def worker(proc_num, queue):
while True:
time.sleep(random.random() * 10)
try:
year = queue.get(block=False)
except Empty:
print(proc_num, "Finished")
return
positive_seeds, negative_seeds = seeds.adj_seeds()
year = str(year)
print(proc_num, "On year", year)
words = vocab.pos_words(year, "jj")
embed = create_representation("SVD", constants.COHA_EMBEDDINGS + year)
embed_words = set(embed.iw)
words = words.intersection(embed_words)
polarities = polarity_induction_methods.bootstrap(
embed.get_subembed(
words.union(positive_seeds).union(negative_seeds)),
positive_seeds,
negative_seeds,
score_method=polarity_induction_methods.random_walk,
num_boots=50,
n_procs=20,
return_all=True,
beta=0.9,
nn=25)
util.write_pickle(polarities,
constants.POLARITIES + year + '-coha-adj-boot.pkl')
def evaluate_twitter_methods():
np.random.seed(0)
print "Getting evalution words and embeddings.."
gi = lexicons.load_lexicon("inquirer", remove_neutral=False)
lexicon = lexicons.load_lexicon("twitter", remove_neutral=True)
scores = lexicons.load_lexicon("twitter-scores", remove_neutral=True)
sent140 = lexicons.load_lexicon("140-scores", remove_neutral=False)
# padding lexicon with neutral from GI
gi_neut = [word for word in gi if gi[word] == 0]
gi_neut = np.random.choice(
gi_neut,
int((float(len(gi_neut)) / (len(gi) - len(gi_neut)) * len(lexicon))))
for word in gi_neut:
lexicon[word] = 0
positive_seeds, negative_seeds = seeds.twitter_seeds()
embed = create_representation(
"GIGA", constants.TWITTER_EMBEDDINGS,
set(lexicon.keys()).union(positive_seeds).union(negative_seeds))
print len(
(set(positive_seeds).union(negative_seeds)).intersection(embed.iw))
embed_words = set(embed.iw)
s140_words = set(sent140.keys())
eval_words = [
word for word in lexicon
if word in s140_words and not word in positive_seeds
and not word in negative_seeds and word in embed_words
]
print "Evaluating with ", len(eval_words), "out of", len(lexicon)
print "Sentiment 140"
evaluate(sent140, lexicon, eval_words, tau_lexicon=scores)
print
print "SentProp"
polarities = run_method(positive_seeds,
negative_seeds,
embed,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.densify,
lr=0.01,
regularization_strength=0.5,
**DEFAULT_ARGUMENTS)
util.write_pickle(polarities, "twitter-test.pkl")
evaluate(polarities, lexicon, eval_words, tau_lexicon=scores)
print "SentProp"
polarities = run_method(
positive_seeds,
negative_seeds,
embed,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.random_walk,
beta=0.9,
nn=25,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=scores)
def worker(proc_num, queue, iter):
while True:
time.sleep(random.random()*10)
try:
year = queue.get(block=False)
except Empty:
print(proc_num, "Finished")
return
np.random.seed()
positive_seeds, negative_seeds = seeds.hist_seeds()
year = str(year)
print(proc_num, "On year", year)
words = vocab.pos_words(year, "ADJ")
embed = create_representation("SVD", constants.COHA_EMBEDDINGS + year)
print(year, len(words))
embed_words = set(embed.iw)
words = words.intersection(embed_words)
print(year, len(words))
# counts = create_representation("Explicit", constants.COHA_COUNTS + year, normalize=False)
# ppmi = create_representation("Explicit", constants.COHA_PPMI + year)
weight = _make_weight(float(year))
print(year, weight)
embed = embed.get_subembed(words)
test_embed = make_synthetic_data(embed, embed, words, weight, seed_offset=iter)
polarities = evaluate_methods.run_method(positive_seeds, negative_seeds,
test_embed,
method=polarity_induction_methods.random_walk,
beta=0.9, nn=25,
**evaluate_methods.DEFAULT_ARGUMENTS)
util.write_pickle(polarities, constants.POLARITIES + year + '-synth-adj-coha-' + str(iter) + '.pkl')
