def test_insufficient_no_points():
"""Test if estimation aborts for too few data points."""
expected_mi, source1, source2, target = _get_gauss_data(n=4)
settings = {
'kraskov_k': 4,
'theiler_t': 0,
'history': 1,
'history_target': 1,
'lag_mi': 1,
'source_target_delay': 1
}
# Test first settings combination with k==N
est = OpenCLKraskovMI(settings)
with pytest.raises(RuntimeError):
est.estimate(source1, target)
est = OpenCLKraskovCMI(settings)
with pytest.raises(RuntimeError):
est.estimate(source1, target, target)
# Test a second combination with a Theiler-correction != 0
settings['theiler_t'] = 1
settings['kraskov_k'] = 2
est = OpenCLKraskovMI(settings)
with pytest.raises(RuntimeError):
est.estimate(source1, target)
est = OpenCLKraskovCMI(settings)
with pytest.raises(RuntimeError):
est.estimate(source1, target, target)
def test_cmi_uncorrelated_gaussians():
"""Test CMI estimator on uncorrelated Gaussian data."""
n_obs = 10000
var1 = np.random.randn(n_obs, 1)
var2 = np.random.randn(n_obs, 1)
var3 = np.random.randn(n_obs, 1)
# Run OpenCL estimator.
settings = {'debug': True, 'return_counts': True}
ocl_est = OpenCLKraskovCMI(settings=settings)
(mi_ocl, dist, n_range_var1, n_range_var2,
n_range_var3) = ocl_est.estimate(var1, var2, var3)
mi_ocl = mi_ocl[0]
# Run JIDT estimator.
jidt_est = JidtKraskovCMI(settings={})
mi_jidt = jidt_est.estimate(var1, var2, var3)
print('JIDT MI result: {0:.4f} nats; OpenCL MI result: {1:.4f} nats; '
'expected to be close to 0 nats for uncorrelated '
'Gaussians.'.format(mi_jidt, mi_ocl))
assert np.isclose(
mi_jidt, 0,
atol=0.05), ('MI estimation for uncorrelated Gaussians using the '
'JIDT estimator failed (error larger 0.05).')
assert np.isclose(
mi_ocl, 0,
atol=0.05), ('MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
assert np.isclose(
mi_ocl, mi_jidt,
atol=0.0001), ('MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
def test_cmi_correlated_gaussians():
"""Test estimators on correlated Gaussian data with conditional."""
expected_mi, source, source_uncorr, target = _get_gauss_data()
# Run OpenCL estimator.
settings = {'debug': True, 'return_counts': True}
ocl_est = OpenCLKraskovCMI(settings=settings)
(mi_ocl, dist, n_range_var1, n_range_var2,
n_range_cond) = ocl_est.estimate(source, target, source_uncorr)
mi_ocl = mi_ocl[0]
# Run JIDT estimator.
jidt_est = JidtKraskovCMI(settings={})
mi_jidt = jidt_est.estimate(source, target, source_uncorr)
print('JIDT MI result: {0:.4f} nats; OpenCL MI result: {1:.4f} nats; '
'expected to be close to {2:.4f} nats for correlated '
'Gaussians.'.format(mi_jidt, mi_ocl, expected_mi))
assert np.isclose(
mi_jidt, expected_mi,
atol=0.05), ('MI estimation for uncorrelated Gaussians using the '
'JIDT estimator failed (error larger 0.05).')
assert np.isclose(
mi_ocl, expected_mi,
atol=0.05), ('MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
assert np.isclose(
mi_ocl, mi_jidt,
atol=0.0001), ('MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
def test_cmi_no_cond_correlated_gaussians():
"""Test estimators on correlated Gaussian data without conditional."""
expected_mi, source, source_uncorr, target = _get_gauss_data()
# Run OpenCL estimator.
settings = {'debug': True}
ocl_est = OpenCLKraskovCMI(settings=settings)
mi_ocl, dist, n_range_var1, n_range_var2 = ocl_est.estimate(source, target)
mi_ocl = mi_ocl[0]
# Run JIDT estimator.
jidt_est = JidtKraskovCMI(settings={})
mi_jidt = jidt_est.estimate(source, target)
cov_effective = np.cov(np.squeeze(source), np.squeeze(target))[1, 0]
expected_mi = math.log(1 / (1 - math.pow(cov_effective, 2)))
print('JIDT MI result: {0:.4f} nats; OpenCL MI result: {1:.4f} nats; '
'expected to be close to {2:.4f} nats for correlated '
'Gaussians.'.format(mi_jidt, mi_ocl, expected_mi))
assert np.isclose(mi_jidt, expected_mi, atol=0.05), (
'MI estimation for uncorrelated Gaussians using the '
'JIDT estimator failed (error larger 0.05).')
