class TestMetrics(unittest.TestCase):
def setUp(self):
self.paris = Paris()
self.louvain_hierarchy = LouvainHierarchy()
def test_undirected(self):
adjacency = cyclic_graph(3)
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_cost(adjacency, dendrogram), 2.666, 2)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.111, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.0632, 3)
self.assertAlmostEqual(
tree_sampling_divergence(adjacency, dendrogram, normalized=False),
0.0256, 3)
adjacency = test_graph()
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_cost(adjacency, dendrogram), 4.26, 2)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.573, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.304, 2)
dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_cost(adjacency, dendrogram), 4.43, 2)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.555, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.286, 2)
def test_directed(self):
adjacency = test_digraph()
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.566, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.318, 2)
dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.55, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.313, 2)
def test_disconnected(self):
adjacency = test_graph_disconnect()
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.682, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.464, 2)
dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.670, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.594, 2)
def test_options(self):
adjacency = test_graph()
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(
dasgupta_score(adjacency, dendrogram, weights='degree'), 0.573, 2)
self.assertAlmostEqual(
tree_sampling_divergence(adjacency, dendrogram, weights='uniform'),
0.271, 2)
self.assertAlmostEqual(
tree_sampling_divergence(adjacency, dendrogram, normalized=False),
0.367, 2)
class TestMetrics(unittest.TestCase):
def setUp(self):
self.paris = Paris()
self.louvain_hierarchy = LouvainHierarchy()
def test_undirected(self):
adjacency = test_graph()
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_cost(adjacency, dendrogram), 3.98, 2)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.602, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.450, 2)
dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_cost(adjacency, dendrogram), 4.45, 2)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.555, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.431, 2)
def test_directed(self):
adjacency = test_digraph()
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.586, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.376, 2)
dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.56, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.357, 2)
def test_disconnected(self):
adjacency = test_graph_disconnect()
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.752, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.627, 2)
dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.691, 2)
self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram),
0.549, 2)
def test_options(self):
adjacency = test_graph()
dendrogram = self.paris.fit_transform(adjacency)
self.assertAlmostEqual(
dasgupta_score(adjacency, dendrogram, weights='degree'), 0.602, 2)
self.assertAlmostEqual(
tree_sampling_divergence(adjacency, dendrogram, weights='uniform'),
0.307, 2)
self.assertAlmostEqual(
tree_sampling_divergence(adjacency, dendrogram, normalized=False),
0.545, 2)
def compute_rank(self, file_name):
x = csr_matrix((self.v, (self.b, self.a)),
shape=(len(self.destin_idx), len(self.destin_idx)),
dtype=float)
print(x)
adjacency = x.multiply(x.transpose())
pagerank = PageRank()
scores = pagerank.fit_transform(adjacency)
image = svg_graph(adjacency,
names=self.destin_names,
scores=scores,
display_node_weight=True,
node_order=np.argsort(scores))
with open(file_name, "w") as text_file:
print(file_name)
print(scores)
text_file.write(image)
print(self.v)
print(self.destin_names)
paris = Paris()
dendrogram = paris.fit_transform(adjacency)
image = svg_dendrogram(dendrogram,
self.destin_names,
n_clusters=5,
rotate=True)
with open("dento_" + file_name, "w") as text_file:
text_file.write(image)
def _cluster(self):
try:
self.knn_graph
except AttributeError:
var_exists = False
else:
var_exists = True
if not var_exists:
self.make_distance_matrix()
paris = Paris()
self.dendrogram = paris.fit_transform(self.knn_graph)
def test_aggregation(self):
self.adjacency = karate_club()
paris = Paris()
self.dendrogram = paris.fit_transform(self.adjacency)
n_clusters = 5
dendrogram_, counts = aggregate_dendrogram(self.dendrogram,
n_clusters,
return_counts=True)
self.assertEqual(dendrogram_.shape,
(n_clusters - 1, self.dendrogram.shape[1]))
self.assertEqual(counts.sum(), self.dendrogram.shape[0] + 1)
def test_undirected(self):
adjacency = karate_club()
paris = Paris()
dendrogram = paris.fit_transform(adjacency)
image = svg_dendrogram(dendrogram)
self.assertEqual(image[1:4], 'svg')
n = adjacency.shape[0]
image = svg_dendrogram(dendrogram,
names=np.arange(n),
width=200,
height=200,
margin=10,
margin_text=5,
scale=3,
n_clusters=3,
color='green',
colors=['red', 'blue'],
font_size=14,
reorder=True,
rotate=True)
self.assertEqual(image[1:4], 'svg')
image = svg_dendrogram(dendrogram,
names=np.arange(n),
width=200,
height=200,
margin=10,
margin_text=5,
scale=3,
n_clusters=3,
color='green',
colors={
0: 'red',
1: 'blue'
},
font_size=14,
reorder=False,
rotate=True)
self.assertEqual(image[1:4], 'svg')
svg_dendrogram_top(dendrogram,
names=np.arange(n),
width=200,
height=200,
margin=10,
margin_text=5,
scale=3,
n_clusters=3,
color='green',
colors=np.array(['red', 'black', 'blue']),
font_size=14,
reorder=False,
rotate_names=True,
line_width=0.1)
def test_directed(self):
graph = painters(True)
adjacency = graph.adjacency
names = graph.names
paris = Paris()
dendrogram = paris.fit_transform(adjacency)
image = svg_dendrogram(dendrogram)
self.assertEqual(image[1:4], 'svg')
image = svg_dendrogram(dendrogram,
names=names,
width=200,
height=200,
margin=10,
margin_text=5,
scale=3,
n_clusters=3,
color='green',
font_size=14,
reorder=True,
rotate=True)
self.assertEqual(image[1:4], 'svg')
def test_options(self):
paris = Paris(weights='uniform')
adjacency = test_graph()
dendrogram = paris.fit_transform(adjacency)
n = adjacency.shape[0]
self.assertEqual(dendrogram.shape, (n - 1, 4))
def setUp(self):
paris = Paris()
adjacency = karate_club()
self.dendrogram = paris.fit_transform(adjacency)
def find_modules(
adata,
n_levels=100,
level_start=2,
level_end_size=2,
n_pcs=100,
layer=None,
corr='pearson',
corr_threshold=0,
corr_power=2,
method='complete',
metric='correlation',
smallest_module=3,
key_added=None,
):
if n_pcs is not None:
print('Fitting PCA...')
