infra: black format unit tests (#2305)

Author: ajaykarpur

tests/unit/sagemaker/model/test_framework_model.py

@@ -56,7 +56,7 @@ def __init__(self, sagemaker_session, **kwargs):
             ROLE,
             ENTRY_POINT,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )
 
     def create_predictor(self, endpoint_name):
@@ -71,7 +71,7 @@ def __init__(self, sagemaker_session, entry_point, **kwargs):
             ROLE,
             entry_point=entry_point,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )
 
     def create_predictor(self, endpoint_name):

tests/unit/sagemaker/tensorflow/test_estimator.py

@@ -159,7 +159,7 @@ def _build_tf(
     py_version=None,
     instance_type=None,
     base_job_name=None,
-    **kwargs
+    **kwargs,
 ):
     return TensorFlow(
         entry_point=SCRIPT_PATH,
@@ -170,7 +170,7 @@ def _build_tf(
         instance_count=INSTANCE_COUNT,
         instance_type=instance_type if instance_type else INSTANCE_TYPE,
         base_job_name=base_job_name,
-        **kwargs
+        **kwargs,
     )
 
 

tests/unit/sagemaker/tensorflow/test_estimator_init.py

@@ -35,7 +35,7 @@ def _build_tf(sagemaker_session, **kwargs):
         role="dummy-role",
         instance_count=1,
         instance_type="ml.c4.xlarge",
-        **kwargs
+        **kwargs,
     )
 
 

tests/unit/test_amazon_estimator.py

@@ -86,7 +86,7 @@ def test_init_enable_network_isolation(sagemaker_session):
         num_components=55,
         sagemaker_session=sagemaker_session,
         enable_network_isolation=True,
-        **COMMON_ARGS
+        **COMMON_ARGS,
     )
     assert pca.num_components == 55
     assert pca.enable_network_isolation() is True
@@ -99,7 +99,7 @@ def test_init_all_pca_hyperparameters(sagemaker_session):
         subtract_mean=True,
         extra_components=33,
         sagemaker_session=sagemaker_session,
-        **COMMON_ARGS
+        **COMMON_ARGS,
     )
     assert pca.num_components == 55
     assert pca.algorithm_mode == "randomized"
@@ -112,7 +112,7 @@ def test_init_estimator_args(sagemaker_session):
         max_run=1234,
         sagemaker_session=sagemaker_session,
         data_location="s3://some-bucket/some-key/",
-        **COMMON_ARGS
+        **COMMON_ARGS,
     )
     assert pca.instance_type == COMMON_ARGS["instance_type"]
     assert pca.instance_count == COMMON_ARGS["instance_count"]
@@ -133,7 +133,7 @@ def test_data_location_does_not_call_default_bucket(sagemaker_session):
         num_components=2,
         sagemaker_session=sagemaker_session,
         data_location=data_location,
-        **COMMON_ARGS
+        **COMMON_ARGS,
     )
     assert pca.data_location == data_location
     assert not sagemaker_session.default_bucket.called
@@ -205,7 +205,7 @@ def test_fit_ndarray(time, sagemaker_session):
         num_components=55,
         sagemaker_session=sagemaker_session,
         data_location="s3://{}/key-prefix/".format(BUCKET_NAME),
-        **kwargs
+        **kwargs,
     )
     train = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 8.0], [44.0, 55.0, 66.0]]
     labels = [99, 85, 87, 2]
@@ -233,7 +233,7 @@ def test_fit_pass_experiment_config(sagemaker_session):
         num_components=55,
         sagemaker_session=sagemaker_session,
         data_location="s3://{}/key-prefix/".format(BUCKET_NAME),
-        **kwargs
+        **kwargs,
     )
     train = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 8.0], [44.0, 55.0, 66.0]]
     labels = [99, 85, 87, 2]

tests/unit/test_chainer.py

@@ -88,7 +88,7 @@ def _chainer_estimator(
     num_processes=None,
     process_slots_per_host=None,
     additional_mpi_options=None,
-    **kwargs
+    **kwargs,
 ):
     return Chainer(
         entry_point=SCRIPT_PATH,
@@ -103,7 +103,7 @@ def _chainer_estimator(
         num_processes=num_processes,
         process_slots_per_host=process_slots_per_host,
         additional_mpi_options=additional_mpi_options,
-        **kwargs
+        **kwargs,
     )
 
