xiaohan2012

import ray
from tqdm import tqdm


class RayProgressBar:
    @staticmethod
    def num_jobs_done_iter(obj_ids):
        while obj_ids:
            done, obj_ids = ray.wait(obj_ids)
            yield ray.get(done[0])

xiaohan2012

{
  "embeddings": [
    {
      "tensorName": "20newsgroup",
      "tensorShape": [
        1727,
        100
      ],
      "tensorPath": "https://gist.githubusercontent.com/xiaohan2012/ea60050a0093f86237210762804d066b/raw/16a004a3f95b00d0e416f4f61bca79b663904735/20news-embedding.tsv",
      "metadataPath": "https://gist.githubusercontent.com/xiaohan2012/bba8d962a560983b2a4f1e6302f77766/raw/594edaed48724a03c1575fe98a0e974cd8ae8c6f/20newsgroup-labels.tsv"

xiaohan2012

1
0
1
1
1
0
0
0
1
0

xiaohan2012

0.4290986657142639	0.42358970642089844	0.42887815833091736	0.42253378033638	0.5642207860946655	0.4708421230316162	0.4873978793621063	0.42862436175346375	0.4253680408000946	0.43093568086624146	0.7340912818908691	0.3532145619392395	0.42910027503967285	0.42882195115089417	0.8975470066070557	0.4787288010120392	0.43022769689559937	0.42916831374168396	0.8609343767166138	0.8841681480407715	0.9066706895828247	0.7836756706237793	0.42883408069610596	0.7690004706382751	0.4311878979206085	0.43890127539634705	0.7569071054458618	0.42820173501968384	0.8944039940834045	0.4259122610092163	0.8377361297607422	0.683721125125885	0.42419368028640747	0.42937594652175903	0.4367840886116028	0.8066317439079285	0.9182034730911255	0.5937310457229614	0.49091818928718567	0.4636102318763733	0.43012914061546326	0.8237452507019043	0.9045807719230652	0.4264647662639618	0.42861756682395935	0.6517515182495117	0.845058023929596	0.4262949824333191	0.7197322249412537	0.4352913200855255	0.4283246397972107	0.470052033662796	0.8975110054016113	0.4232

xiaohan2012

[{"label":"State 0","isSelected":true,"tSNEIteration":342,"tSNEPerplexity":5,"tSNELearningRate":1,"tSNEis3d":false,"pcaComponentDimensions":[0,1,2],"projections":[{"pca-0":-0.1805076003074646,"pca-1":-0.052298735827207565,"pca-2":0.7568523287773132,"pca-3":-0.44091248512268066,"pca-4":-0.30822834372520447,"pca-5":-0.13008351624011993,"pca-6":0.004937216639518738,"pca-7":0.24454794824123383,"pca-8":0.036458518356084824,"pca-9":-0.011632245033979416,"tsne-0":25.64325238381404,"tsne-1":-2.2261678929273576},{"pca-0":-0.33329975605010986,"pca-1":0.5092434287071228,"pca-2":0.14169825613498688,"pca-3":0.3302021920681,"pca-4":0.5076401829719543,"pca-5":-0.2071685791015625,"pca-6":-0.15575245022773743,"pca-7":-0.16170069575309753,"pca-8":0.21544840931892395,"pca-9":0.047406088560819626,"tsne-0":-3.150499961645024,"tsne-1":3.058536067646262},{"pca-0":-0.4729415476322174,"pca-1":-0.227816641330719,"pca-2":-0.5151332020759583,"pca-3":-0.5534241199493408,"pca-4":0.043921008706092834,"pca-5":-0.04174278303980827,"pca-6":-0

xiaohan2012

{
  "embeddings": [
    {
      "tensorName": "Data science label embedding (from stackexchange)",
      "tensorShape": [
        328,
        48
      ],
      "tensorPath": "https://gist.githubusercontent.com/xiaohan2012/493ae0ac0b813ae0d720cc13315009ea/raw/80dc605d270a366bb3d1282fd2a2a040d2cff4bd/datascience-label-embedding.tsv",
      "metadataPath": "https://gist.githubusercontent.com/xiaohan2012/93d7d5048eaf0aa67250fdc30e3a6c72/raw/0d0da39df461b2f8a16b7f8955289ed1361d992b/datascience-label-text.tsv",

xiaohan2012

nltk
computer-vision
missing-data
gensim
smote
classification
word2vec
powerbi
rnn
education

xiaohan2012

0.8404189348220825	0.7459937930107117	0.6308125257492065	0.873324990272522	0.9206571578979492	0.8535201549530029	0.8021265268325806	0.9472481608390808	0.8903049230575562	0.9364266395568848	0.8126969933509827	0.6442192196846008	0.9481191039085388	0.7041640877723694	0.9348658323287964	0.5755905508995056	0.40711039304733276	0.27529847621917725	0.9251526594161987	0.9246417284011841	0.7352113723754883	0.01757754199206829	0.6355307698249817	0.972328782081604	0.9226069450378418	0.6047204732894897	0.41323211789131165	0.33520519733428955	0.617024302482605	0.9292117953300476	0.9471990466117859	0.7368165254592896	0.8655673861503601	0.9826233983039856	0.819502592086792	0.8250176906585693	0.8147366642951965	0.6649282574653625	0.6875866055488586	0.6668310165405273	0.88420170545578	0.5418125987052917	0.6760655641555786	0.7673236727714539	0.6285435557365417	0.9396171569824219	0.2997831106185913	0.9628713130950928
0.8436189889907837	0.794937014579773	0.75254225730896	0.7011001110076904	0.9641327857971191	0.7552525401115417	0.

xiaohan2012

import tensorflow as tf

checkpoint_file = 'runs/deepwalk/checkpoints/model-25000'

sess = tf.InteractiveSession()

saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)

embedding_table = sess.graph.get_operation_by_name('embedding/table')

xiaohan2012

CXX=g++
CPPFLAGS=-std=c++11 -O2 -Wall
LINK_OPTS=-shared -Wl,--export-dynamic
COMPILE_OPTS=-fPIC
INCLUDES=-I /usr/include/python3.5/
LIBS=-L /usr/lib/x86_64-linux-gnu/ -l boost_python-py35

%.so: %.o
        $(CXX) $^ $(CPPFLAGS) $(LINK_OPTS) $(LIBS) -o $@