def test_classify():
from util import io
from data import real_data
loc = "_spns"
ident = "rdc=" + str(0.3) + "_mis=" + str(0.1)
spn, _ = io.load(ident, "titanic", loc)
value_dict = real_data.get_titanic_value_dict()
#spn = fn.marg(spn, keep=[0,1,2,4,5,7])
ranges = np.array(
[[None, NominalRange([1]), None, None, None, None, None, None],
[None, NominalRange([0]), None, None, None, None, None, None],
[None, NominalRange([0]), None, None, None, None, None, None]])
res = fn.classifies(spn, target_id=0, ranges=ranges, value_dict=value_dict)
print(res)
res = fn.classify(spn, target_id=0)
print(res)
df, _ = real_data.get_titanic()
a = {v[1]: v[2] for _, v in value_dict.items() if v[0] == "discrete"}
df = df.replace(a)
preds = fn.classify_dataset(spn,
target_id=0,
df=df,
transform=True,
value_dict=value_dict)
print(preds)
def test_mpe_old():
from util import io
from data import real_data
loc = "_spns"
ident = "rdc=" + str(0.3) + "_mis=" + str(0.1)
spn, _ = io.load(ident, "titanic", loc)
value_dict = real_data.get_titanic_value_dict()
#spn = fn.marg(spn, keep=[0,1,2,4,5,7])
df, _ = real_data.get_titanic()
input_data = df.values
res = fn.mpe_spflow(spn, 0, input_data)
print(res)
df, _ = real_data.get_titanic()
a = {v[1]: v[2] for _, v in value_dict.items() if v[0] == "discrete"}
df = df.replace(a)
preds = fn.classify_dataset(spn,
target_id=0,
df=df,
transform=True,
value_dict=value_dict)
print(preds)
def score(jobman,path):
hp = jobman.state
nsenna = 30000
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = identity)
load(embedding,path+'/embedding.pkl')
words = parse_data()
scores = []
esims = []
msim = []
hsim = []
Em = embedding.params['e_weights'].get_value(borrow=True)
for i,(w1,w2,s) in enumerate(words):
sys.stdout.flush()
w1em = Em[w1]
w2em = Em[w2]
esim = -((w1em - w2em)**2).sum()
esims.append(esim)
hsim.append(s)
print 'Embeddings:',scipy.stats.spearmanr(numpy.array(hsim), numpy.array(esims))[0]
def score(jobman, path):
hp = jobman.state
nsenna = 30000
embedding = cae(i_size=nsenna, h_size=hp["embedsize"], e_act=identity)
load(embedding, path + "/embedding.pkl")
words = parse_data()
scores = []
esims = []
msim = []
hsim = []
Em = embedding.params["e_weights"].get_value(borrow=True)
for i, (w1, w2, s) in enumerate(words):
sys.stdout.flush()
w1em = Em[w1]
w2em = Em[w2]
esim = -((w1em - w2em) ** 2).sum()
esims.append(esim)
hsim.append(s)
print "Embeddings:", scipy.stats.spearmanr(numpy.array(hsim), numpy.array(esims))[0]
def score(jobman, path):
hp = jobman.state
nsenna = 30000
PATH = "/scratch/rifaisal/msrtest/test/"
delta = hp["wsize"] / 2
rest = hp["wsize"] % 2
sent = T.matrix()
embedding = cae(i_size=nsenna, h_size=hp["embedsize"], e_act=identity)
H = ae(i_size=hp["embedsize"] * hp["wsize"], h_size=hp["hsize"], e_act=T.tanh)
L = logistic(i_size=hp["hsize"], h_size=1, act=identity)
load(embedding, path + "/embedding.pkl")
load(H, path + "/hidden.pkl")
load(L, path + "/logistic.pkl")
posit_embed = T.dot(sent, embedding.params["e_weights"]).reshape((1, hp["embedsize"] * hp["wsize"]))
posit_score = H.encode(posit_embed)
scoreit = theano.function([sent], posit_score)
sentences = parse_data()
scores = []
esims = []
msim = []
hsim = []
Em = embedding.params["e_weights"].get_value(borrow=True)
for i, (sc, w1, w2, c1, c2) in enumerate(sentences):
sys.stdout.flush()
c1 = [29999] * 10 + c1 + [29999] * 10
c2 = [29999] * 10 + c2 + [29999] * 10
w1seqs = [c1[10 + idx - delta : 10 + idx + delta + rest] for idx in w1]
w2seqs = [c2[10 + idx - delta : 10 + idx + delta + rest] for idx in w2]
c = []
w1em = Em[c1[10 + w1[0]]]
w2em = Em[c2[10 + w2[0]]]
w1sc = numpy.concatenate([scoreit(idx2mat(w1seqs[0], nsenna)).flatten(), Em[c1[10 + w1[0]]]])
w2sc = numpy.concatenate([scoreit(idx2mat(w2seqs[0], nsenna)).flatten(), Em[c2[10 + w2[0]]]])
metric = L.params["weights"].get_value(borrow=True).flatten()
sim = -(((w1sc - w2sc)) ** 2).sum()
esim = -((w1em - w2em) ** 2).sum()
msim.append(sim)
esims.append(esim)
hsim.append(numpy.mean(sc))
print "Model:", scipy.stats.spearmanr(numpy.array(hsim), numpy.array(msim))[
0
], ", Embeddings:", scipy.stats.spearmanr(numpy.array(hsim), numpy.array(esims))[0]
def evaluate_spn1_method(rdc_thresholds,
min_instances_slices,
min_supports,
dataset_name,
binary_positive=True):
for rdc_threshold in rdc_thresholds:
for min_instances_slice in min_instances_slices:
loc = "_spns"
ident = "rdc=" + str(rdc_threshold) + "_mis=" + str(
min_instances_slice)
spn, const_time = io.load(ident, dataset_name, loc)
for min_support in min_supports:
item_sets, exc_time = methods.spn1(
spn, min_support, binary_positive=binary_positive)
io.save([item_sets, exc_time],
"spn1_" + ident + "_minSup=" + str(min_support),
"freq_sets/" + dataset_name,
loc="_results")
def run(jobman, debug=False):
expstart = time.time()
hp = jobman.state
if not os.path.exists("files/"):
os.mkdir("files/")
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(open("/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl"))
i2w = dict((v, k) for k, v in w2i.iteritems())
i2w[0] = "UNK"
senna = [i2w[i] for i in range(len(i2w.keys()))]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp["embedsize"], e_act=identity)
H = ae(i_size=hp["embedsize"] * hp["wsize"], h_size=hp["hsize"], e_act=T.tanh)
L = logistic(i_size=hp["hsize"], h_size=1, act=identity)
path = hp["loadpath"]
if path:
load(embedding, path + "/embedding.pkl")
load(H, path + "/hidden.pkl")
load(L, path + "/logistic.pkl")
hp["embedsize"] = embedding.params["e_weights"].get_value(borrow=True).shape[1]
