def test_precision_recall(self):
Iref = [[1, 2, 3], [5, 6], [], []]
Inew = [[1, 2], [6, 7], [1], []]
lims_ref = np.cumsum([0] + [len(x) for x in Iref])
Iref = np.hstack(Iref)
lims_new = np.cumsum([0] + [len(x) for x in Inew])
Inew = np.hstack(Inew)
precision, recall = evaluation.range_PR(lims_ref, Iref, lims_new, Inew)
print(precision, recall)
self.assertEqual(precision, 0.6)
self.assertEqual(recall, 0.6)
def test_PR_multiple(self):
metric = faiss.METRIC_L2
ds = datasets.SyntheticDataset(32, 1000, 1000, 10)
xq = ds.get_queries()
xb = ds.get_database()
# good for ~10k results
threshold = 15
index = faiss.IndexFlat(32, metric)
index.add(xb)
ref_lims, ref_D, ref_I = index.range_search(xq, threshold)
# now make a slightly suboptimal index
index2 = faiss.index_factory(32, "PCA16,Flat")
index2.train(ds.get_train())
index2.add(xb)
# PCA reduces distances so will have more results
new_lims, new_D, new_I = index2.range_search(xq, threshold)
all_thr = np.array([5.0, 10.0, 12.0, 15.0])
for mode in "overall", "average":
ref_precisions = np.zeros_like(all_thr)
ref_recalls = np.zeros_like(all_thr)
for i, thr in enumerate(all_thr):
lims2, _, I2 = evaluation.filter_range_results(
new_lims, new_D, new_I, thr)
prec, recall = evaluation.range_PR(ref_lims,
ref_I,
lims2,
I2,
mode=mode)
ref_precisions[i] = prec
ref_recalls[i] = recall
precisions, recalls = evaluation.range_PR_multiple_thresholds(
ref_lims, ref_I, new_lims, new_D, new_I, all_thr, mode=mode)
np.testing.assert_array_almost_equal(ref_precisions, precisions)
np.testing.assert_array_almost_equal(ref_recalls, recalls)