def run(count_path, out_path, smooth=0, cds=True, normalize=False, neg=1):
counts = create_representation("Explicit", count_path, normalize=False)
old_mat = counts.m
index = counts.wi
smooth = old_mat.sum() * smooth
# getting marginal probs
row_probs = old_mat.sum(1) + smooth
col_probs = old_mat.sum(0) + smooth
if cds:
col_probs = np.power(col_probs, 0.75)
row_probs = row_probs / row_probs.sum()
col_probs = col_probs / col_probs.sum()
# building PPMI matrix
ppmi_mat = make_ppmi_mat(old_mat,
row_probs,
col_probs,
smooth,
neg=neg,
normalize=normalize)
import pyximport
pyximport.install(setup_args={"include_dirs": np.get_include()})
from representations import sparse_io
sparse_io.export_mat_eff(ppmi_mat.row, ppmi_mat.col, ppmi_mat.data,
out_path + ".bin")
util.write_pickle(index, out_path + "-index.pkl")
def worker(proc_num, queue, iter):
while True:
time.sleep(random.random()*10)
try:
year = queue.get(block=False)
except Empty:
print proc_num, "Finished"
return
np.random.seed()
positive_seeds, negative_seeds = seeds.hist_seeds()
year = str(year)
print proc_num, "On year", year
words = vocab.pos_words(year, "ADJ")
embed = create_representation("SVD", constants.COHA_EMBEDDINGS + year)
print year, len(words)
embed_words = set(embed.iw)
words = words.intersection(embed_words)
print year, len(words)
# counts = create_representation("Explicit", constants.COHA_COUNTS + year, normalize=False)
# ppmi = create_representation("Explicit", constants.COHA_PPMI + year)
weight = _make_weight(float(year))
print year, weight
embed = embed.get_subembed(words)
test_embed = make_synthetic_data(embed, embed, words, weight, seed_offset=iter)
polarities = evaluate_methods.run_method(positive_seeds, negative_seeds,
test_embed,
method=polarity_induction_methods.random_walk,
beta=0.9, nn=25,
**evaluate_methods.DEFAULT_ARGUMENTS)
util.write_pickle(polarities, constants.POLARITIES + year + '-synth-adj-coha-' + str(iter) + '.pkl')
def run(subreddit, smooth=0, cds=True, normalize=False, neg=1):
const = get_constants(subreddit)
file_indices = const['INDICES']
file_counts = const['COUNTS']
file_ppmi = const['PPMI']
file_ppmi_index = const['PPMI_INDEX']
counts = create_representation('Explicit',
file_counts,
file_indices,
normalize=False)
old_mat = counts.m
index = counts.wi
smooth = old_mat.sum() * smooth
# getting marginal probs
row_probs = old_mat.sum(1) + smooth
col_probs = old_mat.sum(0) + smooth
if cds:
col_probs = np.power(col_probs, 0.75)
row_probs = row_probs / row_probs.sum()
col_probs = col_probs / col_probs.sum()
# building PPMI matrix
ppmi_mat = make_ppmi_mat(old_mat,
row_probs,
col_probs,
smooth,
neg=neg,
normalize=normalize)
sparse_io.export_mat_eff(ppmi_mat.row, ppmi_mat.col, ppmi_mat.data,
file_ppmi.encode())
util.write_pickle(index, file_ppmi_index)
def run(in_file, out_path, dim=300, keep_words=None):
base_embed = Explicit.load(in_file, normalize=False)
if keep_words != None:
base_embed = base_embed.get_subembed(keep_words)
u, s, v = randomized_svd(base_embed.m, n_components=dim, n_iter=5)
np.save(out_path + "-u.npy", u)
np.save(out_path + "-v.npy", v)
np.save(out_path + "-s.npy", s)
util.write_pickle(base_embed.iw, out_path + "-vocab.pkl")
def run(in_file, out_path, dim=300, keep_words=None):
base_embed = Explicit.load(in_file, normalize=False)
if keep_words != None:
base_embed = base_embed.get_subembed(keep_words)
u, s, v = randomized_svd(base_embed.m, n_components=dim, n_iter=5)
np.save(out_path + "-u.npy", u)
np.save(out_path + "-v.npy", v)
np.save(out_path + "-s.npy", s)
util.write_pickle(base_embed.iw, out_path + "-vocab.pkl")
def evaluate_methods():
"""
Evaluates different methods on standard English.
