surprise package updates

ai/recommender/surprise_recommenders.py

@@ -95,7 +95,7 @@ def __init__(self,
         self.first_fit = True
 
         # reader for translating btw PennAI results and Suprise training set
-        self.reader = Reader()
+        self.reader = Reader(rating_scale=(0,1))
 
         self.ml_type = ml_type
 
@@ -224,7 +224,7 @@ def _update_training_data(self, results_data, shuffle=False):
         data = Dataset.load_from_df(self.results_df[['_id',
                                                      'algorithm-parameters',
                                                      'score']],
-                                    self.reader, rating_scale=(0,1))
+                                    self.reader)
         # build training set from the data
         self.trainset = data.build_full_trainset()
         logger.debug('self.trainset # of ML-P combos: ' +

docker/lab/Dockerfile

@@ -22,8 +22,6 @@ RUN apt-get update --fix-missing && apt-get install -y \
 # setup python environment
 COPY ${docker_filepath}/requirements.txt /root/
 RUN pip install --no-cache-dir -r /root/requirements.txt
-### bill's surprise fork
-RUN pip install --no-cache-dir git+https://github.com/lacava/surprise.git@1.0.8.3
 
 ## Webserver
 

docker/lab/Dockerfile_production

@@ -27,8 +27,6 @@ RUN apt-get update --fix-missing && apt-get install -y \
 # setup python environment
 COPY ${docker_filepath}/requirements.txt /root/
 RUN pip install --no-cache-dir -r /root/requirements.txt
-### bill's surprise fork
-RUN pip install --no-cache-dir git+https://github.com/lacava/surprise.git@1.0.8.3
 
 ## Webserver
 

docker/lab/files/requirements.txt

@@ -8,3 +8,4 @@ xgboost==0.82
 simplejson==3.16.0
 requests==2.22.0
 joblib==0.16.0
+-e git+https://github.com/lacava/surprise.git@1.1.1.1#egg=scikit-surprise

docs/Dockerfile

@@ -8,6 +8,3 @@ RUN pip install --no-cache-dir -r /root/lab_requirements.txt
 
 COPY /docker/machine/files/requirements.txt /root/mach_requirements.txt
 RUN pip install --no-cache-dir -r /root/mach_requirements.txt
-
-### Bill's surprise fork
-RUN pip install --no-cache-dir git+https://github.com/lacava/surprise.git@master

docs/guides/Scikit_Learn_API_Guide.md

@@ -28,10 +28,10 @@ NumPy, SciPy, scikit-learn, pandas and joblib can be installed in Anaconda via t
 conda install numpy scipy scikit-learn pandas joblib simplejson
 ```
 
-Surprise was tweaked by William La Cava for PennAI AI engine and it can be install with `pip` via the command below. **Note: [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) is required for building the surprise package in Windows OS. Please download and run the installer with selecting "C++ Build tools". Additionally, the latest version of [`cython`](https://cython.org) is required and it can be installed/updated via `pip install --upgrade cython`.**
+Surprise was tweaked for the PennAI AI engine and it can be install with `pip` via the command below. **Note: [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) is required for building the surprise package in Windows OS. Please download and run the installer with selecting "C++ Build tools". Additionally, the latest version of [`cython`](https://cython.org) is required and it can be installed/updated via `pip install --upgrade cython`.**
 
 ```Shell
-pip install --no-cache-dir git+https://github.com/lacava/surprise.git@1.0.8.3
+pip install --no-cache-dir git+https://github.com/lacava/surprise.git@1.1.1.1
 ```
 
 Finally to install AI engine itself, run the following command:

tests/unit/Dockerfile

@@ -27,10 +27,6 @@ RUN pip install --no-cache-dir -r /root/lab_requirements.txt
 COPY /docker/machine/files/requirements.txt /root/mach_requirements.txt
 RUN pip install --no-cache-dir -r /root/mach_requirements.txt
 
-### Bill's surprise fork
-RUN pip install --no-cache-dir git+https://github.com/lacava/surprise.git@1.0.8.3
-
-
 # install lab/node_modules to an anon volume
 WORKDIR /appsrc/lab
 COPY lab/package.json /appsrc/lab/