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_clustering_two_methods_one_w_no_distance_matrix(self, ens1):
pytest.importorskip('sklearn')
cluster_collection = encore.cluster(
[ens1],
method=[encore.KMeans(17),
encore.AffinityPropagationNative()])
assert len(cluster_collection[0]) == len(cluster_collection[0]), \
"Unexpected result: {0}".format(cluster_collection)
def test_clustering_method_w_no_distance_matrix(self):
cluster_collection = encore.cluster([self.ens1],
method=encore.KMeans(10))
print(cluster_collection)
assert_equal(
len(cluster_collection),
10,
err_msg="Unexpected result: {0}".format(cluster_collection))
def test_clustering_KMeans_direct(self, ens1):
pytest.importorskip('sklearn')
clusters = 10
method = encore.KMeans(clusters)
coordinates = ens1.trajectory.timeseries(order='fac')
coordinates = np.reshape(coordinates, (coordinates.shape[0], -1))
cluster_assignment, details = method(coordinates)
assert len(set(cluster_assignment)) == clusters, \
"Unexpected result: {0}".format(cluster_assignment)
def test_clustering_two_methods_one_w_no_distance_matrix(self):
cluster_collection = encore.cluster(
[self.ens1],
method=[encore.KMeans(17),
encore.AffinityPropagationNative()])
print(cluster_collection)
assert_equal(
len(cluster_collection[0]),
len(cluster_collection[0]),
err_msg="Unexpected result: {0}".format(cluster_collection))
def test_clustering_KMeans_direct(self):
clusters = 10
method = encore.KMeans(clusters)
coordinates = self.ens1.trajectory.timeseries(format='fac')
coordinates = np.reshape(coordinates, (coordinates.shape[0], -1))
cluster_assignment, details = method(coordinates)
assert_equal(
len(set(cluster_assignment)),
clusters,
err_msg="Unexpected result: {0}".format(cluster_assignment))
def test_clustering_method_w_no_distance_matrix(self, ens1):
pytest.importorskip('sklearn')
cluster_collection = encore.cluster([ens1], method=encore.KMeans(10))
assert len(cluster_collection) == 10, \
"Unexpected result: {0}".format(cluster_collection)