"""
print "Getting evalution words.."
np.random.seed(0)
lexicon = lexicons.load_lexicon("inquirer", remove_neutral=False)
kuperman = lexicons.load_lexicon("kuperman", remove_neutral=False)
eval_words = set(lexicon.keys())
# load in WordNet lexicon and pad with zeros for missing words
# (since these are implicitly zero for this method)
qwn = lexicons.load_lexicon("qwn-scores")
for word in lexicon:
if not word in qwn:
qwn[word] = 0
positive_seeds, negative_seeds = seeds.hist_seeds()
common_embed = create_representation(
"GIGA", constants.GOOGLE_EMBEDDINGS,
eval_words.union(positive_seeds).union(negative_seeds))
embed_words = set(common_embed.iw)
eval_words = eval_words.intersection(embed_words)
eval_words = [
word for word in eval_words
if not word in positive_seeds and not word in negative_seeds
]
print "Evaluating with ", len(eval_words), "out of", len(lexicon)
# print
# print "WordNet:"
# evaluate(qwn, lexicon, eval_words, tau_lexicon=kuperman)
#
# print "Densifier:"
# polarities = run_method(positive_seeds, negative_seeds,
# common_embed.get_subembed(set(eval_words).union(negative_seeds).union(positive_seeds)),
# method=polarity_induction_methods.bootstrap, score_method=polarity_induction_methods.densify,
# **DEFAULT_ARGUMENTS)
# evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
print "SentProp:"
polarities = run_method(
positive_seeds,
negative_seeds,
common_embed.get_subembed(
set(eval_words).union(negative_seeds).union(positive_seeds)),
method=polarity_induction_methods.label_propagate_probabilistic,
#method=polarity_induction_methods.bootstrap,
beta=0.99,
nn=10,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
util.write_pickle(polarities, "tmp/gi-cc-walk-pols.pkl")
def run(index_path, in_file, out_path, dim=300, keep_words=None):
base_embed = Explicit.load(in_file, index_path, normalize=False)
if keep_words != None:
base_embed = base_embed.get_subembed(keep_words)
print("Factorising {} with shape {}".format(base_embed.m.nnz,
base_embed.m.shape))
u, s, v = randomized_svd(base_embed.m, n_components=dim, n_iter=5)
np.save(out_path + "-u.npy", u)
np.save(out_path + "-v.npy", v)
np.save(out_path + "-s.npy", s)
util.write_pickle(base_embed.iw, out_path + "-vocab.pkl")
def run_sentprop(subreddit,
ppmi_svd_dir,
socialsent_lexicons_dir,
vocab_dir,
topn=5000,
bstrp=False,
nn=25,
beta=0.9):
#program = 'python make_sent_lexicons.py ' + subreddit + " " + ppmi_svd_dir + " " + socialsent_lexicons_dir + " " + vocab_dir
#os.system(program)
#stop_words = set(stopwords.words('english'))
#stop_words.add('<#S#>') #dummy token
fname = os.path.join(vocab_dir, subreddit + '.txt')
with open(fname, 'r') as f:
words = f.readlines()
top_words = [w.split()[0] for w in words][:topn]
pos_seeds, neg_seeds = seeds.twitter_seeds(
) #Twitter seed words (from socialsent package)
vector_file = os.path.join(ppmi_svd_dir, subreddit + '.txt')
embeddings = create_representation(
'GIGA', vector_file,
set(top_words).union(pos_seeds).union(neg_seeds)) # sub_vecs
if bstrp:
polarities = bootstrap(embeddings,
pos_seeds,
neg_seeds,
return_all=True,
nn=nn,
beta=beta,
num_boots=50,
n_procs=10) # NEW
outfile = os.path.join(socialsent_lexicons_dir,
subreddit + '.pkl') # NEW
util.write_pickle(polarities, outfile) # NEW
else:
polarities = random_walk(embeddings,
pos_seeds,
neg_seeds,
beta=beta,
nn=nn,
num_boots=50,
n_procs=10)
sorted_x = sorted(polarities.items(), key=operator.itemgetter(1))
outfile = os.path.join(socialsent_lexicons_dir, subreddit + '.txt')
with open(outfile, 'w') as f:
tsvin = csv.writer(f, delimiter='\t')
for word in sorted_x:
tsvin.writerow(word)
def evaluate_methods():
"""
Evaluates different methods on standard English.