assert np.isclose(mi_ocl, expected_mi, atol=0.05), (
'MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
assert np.isclose(mi_ocl, mi_jidt, atol=0.0001), (
'MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
def test_multi_gpu():
"""Test use of multiple GPUs."""
expected_mi, source, source_uncorr, target = _get_gauss_data()
settings = {'debug': True, 'return_counts': True}
# Get no. available devices on current platform.
device_list, _, _ = OpenCLKraskovCMI()._get_device(gpuid=0)
print(device_list)
n_devices = len(device_list)
# Try initialising estimator with unavailable GPU ID
with pytest.raises(RuntimeError):
settings['gpuid'] = n_devices + 1
OpenCLKraskovCMI(settings=settings)
# Run OpenCL estimator on available device with highest available ID.
settings['gpuid'] = n_devices - 1
ocl_est = OpenCLKraskovCMI(settings=settings)
(mi_ocl, dist, n_range_var1, n_range_var2,
n_range_cond) = ocl_est.estimate(source, target, source_uncorr)
mi_ocl = mi_ocl[0]
print('Expected MI: {0:.4f} nats; OpenCL MI result: {1:.4f} nats; '
'expected to be close to 0 nats for uncorrelated '
'Gaussians.'.format(expected_mi, mi_ocl))
assert np.isclose(
mi_ocl, expected_mi,
atol=0.05), ('MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
def test_multiple_runs_two_dim():
"""Test kNN with two chunks of 2D data in the same call."""
settings = {
'theiler_t': 0,
'knn_k': 1,
'gpu_id': 0,
'debug': True,
'max_mem': 5 * 1024 * 1024}
EST_MI = OpenCLKraskovMI(settings)
EST_CMI = OpenCLKraskovCMI(settings)
n_chunks = 50000
pointset1 = np.array(
[[-1, 0.5, 1.1, 2, 10, 11, 10.5, -100, -50, 666, 9999, 9999],
[-1, 0.5, 1.1, 2, 98, -9, -200, 45.3, -53, 0.1, 9999, 9999]]).T.copy()
pointset1 = np.tile(pointset1, (n_chunks, 1))
pointset2 = np.ones(pointset1.shape) * 9999
pointset3 = np.ones(pointset1.shape) * 9999
# Call MI estimator
mi, dist1, npoints_x, npoints_y = EST_MI.estimate(
pointset1, pointset2, n_chunks=n_chunks)
assert np.isclose(dist1[0], 1.5), 'Distances 0 not correct.'
assert np.isclose(dist1[1], 0.6), 'Distances 1 not correct.'
assert np.isclose(dist1[2], 0.6), 'Distances 2 not correct.'
assert np.isclose(dist1[3], 0.9), 'Distances 3 not correct.'
# Call CMI estimator with pointset2 as conditional (otherwise the MI
# estimator is called internally and the CMI estimator is never tested).
cmi, dist2, npoints_x, npoints_y, npoints_c = EST_CMI.estimate(
pointset1, pointset2, pointset3, n_chunks=n_chunks)
assert np.isclose(dist2[0], 1.5), 'Distances 0 not correct.'
assert np.isclose(dist2[1], 0.6), 'Distances 1 not correct.'
assert np.isclose(dist2[2], 0.6), 'Distances 2 not correct.'
assert np.isclose(dist2[3], 0.9), 'Distances 3 not correct.'
def test_cmi_uncorrelated_gaussians():
"""Test CMI estimator on uncorrelated Gaussian data."""
n_obs = 10000
var1 = np.random.randn(n_obs, 1)
var2 = np.random.randn(n_obs, 1)
var3 = np.random.randn(n_obs, 1)
# Run OpenCL estimator.
settings = {'debug': True}
ocl_est = OpenCLKraskovCMI(settings=settings)
(mi_ocl, dist, n_range_var1,
n_range_var2, n_range_var3) = ocl_est.estimate(var1, var2, var3)
mi_ocl = mi_ocl[0]
# Run JIDT estimator.
jidt_est = JidtKraskovCMI(settings={})
mi_jidt = jidt_est.estimate(var1, var2, var3)
print('JIDT MI result: {0:.4f} nats; OpenCL MI result: {1:.4f} nats; '
'expected to be close to 0 nats for uncorrelated '
'Gaussians.'.format(mi_jidt, mi_ocl))
assert np.isclose(mi_jidt, 0, atol=0.05), (
'MI estimation for uncorrelated Gaussians using the '
'JIDT estimator failed (error larger 0.05).')
