

layout = go.Layout(plot_bgcolor="#E5E5E5", paper_bgcolor="#E5E5E5")

fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename="parcoords-basic")