def knearest_tanimoto_search(query_arena, target_arena, k, threshold):
require_matching_sizes(query_arena, target_arena)
num_queries = len(query_arena)
results = SearchResults(num_queries, target_arena.ids)
_chemfp.knearest_tanimoto_arena(
k, threshold, target_arena.num_bits, query_arena.start_padding,
query_arena.end_padding, query_arena.storage_size, query_arena.arena,
query_arena.start, query_arena.end, target_arena.start_padding,
target_arena.end_padding, target_arena.storage_size,
target_arena.arena, target_arena.start, target_arena.end,
target_arena.popcount_indices, results, 0)
_chemfp.knearest_results_finalize(results, 0, num_queries)
return results
def knearest_tanimoto_search_fp(query_fp, target_arena, k, threshold):
require_matching_fp_size(query_fp, target_arena)
query_start_padding, query_end_padding, query_fp = _chemfp.align_fingerprint(
query_fp, target_arena.alignment, target_arena.storage_size)
if k < 0:
raise ValueError("k must be non-negative")
results = SearchResults(1)
_chemfp.knearest_tanimoto_arena(
k, threshold, target_arena.num_bits, query_start_padding,
query_end_padding, target_arena.storage_size, query_fp, 0, 1,
target_arena.start_padding, target_arena.end_padding,
target_arena.storage_size, target_arena.arena, target_arena.start,
target_arena.end, target_arena.popcount_indices, results, 0)
_chemfp.knearest_results_finalize(results, 0, 1)
return results[0]
def knearest_tanimoto_search_arena(query_arena,
target_arena,
k=3,
threshold=0.7):
"""Search for the `k` nearest hits in the `target_arena` at least `threshold` similar to the fingerprints in `query_arena`
The hits in the `SearchResults` are ordered by decreasing similarity score.
Example::
queries = chemfp.load_fingerprints("queries.fps")
targets = chemfp.load_fingerprints("targets.fps")
results = chemfp.search.knearest_tanimoto_search_arena(queries, targets, k=3, threshold=0.5)
for query_id, query_hits in zip(queries.ids, results):
if len(query_hits) >= 2:
print query_id, "->", ", ".join(query_hits.get_ids())
:param query_arena: The query fingerprints.
:type query_arena: a FingerprintArena
:param target_arena: The target fingerprints.
:type target_arena: a FingerprintArena
:param k: the number of nearest neighbors to find.
:type k: positive integer
:param threshold: The minimum score threshold.
:type threshold: float between 0.0 and 1.0, inclusive
:returns: a SearchResults instance
"""
_require_matching_sizes(query_arena, target_arena)
num_queries = len(query_arena)
results = SearchResults(num_queries, target_arena.arena_ids)
_chemfp.knearest_tanimoto_arena(
k, threshold, target_arena.num_bits, query_arena.start_padding,
query_arena.end_padding, query_arena.storage_size, query_arena.arena,
query_arena.start, query_arena.end, target_arena.start_padding,
target_arena.end_padding, target_arena.storage_size,
target_arena.arena, target_arena.start, target_arena.end,
target_arena.popcount_indices, results, 0)
_chemfp.knearest_results_finalize(results, 0, num_queries)
return results
def knearest_tanimoto_search_arena(query_arena, target_arena, k=3, threshold=0.7):
"""Search for the `k` nearest hits in the `target_arena` at least `threshold` similar to the fingerprints in `query_arena`
The hits in the `SearchResults` are ordered by decreasing similarity score.
Example::
queries = chemfp.load_fingerprints("queries.fps")
targets = chemfp.load_fingerprints("targets.fps")
results = chemfp.search.knearest_tanimoto_search_arena(queries, targets, k=3, threshold=0.5)
for query_id, query_hits in zip(queries.ids, results):
if len(query_hits) >= 2:
print query_id, "->", ", ".join(query_hits.get_ids())
:param query_arena: The query fingerprints.
:type query_arena: a FingerprintArena
:param target_arena: The target fingerprints.
:type target_arena: a FingerprintArena
:param k: the number of nearest neighbors to find.