assert np.isclose(mi_ocl, 0, atol=0.05), (
'MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
assert np.isclose(mi_ocl, mi_jidt, atol=0.0001), (
'MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
def test_cmi_correlated_gaussians():
"""Test estimators on correlated Gaussian data with conditional."""
expected_mi, source, source_uncorr, target = _get_gauss_data()
# Run OpenCL estimator.
settings = {'debug': True}
ocl_est = OpenCLKraskovCMI(settings=settings)
(mi_ocl, dist, n_range_var1,
n_range_var2, n_range_cond) = ocl_est.estimate(source, target,
source_uncorr)
mi_ocl = mi_ocl[0]
# Run JIDT estimator.
jidt_est = JidtKraskovCMI(settings={})
mi_jidt = jidt_est.estimate(source, target, source_uncorr)
print('JIDT MI result: {0:.4f} nats; OpenCL MI result: {1:.4f} nats; '
'expected to be close to {2:.4f} nats for correlated '
'Gaussians.'.format(mi_jidt, mi_ocl, expected_mi))
assert np.isclose(mi_jidt, expected_mi, atol=0.05), (
'MI estimation for uncorrelated Gaussians using the '
'JIDT estimator failed (error larger 0.05).')
assert np.isclose(mi_ocl, expected_mi, atol=0.05), (
'MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
assert np.isclose(mi_ocl, mi_jidt, atol=0.0001), (
'MI estimation for uncorrelated Gaussians using the '
'OpenCL estimator failed (error larger 0.05).')
def test_amd_data_padding():
"""Test padding necessary for AMD devices."""
expected_mi, source, source_uncorr, target = _get_gauss_data()
settings = {'debug': True, 'return_counts': True}
est_mi = OpenCLKraskovMI(settings=settings)
est_cmi = OpenCLKraskovCMI(settings=settings)
# Run OpenCL estimator for various data sizes.
for n in [11, 13, 25, 64, 100, 128, 999, 10000, 3781, 50000]:
for n_chunks in [1, 3, 10, 50, 99]:
data_run_source = np.tile(source[:n], (n_chunks, 1))
data_run_target = np.tile(target[:n], (n_chunks, 1))
mi, dist, n_range_var1, n_range_var2 = est_mi.estimate(
data_run_source, data_run_target, n_chunks=n_chunks)
cmi, dist, n_range_var1, n_range_var2 = est_cmi.estimate(
data_run_source, data_run_target, n_chunks=n_chunks)
# Run OpenCL esitmator for various no. points and check result for
# correctness. Note that for smaller sample sizes the error becomes too
# large.
n_chunks = 1
for n in [832, 999, 10000, 3781, 50000]:
data_run_source = np.tile(source[:n], (n_chunks, 1))
data_run_target = np.tile(target[:n], (n_chunks, 1))
mi, dist, n_range_var1, n_range_var2 = est_mi.estimate(
data_run_source, data_run_target, n_chunks=n_chunks)
cmi, dist, n_range_var1, n_range_var2 = est_cmi.estimate(
data_run_source, data_run_target, n_chunks=n_chunks)
print('{0} points, {1} chunks: OpenCL MI result: {2:.4f} nats; '
'expected to be close to {3:.4f} nats for correlated '
'Gaussians.'.format(n, n_chunks, mi[0], expected_mi))
assert np.isclose(mi[0], expected_mi, atol=0.05), (
'MI estimation for uncorrelated Gaussians using the OpenCL '
'estimator failed (error larger 0.05).')
print('OpenCL CMI result: {0:.4f} nats; expected to be close to '
'{1:.4f} nats for correlated Gaussians.'.format(
cmi[0], expected_mi))
assert np.isclose(cmi[0], expected_mi, atol=0.05), (
'CMI estimation for uncorrelated Gaussians using the OpenCL '
'estimator failed (error larger 0.05).')
