Integrating a user-provided simulator in an end-to-end AutoEmulate workflow

Overview

In this workflow we demonstrate the integration of a Cardiovascular simulator, Naghavi Model from ModularCirc in the end-to-end AutoEmulate workflow.

Naghavi model is a 0D (zero-dimensional) computational model of the cardiovascular system, which is used to simulate blood flow and pressure dynamics in the heart and blood vessels.

This demo includes:

• Setting up parameter ranges

• Creating samples

• Running the simulator to generate training data for the emulator

• Using AutoEmulate to find the best pre-processing technique and model tailored to the simulation data

• Applying history matching to refine the model and enhance parameter ranges

• Sensitivity Analysis

Additional dependency requirements

In this demonstration we are using the Naghavi Model Simulator from ModularCirc library. Therefore, the user needs to install the ModularCirc library in their existing AutoEmulate virtual environemnt as an additional dependency.

! pip install git+https://github.com/alan-turing-institute/ModularCirc.git@dev

Workflow

1 - Create a dictionary called parameters_range which contains the name of the simulator input parameters and their range.

from autoemulate.simulations.naghavi_cardiac_ModularCirc import extract_parameter_ranges
# Usage example:
parameters_range = extract_parameter_ranges('../data/naghavi_model_parameters.json')
parameters_range

{'ao.r': (120.0, 360.0),
 'ao.c': (0.15, 0.44999999999999996),
 'art.r': (562.5, 1687.5),
 'art.c': (1.5, 4.5),
 'ven.r': (4.5, 13.5),
 'ven.c': (66.65, 199.95000000000002),
 'av.r': (3.0, 9.0),
 'mv.r': (2.05, 6.1499999999999995),
 'la.E_pas': (0.22, 0.66),
 'la.E_act': (0.225, 0.675),
 'la.v_ref': (5.0, 15.0),
 'la.k_pas': (0.01665, 0.07500000000000001),
 'lv.E_pas': (0.5, 1.5),
 'lv.E_act': (1.5, 4.5),
 'lv.v_ref': (5.0, 15.0),
 'lv.k_pas': (0.00999, 0.045)}

2 - Use LatinHypercube method from AutoEmulate to generate initial samples using the parameters range.

import pandas as pd
import numpy as np
from autoemulate.experimental_design import LatinHypercube

# Generate Latin Hypercube samples
N_samples = 100
lhd = LatinHypercube(list(parameters_range.values()))
sample_array = lhd.sample(N_samples)
sample_df = pd.DataFrame(sample_array, columns=parameters_range.keys())

print("Number of parameters:", sample_df.shape[1], "Number of samples from each parameter:", sample_df.shape[0])
sample_df.head()

Number of parameters: 16 Number of samples from each parameter: 100

	ao.r	ao.c	art.r	art.c	ven.r	ven.c	av.r	mv.r	la.E_pas	la.E_act	la.v_ref	la.k_pas	lv.E_pas	lv.E_act	lv.v_ref	lv.k_pas
0	296.440885	0.396672	1015.055683	1.988679	6.507542	167.642001	5.485211	3.162309	0.233027	0.326324	12.565421	0.022142	1.443565	3.103660	7.226351	0.025244
1	170.936176	0.430408	810.882333	2.385469	10.865371	147.729601	5.217009	5.843980	0.254431	0.435555	14.803089	0.035664	0.854612	2.958498	8.912569	0.039591
2	224.910382	0.215161	1669.192723	1.551490	10.593887	88.536086	4.763047	3.939957	0.516216	0.304267	6.212975	0.020790	1.461334	4.461589	8.717625	0.031072
3	315.206213	0.151029	1201.130539	3.547849	6.280902	139.960518	4.016971	5.931769	0.629961	0.387896	14.515793	0.048657	1.499412	3.940640	14.773632	0.010744
4	220.269324	0.387451	933.723349	3.972825	5.981294	165.658227	6.785018	3.791956	0.381240	0.458787	11.295892	0.064039	0.929570	3.532311	9.808989	0.010605

3 - Wrap your Simulator in the AutoEmulate Simulator Base Class.

from autoemulate.simulations.naghavi_cardiac_ModularCirc import NaghaviSimulator
# Initialize simulator with specific outputs
simulator = NaghaviSimulator(
    parameters_range=parameters_range, 
    output_variables=['lv.P_i', 'lv.P_o'],  # Only the ones you're interested in
    n_cycles=300, 
    dt=0.001,
)

4 - Run the simulator using run_batch_simulations to obtain data for training AutoEmulate.

