diff -up MDAnalysis-0.16.2/MDAnalysisTests-0.16.2/MDAnalysisTests/analysis/test_encore.py.skip-32bit MDAnalysis-0.16.2/MDAnalysisTests-0.16.2/MDAnalysisTests/analysis/test_encore.py
--- MDAnalysis-0.16.2/MDAnalysisTests-0.16.2/MDAnalysisTests/analysis/test_encore.py.skip-32bit 2017-06-30 14:14:37.802409132 +0200
+++ MDAnalysis-0.16.2/MDAnalysisTests-0.16.2/MDAnalysisTests/analysis/test_encore.py 2017-06-30 14:17:13.309229660 +0200
@@ -283,21 +283,6 @@ inconsistent results")
assert_almost_equal(result_value, expected_value, decimal=-3,
err_msg="Unexpected value for Harmonic Ensemble Similarity: {0:f}. Expected {1:f}.".format(result_value, expected_value))
- def test_ces_to_self(self):
- results, details = \
- encore.ces([self.ens1, self.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):
- results, details = encore.ces([self.ens1, self.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_dres_to_self(self):
results, details = encore.dres([self.ens1, self.ens1])
@@ -324,13 +309,6 @@ inconsistent results")
assert_almost_equal(result_value, expected_value, decimal=1,
err_msg="Unexpected value for Dim. reduction Ensemble Similarity: {0:f}. Expected {1:f}.".format(result_value, expected_value))
- def test_ces_convergence(self):
- expected_values = [0.3443593, 0.1941854, 0.06857104, 0.]
- results = encore.ces_convergence(self.ens1, 5)
- print (results)
- for i,ev in enumerate(expected_values):
- assert_almost_equal(ev, results[i], decimal=2,
- err_msg="Unexpected value for Clustering Ensemble similarity in convergence estimation")
def test_dres_convergence(self):
expected_values = [ 0.3, 0.]
@@ -351,44 +329,6 @@ inconsistent results")
assert_almost_equal(stdev, expected_stdev, decimal=-2,
err_msg="Unexpected standard daviation for bootstrapped samples in Harmonic Ensemble imilarity")
- @dec.slow
- def test_ces_error_estimation(self):
- expected_average = 0.03
- expected_stdev = 0.31
- averages, stdevs = encore.ces([self.ens1, self.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")
-
- @dec.skipif(module_not_found('sklearn'),
- "Test skipped because sklearn is not available.")
- @dec.slow
- def test_ces_error_estimation_ensemble_bootstrap(self):
- # Error estimation using a method that does not take a distance
- # matrix as input, and therefore relies on bootstrapping the ensembles
- # instead
- expected_average = 0.03
- expected_stdev = 0.02
- averages, stdevs = encore.ces([self.ens1, self.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")
@dec.slow
def test_dres_error_estimation(self):
@@ -454,38 +394,6 @@ class TestEncoreClustering(TestCase):
del cls.ens1_template
del cls.ens2_template
- @dec.slow
- def test_clustering_one_ensemble(self):
- cluster_collection = encore.cluster(self.ens1)
- expected_value = 7
- assert_equal(len(cluster_collection), expected_value,
- err_msg="Unexpected results: {0}".format(cluster_collection))
-
- @dec.slow
- def test_clustering_two_ensembles(self):
- cluster_collection = encore.cluster([self.ens1, self.ens2])
- expected_value = 14
- assert_equal(len(cluster_collection), expected_value,
- err_msg="Unexpected results: {0}".format(cluster_collection))
-
- @dec.slow
- def test_clustering_two_methods(self):
- cluster_collection = encore.cluster(
- [self.ens1],
- method=[encore.AffinityPropagationNative(),
- encore.AffinityPropagationNative()])
- assert_equal(len(cluster_collection[0]), len(cluster_collection[1]),
- err_msg="Unexpected result: {0}".format(cluster_collection))
-
- @dec.slow
- def test_clustering_AffinityPropagationNative_direct(self):
- method = encore.AffinityPropagationNative()
- distance_matrix = encore.get_distance_matrix(self.ens1)
- cluster_assignment, details = method(distance_matrix)
- expected_value = 7
- assert_equal(len(set(cluster_assignment)), expected_value,
- err_msg="Unexpected result: {0}".format(
- cluster_assignment))
@dec.slow
@dec.skipif(module_not_found('sklearn'),
@@ -549,29 +457,7 @@ class TestEncoreClustering(TestCase):
assert_equal(len(cluster_collection), 10,
err_msg="Unexpected result: {0}".format(cluster_collection))
- @dec.slow
- @dec.skipif(module_not_found('sklearn'),
- "Test skipped because sklearn is not available.")
- 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))
- @dec.slow
- @dec.skipif(module_not_found('sklearn'),
- "Test skipped because sklearn is not available.")
- def test_sklearn_affinity_propagation(self):
- cc1 = encore.cluster([self.ens1])
- cc2 = encore.cluster([self.ens1],
- method=encore.AffinityPropagation())
- assert_equal(len(cc1), len(cc2),
- err_msg="Native and sklearn implementations of affinity "
- "propagation don't agree: mismatch in number of "
- "clusters: {0} {1}".format(len(cc1), len(cc2)))
@@ -662,15 +548,6 @@ class TestEncoreClusteringSklearn(TestCa
for j in range(i,dimension):
self.distance_matrix[i, j] = distances[i,j]
- def test_one(self):
- preference = -float(np.median(self.distance_matrix.as_array()) * 10.)
- clustering_method = encore.AffinityPropagationNative(preference=preference)
- ccs = encore.cluster(None,
- distance_matrix=self.distance_matrix,
- method=clustering_method)
- assert_equal(self.n_clusters, len(ccs),
- err_msg="Basic clustering test failed to give the right"
- "number of clusters: {0} vs {1}".format(self.n_clusters, len(ccs)))
class TestEncoreDimensionalityReduction(TestCase):