hp["hsize"] = H.params["e_weights"].get_value(borrow=True).shape[1]
jobman.save()
del H.params["d_bias"]
del embedding.params["d_bias"]
del embedding.params["e_bias"]
lr = hp["lr"]
h_size = hp["hsize"]
bs = hp["bs"]
posit_embed = theano.sparse.dot(s_valid, embedding.params["e_weights"]).reshape(
(1 + hp["nneg"], hp["embedsize"] * hp["wsize"])
)
# posit_embed = T.dot(s_valid, embedding.params['e_weights']).reshape((1+hp['nneg'],hp['embedsize']*hp['wsize']))
# negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
# negat_score = L.encode(H.encode(negat_embed))
C = posit_score[1:] - posit_score[0] + hp["margin"]
CC = (rect(C)).mean()
opt = theano.function(
[s_valid], (rect(C)).mean(), updates=dict(L.update(CC, lr) + H.update(CC, lr) + embedding.update_norm(CC, lr))
)
def saveexp():
save(embedding, fname + "embedding.pkl")
save(H, fname + "hidden.pkl")
save(L, fname + "logistic.pkl")
delta = hp["wsize"] / 2
rest = hp["wsize"] % 2
wsimtester = wsim()
freq_idx = cPickle.load(open("/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl"))[:1000]
fname = ""
tseenwords = not debug
for e in range(hp["epoch"]):
hp["split"] = numpy.random.randint(285)
sentences = cPickle.load(
open("/mnt/scratch/bengio/bengio_group/data/gutenberg/small_ints_50000/split" + str(hp["split"]) + ".pkl")
)
nsent = len(sentences)
bigc = []
bigr = []
E = embedding.params["e_weights"].get_value(borrow=True)
seen_words = 0
for i, s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp["wsize"] + 2:
continue
c = []
r = []
if debug:
print " *** Processing document", i, "with", nword,
sys.stdout.flush()
for j in range(delta, nword - delta):
cstart = time.time()
pchunk = s[j - delta : j + delta + rest]
nchunk = []
st = s[j - delta : j]
en = s[j + 1 : j + delta + rest]
rndidx = numpy.random.randint(nsenna, size=(hp["nneg"],))
nchunk = []
for kk in range(hp["nneg"]):
nchunk += st + [rndidx[kk]] + en
# assert len(nchunk) == len(pchunk)*hp['nneg']
# p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
pn = idx2spmat(pchunk + nchunk, nsenna)
# assert pn[0,pchunk[0]] == 1
ctime = time.time() - cstart
tstart = time.time()
l = opt(pn)
c.append(l)
if debug:
print ".",
break
if debug:
print ""
bigc += [numpy.array(c).sum()]
if tseenwords > hp["wsimfreq"] or debug:
hp["wsim"] = wsimtester.score(embedding.params["e_weights"].get_value(borrow=True))
print i, "WordSim Sp Corr:", hp["wsim"]
sys.stdout.flush()
hp["score"] = numpy.array(bigc).mean()
hp["e"] = e
hp["i"] = i
print e, i, "NN Score:", hp["score"]
tseenwords = 0
jobman.save()
if seen_words > hp["freq"] or debug:
seen_words = 0
ne = knn(freq_idx, embedding.params["e_weights"].get_value(borrow=True))
open("files/" + fname + "nearest.txt", "w").write(display(ne, i2w))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def run(jobman, debug=False):
hp = jobman.state
# Symbolic variables
s_posit = T.matrix() #theano.sparse.csr_matrix()
s_negat = T.matrix() #theano.sparse.csr_matrix()
s_valid = theano.sparse.csr_matrix()
sentences = cPickle.load(
open('/data/lisatmp2/rifaisal/guten_subset_idx.pkl'))
validsentence = sentences[-10:]
sentences = sentences[:-10]
senna = cPickle.load(open('/data/lisatmp2/rifaisal/senna.pkl'))
gsubset = cPickle.load(
open('/data/lisatmp2/rifaisal/guten_vocab_subset.pkl')).flatten(
).tolist()
hashtab = dict(zip(gsubset, range(len(gsubset))))
senna = numpy.array(senna)[gsubset].tolist()
nsent = len(sentences)
nsenna = len(senna)
# Layers
embedding = logistic(i_size=nsenna, h_size=hp['embedsize'], act=identity)
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) #, act = identity)
valid_embedding = sparse.supervised.logistic(i_size=nsenna,
h_size=hp['embedsize'],
act=identity)
#valid_embedding.params['weights'].set_value(embedding.params['weights'].get_value(borrow=True))
#valid_embedding.params['bias'].set_value(embedding.params['bias'].get_value(borrow=True))
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'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat],
C.mean(),
updates=dict(
L.update(CC, lr) + H.update(CC, lr) +
embedding.update_norm(CC, lr)))
#validfct = theano.function([s_valid],valid_score)
#print 'Random Valid Mean rank',evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
#load(valid_embedding,'files/gutensubsetdense_exp.py_embedding.pkl')
load(embedding, 'files/gutensubsetdense_exp.py_embedding.pkl')
load(H, 'files/gutensubsetdense_exp.py_hidden.pkl')
load(L, 'files/gutensubsetdense_exp.py_logistic.pkl')
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
freq_idx = cPickle.load(
open('/data/lisatmp2/rifaisal/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 = []
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']
#start = time.time()
p, n = (idx2mat(pchunk, nsenna), idx2mat(nchunk, nsenna))
#print 'Select row:',time.time()-start,
#start = time.time()
c.append(opt(p, n))
#print 'grad up:',time.time()-start
if i % hp['freq'] == 0:
print e, i, numpy.array(c).mean(0)
ne = knn(freq_idx,
embedding.params['weights'].get_value(borrow=True))
save(embedding, fname + 'embedding.pkl')
save(H, fname + 'hidden.pkl')
save(L, fname + 'logistic.pkl')
sys.stdout.flush()
open('files/' + fname + 'nearest.txt',
'w').write(display(ne, senna))
#print 'Valid Mean rank',evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
save(embedding, fname + 'embedding.pkl')
save(H, fname + 'hidden.pkl')
save(L, fname + 'logistic.pkl')
def msrerror(vocab, jobman):
hp = jobman.state
nsenna = 30000
PATH = '/scratch/rifaisal/msrtest/test/'
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
sent = T.matrix()