# Test debugging switched off
settings = {'debug': False, 'return_counts': False}
est_mi = OpenCLKraskovMI(settings=settings)
est_cmi = OpenCLKraskovCMI(settings=settings)
mi = est_mi.estimate(source, target)
cmi = est_cmi.estimate(source, target)
settings['local_values'] = True
est_mi = OpenCLKraskovMI(settings=settings)
est_cmi = OpenCLKraskovCMI(settings=settings)
mi = est_mi.estimate(source, target)
cmi = est_cmi.estimate(source, target)
def test_multiple_runs_two_dim():
"""Test kNN with two chunks of 2D data in the same call."""
settings = {
'theiler_t': 0,
'knn_k': 1,
'gpu_id': 0,
'debug': True,
'max_mem': 5 * 1024 * 1024
}
EST_MI = OpenCLKraskovMI(settings)
EST_CMI = OpenCLKraskovCMI(settings)
n_chunks = 50000
pointset1 = np.array([[1, 1.1, -1, -1.2, 1, 1.1, -1, -1.2],
[1, 1, -1, -1, 1, 1, -1, -1]]).T.copy()
pointset1 = np.tile(pointset1, (n_chunks // 2, 1))
pointset2 = np.ones(pointset1.shape) * 9999
pointset3 = np.ones(pointset1.shape) * 9999
# Points: X Y y
# 1 1 | o o
# 1.1 1 |
# -1 -1 ----+----x
# -1.2 -1 |
# o o |
# Call MI estimator
mi, dist1, npoints_x, npoints_y = EST_MI.estimate(pointset1,
pointset2,
n_chunks=n_chunks)
print(dist1[0:8])
assert np.isclose(dist1[0], 0.1), 'Distance 0 not correct.'
assert np.isclose(dist1[1], 0.1), 'Distance 1 not correct.'
assert np.isclose(dist1[2], 0.2), 'Distance 2 not correct.'
assert np.isclose(dist1[3], 0.2), 'Distance 3 not correct.'
assert np.isclose(dist1[4], 0.1), 'Distance 4 not correct.'
assert np.isclose(dist1[5], 0.1), 'Distance 5 not correct.'
assert np.isclose(dist1[6], 0.2), 'Distance 6 not correct.'
assert np.isclose(dist1[7], 0.2), 'Distance 7 not correct.'
# Call CMI estimator with pointset2 as conditional (otherwise the MI
# estimator is called internally and the CMI estimator is never tested).
cmi, dist2, npoints_x, npoints_y, npoints_c = EST_CMI.estimate(
pointset1, pointset2, pointset3, n_chunks=n_chunks)
assert np.isclose(dist2[0], 0.1), 'Distance 0 not correct.'
assert np.isclose(dist2[1], 0.1), 'Distance 1 not correct.'
assert np.isclose(dist2[2], 0.2), 'Distance 2 not correct.'
assert np.isclose(dist2[3], 0.2), 'Distance 3 not correct.'
assert np.isclose(dist2[4], 0.1), 'Distance 4 not correct.'
assert np.isclose(dist2[5], 0.1), 'Distance 5 not correct.'
assert np.isclose(dist2[6], 0.2), 'Distance 6 not correct.'
assert np.isclose(dist2[7], 0.2), 'Distance 7 not correct.'
def test_user_input():
est_mi = OpenCLKraskovMI()
est_cmi = OpenCLKraskovCMI()
N = 1000
# Unequal variable dimensions.
with pytest.raises(AssertionError):
est_mi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N + 1, 1))
with pytest.raises(AssertionError):
est_cmi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N + 1, 1),
conditional=np.random.randn(N, 1))
with pytest.raises(AssertionError):
est_cmi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N, 1),
conditional=np.random.randn(N + 1, 1))
# No. chunks doesn't fit the signal length.
with pytest.raises(AssertionError):
est_mi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N, 1),
n_chunks=7)
with pytest.raises(AssertionError):
est_cmi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N, 1),
conditional=np.random.randn(N, 1),
n_chunks=7)
def test_amd_data_padding():
"""Test padding necessary for AMD devices."""
