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For a more updated version with some of the functionality of this repo, check out this project https://github.com/fsahli/AtrialMFclass

Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models

Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl and Daniel E. Hurtado

This code implements a Gaussian process multi-fidelity classifier in pyMC3. There are four types of classifiers available:

  • Gaussian process classifier: GPclassifier
  • Sparse Gaussian process classifier: SGPclassifier
  • Multi-fidelity classifier: MFGPclassifier
  • Sparse multi-fidelity classifier: SMFGPclassifier

All the classifiers have the following methods:

  • classifier.create_model(): to initialize the model.
  • classifier.sample_model(): to perform the inference using NUTS. This is tipically the step that takes longer.
  • classifier.sample_predictive(X_star, n_samples = 100): generate n_samples samples at the locations X_star for the latent (high fidelity) Gaussian process. To obtain a single prediction for each point use:
from GPmodels import invlogit
y_star = invlogit(classifier.sample_predictive(X_star, n_samples = 100).mean(0))
  • classifier.active_learning(N = 15, plot = False): active learning option, where the next point is adaptively sample to improve accuracy.
  • classifier.plot(): plot the classifier and the active learning distribution. Works only for 2D examples.
  • classifier.test_model(): compute the accuracy at the X_test locations given in the initialization

Dependencies:

All the details can be found in the article:

@article{SahliCostabal2019,
title = "Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models",
journal = "Computer Methods in Applied Mechanics and Engineering",
volume = "357",
pages = "112602",
year = "2019",
issn = "0045-7825",
doi = "https://doi.org/10.1016/j.cma.2019.112602",
url = "http://www.sciencedirect.com/science/article/pii/S0045782519304785",
author = "Francisco Sahli Costabal and Paris Perdikaris and Ellen Kuhl and Daniel E. Hurtado"
}

If you are going to use this code for a publication, please cite this article.

Demo

%load_ext autoreload
%autoreload 2

import numpy as np
import matplotlib.pyplot as plt
from pyDOE import lhs
from GPmodels import GPclassifier, MFGPclassifier, SMFGPclassifier, SGPclassifier
from scipy.cluster.vq import kmeans

plt.rcParams['image.cmap'] = 'coolwarm'
np.random.seed(2)

Define the functions used in the first example of the publication

def low_fidelity(X):
    return (0.45 + np.sin(2.2*X[:,0]*np.pi)/2.5 - X[:,1]) > 0
    
def high_fidelity(X):
    return ( 0.5 + np.sin(2.5*X[:,0]*np.pi)/3 - X[:,1]) > 0
    
def low_fidelity_boundary(X):
    return (0.45 + np.sin(2.2*X[:,0]*np.pi)/2.5)
    
def high_fidelity_boundary(X):
    return (0.5 + np.sin(2.5*X[:,0]*np.pi)/3)

Generate some data

# upper and lower bounds of the data. Used for normalization
# and to set the parameter space in the active learning case
lb = np.array([0,0])
ub = np.array([1,1])

N_L = 50 # number of low fidelity points
N_H = 10 # number of high fidelity points
X_L= lhs(2,N_L) # generate data with a Latin hypercube desing
labels_low = low_fidelity(X_L)
Y_L = 1.0*labels_low

# to generate the high fidelity data we choose some points
# from both of classes in the low fidelity data
ind1 = np.where(Y_L > 0)[0]
ind0 = np.where(Y_L == 0)[0]

X_H1 = X_L[np.random.choice(ind1, N_H/2, replace = False)]
X_H0 = X_L[np.random.choice(ind0, N_H/2, replace = False)]

X_H = np.concatenate((X_H1, X_H0))

labels_high = high_fidelity(X_H)
Y_H = 1.0*labels_high

X_test = lhs(2,1000) # to test the accuracy

... and plot it

fig = plt.figure()
fig.set_size_inches((8,8))

plt.scatter(X_L[:,0], X_L[:,1], c = Y_L, marker = 'x', label = 'low-fidelity data')
plt.scatter(X_H[:,0], X_H[:,1], c = Y_H, label = 'high-fidelity data')

x = np.linspace(0, 1, 100)[:,None]

plt.plot(x, low_fidelity_boundary(x), 'k--', label = 'low-fidelity boundary')
plt.plot(x , high_fidelity_boundary(x), 'k', label = 'high-fidelity boundary')

plt.legend(frameon = False)

<matplotlib.legend.Legend at 0x1c22036150>

png

Gaussian process classifier

Train the single fidelity classifier on the low fidelity data

GPc = GPclassifier(X_L, Y_L, lb, ub, low_fidelity, X_test = X_test)
# the low_fidelity function is needed for active learning. 
# otherwise set to None
GPc.create_model()
GPc.sample_model()
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
/Users/fsc/anaconda/envs/ml/lib/python2.7/site-packages/theano/tensor/basic.py:6592: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  result[diagonal_slice] = x
Multiprocess sampling (2 chains in 4 jobs)
NUTS: [fp_rotated_, eta_L, l_L]
The number of effective samples is smaller than 25% for some parameters.

GPc.plot() # only works for 2D
100%|██████████| 500/500 [00:38<00:00, 12.93it/s]

png

Sparse Gaussian process classifier

Train the sparse single fidelity classifier on the low fidelity data

X_Lu = kmeans(X_L,30)[0] # k-means clustering to obtain the position of the inducing points

SGPc = SGPclassifier(X_L, Y_L, X_Lu, lb, ub, low_fidelity, X_test = X_test)
# the low_fidelity function is needed for active learning. 
# otherwise set to None
SGPc.create_model()
SGPc.sample_model()
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (2 chains in 4 jobs)
NUTS: [fp_rotated_, u_rotated_, eta_L, l_L]
The number of effective samples is smaller than 25% for some parameters.

SGPc.plot() # only works for 2D
100%|██████████| 500/500 [00:49<00:00, 10.03it/s]

png

Multi-fidelity Gaussian process classifier

Train the multi-fidelity classifier on low fidelity and high fidelity data

MFGPc = MFGPclassifier(X_L, Y_L, X_H, Y_H, lb, ub, high_fidelity, X_test = X_test)
# the high_fidelity function is needed for active learning. 
# otherwise set to None
MFGPc.create_model()
MFGPc.sample_model()
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (2 chains in 4 jobs)
NUTS: [fp_rotated_, eta_H, delta, l_H, eta_L, l_L]
There was 1 divergence after tuning. Increase `target_accept` or reparameterize.
The number of effective samples is smaller than 25% for some parameters.

MFGPc.plot()
100%|██████████| 500/500 [00:40<00:00, 12.22it/s]

png

Sparse ulti-fidelity Gaussian process classifier

Train the sparse multi-fidelity classifier on low fidelity and high fidelity data

X_Lu = kmeans(X_L,30)[0] # k-means clustering to obtain the position of the inducing points
X_Hu = X_H # we use the high fidelity points as inducing points as well because there only 10.

SMFGPc = SMFGPclassifier(X_L, Y_L, X_H, Y_H, X_Lu, X_Hu, lb, ub, high_fidelity, X_test = X_test)
SMFGPc.create_model()
SMFGPc.sample_model()
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (2 chains in 4 jobs)
NUTS: [fp_rotated_, u_rotated_, eta_H, delta, l_H, eta_L, l_L]
There was 1 divergence after tuning. Increase `target_accept` or reparameterize.
There was 1 divergence after tuning. Increase `target_accept` or reparameterize.
The number of effective samples is smaller than 25% for some parameters.

SMFGPc.plot()
100%|██████████| 500/500 [01:32<00:00,  6.08it/s]

png