# Run batch simulations with the samples generated in Cell 1
results = simulator.run_batch_simulations(sample_df)

# Convert results to DataFrame for analysis
results_df = pd.DataFrame(results)

Successfully completed 100/100 simulations (100.0%)

results_df

	0	1	2	3	4	5	6	7
0	15.168249	20.161629	18.472553	4.993380	15.168249	20.161629	18.472553	4.993380
1	36.019994	39.135503	37.573478	3.115509	36.019994	39.135503	37.573478	3.115509
2	26.756008	26.886811	26.814212	0.130803	26.756008	26.886811	26.814212	0.130803
3	2.411396	7.086565	5.246729	4.675169	2.411396	7.086565	5.246729	4.675169
4	1.594468	5.884158	4.283615	4.289690	1.594468	5.884158	4.283615	4.289690
...	...	...	...	...	...	...	...	...
95	4.781580	15.237328	13.694896	10.455748	4.781580	15.237328	13.694896	10.455748
96	11.317115	20.156449	18.979320	8.839334	11.317115	20.156449	18.979320	8.839334
97	18.666848	23.334430	21.667915	4.667582	18.666848	23.334430	21.667915	4.667582
98	12.985272	24.617342	23.214435	11.632069	12.985272	24.617342	23.214435	11.632069
99	9.713965	15.335051	14.554911	5.621086	9.713965	15.335051	14.554911	5.621086

100 rows × 8 columns

Note that the first 4 steps can be replaced by having stored the output of your simulation in a file and then reading them in to a dataframe. However the purpose of this article is to demonstrate the use of a User-provided simulator in an end-to-end workflow.

simulator.output_names

['lv.P_i_min',
 'lv.P_i_max',
 'lv.P_i_mean',
 'lv.P_i_range',
 'lv.P_o_min',
 'lv.P_o_max',
 'lv.P_o_mean',
 'lv.P_o_range']

Test your simulator with our test function to make sure it is compatible with AutoEmulate pipeline (Feature not provided yet).

# this should be replaced with a test written specically to test the simulator written by the user
# ! pytest ../../tests/test_base_simulator.py

5 - Setup AutoEmulate.

• User should choose from the available target pre-processing methods the methods they would like to investigate.

• User should choose from the available models the models they would like to investigate.

• Setup AutoEmulate

import numpy as np
from autoemulate.compare import AutoEmulate
from autoemulate.plotting import _predict_with_optional_std


preprocessing_methods = [{"name" : "PCA", "params" : {"reduced_dim": 2}}]
em = AutoEmulate()
em.setup(sample_df, results, models=["gp"], scale_output = True, reduce_dim_output=True, preprocessing_methods=preprocessing_methods)

AutoEmulate is set up with the following settings:

	Values
Simulation input shape (X)	(100, 16)
Simulation output shape (y)	(100, 8)
Proportion of data for testing (test_set_size)	0.2
Scale input data (scale)	True
Scaler (scaler)	StandardScaler
Scale output data (scale_output)	True
Scaler output (scaler_output)	StandardScaler
Do hyperparameter search (param_search)	False
Reduce input dimensionality (reduce_dim)	False
Reduce output dimensionality (reduce_dim_output)	True
Dimensionality output reduction methods (dim_reducer_output)	PCA
Cross validator (cross_validator)	KFold
Parallel jobs (n_jobs)	1

6 - Run compare to train AutoEmulate and extract the best model.

best_model = em.compare()

7 - Examine the summary of cross-validation.

em.summarise_cv()

	preprocessing	model	short	fold	rmse	r2
0	PCA	GaussianProcess	gp	4	1.640022	0.916621
1	PCA	GaussianProcess	gp	2	3.126992	0.874326
2	PCA	GaussianProcess	gp	0	2.790274	0.864704
3	PCA	GaussianProcess	gp	3	3.622270	0.846636
4	PCA	GaussianProcess	gp	1	2.794191	0.774314

best_model

InputOutputPipeline(regressor=Pipeline(steps=[('scaler', StandardScaler()),
                                              ('model', GaussianProcess())]),
                    transformer=Pipeline(steps=[('scaler_output',
                                                 StandardScaler()),
                                                ('dim_reducer_output',
                                                 PCA(n_components=2))]))

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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8 - Extract the desired model, run evaluation and refit using the whole dataset.

