Use latest version of black fo fix lint check

examples/statsmodels/train.py

@@ -28,7 +28,7 @@ def main():
     np.random.seed(9876789)
     nsamples = 100
     x = np.linspace(0, 10, 100)
-    X = np.column_stack((x, x ** 2))
+    X = np.column_stack((x, x**2))
     beta = np.array([1, 0.1, 10])
     e = np.random.normal(size=nsamples)
     X = sm.add_constant(X)

mlflow/pytorch/_pytorch_autolog.py

@@ -273,7 +273,6 @@ def on_test_end(self, trainer, pl_module):
     def _get_step_metrics(trainer):
         return trainer.callback_metrics
 
-
 else:
 
     def _get_step_metrics(trainer):

mlflow/store/db/utils.py

@@ -161,7 +161,7 @@ def create_sqlalchemy_engine_with_retry(db_uri):
             return engine
         except Exception as e:
             if attempts < MAX_RETRY_COUNT:
-                sleep_duration = 0.1 * ((2 ** attempts) - 1)
+                sleep_duration = 0.1 * ((2**attempts) - 1)
                 _logger.warning(
                     "SQLAlchemy engine could not be created. The following exception is caught.\n"
                     "%s\nOperation will be retried in %.1f seconds",

requirements/lint-requirements.txt

@@ -1,3 +1,3 @@
 pylint==2.11.1
 rstcheck==3.2
-black==21.10b0
+black==22.3.0

tests/examples/test_examples.py

@@ -34,7 +34,7 @@ def remove_conda_env(env_name):
 
 def get_free_disk_space():
     # https://stackoverflow.com/a/48929832/6943581
-    return shutil.disk_usage("/")[-1] / (2 ** 30)
+    return shutil.disk_usage("/")[-1] / (2**30)
 
 
 def is_conda_yaml(path):

tests/generate_ui_test_data.py

@@ -72,7 +72,7 @@ def rand_str(max_len=40):
         parameters = {
             "this is a pretty long parameter name": "NA10921-test_file_2018-08-10.txt",
         }
-        metrics = {"grower": [i ** 1.2 for i in range(10)]}
+        metrics = {"grower": [i**1.2 for i in range(10)]}
         log_params(parameters)
         log_metrics(metrics)
 

tests/pylint_plugins/utils.py

@@ -8,4 +8,4 @@ def get_pylint_msg_ids():
     res = subprocess.run(["pylint", "--list-msgs"], stdout=subprocess.PIPE, check=True)
     stdout = res.stdout.decode("utf-8")
     letters = "".join(MSG_TYPES.keys())
-    return set(re.findall(fr"\(([{letters}][0-9]{{4}})\)", stdout))
+    return set(re.findall(rf"\(([{letters}][0-9]{{4}})\)", stdout))

tests/statsmodels/model_fixtures.py

@@ -21,7 +21,7 @@ def ols_model(**kwargs):
     np.random.seed(9876789)
     nsamples = 100
     x = np.linspace(0, 10, 100)
-    X = np.column_stack((x, x ** 2))
+    X = np.column_stack((x, x**2))
     beta = np.array([1, 0.1, 10])
     e = np.random.normal(size=nsamples)
     X = sm.add_constant(X)
@@ -65,7 +65,7 @@ def gls_model():
     res_fit = sm.OLS(ols_resid[1:], ols_resid[:-1]).fit()
     rho = res_fit.params
     order = toeplitz(np.arange(16))
-    sigma = rho ** order
+    sigma = rho**order
     gls = sm.GLS(data.endog, data.exog, sigma=sigma)
     model = gls.fit()