def test_update_w_prepare_query_accuracy(nn_data, metric):
nnd = NNDescent(
nn_data[200:800],
metric=metric,
n_neighbors=10,
random_state=None,
compressed=False,
)
nnd.prepare()
nnd.update(xs_fresh=nn_data[800:])
nnd.prepare()
knn_indices, _ = nnd.query(nn_data[:200], k=10, epsilon=0.2)
true_nnd = NearestNeighbors(metric=metric).fit(nn_data[200:])
true_indices = true_nnd.kneighbors(nn_data[:200],
10,
return_distance=False)
num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
percent_correct = num_correct / (true_indices.shape[0] * 10)
assert percent_correct >= 0.95, ("NN-descent query did not get 95% "
"accuracy on nearest neighbors")
def test_tree_numbers_after_multiple_updates(n_trees):
trees_after_update = max(1, int(np.round(n_trees / 3)))
nnd = NNDescent(np.array([[1.0]]), n_neighbors=1, n_trees=n_trees)
assert nnd.n_trees == n_trees, "NN-descent update changed the number of trees"
assert (
nnd.n_trees_after_update == trees_after_update
), "The value of the n_trees_after_update in NN-descent after update(s) is wrong"
for i in range(5):
nnd.update(xs_fresh=np.array([[i]], dtype=np.float64))
assert (
nnd.n_trees == trees_after_update
), "The value of the n_trees in NN-descent after update(s) is wrong"
assert (
nnd.n_trees_after_update == trees_after_update
), "The value of the n_trees_after_update in NN-descent after update(s) is wrong"
"accuracy on nearest neighbors")
k = 10
xs_orig, xs_fresh, xs_updated, indices_updated = update_data[case]
queries1 = xs_orig
# original
index = NNDescent(xs_orig,
metric=metric,
n_neighbors=40,
random_state=1234)
index.prepare()
evaluate(index, xs_orig, queries1)
# updated
index.update(xs_fresh=xs_fresh,
xs_updated=xs_updated,
updated_indices=indices_updated)
if xs_fresh is not None:
xs = np.vstack((xs_orig, xs_fresh))
queries2 = np.vstack((queries1, xs_fresh))
else:
xs = xs_orig
queries2 = queries1
if indices_updated is not None:
xs[indices_updated] = xs_updated
evaluate(index, xs, queries2)
if indices_updated is not None:
evaluate(index, xs, xs_updated)
@pytest.mark.parametrize("n_trees", [1, 2, 3, 10])
