style

python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py

@@ -295,7 +295,7 @@ def _mg_call_plc_uniform_neighbor_sample(
             renumber=renumber,
             use_legacy_names=use_legacy_names,
             compression=compression,
-            include_hop_column=include_hop_column
+            include_hop_column=include_hop_column,
         )
         if with_edge_properties
         else create_empty_df(indices_t, weight_t)
@@ -522,10 +522,8 @@ def uniform_neighbor_sample(
                         Contains the batch offsets for the renumber maps
     """
 
-    if compression not in ['COO', 'CSR', 'CSC', 'DCSR', 'DCSC']:
-        raise ValueError(
-            "compression must be one of COO, CSR, CSC, DCSR, or DCSC"
-        )
+    if compression not in ["COO", "CSR", "CSC", "DCSR", "DCSC"]:
+        raise ValueError("compression must be one of COO, CSR, CSC, DCSR, or DCSC")
 
     if with_edge_properties:
         warning_msg = (

python/cugraph/cugraph/sampling/uniform_neighbor_sample.py

@@ -236,10 +236,8 @@ def uniform_neighbor_sample(
         major_col_name = "majors"
         minor_col_name = "minors"
 
-    if compression not in ['COO', 'CSR', 'CSC', 'DCSR', 'DCSC']:
-        raise ValueError(
-            "compression must be one of COO, CSR, CSC, DCSR, or DCSC"
-        )
+    if compression not in ["COO", "CSR", "CSC", "DCSR", "DCSC"]:
+        raise ValueError("compression must be one of COO, CSR, CSC, DCSR, or DCSC")
 
     if (
         (compression != "COO")

python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py

@@ -304,7 +304,7 @@ def test_bulk_sampler_csr(scratch_dir):
     el = email_Eu_core.get_edgelist()
 
     G = cugraph.Graph(directed=True)
-    G.from_cudf_edgelist(el, source='src', destination='dst')
+    G.from_cudf_edgelist(el, source="src", destination="dst")
 
     samples_path = os.path.join(scratch_dir, "test_bulk_sampler_csr")
     create_directory_with_overwrite(samples_path)
@@ -318,21 +318,25 @@ def test_bulk_sampler_csr(scratch_dir):
         batches_per_partition=7,
         renumber=True,
         use_legacy_names=False,
-        compression='CSR',
+        compression="CSR",
         compress_per_hop=False,
-        prior_sources_behavior='exclude',
-        include_hop_column=False
+        prior_sources_behavior="exclude",
+        include_hop_column=False,
     )
 
     seeds = G.select_random_vertices(62, 1000)
-    batch_ids = cudf.Series(cupy.repeat(cupy.arange(int(1000/7)+1,dtype='int32'), 7)[:1000]).sort_values()
+    batch_ids = cudf.Series(
+        cupy.repeat(cupy.arange(int(1000 / 7) + 1, dtype="int32"), 7)[:1000]
+    ).sort_values()
 
-    batch_df = cudf.DataFrame({
-        'seed': seeds,
-        'batch': batch_ids,
-    })
+    batch_df = cudf.DataFrame(
+        {
+            "seed": seeds,
+            "batch": batch_ids,
+        }
+    )
 
-    bs.add_batches(batch_df, start_col_name='seed', batch_col_name='batch')
+    bs.add_batches(batch_df, start_col_name="seed", batch_col_name="batch")
     bs.flush()
 
     assert len(os.listdir(samples_path)) == 21

python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py

@@ -251,12 +251,12 @@ def test_bulk_sampler_empty_batches(dask_client, scratch_dir):
 
 @pytest.mark.mg
 @pytest.mark.parametrize("mg_input", [True, False])
-def test_bulk_sampler_csr(dask_client,scratch_dir,mg_input):
+def test_bulk_sampler_csr(dask_client, scratch_dir, mg_input):
     nworkers = len(dask_client.scheduler_info()["workers"])
-    el = dask_cudf.from_cudf(email_Eu_core.get_edgelist(), npartitions=nworkers*2)
+    el = dask_cudf.from_cudf(email_Eu_core.get_edgelist(), npartitions=nworkers * 2)
 
     G = cugraph.Graph(directed=True)
-    G.from_dask_cudf_edgelist(el, source='src', destination='dst')
+    G.from_dask_cudf_edgelist(el, source="src", destination="dst")
 
     samples_path = os.path.join(scratch_dir, "mg_test_bulk_sampler_csr")
     create_directory_with_overwrite(samples_path)
@@ -270,29 +270,30 @@ def test_bulk_sampler_csr(dask_client,scratch_dir,mg_input):
         batches_per_partition=7,
         renumber=True,
         use_legacy_names=False,
-        compression='CSR',
+        compression="CSR",
         compress_per_hop=False,
-        prior_sources_behavior='exclude',
-        include_hop_column=False
+        prior_sources_behavior="exclude",
+        include_hop_column=False,
     )
 
     seeds = G.select_random_vertices(62, 1000)
-    batch_ids = cudf.Series(cupy.repeat(cupy.arange(int(1000/7)+1,dtype='int32'), 7)[:1000]).sort_values()
+    batch_ids = cudf.Series(
+        cupy.repeat(cupy.arange(int(1000 / 7) + 1, dtype="int32"), 7)[:1000]
+    ).sort_values()
 
-    batch_df = cudf.DataFrame({
-        'seed': seeds.compute().values,
-        'batch': batch_ids,
-    })
+    batch_df = cudf.DataFrame(
+        {
+            "seed": seeds.compute().values,
+            "batch": batch_ids,
+        }
+    )
 
     if mg_input:
-        batch_df = dask_cudf.from_cudf(
-            batch_df,
-            npartitions=2
-        )
+        batch_df = dask_cudf.from_cudf(batch_df, npartitions=2)
 
-    bs.add_batches(batch_df, start_col_name='seed', batch_col_name='batch')
+    bs.add_batches(batch_df, start_col_name="seed", batch_col_name="batch")
     bs.flush()
 
     assert len(os.listdir(samples_path)) == 21
 
-    shutil.rmtree(samples_path)
+    shutil.rmtree(samples_path)