Update to fbprophet 0.4.post2

Updated the installation to use the latest version of Facebook Prophet. Solves the issue of incompatibility with new version of numpy.

Qlik-Py-Init.bat

@@ -15,7 +15,7 @@ echo Installing required packages... & echo.
 python -m pip install --upgrade setuptools pip
 pip install grpcio grpcio-tools numpy scipy pandas cython
 pip install pystan==2.17
-pip install fbprophet==0.3.post2
+pip install fbprophet
 pip install -U scikit-learn
 pip install hdbscan
 pip install -U skater

Qlik-Py-Init.ps1

@@ -9,7 +9,7 @@ Write-Output "Installing required packages..."
 python -m pip install --upgrade setuptools pip
 pip install grpcio grpcio-tools numpy scipy pandas cython
 pip install pystan==2.17
-pip install fbprophet==0.3.post2
+pip install fbprophet
 pip install -U scikit-learn
 pip install hdbscan
 pip install -U skater

README.md

@@ -12,9 +12,11 @@
 
 
 ## Introduction
-Qlik's advanced analytics integration provides a path to making modern algorithms more accessible to the wider business audience. This project is an attempt to show what's possible.
+Qlik's advanced analytics integration provides a path to making modern data science algorithms accessible to the wider business audience. This project is an attempt to show what's possible.
 
-This repository provides a server side extension (SSE) for Qlik Sense built using Python. The intention is to provide a set of functions for data science that can be used as expressions in Qlik. Sample Qlik Sense apps are also included and explained so that the techniques shown here can be easily replicated.
+This repository provides a server side extension (SSE) for Qlik Sense built using Python. The intention is to provide a set of functions for data science that can be used as expressions in Qlik. 
+
+Sample Qlik Sense apps are included and explained so that the techniques shown here can be easily replicated.
 
 The current implementation includes:
 

core/_prophet.py

@@ -5,7 +5,7 @@
 import locale
 import numpy as np
 import pandas as pd
-from fbprophet import Prophet
+from fbprophet import Prophet, plot
 import _utils as utils
 import ServerSideExtension_pb2 as SSE
 
@@ -495,7 +495,7 @@ def _forecast(self):
                 # Prepare the seasonality data frame
                 # Parameter start needs to be any arbitrary week starting on a Sunday
                 days = (pd.date_range(start='2017-01-01', periods=7) + pd.Timedelta(days=self.weekly_start))
-                df_w = self.model.seasonality_plot_df(days)
+                df_w = plot.seasonality_plot_df(self.model, days)
 
                 # Calculate seasonal components 
                 self.forecast = self.model.predict_seasonal_components(df_w)
@@ -504,7 +504,7 @@ def _forecast(self):
                 # Prepare the seasonality data frame
                 # Parameter start needs to be 1st January for any arbitrary year
                 days = (pd.date_range(start='2017-01-01', periods=365) + pd.Timedelta(days=self.yearly_start))
-                df_y = self.model.seasonality_plot_df(days)
+                df_y = plot.seasonality_plot_df(self.model, days)
 
                 # Calculate seasonal components 
                 self.forecast = self.model.predict_seasonal_components(df_y)
@@ -520,7 +520,7 @@ def _forecast(self):
                 # However, it seems to make more sense to use period given the expected usage in Qlik
                 intervals = pd.to_datetime(np.linspace(start.value, end.value, period)) 
 
-                df_x = self.model.seasonality_plot_df(intervals)
+                df_x = plot.seasonality_plot_df(self.model, intervals)
 
                 # Calculate seasonal components 
                 self.forecast = self.model.predict_seasonal_components(df_x)

docs/README.md

@@ -12,9 +12,11 @@
 
 
 ## Introduction
-Qlik's advanced analytics integration provides a path to making modern algorithms more accessible to the wider business audience. This project is an attempt to show what's possible.
+Qlik's advanced analytics integration provides a path to making modern data science algorithms accessible to the wider business audience. This project is an attempt to show what's possible.
 
-This repository provides a server side extension (SSE) for Qlik Sense built using Python. The intention is to provide a set of functions for data science that can be used as expressions in Qlik. Sample Qlik Sense apps are also included and explained so that the techniques shown here can be easily replicated.
+This repository provides a server side extension (SSE) for Qlik Sense built using Python. The intention is to provide a set of functions for data science that can be used as expressions in Qlik. 
+
+Sample Qlik Sense apps are included and explained so that the techniques shown here can be easily replicated.
 
 The current implementation includes:
 

offline-install/Qlik-Py-Init Offline.bat

@@ -15,7 +15,7 @@ echo.
 echo Installing required packages... & echo.
 pip install --no-index --find-links=offline grpcio grpcio-tools numpy scipy pandas cython
 pip install --no-index --find-links=offline pystan==2.17
-pip install --no-index --find-links=offline fbprophet==0.3.post2
+pip install --no-index --find-links=offline fbprophet
 pip install --no-index --find-links=offline scikit-learn
 pip install --no-index --find-links=offline setuptools wheel hdbscan
 pip install --no-index --find-links=offline skater