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act=identity)
H = ae(i_size=hp['embedsize'] * hp['wsize'],
h_size=hp['hsize'],
e_act=T.tanh)
L = logistic(i_size=hp['hsize'], h_size=1, act=identity)
path = hp['loadpath']
load(embedding, path + '/embedding.pkl')
load(H, path + '/hidden.pkl')
load(L, path + '/logistic.pkl')
posit_embed = T.dot(sent, embedding.params['e_weights']).reshape(
(1, hp['embedsize'] * hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
fct = theano.function([sent], posit_score)
sentences = idxdataset(vocab)
scores = []
for i, s in enumerate(sentences):
print i,
sys.stdout.flush()
nword = len(s)
if nword < hp['wsize'] + 2:
#print i,'Failure'
s += [29999] * 3
c = []
for j in range(delta, nword - delta):
pchunk = s[j - delta:j + delta + rest]
p = idx2mat(pchunk, nsenna)
l = fct(p)
c.append(l)
if not len(c):
print 'pas bim'
scores.append(0)
else:
scores.append(numpy.mean(c))
#if i%5 == 0:
# print scores[i-5:i]
score_template = open(PATH + 'data/Holmes.lm_format.questions.txt')
score_output = open('energy.lm_output.txt', 'w')
sentencelist = score_template.readlines()
for sc, sentence in zip(scores, sentencelist):
score_output.write(sentence.split('\n')[0] + '\t' + str(sc) + '\n')
score_output.close()
pipebestof5 = subprocess.Popen(
['perl', PATH + 'bestof5.pl', './energy.lm_output.txt'],
stdout=subprocess.PIPE)
energyanswer = open('./energy.answers', 'w')
for line in pipebestof5.stdout:
energyanswer.write(line)
energyanswer.close()
pipescore = subprocess.Popen([
'perl', PATH + 'score.pl', './energy.answers',
PATH + 'data/Holmes.lm_format.answers.txt'
],
stdout=subprocess.PIPE)
legend = ['correct', '%correct', 'valid', 'test']
out = zip(legend,
[r.split('\n')[0] for r in pipescore.stdout.readlines()[-4:]])
res = dict(out)
res = dict((k, float(v)) for k, v in res.iteritems())
print res
print out
help='Dropout applied to decoder outputs and'
' hidden states')
parser.add_argument('--teacher_forcing', type=float, default=1.0,
help='Ratio of decoder`s own predictions and true'
' target values used during training.')
parser.add_argument('--bidirectional', action='store_true', default=False,
help='Set encoder to compute forward and backward'
' hidden states.')
args = parser.parse_args()
if validate_args(args):
lang, datasets = io.load(args.data)
vocab = {
'src': Vocab(lang['vocab']['src']),
'tgt': Vocab(lang['vocab']['tgt']),
'stack': Vocab(lang['vocab']['stack']),
'operator': Vocab(lang['vocab']['operator'])
}
log = Logger(out_path=args.out)
line = log.add_text('')
log.start()
logger = {
'log': log,
'line': line
}
return all_diffs
else:
raise Exception("Unknown attribute-type: " + str(value_info[0]))
if __name__ == '__main__':
from util import io
from data import real_data
loc = "_spns"
ident = "rdc=" + str(0.3) + "_mis=" + str(0.1)
spn, _ = io.load(ident, "titanic", loc)
value_dict = real_data.get_titanic_value_dict()
spn = fn.marg(spn, keep=[0,1,2,3,4,5,6,7])
#spn = example_spns.get_credit_spn()
fis = feature_importance(spn, 1, value_dict=value_dict)
print(fis)
fis = feature_importance_weighted(spn, 1, value_dict=value_dict)
print(fis)
def visualize_stats(dataset_name,
min_supports,
gt_method="apriori1",
other_methods=[
"fpgrowth2", "spn1_rdc=0.3_mis=0.1",
"spn1_rdc=0.3_mis=0.01", "spn1_rdc=0.2_mis=0.1",
"spn1_rdc=0.2_mis=0.01", "spn1_rdc=0.1_mis=0.1",
"spn1_rdc=0.1_mis=0.01"
]):
folder = "freq_sets/" + dataset_name
vis_dict = {
"item_sets": {},
"n_items": {},
"exc_times": {},
"same_items": {},
"same_errors": {},
"missing_items": {},
"missing_probs": {},
"new_items": {},
"new_probs": {}
}
for method in [gt_method] + other_methods:
vis_dict["item_sets"][method] = []
vis_dict["n_items"][method] = []
vis_dict["exc_times"][method] = []
vis_dict["same_items"][method] = []
vis_dict["missing_items"][method] = []
vis_dict["new_items"][method] = []
vis_dict["same_errors"][method] = []
vis_dict["new_probs"][method] = []
vis_dict["missing_probs"][method] = []
for i, min_sup in enumerate(min_supports):
method_itemsets, method_exc_time = io.load(method + "_minSup=" +
str(min_sup),
folder,
loc="_results")
vis_dict["item_sets"][method].append(method_itemsets)
vis_dict["n_items"][method].append(len(method_itemsets))
vis_dict["exc_times"][method].append(method_exc_time)
same_sets, missing_sets, different_sets = __compare_itemsets(
vis_dict["item_sets"][gt_method][i], method_itemsets)
vis_dict["same_items"][method].append(len(same_sets))
vis_dict["missing_items"][method].append(len(missing_sets))
vis_dict["new_items"][method].append(len(different_sets))
vis_dict["same_errors"][method].append(
[s1 - s2 for (_, s1, s2) in same_sets])
vis_dict["new_probs"][method].append(
[sup for (sup, _) in different_sets])
vis_dict["missing_probs"][method].append(
[sup for (sup, _) in missing_sets])
plot_names = list(vis_dict.keys())
plot_names.remove("item_sets")
ncols = 1
nrows = len(plot_names)
figsize_x = 12
figsize_y = 20
_, axes = plt.subplots(nrows,
ncols,
figsize=(figsize_x, figsize_y),
squeeze=False,
sharex=False)
r = np.arange(len(min_supports))
n_methods = 1 + len(other_methods)
barWidth = 1 / (1 + n_methods)
for j, plot_name in enumerate(plot_names):
if plot_name in ["same_errors", "new_probs", "missing_probs"]:
for i, (name, vals) in enumerate(vis_dict[plot_name].items()):
#positions = np.arange(len(method_box_plots[i]))+ (i/(2 + len(method_box_plots))) - (len(method_box_plots)/2)/len(method_box_plots) + 1.5/len(method_box_plots)
axes[j][0].boxplot(vals,
positions=r + i * barWidth,
widths=barWidth)
axes[j][0].set_title(plot_name)
axes[j][0].set_xlabel('min support')
