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gutenaesharedmonk_exp.py
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gutenaesharedmonk_exp.py
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from dataset.DatasetInterfaces import root
from util.embedding import knn, display
from util.cost import nll, hardmax
from util.expr import rect, identity, hardtanh
from util.io import save, load
from util.sparse import idx2spmat, idx2mat, idx2vec
import evaluation
from unsupervised import cae,ae
from supervised import logistic
from jobscheduler import JobmanInterface as JB
from tools.hyperparams import hyperparams
import sampler
import sparse.supervised
import theano
import theano.sparse
import theano.tensor as T
from theano import function
import numpy
import sys,pdb,cPickle
import time
def run(jobman,debug = False):
expstart = time.time()
hp = jobman.state
# Symbolic variables
s_bow = T.matrix()
s_posit = T.matrix()#theano.sparse.csr_matrix()
s_negat = T.matrix()#theano.sparse.csr_matrix()
sentences = cPickle.load(open('/scratch/rifaisal/data/guten/guten_subset_idx.pkl'))
senna = cPickle.load(open('/scratch/rifaisal/data/guten/senna.pkl'))
gsubset = cPickle.load(open('/scratch/rifaisal/data/guten/guten_vocab_subset.pkl')).flatten().tolist()
hashtab = dict( zip( gsubset, range( len( gsubset))))
senna = numpy.array(senna)[gsubset].tolist()
s_valid = theano.sparse.csr_matrix()
validsentence = sentences[-10:]
sentences = sentences[:-10]
nsent = len(sentences)
nsenna = len(senna)
# Layers
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = T.nnet.sigmoid)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = rect, d_act = hardtanh)
L = logistic(i_size = hp['hsize'], h_size = 1)
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = T.nnet.sigmoid)
valid_embedding.params['weights'] = embedding.params['e_weights']
valid_embedding.params['bias'] = embedding.params['e_bias']
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = embedding.encode(s_posit).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = embedding.encode(s_negat).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
valid_embed = valid_embedding.encode(s_valid).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
negat_score = L.encode(H.encode(negat_embed))
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
rec = embedding.reconstruct(s_bow, loss='ce')
CC = (rect(C)).mean() + hp['lambda'] * rec
opt = theano.function([s_posit, s_negat, s_bow],
[C.mean(),rec],
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update(CC,lr)) )
validfct = theano.function([s_valid],valid_score)
def saveexp():
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
print 'Saved successfully'
delta = hp['wsize']/2
rest = hp['wsize']%2
freq_idx = cPickle.load(open('/scratch/rifaisal/data/guten/gutten_sorted_vocab.pkl'))[:1000]
freq_idx = [ hashtab[idx] for idx in freq_idx ]
fname = sys.argv[0]+'_'
for e in range(hp['epoch']):
c = []
r = []
for i in range(nsent):
rsent = numpy.random.randint(nsent-1)
nword = len(sentences[rsent])
if nword < hp['wsize'] + 2:
continue
pidx = numpy.random.randint(low = delta, high = nword-delta)
pchunk = sentences[rsent][pidx-delta:pidx+delta+rest]
nchunk = []
st = sentences[rsent][pidx-delta:pidx]
en = sentences[rsent][pidx+1:pidx+delta+rest]
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for j in range(hp['nneg']):
nchunk += en + [rndidx[j]] + st
assert len(nchunk) == len(pchunk)*hp['nneg']
p, n, b = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna), idx2vec(sentences[rsent],nsenna))
l,g = opt(p,n,b)
c.append(l)
r.append(g)
if (time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (i+1)%(50*hp['freq']) == 0:
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
hp['e'] = e
hp['i'] = i
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
if i%hp['freq'] == 0:
hp['score'] = numpy.array(c).mean()
hp['rec'] = numpy.array(r).mean()
print e,i,'NN Score:', hp['score'], 'Reconstruction:', hp['rec']
ne = knn(freq_idx,embedding.params['e_weights'].get_value(borrow=True))
open('files/'+fname+'nearest.txt','w').write(display(ne,senna))
saveexp()
sys.stdout.flush()
jobman.save()
save()
def jobman_entrypoint(state, channel):
jobhandler = JB.JobHandler(state,channel)
run(jobhandler,False)
return 0
if __name__ == "__main__":
HP_init = [ ('values','dataset',['/data/lisatmp/mullerx/jfk3/amz_dvd_3_files_dict.pkl']),
('values','adjmat',['/data/lisatmp/mesnilgr/datasets/jfkd/adjacency/adjacenty_lil_matrix_indomain_movies.npy']),
('values','epoch',[100]),
('values','freq',[10000]),
('values','hsize',[100]),
('values','embedsize',[50,100,200]),
('values','wsize',[5]),
('values','npos',[1]),
('values','nneg',[10]),
('values','lr',[0.001,.0001,.01]),
('values','lambda',[.01,.001,.0001]),
('values','margin',[1.]),
('values','bs',[10]) ]
hp_sampler = hyperparams(HP_init)
jobparser = JB.JobParser(jobman_entrypoint,hp_sampler)