"""
print "Getting evalution words.."
np.random.seed(0)
lexicon = lexicons.load_lexicon("inquirer", remove_neutral=False)
kuperman = lexicons.load_lexicon("kuperman", remove_neutral=False)
eval_words = set(lexicon.keys())
# load in WordNet lexicon and pad with zeros for missing words
# (since these are implicitly zero for this method)
qwn = lexicons.load_lexicon("qwn-scores")
for word in lexicon:
if not word in qwn:
qwn[word] = 0
positive_seeds, negative_seeds = seeds.hist_seeds()
common_embed = create_representation("GIGA", constants.GOOGLE_EMBEDDINGS,
eval_words.union(positive_seeds).union(negative_seeds))
embed_words = set(common_embed.iw)
eval_words = eval_words.intersection(embed_words)
eval_words = [word for word in eval_words
if not word in positive_seeds
and not word in negative_seeds]
print "Evaluating with ", len(eval_words), "out of", len(lexicon)
# print
# print "WordNet:"
# evaluate(qwn, lexicon, eval_words, tau_lexicon=kuperman)
#
# print "Densifier:"
# polarities = run_method(positive_seeds, negative_seeds,
# common_embed.get_subembed(set(eval_words).union(negative_seeds).union(positive_seeds)),
# method=polarity_induction_methods.bootstrap, score_method=polarity_induction_methods.densify,
# **DEFAULT_ARGUMENTS)
# evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
print "SentProp:"
polarities = run_method(positive_seeds, negative_seeds,
common_embed.get_subembed(set(eval_words).union(negative_seeds).union(positive_seeds)),
method=polarity_induction_methods.label_propagate_probabilistic,
#method=polarity_induction_methods.bootstrap,
beta=0.99, nn=10,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
util.write_pickle(polarities, "tmp/gi-cc-walk-pols.pkl")
def evaluate_twitter_methods():
np.random.seed(0)
print "Getting evalution words and embeddings.."
gi = lexicons.load_lexicon("inquirer", remove_neutral=False)
lexicon = lexicons.load_lexicon("twitter", remove_neutral=True)
scores = lexicons.load_lexicon("twitter-scores", remove_neutral=True)
sent140 = lexicons.load_lexicon("140-scores", remove_neutral=False)
# padding lexicon with neutral from GI
gi_neut = [word for word in gi if gi[word] == 0]
gi_neut = np.random.choice(gi_neut, int( (float(len(gi_neut))/(len(gi)-len(gi_neut)) * len(lexicon))))
for word in gi_neut:
lexicon[word] = 0
positive_seeds, negative_seeds = seeds.twitter_seeds()
embed = create_representation("GIGA", constants.TWITTER_EMBEDDINGS, set(lexicon.keys()).union(positive_seeds).union(negative_seeds))
print len((set(positive_seeds).union(negative_seeds)).intersection(embed.iw))
embed_words = set(embed.iw)
s140_words = set(sent140.keys())
eval_words = [word for word in lexicon if word in s140_words and
not word in positive_seeds
and not word in negative_seeds
and word in embed_words]
print "Evaluating with ", len(eval_words), "out of", len(lexicon)
print "Sentiment 140"
evaluate(sent140, lexicon, eval_words, tau_lexicon=scores)
print
print "SentProp"
polarities = run_method(positive_seeds, negative_seeds,
embed,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.densify,
lr=0.01, regularization_strength=0.5,
**DEFAULT_ARGUMENTS)
util.write_pickle(polarities, "twitter-test.pkl")
evaluate(polarities, lexicon, eval_words, tau_lexicon=scores)
print "SentProp"
polarities = run_method(positive_seeds, negative_seeds,
embed,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.random_walk,
beta=0.9, nn=25,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=scores)
def main(subreddit):
out_path = OUT.format(subreddit)
util.mkdir(out_path)
print "Getting and writing dictionary..."
gdict = util.load_pickle(DICTS.format(subreddit))
gdict.filter_extremes(no_above=0.5, no_below=100)
gdict.compactify()
util.write_pickle(gdict.token2id, out_path + "index.pkl")
print "Generating word co-occurrences..."
cooccurgen.run(word_gen(COMMENTS.format(subreddit), gdict), gdict.token2id, 4, out_path + "counts.bin")
print "Generating PPMI vectors..."