X = sc.pp.pca(adata,
n_comps=n_pcs,
copy=True,
use_highly_variable=False).varm['PCs'].T
else:
if layer is None:
X = adata.X
else:
X = adata.layers[layer]
X = X.A if sp.sparse.issparse(X) else X
key_added = '' if key_added is None else '_' + key_added
if method == 'paris':
from sknetwork.hierarchy import Paris
if corr == 'pearson':
corr_df = np.corrcoef(X, rowvar=False)
elif corr == 'spearman':
corr_df = sp.stats.spearmanr(X)[0]
else:
raise ValueError('Unknown corr')
corr_df = pd.DataFrame(corr_df,
columns=adata.var_names,
index=adata.var_names)
adata.varp[f'paris_corr_raw{key_added}'] = corr_df.copy()
if corr_threshold is not None:
corr_df[corr_df < corr_threshold] = corr_threshold
if corr_power is not None:
corr_df = corr_df.pow(corr_power)
adata.varp[f'paris_corr{key_added}'] = corr_df.values
print('Fitting the Paris model...')
# TODO: consider BiParis on the tp10k matrix
model = Paris()
corr_mat = sp.sparse.csr_matrix(corr_df.values)
dendro = model.fit_transform(corr_mat)
else:
print('Hierarchical clustering...')
dendro = linkage(X.T, method=method, metric=metric)
level_end = round(adata.n_vars / level_end_size)
dfs = []
n_cl_list = np.linspace(level_start, level_end, n_levels, dtype=int)
if len(set(n_cl_list)) != len(n_cl_list):
n_cl_list = pd.Series(n_cl_list)
n_cl_list = n_cl_list[~n_cl_list.duplicated()].tolist()
print(
f'Not enough clusters for n_levels={n_levels}, reducing to {len(n_cl_list)}...'
)
print('Cutting trees :( ...')
cl = cut_tree(dendro, n_clusters=n_cl_list)
dfs = pd.DataFrame(cl.astype(str),
index=adata.var_names,
columns=[f'level_{i}' for i in range(len(n_cl_list))])
assert np.all(dfs.nunique().values == n_cl_list)
for key in dfs.columns:
dfs[key] = pd.Categorical(dfs[key],
categories=natsorted(np.unique(dfs[key])))
adata.varm[f'paris_partitions{key_added}'] = dfs
adata.uns[f'paris{key_added}'] = {
'dendrogram': dendro,
'params': {
layer: layer,
n_levels: n_levels,
corr: corr
},
}
_build_module_dict(adata,
level=None,
paris_key=None if not key_added else key_added[1:])
print('Removing duplicates and small modules...')
# remove duplicate modules
df = pd.DataFrame(
adata.uns[f'paris{key_added}']['module_dict']).reset_index()
df = df.melt(id_vars=['index'], value_name='genes',
var_name='level').rename(columns={'index': 'module'})
df = df[~df.genes.isnull()]
df['size'] = [len(x) for x in df.genes]
small_idx = df['size'] < smallest_module
dups_idx = df.duplicated('genes')
print(f'{dups_idx.sum()} duplicates found...')
print(f'{small_idx.sum()} small modules found...')
rms = list(df[small_idx
| dups_idx].reset_index(drop=True)[['module',
'level']].itertuples())
for rm in rms:
del adata.uns[f'paris{key_added}']['module_dict'][rm.level][rm.module]
# remove empty levels
empty_levels = [
k for k, v in adata.uns[f'paris{key_added}']['module_dict'].items()
if len(v) == 0
]
print(f'{len(empty_levels)} empty levels found...')
newp = adata.varm[f'paris_partitions{key_added}'].iloc[:, ~adata.varm[
f'paris_partitions{key_added}'].columns.isin(empty_levels)]
adata.varm[f'paris_partitions{key_added}'] = newp
for l in empty_levels:
del adata.uns[f'paris{key_added}']['module_dict'][l]
print(f'{len(df[~(small_idx | dups_idx)])} total modules found.')
print('Calculating module dependencies...')
deps = _calculate_module_dependencies(
adata, paris_key=None if not key_added else key_added[1:])
adata.uns[f'paris{key_added}']['module_dependencies'] = deps
