def find_similarity_matches(
query: chemfp.arena.FingerprintArena,
target: Optional[chemfp.arena.FingerprintArena],
threshold: float = 0.95,
n_threads: int = 1,
) -> Mapping[str, Mapping[str, float]]:
chemfp.set_num_threads(n_threads)
if target is not None:
match_res = chemfp.search.threshold_tanimoto_search_arena(
query, target, threshold=threshold)
else:
match_res = chemfp.search.threshold_tanimoto_search_symmetric(
query, threshold=threshold, include_lower_triangle=False)
return {
q_id: {t: s
for t, s in targets}
for q_id, targets in zip(query.ids, match_res.iter_ids_and_scores())
if len(targets) > 0
}
def distance_matrix(arena, tanimoto_threshold = 0.0):
n = len(arena)
# Start off a similarity matrix with 1.0s along the diagonal
try:
similarities = numpy.identity(n, "d")
except:
raise Exception('Input dataset is to large!')
chemfp.set_num_threads( args.processors )
## Compute the full similarity matrix.
# The implementation computes the upper-triangle then copies
# the upper-triangle into lower-triangle. It does not include
# terms for the diagonal.
results = chemfp.search.threshold_tanimoto_search_symmetric(arena, threshold=tanimoto_threshold)
# Copy the results into the NumPy array.
for row_index, row in enumerate(results.iter_indices_and_scores()):
for target_index, target_score in row:
similarities[row_index, target_index] = target_score
# Return the distance matrix using the similarity matrix
return 1.0 - similarities
def distance_matrix(arena, tanimoto_threshold = 0.0):
n = len(arena)
# Start off a similarity matrix with 1.0s along the diagonal
try:
similarities = numpy.identity(n, "d")
except:
raise Exception('Input dataset is to large!')
chemfp.set_num_threads( args.processors )
## Compute the full similarity matrix.
# The implementation computes the upper-triangle then copies
# the upper-triangle into lower-triangle. It does not include
# terms for the diagonal.
results = chemfp.search.threshold_tanimoto_search_symmetric(arena, threshold=tanimoto_threshold)
# Copy the results into the NumPy array.
for row_index, row in enumerate(results.iter_indices_and_scores()):
for target_index, target_score in row:
similarities[row_index, target_index] = target_score
# Return the distance matrix using the similarity matrix
return 1.0 - similarities
def butina(args):
"""
Taylor-Butina clustering from the chemfp help.
"""
out = args.output_path
targets = chemfp.open(args.input_path, format='fps')
arena = chemfp.load_fingerprints(targets)
chemfp.set_num_threads(args.processors)
results = search.threshold_tanimoto_search_symmetric(
arena, threshold=args.tanimoto_threshold)
results.reorder_all("move-closest-first")
sorted_ids = unix_sort(results)
# Determine the true/false singletons and the clusters
true_singletons = []
false_singletons = []
clusters = []
seen = set()
#for (size, fp_idx, members) in results:
for (size, fp_idx) in sorted_ids:
members = results[fp_idx].get_indices()
#print arena.ids[ fp_idx ], [arena.ids[ m ] for m in members]
if fp_idx in seen:
# Can't use a centroid which is already assigned
continue
seen.add(fp_idx)
if size == 0:
# The only fingerprint in the exclusion sphere is itself
true_singletons.append(fp_idx)
continue
# Figure out which ones haven't yet been assigned
unassigned = set(members) - seen
if not unassigned:
false_singletons.append(fp_idx)
continue
# this is a new cluster
clusters.append((fp_idx, unassigned))
seen.update(unassigned)
len_cluster = len(clusters)
#out.write( "#%s true singletons: %s\n" % ( len(true_singletons), " ".join(sorted(arena.ids[idx] for idx in true_singletons)) ) )
#out.write( "#%s false singletons: %s\n" % ( len(false_singletons), " ".join(sorted(arena.ids[idx] for idx in false_singletons)) ) )
out.write("#%s true singletons\n" % len(true_singletons))
out.write("#%s false singletons\n" % len(false_singletons))
out.write("#clusters: %s\n" % len_cluster)
# Sort so the cluster with the most compounds comes first,
# then by alphabetically smallest id
def cluster_sort_key(cluster):
centroid_idx, members = cluster
return -len(members), arena.ids[centroid_idx]
clusters.sort(key=cluster_sort_key)
for centroid_idx, members in clusters:
centroid_name = arena.ids[centroid_idx]
out.write("%s\t%s\t%s\n" %
(centroid_name, len(members), " ".join(arena.ids[idx]
for idx in members)))
#ToDo: len(members) need to be some biggest top 90% or something ...
for idx in true_singletons:
out.write("%s\t%s\n" % (arena.ids[idx], 0))
out.close()
def butina( args ):
"""
Taylor-Butina clustering from the chemfp help.