axes[j][0].set_xlim(-0.25, len(min_supports))
axes[j][0].set_xticks(np.arange(0, len(min_supports)))
axes[j][0].set_xticklabels(
[str(min_sup) for min_sup in min_supports])
axes[j][0].legend()
else:
for i, (name, vals) in enumerate(vis_dict[plot_name].items()):
axes[j][0].bar(r + i * barWidth,
vals,
width=barWidth,
label=name)
axes[j][0].set_title(plot_name)
axes[j][0].set_xlabel('min support')
if plot_name == "exc_times":
axes[j][0].set_yscale('log')
axes[j][0].set_xlim(-0.25, len(min_supports))
axes[j][0].set_xticks(np.arange(0, len(min_supports)))
axes[j][0].set_xticklabels(
[str(min_sup) for min_sup in min_supports])
axes[j][0].legend()
plt.tight_layout()
plt.savefig("stats.pdf")
plt.show()
def run(jobman,debug = False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
idx_start = T.lscalar()
idx_stop = T.lscalar()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl'))
i2w = dict( (v,k) for k,v in w2i.iteritems() )
i2w[0] = 'UNK'
senna = [ i2w[i] for i in range(len(i2w.keys())) ]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = identity)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = T.tanh)
L = logistic(i_size = hp['hsize'], h_size = 1, act = identity)
del H.params['d_bias']
del embedding.params['d_bias']
del embedding.params['e_bias']
minsize = hp['minsize']
maxsize = hp['maxsize']
dsize = maxsize - minsize +1
H.params['e_bias'] = theano.shared( numpy.array(numpy.zeros((dsize,hp['hsize'])),dtype=theano.config.floatX),name='e_bias')
path = hp['loadpath']
if path:
load(embedding,path+'/embedding.pkl')
#load(H,path+'/hidden.pkl')
#load(L,path+'/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(borrow=True).shape[1]
#hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
H.params['e_bias'] = theano.shared( numpy.array(numpy.zeros((dsize,hp['hsize'])),dtype=theano.config.floatX),name='e_bias')
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
valid_embed = sp.dot(s_valid,valid_embedding.params['weights']).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_embed_left = T.concatenate([posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(posit_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
negat_embed_left = T.concatenate([negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(negat_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
posit_embed_right = T.concatenate([ T.zeros_like(posit_embed[:,:idx_start*hp['embedsize']]),
posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']]],axis=1)
negat_embed_right = T.concatenate([ T.zeros_like(negat_embed[:,:idx_start*hp['embedsize']]),
negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']]],axis=1)
posit_embed = T.concatenate([ T.zeros_like(posit_embed[:,:idx_start*hp['embedsize']]),
posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(posit_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
negat_embed = T.concatenate([ T.zeros_like(negat_embed[:,:idx_start*hp['embedsize']]),
negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(negat_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
#posit_embed = ifelse(T.eq(idx_start, 0), posit_embed_left, posit_embed)
#posit_embed = ifelse(T.eq(idx_stop, hp['maxsize']), posit_embed_right, posit_embed)
#negat_embed = ifelse(T.eq(idx_start, 0), negat_embed_left, negat_embed)
#negat_embed = ifelse(T.eq(idx_stop, hp['maxsize']), negat_embed_right, negat_embed)
Hposit = T.tanh(T.dot(posit_embed,H.params['e_weights']) + H.params['e_bias'][idx_stop-idx_start-minsize,:])
Hnegat = T.tanh(T.dot(negat_embed,H.params['e_weights']) + H.params['e_bias'][idx_stop-idx_start-minsize,:])
posit_score = L.encode(Hposit)
negat_score = L.encode(Hnegat)
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat, idx_start, idx_stop],
(rect(C)).mean(),
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update_norm(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')
delta = hp['wsize']/2
rest = hp['wsize']%2
freq_idx = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl'))[:2000]
fname = ''
validsentence = []# cPickle.load(open('/scratch/rifaisal/data/wiki_april_2010/valid_debug.pkl'))
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(45)
sentences = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/ints_50000/split'+str(hp['split'])+'.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
seen_words = 0
for i,s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['maxsize'] + 2:
continue
rndsize = numpy.random.randint(low=hp['minsize']+1,high=hp['maxsize']-1)
idxsta = numpy.random.randint(low=1, high=hp['maxsize']-rndsize)
idxsto = idxsta+rndsize
print 'r',rndsize,'b',idxsta,'e',idxsto,'shape',H.params['e_bias'].get_value().shape
c =[]
r =[]
if debug:
print ' *** Processing document',i,'with',nword,
sys.stdout.flush()
for j in range(delta,nword-delta):
nd = rndsize/2
rd = rndsize%2
pchunk = s[j-delta:j+delta+rest]
nchunk = []
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for kk in range(hp['nneg']):
tmpchunk = copy.copy(pchunk)
tmpchunk[idxsta+nd] = rndidx[kk]
nchunk += tmpchunk
assert len(nchunk) == len(pchunk)*hp['nneg']
p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
l = opt(p,n, idxsta, idxsto)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if 0:#(time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (tseenwords)>(10*hp['freq']):
tseenwords = 0
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
if seen_words > hp['freq'] or debug:
seen_words = 0
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print ''
print e,i,'NN Score:', hp['score']
if not debug:
