remove redundant dependencies

requirements.txt

@@ -1,38 +1,18 @@
-setuptools==39.1.0  #tensorflow 1.10.0 has requirement setuptools<=39.1.0, but you'll have setuptools 39.2.0 which is incompatible
-absl-py==0.2.2
-astor==0.6.2
-backports.weakref==1.0.post1
-bleach==1.5.0
-decorator==4.3.0
-#enum34==1.1.6  # enum34 is not necessary for python > 3.4
-funcsigs==1.0.2
-gast==0.2.0
-grpcio==1.12.1
-html5lib==0.9999999
-Markdown==2.6.11
-mock==2.0.0
-numpy==1.14.5
-pbr==4.0.4
-protobuf==3.6.0
-scipy==1.1.0
-six==1.11.0
-#sklearn==0.0
-termcolor==1.1.0
-Werkzeug==0.14.1
-# we update to the latest version of the following packages @18 Oct 2018
-# the orignal one: https://github.com/williamleif/GraphSAGE
-#futures==3.2.0
-networkx==2.2
-tensorflow==1.10.0
-tensorboard==1.10.0
-gensim==3.0.1
-scikit-learn==0.19.0  #0.20.0 is OK but may get some warnings
-# if your want utilize your gpu for speeding up, try simply use the following conda command
 # tested in python==3.6.6
 
+numpy==1.14.5
+tensorflow==1.10.0 # to do... compatible with latest tf and tf-gpu
+tensorboard==1.10.0
+networkx==2.2
+gensim==3.0.1
+scikit-learn==0.19.0 # to do... compatible with >0.20
+pandas==0.23.0
+
+# want to use GPU? some useful info:
+
 # either -> conda install tensorflow-gpu==1.10.0  #this version will help you to install cuda and cudnn
 # for cuda driver compatibility: https://docs.nvidia.com/deploy/cuda-compatibility/index.html
 # e.g. if driver 384.xx -> conda install tensorflow-gpu=1.10.0 cudatoolkit=9.0
 
 # or     -> simply build from docker image: docker pull tensorflow/tensorflow:1.10.0-gpu-py3
-# ref: https://www.tensorflow.org/install/docker#gpu_support
+# ref: https://www.tensorflow.org/install/docker#gpu_support