From d8dbb7889086f9f79e19790a127cad6739661558 Mon Sep 17 00:00:00 2001
From: ChunjiangZhu <chunjiang.zhu@uconn.edu>
Date: Fri, 3 Jul 2020 23:59:05 -0400
Subject: [PATCH] add chemfp range query

---
 ann_benchmarks/.DS_Store            | Bin 10244 -> 10244 bytes
 ann_benchmarks/algorithms/chemfp.py |  11 ++++++++---
 ann_benchmarks/plotting/metrics.py  |   2 +-
 ann_benchmarks/runner.py            |   2 +-
 4 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/ann_benchmarks/.DS_Store b/ann_benchmarks/.DS_Store
index 5bdd401ee363832e2914e53a84963cebd538a7bf..f4d2c772a3d3a87f4c84831c6d15b5040581d809 100644
GIT binary patch
delta 320
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WdKfpmO08qr%&zc@W%D-?W@Z3_(@7uz

delta 275
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z^K<fxoN`K2BQlfo^MW(0QYYUM+*Kbhz+aq^U+xJM3n)r0%S<hgPA|$#2`)%X&df_!
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h$+lwBn-hdTvu%!%Ud^(xL4<KLyTUJ)%{-#a%mALiJkJ0C

diff --git a/ann_benchmarks/algorithms/chemfp.py b/ann_benchmarks/algorithms/chemfp.py
index e700a6e..3a6f29e 100644
--- a/ann_benchmarks/algorithms/chemfp.py
+++ b/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 []
diff --git a/ann_benchmarks/plotting/metrics.py b/ann_benchmarks/plotting/metrics.py
index cdf2800..fe3f0d0 100644
--- a/ann_benchmarks/plotting/metrics.py
+++ b/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
diff --git a/ann_benchmarks/runner.py b/ann_benchmarks/runner.py
index f840c6b..9acaa4d 100644
--- a/ann_benchmarks/runner.py
+++ b/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])))