Logic simplification (#2192)

python/prophet/make_holidays.py

@@ -35,8 +35,8 @@ def get_holiday_names(country):
         try:
             holiday_names = getattr(hdays_part1, country)(years=years).values()
         except AttributeError as e:
-            raise AttributeError(
-                "Holidays in {} are not currently supported!".format(country)) from e
+            raise AttributeError(f"Holidays in {country} are not currently supported!") from e
+
     return set(holiday_names)
 
 
@@ -59,8 +59,8 @@ def make_holidays_df(year_list, country, province=None, state=None):
         try:
             holidays = getattr(hdays_part1, country)(prov=province, state=state, years=year_list, expand=False)
         except AttributeError as e:
-            raise AttributeError(
-                "Holidays in {} are not currently supported!".format(country)) from e
+            raise AttributeError(f"Holidays in {country} are not currently supported!") from e
+
     holidays_df = pd.DataFrame([(date, holidays.get_list(date)) for date in holidays], columns=['ds', 'holiday'])
     holidays_df = holidays_df.explode('holiday')
     holidays_df.reset_index(inplace=True, drop=True)

python/prophet/models.py

@@ -16,9 +16,7 @@ import platform
 import logging
 logger = logging.getLogger('prophet.models')
 
-PLATFORM = "unix"
-if platform.platform().startswith("Win"):
-    PLATFORM = "win"
+PLATFORM = "win" if platform.platform().startswith("Win") else "unix"
 
 class IStanBackend(ABC):
     def __init__(self):
@@ -98,15 +96,11 @@ class CmdStanPyBackend(IStanBackend):
             self.stan_fit = self.model.optimize(**args)
         except RuntimeError as e:
             # Fall back on Newton
-            if self.newton_fallback and args['algorithm'] != 'Newton':
-                logger.warning(
-                    'Optimization terminated abnormally. Falling back to Newton.'
-                )
-                args['algorithm'] = 'Newton'
-                self.stan_fit = self.model.optimize(**args)
-            else:
+            if not self.newton_fallback or args['algorithm'] == 'Newton':
                 raise e
-
+            logger.warning('Optimization terminated abnormally. Falling back to Newton.')
+            args['algorithm'] = 'Newton'
+            self.stan_fit = self.model.optimize(**args)
         params = self.stan_to_dict_numpy(
             self.stan_fit.column_names, self.stan_fit.optimized_params_np)
         for par in params:
@@ -188,11 +182,7 @@ class CmdStanPyBackend(IStanBackend):
         end = 0
         two_dims = len(data.shape) > 1
         for cname in column_names:
-            if "." in cname:
-                parsed = cname.split(".")
-            else:
-                parsed = cname.split("[")
-
+            parsed = cname.split(".") if "." in cname else cname.split("[")
             curr = parsed[0]
             if prev is None:
                 prev = curr
@@ -208,10 +198,7 @@ class CmdStanPyBackend(IStanBackend):
                     output[prev] = np.array(data[start:end])
                 prev = curr
                 start = end
-                end += 1
-            else:
-                end += 1
-
+            end += 1
         if prev in output:
             raise RuntimeError(
                 "Found repeated column name"
@@ -233,4 +220,4 @@ class StanBackendEnum(Enum):
         try:
             return StanBackendEnum[name].value
         except KeyError as e:
-            raise ValueError("Unknown stan backend: {}".format(name)) from e
+            raise ValueError(f"Unknown stan backend: {name}") from e

python/setup.py

@@ -128,7 +128,7 @@ def get_backends_from_env() -> List[str]:
 
 
 def build_models(target_dir):
-    print(f"Compiling cmdstanpy model")
+    print("Compiling cmdstanpy model")
     build_cmdstan_model(target_dir)
 
     if 'PYSTAN' in get_backends_from_env():