:type k: positive integer
:param threshold: The minimum score threshold.
:type threshold: float between 0.0 and 1.0, inclusive
:returns: a SearchResults instance
"""
_require_matching_sizes(query_arena, target_arena)
num_queries = len(query_arena)
results = SearchResults(num_queries, target_arena.arena_ids)
_chemfp.knearest_tanimoto_arena(
k, threshold, target_arena.num_bits,
query_arena.start_padding, query_arena.end_padding,
query_arena.storage_size, query_arena.arena, query_arena.start, query_arena.end,
target_arena.start_padding, target_arena.end_padding,
target_arena.storage_size, target_arena.arena, target_arena.start, target_arena.end,
target_arena.popcount_indices,
results, 0)
_chemfp.knearest_results_finalize(results, 0, num_queries)
return results
def knearest_tanimoto_search_fp(query_fp, target_arena, k=3, threshold=0.7):
"""Search for `k`-nearest hits in `target_arena` which are at least `threshold` similar to `query_fp`
The hits in the `SearchResults` are ordered by decreasing similarity score.
Example::
query_id, query_fp = chemfp.load_fingerprints("queries.fps")[0]
targets = chemfp.load_fingerprints("targets.fps")
print list(chemfp.search.knearest_tanimoto_search_fp(query_fp, targets, k=3, threshold=0.0))
:param query_fp: the query fingerprint
:type query_fp: a byte string
:param target_arena: the target arena
:type target_fp: a FingerprintArena
:param k: the number of nearest neighbors to find.
:type k: positive integer
:param threshold: The minimum score threshold.
:type threshold: float between 0.0 and 1.0, inclusive
:returns: a SearchResult
"""
_require_matching_fp_size(query_fp, target_arena)
query_start_padding, query_end_padding, query_fp = _chemfp.align_fingerprint(
query_fp, target_arena.alignment, target_arena.storage_size)
if k < 0:
raise ValueError("k must be non-negative")
results = SearchResults(1, target_arena.arena_ids)
_chemfp.knearest_tanimoto_arena(
k, threshold, target_arena.num_bits,
query_start_padding, query_end_padding, target_arena.storage_size, query_fp, 0, 1,
target_arena.start_padding, target_arena.end_padding,
target_arena.storage_size, target_arena.arena, target_arena.start, target_arena.end,
target_arena.popcount_indices,
results, 0)
_chemfp.knearest_results_finalize(results, 0, 1)
return results[0]
def knearest_tanimoto_search_fp(query_fp, target_arena, k=3, threshold=0.7):
"""Search for `k`-nearest hits in `target_arena` which are at least `threshold` similar to `query_fp`
The hits in the `SearchResults` are ordered by decreasing similarity score.
Example::
query_id, query_fp = chemfp.load_fingerprints("queries.fps")[0]
targets = chemfp.load_fingerprints("targets.fps")
print list(chemfp.search.knearest_tanimoto_search_fp(query_fp, targets, k=3, threshold=0.0))
:param query_fp: the query fingerprint
:type query_fp: a byte string
:param target_arena: the target arena
:type target_fp: a FingerprintArena
:param k: the number of nearest neighbors to find.
:type k: positive integer
:param threshold: The minimum score threshold.
:type threshold: float between 0.0 and 1.0, inclusive
:returns: a SearchResult
"""
_require_matching_fp_size(query_fp, target_arena)
query_start_padding, query_end_padding, query_fp = _chemfp.align_fingerprint(
query_fp, target_arena.alignment, target_arena.storage_size)
if k < 0:
raise ValueError("k must be non-negative")
results = SearchResults(1, target_arena.arena_ids)
_chemfp.knearest_tanimoto_arena(
k, threshold, target_arena.num_bits, query_start_padding,
query_end_padding, target_arena.storage_size, query_fp, 0, 1,
target_arena.start_padding, target_arena.end_padding,
target_arena.storage_size, target_arena.arena, target_arena.start,
target_arena.end, target_arena.popcount_indices, results, 0)
_chemfp.knearest_results_finalize(results, 0, 1)
return results[0]