• You can use the best_model selected by AutoEmulate

• or you can extract the model and pre-processing technique displayed in em.summarise_cv()

gp = em.get_model('GaussianProcess')
em.evaluate(gp)
# for best model change the line above to:
# em.evaluate(best_model)

	model	short	preprocessing	rmse	r2
0	GaussianProcess	gp	PCA	3.9183	0.7697

gp_final = em.refit(gp)
gp_final

InputOutputPipeline(regressor=Pipeline(steps=[('scaler', StandardScaler()),
                                              ('model', GaussianProcess())]),
                    transformer=Pipeline(steps=[('scaler_output',
                                                 StandardScaler()),
                                                ('dim_reducer_output',
                                                 PCA(n_components=2))]))

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em.plot_eval(gp_final)

9 - History Matching

Once you have the final model, running history matching can improve your model. The Implausibility metric is calculated using the following relation for each set of parameter:

\(I_i(\overline{x_0}) = \frac{|z_i - \mathbb{E}(f_i(\overline{x_0}))|}{\sqrt{\text{Var}[z_i - \mathbb{E}(f_i(\overline{x_0}))]}}\) Where if implosibility (\(I_i\)) exceeds a threshhold value, the points will be rulled out. The outcome of history matching are the NORY (Not Ruled Out Yet) and RO (Ruled Out) points.

• create a dictionary of your observations, this should match the output names of your simulator

• create the history matching object

• run history matching

from autoemulate.history_matching import HistoryMatching

# Define observed data with means and variances
observations = {
    'lv.P_i_min': (5.0, 0.1),   # Minimum of minimum LV pressure
    'lv.P_i_max': (20.0, 0.1),   # Maximum of minimum LV pressure
    'lv.P_i_mean': (10.0, 0.1),  # Mean of minimum LV pressure
    'lv.P_i_range': (15.0, 0.5), # Range of minimum LV pressure
    'lv.P_o_min': (1.0, 0.1),  # Minimum of maximum LV pressure
    'lv.P_o_max': (13.0, 0.1),  # Maximum of maximum LV pressure
    'lv.P_o_mean': (12.0, 0.1), # Mean of maximum LV pressure
    'lv.P_o_range': (20.0, 0.5)  # Range of maximum LV pressure
}

# Create history matcher
hm = HistoryMatching(
    simulator=simulator,
    observations=observations,
    threshold=3.0
)

# Run history matching
all_samples, all_impl_scores, emulator = hm.run(
    n_waves=20,
    n_samples_per_wave=20,
    emulator_predict=True,
    initial_emulator=gp_final,
)

History Matching: 100%|██████████| 20/20 [00:00<00:00, 21.60wave/s, samples=20, failed=0, NROY=20, min_impl=0.12, max_impl=2.90]

em.plot_eval(emulator)

10 - use the interactive dashboard to inspect the results of history matching

from autoemulate.history_matching_dashboard import HistoryMatchingDashboard
dashboard = HistoryMatchingDashboard(
    samples=all_samples,
    impl_scores=all_impl_scores,
    param_names=simulator.param_names,  
    output_names=simulator.output_names, 
    )
dashboard.display()

11 - Sensitivity Analysis

Use AutoEmulate to perform sensitivity analysis.

# Extract parameter names and bounds from the dictionary
parameter_names = list(parameters_range.keys())
parameter_bounds = list(parameters_range.values())

# Define the problem dictionary for Sobol sensitivity analysis
problem = {
    'num_vars': len(parameter_names),
    'names': parameter_names,
    'bounds': parameter_bounds
}

si = em.sensitivity_analysis(problem=problem)
si

	output	parameter	index	value	confidence
0	y1	ao.r	S1	0.000054	0.000009
1	y1	ao.c	S1	0.000197	0.000032
2	y1	art.r	S1	0.000349	0.000040
3	y1	art.c	S1	0.000073	0.000011
4	y1	ven.r	S1	0.000271	0.000060
...	...	...	...	...	...
115	y8	(lv.E_pas, lv.v_ref)	S2	-0.004675	0.050165
116	y8	(lv.E_pas, lv.k_pas)	S2	0.098837	0.057726
117	y8	(lv.E_act, lv.v_ref)	S2	0.005969	0.018428
118	y8	(lv.E_act, lv.k_pas)	S2	0.001894	0.022177
119	y8	(lv.v_ref, lv.k_pas)	S2	0.002823	0.018446

1216 rows × 5 columns

Footnote: Testing the dashboard

Sometimes it is hard to know, if the results we are seeing is because the code is not working, or our simulation results are more interesting than we expected. Here is a little test dataset which tests the dashboard, so that you can see how the plots are supposed to look liek and what they shouldf show

# Create a test sample with KNOWN NROY regions
test_samples = np.array([[x, y] for x in np.linspace(0,1,100) 
                               for y in np.linspace(0,1,100)])
test_scores = (abs(test_samples[:, 0]-0.5)+abs(test_samples[:, 1]-0.5)).reshape(-1, 1)

# Should show a clear diagonal pattern
test_dash = HistoryMatchingDashboard(
    samples=test_samples,
    impl_scores=test_scores,
    param_names=["p1", "p2"],
    output_names=["out1"],
    threshold=0.7  # ~50% of points should be NROY
)
test_dash.display()