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()
saveexp()
def run(jobman, debug=False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
idx_start = T.lscalar()
idx_stop = T.lscalar()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(
open(
'/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl'
))
i2w = dict((v, k) for k, v in w2i.iteritems())
i2w[0] = 'UNK'
senna = [i2w[i] for i in range(len(i2w.keys()))]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act=identity)
H = ae(i_size=hp['embedsize'] * hp['wsize'],
h_size=hp['hsize'],
e_act=T.tanh)
L = logistic(i_size=hp['hsize'], h_size=1, act=identity)
del H.params['d_bias']
del embedding.params['d_bias']
del embedding.params['e_bias']
minsize = hp['minsize']
maxsize = hp['maxsize']
dsize = maxsize - minsize + 1
H.params['e_bias'] = theano.shared(numpy.array(numpy.zeros(
(dsize, hp['hsize'])),
dtype=theano.config.floatX),
name='e_bias')
path = hp['loadpath']
if path:
load(embedding, path + '/embedding.pkl')
#load(H,path+'/hidden.pkl')
#load(L,path+'/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(
borrow=True).shape[1]
#hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
H.params['e_bias'] = theano.shared(numpy.array(numpy.zeros(
(dsize, hp['hsize'])),
dtype=theano.config.floatX),
name='e_bias')
valid_embedding = sparse.supervised.logistic(i_size=nsenna,
h_size=hp['embedsize'],
act=identity)
valid_embedding.params['weights'] = sp.shared(
value=scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(
borrow=True)))
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape(
(1, hp['embedsize'] * hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape(
(hp['nneg'], hp['embedsize'] * hp['wsize']))
valid_embed = sp.dot(s_valid, valid_embedding.params['weights']).reshape(
(nsenna, hp['embedsize'] * hp['wsize']))
posit_embed_left = T.concatenate([
posit_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']],
T.zeros_like(posit_embed[:, idx_stop * hp['embedsize']:])
],
axis=1)
negat_embed_left = T.concatenate([
negat_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']],
T.zeros_like(negat_embed[:, idx_stop * hp['embedsize']:])
],
axis=1)
posit_embed_right = T.concatenate([
T.zeros_like(posit_embed[:, :idx_start * hp['embedsize']]),
posit_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']]
],
axis=1)
negat_embed_right = T.concatenate([
T.zeros_like(negat_embed[:, :idx_start * hp['embedsize']]),
negat_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']]
],
axis=1)
posit_embed = T.concatenate([
T.zeros_like(posit_embed[:, :idx_start * hp['embedsize']]),
posit_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']],
T.zeros_like(posit_embed[:, idx_stop * hp['embedsize']:])
],
axis=1)
negat_embed = T.concatenate([
T.zeros_like(negat_embed[:, :idx_start * hp['embedsize']]),
negat_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']],
T.zeros_like(negat_embed[:, idx_stop * hp['embedsize']:])
],
axis=1)
#posit_embed = ifelse(T.eq(idx_start, 0), posit_embed_left, posit_embed)
#posit_embed = ifelse(T.eq(idx_stop, hp['maxsize']), posit_embed_right, posit_embed)
#negat_embed = ifelse(T.eq(idx_start, 0), negat_embed_left, negat_embed)
#negat_embed = ifelse(T.eq(idx_stop, hp['maxsize']), negat_embed_right, negat_embed)
Hposit = T.tanh(
T.dot(posit_embed, H.params['e_weights']) +
H.params['e_bias'][idx_stop - idx_start - minsize, :])
Hnegat = T.tanh(
T.dot(negat_embed, H.params['e_weights']) +
H.params['e_bias'][idx_stop - idx_start - minsize, :])
posit_score = L.encode(Hposit)
negat_score = L.encode(Hnegat)
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat, idx_start, idx_stop],
(rect(C)).mean(),
updates=dict(
L.update(CC, lr) + H.update(CC, lr) +
embedding.update_norm(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')
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
freq_idx = cPickle.load(
open('/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl'
))[:2000]
fname = ''
validsentence = [
] # cPickle.load(open('/scratch/rifaisal/data/wiki_april_2010/valid_debug.pkl'))
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(45)
sentences = cPickle.load(
open(
'/mnt/scratch/bengio/bengio_group/data/gutenberg/ints_50000/split'
+ str(hp['split']) + '.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
seen_words = 0
for i, s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['maxsize'] + 2:
continue
rndsize = numpy.random.randint(low=hp['minsize'] + 1,
high=hp['maxsize'] - 1)
idxsta = numpy.random.randint(low=1, high=hp['maxsize'] - rndsize)
idxsto = idxsta + rndsize
print 'r', rndsize, 'b', idxsta, 'e', idxsto, 'shape', H.params[
'e_bias'].get_value().shape
c = []
r = []
if debug:
print ' *** Processing document', i, 'with', nword,
sys.stdout.flush()
for j in range(delta, nword - delta):
nd = rndsize / 2
rd = rndsize % 2
pchunk = s[j - delta:j + delta + rest]
nchunk = []
rndidx = numpy.random.randint(nsenna, size=(hp['nneg'], ))
nchunk = []
for kk in range(hp['nneg']):
tmpchunk = copy.copy(pchunk)
tmpchunk[idxsta + nd] = rndidx[kk]
nchunk += tmpchunk
assert len(nchunk) == len(pchunk) * hp['nneg']
p, n = (idx2mat(pchunk, nsenna), idx2mat(nchunk, nsenna))
l = opt(p, n, idxsta, idxsto)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if 0: #(time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (tseenwords)>(10*hp['freq']):
tseenwords = 0
valid_embedding.params['weights'] = sp.shared(
value=scipy.sparse.csr_matrix(
embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna,
hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank', mrk
if seen_words > hp['freq'] or debug:
