Use latest CmdStan (#2684)

Author: WardBrian

python/prophet/models.py

@@ -90,7 +90,7 @@ def sampling(self, stan_init, stan_data, samples, **kwargs) -> dict:
 
 
 class CmdStanPyBackend(IStanBackend):
-    CMDSTAN_VERSION = "2.33.1"
+    CMDSTAN_VERSION = "2.37.0"
     def __init__(self):
         import cmdstanpy
         # this must be set before super.__init__() for load_model to work on Windows

python/prophet/tests/test_prophet.py

@@ -64,7 +64,7 @@ def test_fit_predict_large_numbers(self, large_numbers_ts, backend, scaling, exp
         test_days = 30
         train, test = train_test_split(large_numbers_ts, test_days)
         forecaster = Prophet(stan_backend=backend, scaling=scaling)
-        forecaster.fit(train, seed=1237861298)
+        forecaster.fit(train, seed=1237861298, sig_figs=6)
         np.random.seed(876543987)
         future = forecaster.make_future_dataframe(test_days, include_history=False)
         future = forecaster.predict(future)
@@ -248,7 +248,7 @@ def test_make_future_dataframe_include_history(self, daily_univariate_ts, backen
         train = daily_univariate_ts.head(468 // 2).copy()
         #cover history with NAs
         train.loc[train.sample(10).index, "y"] = np.nan
-        
+
         forecaster = Prophet(stan_backend=backend)
         forecaster.fit(train)
         future = forecaster.make_future_dataframe(periods=3, freq="D", include_history=True)

python/setup.py

@@ -20,7 +20,7 @@
 MODEL_DIR = "stan"
 MODEL_TARGET_DIR = os.path.join("prophet", "stan_model")
 
-CMDSTAN_VERSION = "2.33.1"
+CMDSTAN_VERSION = "2.37.0"
 BINARIES_DIR = "bin"
 BINARIES = ["diagnose", "print", "stanc", "stansummary"]
 TBB_PARENT = "stan/lib/stan_math/lib"