expected_mi, source, source_uncorr, target = _get_gauss_data()
settings = {'debug': True}
est_mi = OpenCLKraskovMI(settings=settings)
est_cmi = OpenCLKraskovCMI(settings=settings)
# Run OpenCL estimator for various data sizes.
for n in [11, 13, 25, 64, 100, 128, 999, 10000, 3781, 50000]:
for n_chunks in [1, 3, 10, 50, 99]:
data_run_source = np.tile(source[:n], (n_chunks, 1))
data_run_target = np.tile(target[:n], (n_chunks, 1))
mi, dist, n_range_var1, n_range_var2 = est_mi.estimate(
data_run_source, data_run_target, n_chunks=n_chunks)
cmi, dist, n_range_var1, n_range_var2 = est_cmi.estimate(
data_run_source, data_run_target, n_chunks=n_chunks)
# Run OpenCL esitmator for various no. points and check result for
# correctness. Note that for smaller sample sizes the error becomes too
# large.
n_chunks = 1
for n in [832, 999, 10000, 3781, 50000]:
data_run_source = np.tile(source[:n], (n_chunks, 1))
data_run_target = np.tile(target[:n], (n_chunks, 1))
mi, dist, n_range_var1, n_range_var2 = est_mi.estimate(
data_run_source, data_run_target, n_chunks=n_chunks)
cmi, dist, n_range_var1, n_range_var2 = est_cmi.estimate(
data_run_source, data_run_target, n_chunks=n_chunks)
print('{0} points, {1} chunks: OpenCL MI result: {2:.4f} nats; '
'expected to be close to {3:.4f} nats for correlated '
'Gaussians.'.format(n, n_chunks, mi[0], expected_mi))
assert np.isclose(mi[0], expected_mi, atol=0.05), (
'MI estimation for uncorrelated Gaussians using the OpenCL '
'estimator failed (error larger 0.05).')
print('OpenCL CMI result: {0:.4f} nats; expected to be close to '
'{1:.4f} nats for correlated Gaussians.'.format(
cmi[0], expected_mi))
assert np.isclose(cmi[0], expected_mi, atol=0.05), (
'CMI estimation for uncorrelated Gaussians using the OpenCL '
'estimator failed (error larger 0.05).')
# Test debugging switched off
settings['debug'] = False
mi = est_mi.estimate(source, target)
cmi = est_cmi.estimate(source, target)
settings['local_values'] = True
mi = est_mi.estimate(source, target)
cmi = est_cmi.estimate(source, target)
def test_local_values():
"""Test estimation of local MI and CMI using OpenCL estimators."""
# Get data
n_chunks = 2
expec_mi, source, source_uncorr, target = _get_gauss_data(n=20000)
chunklength = int(source.shape[0] / n_chunks)
# Estimate local values
settings = {'local_values': True}
est_cmi = OpenCLKraskovCMI(settings=settings)
cmi = est_cmi.estimate(source, target, source_uncorr, n_chunks=n_chunks)
est_mi = OpenCLKraskovMI(settings=settings)
mi = est_mi.estimate(source, target, n_chunks=n_chunks)
mi_ch1 = np.mean(mi[0:chunklength])
mi_ch2 = np.mean(mi[chunklength:])
cmi_ch1 = np.mean(cmi[0:chunklength])
cmi_ch2 = np.mean(cmi[chunklength:])
# Estimate non-local values for comparison
settings = {'local_values': False}
est_cmi = OpenCLKraskovCMI(settings=settings)
mi = est_cmi.estimate(source, target, source_uncorr, n_chunks=n_chunks)
est_mi = OpenCLKraskovMI(settings=settings)
cmi = est_mi.estimate(source, target, n_chunks=n_chunks)
# Report results
print('OpenCL MI result: {0:.4f} nats (chunk 1); {1:.4f} nats (chunk 2) '
'expected to be close to {2:.4f} nats for uncorrelated '
'Gaussians.'.format(mi_ch1, mi_ch2, expec_mi))
print('OpenCL CMI result: {0:.4f} nats (chunk 1); {1:.4f} nats (chunk 2) '
'expected to be close to {2:.4f} nats for uncorrelated '
'Gaussians.'.format(cmi_ch1, cmi_ch2, expec_mi))
assert np.isclose(mi_ch1, expec_mi, atol=0.05)
assert np.isclose(mi_ch2, expec_mi, atol=0.05)
assert np.isclose(cmi_ch1, expec_mi, atol=0.05)
assert np.isclose(cmi_ch2, expec_mi, atol=0.05)
assert np.isclose(mi_ch1, mi_ch2, atol=0.05)
assert np.isclose(mi_ch1, mi[0], atol=0.05)
assert np.isclose(mi_ch2, mi[1], atol=0.05)
assert np.isclose(cmi_ch1, cmi_ch2, atol=0.05)
assert np.isclose(cmi_ch1, cmi[0], atol=0.05)
assert np.isclose(cmi_ch2, cmi[1], atol=0.05)
def test_user_input():
est_mi = OpenCLKraskovMI()
est_cmi = OpenCLKraskovCMI()