seen_words = 0
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print ''
print e, i, 'NN Score:', hp['score']
if not debug:
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()
saveexp()
raise Exception(
"Not implemented for other than discrete or numeric ...: " +
str(value_dict[target_id][0]))
return conds, labels
if __name__ == '__main__':
from simple_spn.example import example_spns
from util import io
spn = example_spns.get_gender_spn()
loc = "_spns"
ident = "rdc=" + str(0.3) + "_mis=" + str(0.1)
spn, value_dict, _ = io.load(ident, "titanic", loc)
spn = fn.marg(spn, keep=[0, 1, 2, 3, 6])
#visualize_sub_populations(spn)
#visualize_overall_distribution(spn, value_dict=value_dict, save_path="overall_visualization.pdf")
#visualize_target_based_overall_distribution_single(spn, 0, value_dict=value_dict, save_path="overall_visualization_target_based.pdf")
#visualize_target_based_overall_distribution_compact(spn, 0, value_dict=value_dict, save_path="overall_visualization_target_based_compact.pdf")
visualize_expected_sub_populations(spn,
value_dict=value_dict,
save_path="expectation_line_plot.pdf")
visualized_target_based_expected_sub_populations(
spn,
0,
value_dict=value_dict,
save_path="target_based_expectation_line_plot.pdf")
pass
def run(jobman,debug = False):
hp = jobman.state
# Symbolic variables
s_posit = T.matrix()#theano.sparse.csr_matrix()
s_negat = T.matrix()#theano.sparse.csr_matrix()
s_valid = theano.sparse.csr_matrix()
sentences = cPickle.load(open('/data/lisatmp2/rifaisal/guten_subset_idx.pkl'))
validsentence = sentences[-10:]
sentences = sentences[:-10]
senna = cPickle.load(open('/data/lisatmp2/rifaisal/senna.pkl'))
gsubset = cPickle.load(open('/data/lisatmp2/rifaisal/guten_vocab_subset.pkl')).flatten().tolist()
hashtab = dict( zip( gsubset, range( len( gsubset))))
senna = numpy.array(senna)[gsubset].tolist()
nsent = len(sentences)
nsenna = len(senna)
# Layers
embedding = logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
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)#, act = identity)
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
#valid_embedding.params['weights'].set_value(embedding.params['weights'].get_value(borrow=True))
#valid_embedding.params['bias'].set_value(embedding.params['bias'].get_value(borrow=True))
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'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat],
C.mean(),
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update_norm(CC,lr)) )
#validfct = theano.function([s_valid],valid_score)
#print 'Random Valid Mean rank',evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
#load(valid_embedding,'files/gutensubsetdense_exp.py_embedding.pkl')
load(embedding,'files/gutensubsetdense_exp.py_embedding.pkl')
load(H,'files/gutensubsetdense_exp.py_hidden.pkl')
load(L,'files/gutensubsetdense_exp.py_logistic.pkl')
delta = hp['wsize']/2
rest = hp['wsize']%2
freq_idx = cPickle.load(open('/data/lisatmp2/rifaisal/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 = []
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']
#start = time.time()
p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
#print 'Select row:',time.time()-start,
#start = time.time()
c.append(opt(p,n))
#print 'grad up:',time.time()-start
if i%hp['freq'] == 0:
print e,i, numpy.array(c).mean(0)
ne = knn(freq_idx,embedding.params['weights'].get_value(borrow=True))
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
sys.stdout.flush()
open('files/'+fname+'nearest.txt','w').write(display(ne,senna))
#print 'Valid Mean rank',evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
def load_spn(dataset_name, rdc_threshold, min_instances_slice, nrows=None):
fname = "rdc=" + str(rdc_threshold) + "_mis=" + str(min_instances_slice)
if nrows:
fname = fname + '_n=' + np.format_float_scientific(
nrows, precision=0, trim='-')
return io.load(fname, dataset_name, "_spns")
item_dataset, parametric_types = real_data.get_T10I4D(num_features, 100000)
item_dataset[0][9] = 1
dataset_name = "T10I4D_20"
'''
Create SPNs if necessary
'''
#rdc_thresholds = [0.1]
#min_instances_slices = [0.01]
#learn_SPN.create_parametric_spns(item_dataset, parametric_types, rdc_thresholds, min_instances_slices, folder=dataset_name)
print("fdsafsdfasd")
rdc_threshold = 0.1
min_instances_slice = 0.01
loc = "_spns"
ident = "rdc=" + str(rdc_threshold) + "_mis=" + str(min_instances_slice)
spn, const_time = io.load(ident, dataset_name, loc)
print(spn)
print(const_time)
fn.print_statistics(spn)
t0 = time.time()
res = naive_approach(spn, min_support=0.0001)
print(time.time() - t0)
#print("Number item-sets naive approach: " + str(len(res)))
t0 = time.time()
res = min_sub_population(spn, min_support=0.0001)
print(time.time() - t0)
print("Number item-sets min sub population approach: " + str(len(res)))
def msrerror(vocab,jobman):
hp = jobman.state
nsenna = 30000
PATH = '/scratch/rifaisal/msrtest/test/'
delta = hp['wsize']/2
rest = hp['wsize']%2
sent = T.matrix()
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = identity)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = T.tanh)
L = logistic(i_size = hp['hsize'], h_size = 1, act = identity)
path = hp['loadpath']
load(embedding,path+'/embedding.pkl')
load(H,path+'/hidden.pkl')
load(L,path+'/logistic.pkl')
posit_embed = T.dot(sent, embedding.params['e_weights']).reshape((1,hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
fct = theano.function([sent],posit_score)
sentences = idxdataset(vocab)
scores = []
for i,s in enumerate(sentences):
print i,
