def test_scikit_vs_scipy():
"""Test scikit linkage with full connectivity (i.e. unstructured) vs scipy
"""
n, p, k = 10, 5, 3
rng = np.random.RandomState(0)
# Not using a lil_matrix here, just to check that non sparse
# matrices are well handled
connectivity = np.ones((n, n))
for linkage in _TREE_BUILDERS.keys():
for i in range(5):
X = .1 * rng.normal(size=(n, p))
X -= 4. * np.arange(n)[:, np.newaxis]
X -= X.mean(axis=1)[:, np.newaxis]
out = hierarchy.linkage(X, method=linkage)
children_ = out[:, :2].astype(np.int)
children, _, n_leaves, _ = _TREE_BUILDERS[linkage](X, connectivity)
cut = _hc_cut(k, children, n_leaves)
cut_ = _hc_cut(k, children_, n_leaves)
assess_same_labelling(cut, cut_)
# Test error management in _hc_cut
assert_raises(ValueError, _hc_cut, n_leaves + 1, children, n_leaves)
def test_scikit_vs_scipy():
"""Test scikit linkage with full connectivity (i.e. unstructured) vs scipy
"""
n, p, k = 10, 5, 3
rng = np.random.RandomState(0)
# Not using a lil_matrix here, just to check that non sparse
# matrices are well handled
connectivity = np.ones((n, n))
for linkage in _TREE_BUILDERS.keys():
for i in range(5):
X = .1 * rng.normal(size=(n, p))
X -= 4. * np.arange(n)[:, np.newaxis]
X -= X.mean(axis=1)[:, np.newaxis]
out = hierarchy.linkage(X, method=linkage)
children_ = out[:, :2].astype(np.int)
children, _, n_leaves, _ = _TREE_BUILDERS[linkage](X, connectivity)
cut = _hc_cut(k, children, n_leaves)
cut_ = _hc_cut(k, children_, n_leaves)
assess_same_labelling(cut, cut_)
# Test error management in _hc_cut
assert_raises(ValueError, _hc_cut, n_leaves + 1, children, n_leaves)
def test_scikit_vs_scipy():
# Test scikit linkage with full connectivity (i.e. unstructured) vs scipy
n, p, k = 10, 5, 3
rng = np.random.RandomState(0)
# Not using a lil_matrix here, just to check that non sparse
# matrices are well handled
connectivity = np.ones((n, n))
for linkage in _TREE_BUILDERS.keys():
for i in range(5):
X = .1 * rng.normal(size=(n, p))
X -= 4. * np.arange(n)[:, np.newaxis]
X -= X.mean(axis=1)[:, np.newaxis]
out = hierarchy.linkage(X, method=linkage)
children_ = out[:, :2].astype(np.int, copy=False)
children, _, n_leaves, _ = _TREE_BUILDERS[linkage](X, connectivity)
# Sort the order of child nodes per row for consistency
children.sort(axis=1)
assert_array_equal(children, children_, 'linkage tree differs'
' from scipy impl for'
' linkage: ' + linkage)
cut = _hc_cut(k, children, n_leaves)
cut_ = _hc_cut(k, children_, n_leaves)
assess_same_labelling(cut, cut_)
# Test error management in _hc_cut
assert_raises(ValueError, _hc_cut, n_leaves + 1, children, n_leaves)
def test_scikit_vs_scipy():
# Test scikit linkage with full connectivity (i.e. unstructured) vs scipy
n, p, k = 10, 5, 3
rng = np.random.RandomState(0)
# Not using a lil_matrix here, just to check that non sparse
# matrices are well handled
connectivity = np.ones((n, n))
for linkage in _TREE_BUILDERS.keys():
for i in range(5):
X = .1 * rng.normal(size=(n, p))
X -= 4. * np.arange(n)[:, np.newaxis]
X -= X.mean(axis=1)[:, np.newaxis]
out = hierarchy.linkage(X, method=linkage)
children_ = out[:, :2].astype(np.int, copy=False)
children, _, n_leaves, _ = _TREE_BUILDERS[linkage](X, connectivity)
# Sort the order of child nodes per row for consistency
children.sort(axis=1)
assert_array_equal(
children, children_, 'linkage tree differs'
' from scipy impl for'
' linkage: ' + linkage)
cut = _hc_cut(k, children, n_leaves)
cut_ = _hc_cut(k, children_, n_leaves)
assess_same_labelling(cut, cut_)
# Test error management in _hc_cut
assert_raises(ValueError, _hc_cut, n_leaves + 1, children, n_leaves)
def agglomerative_clustering(edgelist=None, distance_matrix=None, num_clusters=4, method='complete', metric='precomputed'):
""" computes an agglomerative clustering as one of the hierarchical clustering methods """
if edgelist is not None:
distance_matrix, names = utils.edgelist_to_distance_matrix(edgelist)
num_clusters=int(input("Enter the number of clusters: "))
assert isinstance(num_clusters, int)
method_options = list(_TREE_BUILDERS.keys())
print('The list of available methods:', method_options, file=sys.stdout)
in_method = input('Input the method name:')
assert isinstance(in_method, str) # native str on Py2 and Py3
method = in_method.strip()
if method == 'ward':
metric = 'euclidean'
else:
metric_options = ['precomputed', 'cosine', 'euclidean', 'cityblock']
print('The list of available metrics:', metric_options , file=sys.stdout)
in_metric = input('Input the metric name:')
assert isinstance(in_metric, str) # native str on Py2 and Py3
metric = in_metric.strip()
#tree_cutoff_options = [True, False, 'auto']
tree_cutoff_options = []
#for method in method_options:
# for metric in metric_options:
#for tree_cutoff in tree_cutoff_options:
model = sklearn.cluster.AgglomerativeClustering(linkage=method, affinity=metric,
n_clusters=num_clusters, connectivity=distance_matrix, compute_full_tree='auto')
model = model.fit(distance_matrix)
labels = model.labels_
print(method, metric)
#plot_dendrogram(model, labels=labels)
return labels