N = 1000
# Unequal variable dimensions.
with pytest.raises(AssertionError):
est_mi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N + 1, 1))
with pytest.raises(AssertionError):
est_cmi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N + 1, 1),
conditional=np.random.randn(N, 1))
with pytest.raises(AssertionError):
est_cmi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N, 1),
conditional=np.random.randn(N + 1, 1))
# No. chunks doesn't fit the signal length.
with pytest.raises(AssertionError):
est_mi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N, 1),
n_chunks=7)
with pytest.raises(AssertionError):
est_cmi.estimate(var1=np.random.randn(N, 1),
var2=np.random.randn(N, 1),
conditional=np.random.randn(N, 1),
n_chunks=7)
def test_debug_setting():
"""Test setting of debugging options."""
settings = {'debug': False, 'return_counts': True}
# Estimators should raise an error if returning of neighborhood counts is
# requested without the debugging option being set.
with pytest.raises(RuntimeError):
OpenCLKraskovMI(settings=settings)
with pytest.raises(RuntimeError):
OpenCLKraskovCMI(settings=settings)
settings['debug'] = True
est = OpenCLKraskovMI(settings=settings)
res = est.estimate(np.arange(10), np.arange(10))
assert len(res) == 4, (
'Requesting debugging output from MI estimator did not return the '
'correct no. values.')
est = OpenCLKraskovCMI(settings=settings)
res = est.estimate(np.arange(10), np.arange(10), np.arange(10))
assert len(res) == 5, (
'Requesting debugging output from CMI estimator did not return the '
'correct no. values.')
def test_multiple_runs_two_dim():
"""Test kNN with two chunks of 2D data in the same call."""
settings = {
'theiler_t': 0,
'knn_k': 1,
'gpu_id': 0,
'debug': True,
'return_counts': True,
'max_mem': 5 * 1024 * 1024
}
EST_MI = OpenCLKraskovMI(settings)
EST_CMI = OpenCLKraskovCMI(settings)
n_chunks = 50000
pointset1 = np.array(
[[-1, 0.5, 1.1, 2, 10, 11, 10.5, -100, -50, 666, 9999, 9999],
[-1, 0.5, 1.1, 2, 98, -9, -200, 45.3, -53, 0.1, 9999,
9999]]).T.copy()
pointset1 = np.tile(pointset1, (n_chunks, 1))
pointset2 = np.ones(pointset1.shape) * 9999
pointset3 = np.ones(pointset1.shape) * 9999
# Call MI estimator
mi, dist1, npoints_x, npoints_y = EST_MI.estimate(pointset1,
pointset2,
n_chunks=n_chunks)
assert np.isclose(dist1[0], 1.5), 'Distances 0 not correct.'
assert np.isclose(dist1[1], 0.6), 'Distances 1 not correct.'
assert np.isclose(dist1[2], 0.6), 'Distances 2 not correct.'
assert np.isclose(dist1[3], 0.9), 'Distances 3 not correct.'
# Call CMI estimator with pointset2 as conditional (otherwise the MI
# estimator is called internally and the CMI estimator is never tested).
cmi, dist2, npoints_x, npoints_y, npoints_c = EST_CMI.estimate(
pointset1, pointset2, pointset3, n_chunks=n_chunks)
assert np.isclose(dist2[0], 1.5), 'Distances 0 not correct.'
assert np.isclose(dist2[1], 0.6), 'Distances 1 not correct.'
assert np.isclose(dist2[2], 0.6), 'Distances 2 not correct.'
assert np.isclose(dist2[3], 0.9), 'Distances 3 not correct.'
def test_local_values():
"""Test estimation of local MI and CMI using OpenCL estimators."""