sys.stdout.flush()
nword = len(s)
if nword < hp['wsize'] + 2:
#print i,'Failure'
s += [29999]*3
c =[]
for j in range(delta,nword-delta):
pchunk = s[j-delta:j+delta+rest]
p = idx2mat(pchunk,nsenna)
l = fct(p)
c.append(l)
if not len(c):
print 'pas bim'
scores.append(0)
else:
scores.append(numpy.mean(c))
#if i%5 == 0:
# print scores[i-5:i]
score_template = open(PATH+'data/Holmes.lm_format.questions.txt')
score_output = open('energy.lm_output.txt','w')
sentencelist = score_template.readlines()
for sc,sentence in zip(scores, sentencelist):
score_output.write(sentence.split('\n')[0]+'\t'+str(sc)+'\n')
score_output.close()
pipebestof5 = subprocess.Popen(['perl', PATH+'bestof5.pl','./energy.lm_output.txt'],stdout=subprocess.PIPE)
energyanswer = open('./energy.answers','w')
for line in pipebestof5.stdout: energyanswer.write(line)
energyanswer.close()
pipescore = subprocess.Popen(['perl', PATH+'score.pl','./energy.answers',PATH+'data/Holmes.lm_format.answers.txt'],stdout=subprocess.PIPE)
legend = ['correct','%correct','valid','test']
out = zip(legend,[ r.split('\n')[0] for r in pipescore.stdout.readlines()[-4:] ])
res = dict(out)
res = dict( (k,float(v)) for k,v in res.iteritems())
print res
print out
def run(jobman, debug=False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(
open(
'/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl'
))
i2w = dict((v, k) for k, v in w2i.iteritems())
i2w[0] = 'UNK'
senna = [i2w[i] for i in range(len(i2w.keys()))]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act=identity)
H = ae(i_size=hp['embedsize'] * hp['wsize'],
h_size=hp['hsize'],
e_act=T.tanh)
L = logistic(i_size=hp['hsize'], h_size=1, act=identity)
path = hp['loadpath']
if path:
load(embedding, path + '/embedding.pkl')
load(H, path + '/hidden.pkl')
load(L, path + '/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(
borrow=True).shape[1]
hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
del H.params['d_bias']
del embedding.params['d_bias']
del embedding.params['e_bias']
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = theano.sparse.dot(s_valid,
embedding.params['e_weights']).reshape(
(1 + hp['nneg'],
hp['embedsize'] * hp['wsize']))
#posit_embed = T.dot(s_valid, embedding.params['e_weights']).reshape((1+hp['nneg'],hp['embedsize']*hp['wsize']))
#negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
#negat_score = L.encode(H.encode(negat_embed))
C = (posit_score[1:] - posit_score[0] + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_valid], (rect(C)).mean(),
updates=dict(
L.update(CC, lr) + H.update(CC, lr) +
embedding.update_norm(CC, lr)))
def saveexp():
save(embedding, fname + 'embedding.pkl')
save(H, fname + 'hidden.pkl')
save(L, fname + 'logistic.pkl')
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
wsimtester = wsim()
freq_idx = cPickle.load(
open('/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl'
))[:1000]
fname = ''
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(285)
sentences = cPickle.load(
open(
'/mnt/scratch/bengio/bengio_group/data/gutenberg/small_ints_50000/split'
+ str(hp['split']) + '.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
E = embedding.params['e_weights'].get_value(borrow=True)
seen_words = 0
for i, s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['wsize'] + 2:
continue
c = []
r = []
if debug:
print ' *** Processing document', i, 'with', nword,
sys.stdout.flush()
for j in range(delta, nword - delta):
cstart = time.time()
pchunk = s[j - delta:j + delta + rest]
nchunk = []
st = s[j - delta:j]
en = s[j + 1:j + delta + rest]
rndidx = numpy.random.randint(nsenna, size=(hp['nneg'], ))
nchunk = []
for kk in range(hp['nneg']):
nchunk += st + [rndidx[kk]] + en
#assert len(nchunk) == len(pchunk)*hp['nneg']
#p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
pn = idx2spmat(pchunk + nchunk, nsenna)
#assert pn[0,pchunk[0]] == 1
ctime = time.time() - cstart
tstart = time.time()
l = opt(pn)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if tseenwords > hp['wsimfreq'] or debug:
hp['wsim'] = wsimtester.score(
embedding.params['e_weights'].get_value(borrow=True))
print i, 'WordSim Sp Corr:', hp['wsim']
sys.stdout.flush()
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print e, i, 'NN Score:', hp['score']
tseenwords = 0
jobman.save()
if seen_words > hp['freq'] or debug:
seen_words = 0
ne = knn(freq_idx,
embedding.params['e_weights'].get_value(borrow=True))
open('files/' + fname + 'nearest.txt',
'w').write(display(ne, i2w))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def demo_visualize_density():
#data, parametric_types = real_data.get_p_value_dataset()
#learn_SPN.create_parametric_spns(data, parametric_types, [0.3], [0.01], folder="p_value_test")
loc = "_spns"
ident = "rdc=" + str(0.3) + "_mis=" + str(0.01)
spn, _ = io.load(ident, "p_value_test", loc)
value_dict = real_data.get_p_value_test_value_dict()
rang = None
save_path = os.path.dirname(os.path.realpath(
__file__)) + "/../../../_plots/interpretability/blackbox/density1.pdf"
visualize_density(spn,
value_dict,
rang=rang,
max_density=10,
save_path=save_path)
rang = [None] * 5 + [NominalRange([0])]
save_path = os.path.dirname(os.path.realpath(
__file__)) + "/../../../_plots/interpretability/blackbox/density2.pdf"
visualize_density(spn,
value_dict,
rang=rang,
max_density=10,
save_path=save_path)
rang = None
save_path = os.path.dirname(os.path.realpath(
__file__)) + "/../../../_plots/interpretability/blackbox/density3.pdf"
visualize_density_target(spn,
5,
value_dict,
rang=rang,
max_density=10,
save_path=save_path)