"""
out = args.output_path
targets = chemfp.open( args.input_path, format='fps' )
arena = chemfp.load_fingerprints( targets )
chemfp.set_num_threads( args.processors )
results = search.threshold_tanimoto_search_symmetric(arena, threshold = args.tanimoto_threshold)
results.reorder_all("move-closest-first")
sorted_ids = unix_sort(results)
# Determine the true/false singletons and the clusters
true_singletons = []
false_singletons = []
clusters = []
seen = set()
#for (size, fp_idx, members) in results:
for (size, fp_idx) in sorted_ids:
members = results[fp_idx].get_indices()
#print arena.ids[ fp_idx ], [arena.ids[ m ] for m in members]
if fp_idx in seen:
# Can't use a centroid which is already assigned
continue
seen.add(fp_idx)
if size == 0:
# The only fingerprint in the exclusion sphere is itself
true_singletons.append( fp_idx )
continue
# Figure out which ones haven't yet been assigned
unassigned = set(members) - seen
if not unassigned:
false_singletons.append(fp_idx)
continue
# this is a new cluster
clusters.append( (fp_idx, unassigned) )
seen.update(unassigned)
len_cluster = len(clusters)
#out.write( "#%s true singletons: %s\n" % ( len(true_singletons), " ".join(sorted(arena.ids[idx] for idx in true_singletons)) ) )
#out.write( "#%s false singletons: %s\n" % ( len(false_singletons), " ".join(sorted(arena.ids[idx] for idx in false_singletons)) ) )
out.write( "#%s true singletons\n" % len(true_singletons) )
out.write( "#%s false singletons\n" % len(false_singletons) )
out.write( "#clusters: %s\n" % len_cluster )
# Sort so the cluster with the most compounds comes first,
# then by alphabetically smallest id
def cluster_sort_key(cluster):
centroid_idx, members = cluster
return -len(members), arena.ids[centroid_idx]
clusters.sort(key=cluster_sort_key)
for centroid_idx, members in clusters:
centroid_name = arena.ids[centroid_idx]
out.write("%s\t%s\t%s\n" % (centroid_name, len(members), " ".join(arena.ids[idx] for idx in members)))
#ToDo: len(members) need to be some biggest top 90% or something ...
for idx in true_singletons:
out.write("%s\t%s\n" % (arena.ids[idx], 0))
out.close()
def test_set_beyond_max(self):
chemfp.set_num_threads(chemfp.get_max_threads()+1)
self.assertEquals(chemfp.get_num_threads(), chemfp.get_max_threads())
def test_set_to_two(self):
chemfp.set_num_threads(2)
self.assertEquals(chemfp.get_num_threads(), 2)
def tearDown(self):
chemfp.set_num_threads(self._num_threads)
def test_set_to_zero(self):
chemfp.set_num_threads(0)
self.assertEquals(chemfp.get_num_threads(), 1)
def tearDown(self):
chemfp.set_num_threads(self._num_threads)
def test_set_beyond_max(self):
chemfp.set_num_threads(chemfp.get_max_threads() + 1)
self.assertEquals(chemfp.get_num_threads(), chemfp.get_max_threads())
def test_set_to_two(self):
chemfp.set_num_threads(2)
self.assertEquals(chemfp.get_num_threads(), 2)
def test_set_to_one(self):
chemfp.set_num_threads(1)
self.assertEquals(chemfp.get_num_threads(), 1)
def test_set_to_zero(self):
chemfp.set_num_threads(0)
self.assertEquals(chemfp.get_num_threads(), 1)
os.system('ln -s %s %s' % (os.path.realpath(sys.argv[1]), temp_link) )
chemfp_fingerprint_file = temp_link
tanimoto_threshold = float(sys.argv[2])
outfile = sys.argv[3]
processors = int(sys.argv[4])
def get_hit_indicies(hits):
return [id for (id, score) in hits]
out = open(outfile, 'w')
dataset = chemfp.load_fingerprints( chemfp_fingerprint_file )
chemfp.set_num_threads( processors )
search = dataset.threshold_tanimoto_search_arena(dataset, threshold = tanimoto_threshold)
#search = chemfp.search.threshold_tanimoto_search_symmetric (dataset, threshold = tanimoto_threshold)
# Reorder so the centroid with the most hits comes first.
# (That's why I do a reverse search.)
# Ignore the arbitrariness of breaking ties by fingerprint index
results = sorted( ( (len(hits), i, hits) for (i, hits) in enumerate(search.iter_indices_and_scores()) ),reverse=True)
# Determine the true/false singletons and the clusters
true_singletons = []
false_singletons = []
clusters = []
seen = set()
def test_set_to_one(self):
chemfp.set_num_threads(1)
self.assertEquals(chemfp.get_num_threads(), 1)
os.system('ln -s %s %s' % (os.path.realpath(sys.argv[1]), temp_link))
chemfp_fingerprint_file = temp_link
tanimoto_threshold = float(sys.argv[2])
outfile = sys.argv[3]
processors = int(sys.argv[4])
def get_hit_indicies(hits):
return [id for (id, score) in hits]
out = open(outfile, 'w')
dataset = chemfp.load_fingerprints(chemfp_fingerprint_file)
chemfp.set_num_threads(processors)
search = dataset.threshold_tanimoto_search_arena(dataset,
threshold=tanimoto_threshold)
#search = chemfp.search.threshold_tanimoto_search_symmetric (dataset, threshold = tanimoto_threshold)
# Reorder so the centroid with the most hits comes first.
# (That's why I do a reverse search.)
# Ignore the arbitrariness of breaking ties by fingerprint index
results = sorted(
((len(hits), i, hits)
for (i, hits) in enumerate(search.iter_indices_and_scores())),
reverse=True)
# Determine the true/false singletons and the clusters
true_singletons = []
false_singletons = []
