update_v1.0

requirements.txt

@@ -7,7 +7,6 @@ networkx==2.3
 gensim==3.7.3
 scikit-learn==0.19.0 # to do... compatible with >0.20
 pandas==0.23.0
-psutil==5.6.3
 
 # Enable GPU:
 # If using anaconda, run `conda install tensorflow-gpu==1.10.0`
@@ -17,47 +16,3 @@ psutil==5.6.3
 
 # Or simply build from docker image: docker pull tensorflow/tensorflow:1.10.0-gpu-py3
 # ref: https://www.tensorflow.org/install/docker#gpu_support
-
-
-'''
-Package         Version
---------------- --------
-absl-py         0.7.1
-astor           0.8.0
-boto            2.49.0
-boto3           1.9.160
-botocore        1.12.160
-certifi         2019.3.9
-chardet         3.0.4
-decorator       4.4.0
-docutils        0.14
-gast            0.2.2
-gensim          3.7.3
-grpcio          1.21.1
-idna            2.8
-jmespath        0.9.4
-Markdown        3.1.1
-mkl-fft         1.0.12
-mkl-random      1.0.2
-networkx        2.3
-numpy           1.14.5
-pandas          0.23.0
-pip             19.1.1
-protobuf        3.8.0
-psutil          5.6.3
-python-dateutil 2.8.0
-pytz            2019.1
-requests        2.22.0
-s3transfer      0.2.0
-scikit-learn    0.19.0
-scipy           1.1.0
-setuptools      39.1.0
-six             1.12.0
-smart-open      1.8.4
-tensorboard     1.10.0
-tensorflow      1.10.0
-termcolor       1.1.0
-urllib3         1.25.3
-Werkzeug        0.15.4
-wheel           0.33.4
-'''

src/libnrl/abrw.py

@@ -96,8 +96,7 @@ class ABRW(object):
         # n*n*8 is the bytes required by pairwise similarity matrix; 2e9 = 2GB ROM remained for safety reason
         # if your computer have 200G memory, there should be no problem for graph with 100k nodes
         # this naive implementation is **faster** than BallTree implementation, thanks to numpy
-        #if n*n*8 + n*n*8 + n*5000*8 + 2e9 < free_memory and n < 1e5:   # X_sim[n,n] dense + A[n,n] if dense + X[n,5000] if dense with max 5000 feats + 2e9 for safety
-        if False:
+        if False:   # X_sim[n,n] dense + A[n,n] if dense + X[n,5000] if dense with max 5000 feats + 2e9 for safety
             print('naive implementation + intro-select ')
             t1 = time.time()
             X_sim = pairwise_similarity(X.todense())