From 1d398c679d40007ade0dcf81ae0022cceaa1ce8b Mon Sep 17 00:00:00 2001
From: Ben Letham <bletham@gmail.com>
Date: Mon, 3 Dec 2018 19:05:47 -0800
Subject: [PATCH] Allow overriding any of the arguments to stan functions

---
 R/R/prophet.R                  | 18 ++++++++++--------
 python/fbprophet/forecaster.py | 27 +++++++++++++++++----------
 2 files changed, 27 insertions(+), 18 deletions(-)

diff --git a/R/R/prophet.R b/R/R/prophet.R
index 60e7612..75862d7 100644
--- a/R/R/prophet.R
+++ b/R/R/prophet.R
@@ -1213,24 +1213,26 @@ fit.prophet <- function(m, df, ...) {
     m$params$sigma_obs <- 0.
     n.iteration <- 1.
   } else if (m$mcmc.samples > 0) {
-    stan.fit <- rstan::sampling(
-      model,
+    args <- list(
+      object = model,
       data = dat,
       init = stan_init,
-      iter = m$mcmc.samples,
-      ...
+      iter = m$mcmc.samples
     )
+    args <- modifyList(args, list(...))
+    stan.fit <- do.call(rstan::sampling, args)
     m$params <- rstan::extract(stan.fit)
     n.iteration <- length(m$params$k)
   } else {
-    stan.fit <- rstan::optimizing(
-      model,
+    args <- list(
+      object = model,
       data = dat,
       init = stan_init,
       iter = 1e4,
-      as_vector = FALSE,
-      ...
+      as_vector = FALSE
     )
+    args <- modifyList(args, list(...))
+    stan.fit <- do.call(rstan::optimizing, args)
     m$params <- stan.fit$par
     n.iteration <- 1
   }
diff --git a/python/fbprophet/forecaster.py b/python/fbprophet/forecaster.py
index 163e4e5..7c7399c 100644
--- a/python/fbprophet/forecaster.py
+++ b/python/fbprophet/forecaster.py
@@ -1082,27 +1082,34 @@ class Prophet(object):
             for par in self.params:
                 self.params[par] = np.array([self.params[par]])
         elif self.mcmc_samples > 0:
-            stan_fit = model.sampling(
-                dat,
+            args = dict(
+                data=dat,
                 init=stan_init,
                 iter=self.mcmc_samples,
-                **kwargs
             )
+            args.update(kwargs)
+            stan_fit = model.sampling(**args)
             for par in stan_fit.model_pars:
                 self.params[par] = stan_fit[par]
                 # Shape vector parameters
                 if par in ['delta', 'beta'] and len(self.params[par].shape) < 2:
                     self.params[par] = self.params[par].reshape((-1, 1))
         else:
+            args = dict(
+                data=dat,
+                init=stan_init,
+                iter=1e4,
+            )
+            args.update(kwargs)
             try:
-                params = model.optimizing(
-                    dat, init=stan_init, iter=1e4, **kwargs)
+                params = model.optimizing(**args)
             except RuntimeError:
-                kwargs.pop('algorithm', None)
-                params = model.optimizing(
-                    dat, init=stan_init, iter=1e4, algorithm='Newton',
-                    **kwargs
-                )
+                if 'algorithm' not in args:
+                    # Fall back on Newton
+                    args['algorithm'] = 'Newton'
+                    params = model.optimizing(**args)
+                else:
+                    raise
             for par in params:
                 self.params[par] = params[par].reshape((1, -1))