# Get data
n_chunks = 2
expec_mi, source, source_uncorr, target = _get_gauss_data(n=20000,
seed=SEED)
chunklength = int(source.shape[0] / n_chunks)
# Estimate local values
settings = {'local_values': True}
est_cmi = OpenCLKraskovCMI(settings=settings)
cmi = est_cmi.estimate(source, target, source_uncorr, n_chunks=n_chunks)
est_mi = OpenCLKraskovMI(settings=settings)
mi = est_mi.estimate(source, target, n_chunks=n_chunks)
mi_ch1 = np.mean(mi[0:chunklength])
mi_ch2 = np.mean(mi[chunklength:])
cmi_ch1 = np.mean(cmi[0:chunklength])
cmi_ch2 = np.mean(cmi[chunklength:])
# Estimate non-local values for comparison
settings = {'local_values': False}
est_cmi = OpenCLKraskovCMI(settings=settings)
mi = est_cmi.estimate(source, target, source_uncorr, n_chunks=n_chunks)
est_mi = OpenCLKraskovMI(settings=settings)
cmi = est_mi.estimate(source, target, n_chunks=n_chunks)
# Report results
print('OpenCL MI result: {0:.4f} nats (chunk 1); {1:.4f} nats (chunk 2) '
'expected to be close to {2:.4f} nats for uncorrelated '
'Gaussians.'.format(mi_ch1, mi_ch2, expec_mi))
print('OpenCL CMI result: {0:.4f} nats (chunk 1); {1:.4f} nats (chunk 2) '
'expected to be close to {2:.4f} nats for uncorrelated '
'Gaussians.'.format(cmi_ch1, cmi_ch2, expec_mi))
assert np.isclose(mi_ch1, expec_mi, atol=0.05)
assert np.isclose(mi_ch2, expec_mi, atol=0.05)
assert np.isclose(cmi_ch1, expec_mi, atol=0.05)
assert np.isclose(cmi_ch2, expec_mi, atol=0.05)
assert np.isclose(mi_ch1, mi_ch2, atol=0.05)
assert np.isclose(mi_ch1, mi[0], atol=0.05)
assert np.isclose(mi_ch2, mi[1], atol=0.05)
assert np.isclose(cmi_ch1, cmi_ch2, atol=0.05)
assert np.isclose(cmi_ch1, cmi[0], atol=0.05)
assert np.isclose(cmi_ch2, cmi[1], atol=0.05)
# Skip test module if pyopencl is not installed
pytest.importorskip('pyopencl')
settings = {
'theiler_t': 0,
'kraskov_k': 1,
'noise_level': 0,
'gpu_id': 0,
'debug': True,
'return_counts': True,
'verbose': True
}
EST_MI = OpenCLKraskovMI(settings)
EST_CMI = OpenCLKraskovCMI(settings)
CHUNK_LENGTH = 500000 # add noise to beginning of chunks to achieve this
def test_three_large_chunks():
"""Test kNN with three large chunks, put test points at chunk end."""
# Data for three individual chunks
n_chunks = 3
chunk1 = np.expand_dims(np.hstack(
(np.ones(CHUNK_LENGTH - 4) * 9999, [5, 6, -5, -7])),
axis=1)
chunk2 = np.expand_dims(np.hstack(
(np.ones(CHUNK_LENGTH - 4) * 9999, [50, -50, 60, -70])),
axis=1)
chunk3 = np.expand_dims(np.hstack(
print('Estimated CMI: {0:.5f}, expected CMI: {1:.5f}'.format(cmi, expected_mi))
est = JidtGaussianMI(settings)
mi = est.estimate(source_cor, target)
print('Estimated MI: {0:.5f}, expected MI: {1:.5f}'.format(mi, expected_mi))
settings['history_target'] = 1
est = JidtGaussianTE(settings)
te = est.estimate(source_cor[1:n], target[0:n - 1])
print('Estimated TE: {0:.5f}, expected TE: {1:.5f}'.format(te, expected_mi))
settings['history'] = 1
est = JidtGaussianAIS(settings)
ais = est.estimate(target)
print('Estimated AIS: {0:.5f}, expected AIS: ~0'.format(ais))
# OpenCL Kraskov estimators
settings = {}
est = OpenCLKraskovCMI(settings)
cmi = est.estimate(source_cor, target, source_uncor)
print('Estimated CMI: {0:.5f}, expected CMI: {1:.5f}'.format(
cmi[0], expected_mi))
est = OpenCLKraskovMI(settings)
mi = est.estimate(source_cor, target)
print('Estimated MI: {0:.5f}, expected MI: {1:.5f}'.format(mi[0], expected_mi))
# Generate binary test data
alph_x = 2
alph_y = 2
alph_z = 2
x = np.random.randint(0, alph_x, n)
y = np.random.randint(0, alph_y, n)
z = np.logical_xor(x, y).astype(int)