Version bump for release and re-black

pynndescent/distances.py

@@ -512,9 +512,9 @@ def tsss(x, y):
     norm_y = np.sqrt(norm_y)
     magnitude_difference = np.abs(norm_x - norm_y)
     d_cos /= norm_x * norm_y
-    theta = np.arccos(d_cos) + np.radians(10) # Add 10 degrees as an "epsilon" to
+    theta = np.arccos(d_cos) + np.radians(10)  # Add 10 degrees as an "epsilon" to
     # avoid problems
-    sector = ((np.sqrt(d_euc_squared) + magnitude_difference)**2) * theta
+    sector = ((np.sqrt(d_euc_squared) + magnitude_difference) ** 2) * theta
     triangle = norm_x * norm_y * np.sin(theta) / 2.0
     return triangle * sector
 
@@ -817,8 +817,10 @@ def kantorovich(x, y, cost=_dummy_cost, max_iter=100000):
     "l2": {"dist": squared_euclidean, "correction": np.sqrt},
     "cosine": {"dist": alternative_cosine, "correction": correct_alternative_cosine},
     "dot": {"dist": alternative_dot, "correction": correct_alternative_cosine},
-    "true_angular": {"dist": alternative_cosine, "correction":
-        true_angular_from_alt_cosine},
+    "true_angular": {
+        "dist": alternative_cosine,
+        "correction": true_angular_from_alt_cosine,
+    },
     "hellinger": {
         "dist": alternative_hellinger,
         "correction": correct_alternative_hellinger,

pynndescent/graph_utils.py

@@ -54,7 +54,11 @@ def create_component_search(index):
         },
     )
     def custom_search_closure(
-        query_points, candidate_indices, k, epsilon, visited,
+        query_points,
+        candidate_indices,
+        k,
+        epsilon,
+        visited,
     ):
         result = make_heap(query_points.shape[0], k)
         distance_scale = 1.0 + epsilon
@@ -176,7 +180,8 @@ def adjacency_matrix_representation(neighbor_indices, neighbor_distances):
     neighbor_distances[neighbor_distances == 0.0] = FLOAT32_EPS
 
     result.row = np.repeat(
-        np.arange(neighbor_indices.shape[0], dtype=np.int32), neighbor_indices.shape[1],
+        np.arange(neighbor_indices.shape[0], dtype=np.int32),
+        neighbor_indices.shape[1],
     )
     result.col = neighbor_indices.ravel()
     result.data = neighbor_distances.ravel()
@@ -191,6 +196,7 @@ def adjacency_matrix_representation(neighbor_indices, neighbor_distances):
 
     return result
 
+
 def connect_graph(graph, index, search_size=10, n_jobs=None):
 
     search_closure = create_component_search(index)
@@ -225,7 +231,8 @@ def new_edge(c1, c2):
         return i, j, d
 
     new_edges = joblib.Parallel(n_jobs=n_jobs, prefer="threads")(
-        joblib.delayed(new_edge)(c1, c2) for c1, c2 in combinations(range(n_components), 2)
+        joblib.delayed(new_edge)(c1, c2)
+        for c1, c2 in combinations(range(n_components), 2)
     )
 
     for i, j, d in new_edges:

pynndescent/sparse.py

@@ -823,7 +823,7 @@ def diversify_csr(
     "canberra": sparse_canberra,
     "kantorovich": sparse_kantorovich,
     "wasserstein": sparse_kantorovich,
-    'braycurtis': sparse_bray_curtis,
+    "braycurtis": sparse_bray_curtis,
     # Binary distances
     "hamming": sparse_hamming,
     "jaccard": sparse_jaccard,

pynndescent/tests/test_pynndescent_.py

@@ -438,6 +438,7 @@ def test_compressed_pickle_unpickle():
     np.testing.assert_equal(neighbors1, neighbors2)
     np.testing.assert_equal(distances1, distances2)
 
+
 def test_transformer_pickle_unpickle():
     seed = np.random.RandomState(42)
 
@@ -456,6 +457,7 @@ def test_transformer_pickle_unpickle():
     np.testing.assert_equal(result1.indices, result2.indices)
     np.testing.assert_equal(result1.data, result2.data)
 
+
 def test_joblib_dump():
     seed = np.random.RandomState(42)
 

setup.py

@@ -8,7 +8,7 @@ def readme():
 
 configuration = {
     "name": "pynndescent",
-    "version": "0.5.1",
+    "version": "0.5.2",
     "description": "Nearest Neighbor Descent",
     "long_description": readme(),
     "classifiers": [