From 0679e69dbaee33b5ae379a62e8ce03aa3dd67b8e Mon Sep 17 00:00:00 2001
From: Alexander Gawrilow <alexander.gawrilow@nesto-software.de>
Date: Tue, 23 Jul 2019 10:59:28 +0200
Subject: [PATCH] implemented disabling calculation of uncertainties if
 uncertainty_samples is 0

---
 python/fbprophet/forecaster.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/python/fbprophet/forecaster.py b/python/fbprophet/forecaster.py
index 29240e9..905aff3 100644
--- a/python/fbprophet/forecaster.py
+++ b/python/fbprophet/forecaster.py
@@ -75,7 +75,8 @@ class Prophet(object):
         model. If mcmc.samples>0, this will be integrated over all model
         parameters, which will include uncertainty in seasonality.
     uncertainty_samples: Number of simulated draws used to estimate
-        uncertainty intervals.
+        uncertainty intervals. Settings this value to 0 or False will disable
+        uncertainty estimation and speed up the calculation.
     """
 
     def __init__(
@@ -1173,7 +1174,10 @@ class Prophet(object):
 
         df['trend'] = self.predict_trend(df)
         seasonal_components = self.predict_seasonal_components(df)
-        intervals = self.predict_uncertainty(df)
+        if self.uncertainty_samples:
+            intervals = self.predict_uncertainty(df)
+        else:
+            intervals = None
 
         # Drop columns except ds, cap, floor, and trend
         cols = ['ds', 'trend']
@@ -1289,8 +1293,9 @@ class Prophet(object):
         seasonal_features, _, component_cols, _ = (
             self.make_all_seasonality_features(df)
         )
-        lower_p = 100 * (1.0 - self.interval_width) / 2
-        upper_p = 100 * (1.0 + self.interval_width) / 2
+        if self.uncertainty_samples:
+            lower_p = 100 * (1.0 - self.interval_width) / 2
+            upper_p = 100 * (1.0 + self.interval_width) / 2
 
         X = seasonal_features.values
         data = {}
@@ -1301,12 +1306,13 @@ class Prophet(object):
             if component in self.component_modes['additive']:
                 comp *= self.y_scale
             data[component] = np.nanmean(comp, axis=1)
-            data[component + '_lower'] = np.nanpercentile(
-                comp, lower_p, axis=1,
-            )
-            data[component + '_upper'] = np.nanpercentile(
-                comp, upper_p, axis=1,
-            )
+            if self.uncertainty_samples:
+                data[component + '_lower'] = np.nanpercentile(
+                    comp, lower_p, axis=1,
+                )
+                data[component + '_upper'] = np.nanpercentile(
+                    comp, upper_p, axis=1,
+                )
         return pd.DataFrame(data)
 
     def sample_posterior_predictive(self, df):