 

tests/unit/test_estimator.py

@@ -140,7 +140,7 @@ def create_model(
         vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
         enable_network_isolation=None,
         model_dir=None,
-        **kwargs
+        **kwargs,
     ):
         if enable_network_isolation is None:
             enable_network_isolation = self.enable_network_isolation()
@@ -151,7 +151,7 @@ def create_model(
             entry_point=entry_point,
             enable_network_isolation=enable_network_isolation,
             role=role,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -171,7 +171,7 @@ def __init__(self, sagemaker_session, entry_point=None, role=ROLE, **kwargs):
             role,
             entry_point or ENTRY_POINT,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )
 
     def create_predictor(self, endpoint_name):

tests/unit/test_fm.py

@@ -115,7 +115,7 @@ def test_all_hyperparameters(sagemaker_session):
         factors_init_scale=1.101,
         factors_init_sigma=1.202,
         factors_init_value=1.303,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
     assert fm.hyperparameters() == dict(
         num_factors=str(ALL_REQ_ARGS["num_factors"]),

tests/unit/test_image.py

@@ -751,10 +751,10 @@ def test_ecr_login_needed(check_output):
     token_response = "AWS:%s" % token
     b64_token = base64.b64encode(token_response.encode("utf-8"))
     response = {
-        u"authorizationData": [
+        "authorizationData": [
             {
-                u"authorizationToken": b64_token,
-                u"proxyEndpoint": u"https://520713654638.dkr.ecr.us-east-1.amazonaws.com",
+                "authorizationToken": b64_token,
+                "proxyEndpoint": "https://520713654638.dkr.ecr.us-east-1.amazonaws.com",
             }
         ],
         "ResponseMetadata": {

tests/unit/test_ipinsights.py

@@ -103,7 +103,7 @@ def test_all_hyperparameters(sagemaker_session):
         random_negative_sampling_rate=5,
         shuffled_negative_sampling_rate=5,
         weight_decay=5.0,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
     assert ipinsights.hyperparameters() == dict(
         num_entity_vectors=str(ALL_REQ_ARGS["num_entity_vectors"]),

tests/unit/test_job.py

@@ -107,7 +107,7 @@ def __init__(self, sagemaker_session, **kwargs):
             ROLE,
             SCRIPT_NAME,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )
 
     def prepare_container_def(self, instance_type, accelerator_type=None):

tests/unit/test_kmeans.py

@@ -93,7 +93,7 @@ def test_all_hyperparameters(sagemaker_session):
         epochs=10,
         center_factor=2,
         eval_metrics=["msd", "ssd"],
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
     assert kmeans.hyperparameters() == dict(
         k=str(ALL_REQ_ARGS["k"]),

tests/unit/test_knn.py

@@ -34,7 +34,7 @@
 }
 ALL_REQ_ARGS = dict(
     {"k": K, "sample_size": SAMPLE_SIZE, "predictor_type": PREDICTOR_TYPE_REGRESSOR},
-    **COMMON_TRAIN_ARGS
+    **COMMON_TRAIN_ARGS,
 )
 
 REGION = "us-west-2"
@@ -104,7 +104,7 @@ def test_all_hyperparameters_regressor(sagemaker_session):
         index_metric="COSINE",
         faiss_index_ivf_nlists="auto",
         faiss_index_pq_m=1,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
     assert knn.hyperparameters() == dict(
         k=str(ALL_REQ_ARGS["k"]),
@@ -130,7 +130,7 @@ def test_all_hyperparameters_classifier(sagemaker_session):
         index_type="faiss.IVFFlat",
         index_metric="L2",
         faiss_index_ivf_nlists="20",
-        **test_params
+        **test_params,
     )
     assert knn.hyperparameters() == dict(
         k=str(ALL_REQ_ARGS["k"]),

