def test_ces_error_estimation(self, ens1):
expected_average = 0.03
expected_stdev = 0.31
averages, stdevs = encore.ces(
[ens1, ens1],
estimate_error=True,
bootstrapping_samples=10,
clustering_method=encore.AffinityPropagationNative(
preference=-2.0),
selection="name CA and resnum 1-10")
average = averages[0, 1]
stdev = stdevs[0, 1]
assert_almost_equal(
average,
expected_average,
decimal=1,
err_msg=
"Unexpected average value for bootstrapped samples in Clustering Ensemble similarity"
)
assert_almost_equal(
stdev,
expected_stdev,
decimal=0,
err_msg=
"Unexpected standard daviation for bootstrapped samples in Clustering Ensemble similarity"
)
def test_ces_error_estimation_ensemble_bootstrap(self, ens1):
# Error estimation using a method that does not take a distance
# matrix as input, and therefore relies on bootstrapping the ensembles
# instead
pytest.importorskip('sklearn')
expected_average = 0.03
expected_stdev = 0.02
averages, stdevs = encore.ces(
[ens1, ens1],
estimate_error=True,
bootstrapping_samples=10,
clustering_method=encore.KMeans(n_clusters=2),
selection="name CA and resnum 1-10")
average = averages[0, 1]
stdev = stdevs[0, 1]
assert_almost_equal(
average,
expected_average,
decimal=1,
err_msg=
"Unexpected average value for bootstrapped samples in Clustering Ensemble similarity"
)
assert_almost_equal(
stdev,
expected_stdev,
decimal=1,
err_msg=
"Unexpected standard daviation for bootstrapped samples in Clustering Ensemble similarity"
)
def test_ces_to_self(self, ens1):
results, details = \
encore.ces([ens1, ens1],
clustering_method=encore.AffinityPropagationNative(preference = -3.0))
result_value = results[0,1]
expected_value = 0.
assert_almost_equal(result_value, expected_value,
err_msg="ClusteringEnsemble Similarity to itself not zero: {0:f}".format(result_value))
def test_ces(self, ens1, ens2):
results, details = encore.ces([ens1, ens2])
result_value = results[0, 1]
expected_value = 0.51
assert_almost_equal(
result_value,
expected_value,
decimal=2,
err_msg=
"Unexpected value for Cluster Ensemble Similarity: {0:f}. Expected {1:f}."
.format(result_value, expected_value))
def test_ces_to_self(self, ens1):
results, details = \
encore.ces([ens1, ens1],
clustering_method=encore.AffinityPropagationNative(preference = -3.0))
result_value = results[0, 1]
expected_value = 0.
assert_almost_equal(
result_value,
expected_value,
err_msg="ClusteringEnsemble Similarity to itself not zero: {0:f}".
format(result_value))
def test_ces_error_estimation(self, ens1):
expected_average = 0.03
expected_stdev = 0.31
averages, stdevs = encore.ces([ens1, ens1],
estimate_error = True,
bootstrapping_samples=10,
clustering_method=encore.AffinityPropagationNative(preference=-2.0),
selection="name CA and resnum 1-10")
average = averages[0,1]
stdev = stdevs[0,1]
assert_almost_equal(average, expected_average, decimal=1,
err_msg="Unexpected average value for bootstrapped samples in Clustering Ensemble similarity")
assert_almost_equal(stdev, expected_stdev, decimal=0,
err_msg="Unexpected standard daviation for bootstrapped samples in Clustering Ensemble similarity")
def test_ces_error_estimation_ensemble_bootstrap(self, ens1):
# Error estimation using a method that does not take a distance
# matrix as input, and therefore relies on bootstrapping the ensembles
# instead
pytest.importorskip('sklearn')
expected_average = 0.03
expected_stdev = 0.02
averages, stdevs = encore.ces([ens1, ens1],
estimate_error = True,
bootstrapping_samples=10,
clustering_method=encore.KMeans(n_clusters=2),
selection="name CA and resnum 1-10")
average = averages[0,1]
stdev = stdevs[0,1]
assert_almost_equal(average, expected_average, decimal=1,
err_msg="Unexpected average value for bootstrapped samples in Clustering Ensemble similarity")
assert_almost_equal(stdev, expected_stdev, decimal=1,
err_msg="Unexpected standard daviation for bootstrapped samples in Clustering Ensemble similarity")
from MDAnalysis import Universe
import MDAnalysis.analysis.encore as encore
ens1 = Universe("results/full_bayesian/calmodulin/saxs/saxs_ensemble.pdb", multiframe=True)
ens2 = Universe("results/full_bayesian/calmodulin/saxs_CS/saxs_CS_ensemble.pdb", multiframe=True)
ens3 = Universe("results/full_bayesian/calmodulin/saxs_EP/saxs_EP_ensemble.pdb", multiframe=True)
ens4 = Universe("results/full_bayesian/calmodulin/saxs_CS_EP/saxs_EP_CS_ensemble.pdb", multiframe=True)
#rmsd_matrix = encore.get_distance_matrix(encore.utils.merge_universes([ens1, ens2, ens3, ens4]))
#weights = [[0.21,0.05,0.16,0.43,0.16],[0.18,0.19,0.44,0.04,0.18],[0.53,0.42,0.05],[0.26,0.26,0.16,0.32]]
print (encore.ces([ens1, ens2, ens3, ens4]))
def test_ces(self, ens1, ens2):
results, details = encore.ces([ens1, ens2])
result_value = results[0,1]
expected_value = 0.51
assert_almost_equal(result_value, expected_value, decimal=2,
err_msg="Unexpected value for Cluster Ensemble Similarity: {0:f}. Expected {1:f}.".format(result_value, expected_value))