loc = "_spns"
ident = "rdc=" + str(0.3) + "_mis=" + str(0.01)
spn, _ = io.load(ident, "titanic", loc)
value_dict = real_data.get_titanic_value_dict()
rang = None
save_path = os.path.dirname(os.path.realpath(
__file__)) + "/../../../_plots/interpretability/blackbox/density5.pdf"
visualize_density(spn, value_dict, max_density=0.1, save_path=save_path)
rang = None
save_path = os.path.dirname(os.path.realpath(
__file__)) + "/../../../_plots/interpretability/blackbox/density6.pdf"
visualize_density_target(spn,
0,
value_dict,
max_density=0.1,
save_path=save_path)
rang = None
save_path = os.path.dirname(os.path.realpath(
__file__)) + "/../../../_plots/interpretability/blackbox/density7.pdf"
visualize_density_target(spn,
2,
value_dict,
max_density=0.1,
save_path=save_path)
rang = [None] * 2 + [NominalRange([0])] + [None] * 5
save_path = os.path.dirname(os.path.realpath(
__file__)) + "/../../../_plots/interpretability/blackbox/density8.pdf"
visualize_density_target(spn,
0,
value_dict,
max_density=0.1,
save_path=save_path)
def score(jobman, path):
hp = jobman.state
nsenna = 30000
PATH = '/scratch/rifaisal/msrtest/test/'
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
sent = T.matrix()
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act=identity)
H = ae(i_size=hp['embedsize'] * hp['wsize'],
h_size=hp['hsize'],
e_act=T.tanh)
L = logistic(i_size=hp['hsize'], h_size=1, act=identity)
load(embedding, path + '/embedding.pkl')
load(H, path + '/hidden.pkl')
load(L, path + '/logistic.pkl')
posit_embed = T.dot(sent, embedding.params['e_weights']).reshape(
(1, hp['embedsize'] * hp['wsize']))
posit_score = H.encode(posit_embed)
scoreit = theano.function([sent], posit_score)
sentences = parse_data()
scores = []
esims = []
msim = []
hsim = []
Em = embedding.params['e_weights'].get_value(borrow=True)
for i, (sc, w1, w2, c1, c2) in enumerate(sentences):
sys.stdout.flush()
c1 = [29999] * 10 + c1 + [29999] * 10
c2 = [29999] * 10 + c2 + [29999] * 10
w1seqs = [c1[10 + idx - delta:10 + idx + delta + rest] for idx in w1]
w2seqs = [c2[10 + idx - delta:10 + idx + delta + rest] for idx in w2]
c = []
w1em = Em[c1[10 + w1[0]]]
w2em = Em[c2[10 + w2[0]]]
w1sc = numpy.concatenate([
scoreit(idx2mat(w1seqs[0], nsenna)).flatten(), Em[c1[10 + w1[0]]]
])
w2sc = numpy.concatenate([
scoreit(idx2mat(w2seqs[0], nsenna)).flatten(), Em[c2[10 + w2[0]]]
])
metric = L.params['weights'].get_value(borrow=True).flatten()
sim = -(((w1sc - w2sc))**2).sum()
esim = -((w1em - w2em)**2).sum()
msim.append(sim)
esims.append(esim)
hsim.append(numpy.mean(sc))
print 'Model:', scipy.stats.spearmanr(
numpy.array(hsim),
numpy.array(msim))[0], ', Embeddings:', scipy.stats.spearmanr(
numpy.array(hsim), numpy.array(esims))[0]
def run(jobman,debug = False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
s_valid = theano.sparse.csr_matrix()
#vocab = cPickle.load(open('/scratch/rifaisal/data/guten/senna.pkl'))
#senna = cPickle.load(open('/scratch/rifaisal/data/wiki_april_2010/WestburyLab.wikicorp.201004_vocab30k.pkl'))
w2i = cPickle.load(open('/scratch/rifaisal/data/gutenberg_aistats/merged_word2idx.pkl'))
i2w = dict( (v,k) for k,v in w2i.iteritems() )
i2w[0] = 'UNK'
senna = [ i2w[i] for i in range(len(i2w.keys())) ]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = identity)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = T.tanh)
L = logistic(i_size = hp['hsize'], h_size = 1, act = identity)
path = hp['loadpath']
if path:
load(embedding,path+'/embedding.pkl')
load(H,path+'/hidden.pkl')
load(L,path+'/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(borrow=True).shape[1]
hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
valid_embedding.params['bias'] = embedding.params['e_bias']
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
valid_embed = sp.dot(s_valid,valid_embedding.params['weights']).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'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat],
(rect(C)).mean(),
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update_norm(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')
delta = hp['wsize']/2
rest = hp['wsize']%2
#freq_idx = range(29000,30000)
freq_idx = cPickle.load(open('/scratch/rifaisal/data/gutenberg_aistats/sorted_vocab.pkl'))[:2000]
fname = ''
#validsentence = cPickle.load(open('/scratch/rifaisal/data/gutenberg_aistats/valid.pkl'))
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(45)
sentences = cPickle.load(open('/scratch/rifaisal/data/gutenberg_aistats/split'+str(hp['split'])+'.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
seen_words = 0
for i,s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['wsize'] + 2:
continue
c =[]
r =[]
if debug:
print ' *** Processing document',i,'with',nword,
sys.stdout.flush()
for j in range(delta,nword-delta):
pchunk = s[j-delta:j+delta+rest]
nchunk = []
st = s[j-delta:j]
en = s[j+1:j+delta+rest]
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for kk in range(hp['nneg']):
nchunk += st + [rndidx[kk]] + en
assert len(nchunk) == len(pchunk)*hp['nneg']
p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
l = opt(p,n)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if 0:#(time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (tseenwords)>(10*hp['freq']):
tseenwords = 0
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
if seen_words > hp['freq'] or debug:
seen_words = 0
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print ''
print e,i,'NN Score:', hp['score']
if not debug:
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()
saveexp()