tests/unit/test_lda.py

@@ -83,7 +83,7 @@ def test_all_hyperparameters(sagemaker_session):
         max_restarts=3,
         max_iterations=10,
         tol=3.3,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
     assert lda.hyperparameters() == dict(
         num_topics=str(ALL_REQ_ARGS["num_topics"]),

tests/unit/test_linear_learner.py

@@ -132,7 +132,7 @@ def test_all_hyperparameters(sagemaker_session):
         accuracy_top_k=3,
         f_beta=1.0,
         balance_multiclass_weights=False,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
 
     assert lr.hyperparameters() == dict(

tests/unit/test_ntm.py

@@ -101,7 +101,7 @@ def test_all_hyperparameters(sagemaker_session):
         clip_gradient=0.5,
         weight_decay=0.5,
         learning_rate=0.5,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
     assert ntm.hyperparameters() == dict(
         num_topics=str(ALL_REQ_ARGS["num_topics"]),

tests/unit/test_object2vec.py

@@ -36,7 +36,7 @@
 }
 ALL_REQ_ARGS = dict(
     {"epochs": EPOCHS, "enc0_max_seq_len": ENC0_MAX_SEQ_LEN, "enc0_vocab_size": ENC0_VOCAB_SIZE},
-    **COMMON_TRAIN_ARGS
+    **COMMON_TRAIN_ARGS,
 )
 
 REGION = "us-west-2"
@@ -134,7 +134,7 @@ def test_all_hyperparameters(sagemaker_session):
         enc1_layers=3,
         enc0_freeze_pretrained_embedding=True,
         enc1_freeze_pretrained_embedding=False,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
 
     hp = object2vec.hyperparameters()

tests/unit/test_pca.py

@@ -93,7 +93,7 @@ def test_all_hyperparameters(sagemaker_session):
         algorithm_mode="regular",
         subtract_mean="True",
         extra_components=1,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
     assert pca.hyperparameters() == dict(
         num_components=str(ALL_REQ_ARGS["num_components"]),

tests/unit/test_pipeline_model.py

@@ -47,7 +47,7 @@ def __init__(self, sagemaker_session, **kwargs):
             ROLE,
             ENTRY_POINT,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )
 
     def create_predictor(self, endpoint_name):

tests/unit/test_randomcutforest.py

@@ -96,7 +96,7 @@ def test_all_hyperparameters(sagemaker_session):
         num_trees=NUM_TREES,
         num_samples_per_tree=NUM_SAMPLES_PER_TREE,
         eval_metrics=EVAL_METRICS,
-        **ALL_REQ_ARGS
+        **ALL_REQ_ARGS,
     )
     assert randomcutforest.hyperparameters() == dict(
         num_samples_per_tree=str(NUM_SAMPLES_PER_TREE),

tests/unit/test_sklearn.py

@@ -92,7 +92,7 @@ def _sklearn_estimator(
         instance_type=instance_type if instance_type else INSTANCE_TYPE,
         base_job_name=base_job_name,
         py_version=PYTHON_VERSION,
-        **kwargs
+        **kwargs,
     )
 
 

tests/unit/test_tuner.py

@@ -889,7 +889,7 @@ def test_deploy_optional_params(_get_best_training_job, best_estimator, tuner):
         wait=False,
         model_name=model_name,
         kms_key=kms_key,
-        **kwargs
+        **kwargs,
     )
 
     best_estimator.assert_called_with(training_job)
@@ -905,7 +905,7 @@ def test_deploy_optional_params(_get_best_training_job, best_estimator, tuner):
         model_name=model_name,
         kms_key=kms_key,
         data_capture_config=None,
-        **kwargs
+        **kwargs,
     )
 
 

tests/unit/test_xgboost.py

@@ -93,7 +93,7 @@ def _xgboost_estimator(
     instance_type=None,
     instance_count=1,
     base_job_name=None,
-    **kwargs
+    **kwargs,
 ):
 
     return XGBoost(
@@ -105,7 +105,7 @@ def _xgboost_estimator(
         instance_count=instance_count,
         base_job_name=base_job_name,
         py_version=PYTHON_VERSION,
-        **kwargs
+        **kwargs,
     )