add chemfp range query

ann_benchmarks/algorithms/chemfp.py

@@ -32,11 +32,16 @@ def pre_query(self, v, n):
         queryMatr = numpy.array([v])
         self._queries = Chemfp.matrToArena(queryMatr)
 
-    def query(self, v, n):
-        self._results = chemfp.knearest_tanimoto_search(self._queries, self._target, k=n, threshold=0.0)
+    def query(self, v, n, rq=False):
+        if rq:
+            self._results = chemfp.threshold_tanimoto_search(self._queries, self._target, threshold=1.0-n)
+        else:
+            self._results = chemfp.knearest_tanimoto_search(self._queries, self._target, k=n, threshold=0.0)
 
-    def post_query(self):
+    def post_query(self, rq=False):
         # parse the results
         for (query_id, hits) in self._results:
             if hits:
                 return hits.get_ids()
+            else:
+                return []

ann_benchmarks/plotting/metrics.py

@@ -13,7 +13,7 @@ def rangequery(dataset_distances, run_distances, radius, epsilon=1e-10):
     for true_distances, found_distances in zip(dataset_distances, run_distances):
         true = [d for d in true_distances if d <= radius + epsilon]
         found = [d for d in found_distances if d <= radius + epsilon]
-        print('found: ' + str(len(found)) + '/true: ' + str(len(true)))
+        #print('found: ' + str(len(found)) + '/true: ' + str(len(true)))
         if len(true) == 0:
             if len(found) == 0:
                 total += 1.0

ann_benchmarks/runner.py

@@ -47,7 +47,7 @@ def single_query(v):
 
             # special code for the Risc, DivideSkip, and Chemfp
             if algoname in ['Risc', 'DivideSkip', 'Chemfp']:
-                candidates = algo.post_query()
+                candidates = algo.post_query(rq)
 
             if issparse(X_train):
                 candidates = [(int(idx), float(metrics[distance]['distance'](v, X_train[idx].toarray()[0])))