ppmigen.run(out_path + "counts.bin", out_path + "ppmi", cds=True)
print "Generating SVD vectors..."
makelowdim.run(out_path + "ppmi.bin", out_path + "vecs")
def main(subreddit):
const = get_constants(subreddit)
word_dict = util.load_pickle(const['DICTS'])
word_dict.filter_extremes(no_above=const['NO_ABOVE_2'],
no_below=const['NO_BELOW'])
to_keep = sorted(word_dict.dfs,
key=lambda w: word_dict.dfs[w],
reverse=True)[:5000]
word_dict.filter_tokens(good_ids=to_keep)
print("Create representation...")
sub_vecs = create_representation('SVD', const['VECS'])
if const["GENDER"]:
pos_seeds, neg_seeds = seeds.gender_seeds()
else:
pos_seeds, neg_seeds = seeds.twitter_seeds()
pos_seeds = list(
set(subredditgen.normalize_text(' '.join(pos_seeds),
const['STEMMING'])))
neg_seeds = list(
set(subredditgen.normalize_text(' '.join(neg_seeds),
const['STEMMING'])))
print("Get sub embedding...")
sub_vecs = sub_vecs.get_subembed(
set(word_dict.token2id.keys()).union(pos_seeds).union(neg_seeds))
print("Bootstrapping...")
print("using seeds {} {}".format(pos_seeds, neg_seeds))
pols = polarity_induction_methods.bootstrap(
sub_vecs,
pos_seeds,
neg_seeds,
return_all=True,
nn=25,
beta=0.9,
boot_size=len(pos_seeds) - 2,
num_boots=30,
n_procs=10,
)
util.write_pickle(pols, const['POLARITIES'])
def run(count_path, out_path, smooth=0, cds=True, normalize=False, neg=1):
counts = create_representation("Explicit", count_path, normalize=False)
old_mat = counts.m
index = counts.wi
smooth = old_mat.sum() * smooth
# getting marginal probs
row_probs = old_mat.sum(1) + smooth
col_probs = old_mat.sum(0) + smooth
if cds:
col_probs = np.power(col_probs, 0.75)
row_probs = row_probs / row_probs.sum()
col_probs = col_probs / col_probs.sum()
# building PPMI matrix
ppmi_mat = make_ppmi_mat(old_mat, row_probs, col_probs, smooth, neg=neg, normalize=normalize)
import pyximport
pyximport.install(setup_args={"include_dirs": np.get_include()})
from representations import sparse_io
sparse_io.export_mat_eff(ppmi_mat.row, ppmi_mat.col, ppmi_mat.data, out_path + ".bin")
util.write_pickle(index, out_path + "-index.pkl")
def main(subreddit):
const = get_constants(subreddit)
if os.path.exists(const['CORPUS']):
print("Loading preexisting corpus...")
corpus = util.load_pickle(const['CORPUS'])
else:
print("Getting and writing dictionary...")
with open(const['OUTPUTS'], "r") as f:
num_lines = sum(1 for line in f)
with open(const['OUTPUTS'], "r") as f:
dicts = (json.loads(comment) for comment in tqdm(f, total=num_lines))
if const["INTERVAL"] is not None:
corpuses = [[] for interval in const["ALL_INTERVALS"]]
for comment in dicts:
i = get_interval_idx(comment["score"])
corpuses[i].append(normalize_text(comment["body"], const['STEMMING']))
for i, interval in enumerate(const["ALL_INTERVALS"]):
util.write_pickle(corpuses[i], get_interval_fname(subreddit, interval))
corpus = corpuses[0]
else:
corpus = [normalize_text(comment["body"], const['STEMMING']) for comment in dicts]
gdict = Dictionary(
corpus
)
gdict.filter_extremes(no_above=const['NO_ABOVE_1'], no_below=const['NO_BELOW'])
gdict.compactify()
util.write_pickle(gdict.token2id, const['INDICES'])
util.write_pickle(gdict, const['DICTS'])
print("Generating word co-occurrences...")
cooccurgen.run(
word_gen(corpus, gdict, subreddit, len(corpus)),
gdict.token2id,
4,
const['COUNTS']
)
print("Generating PPMI vectors...")
ppmigen.run(subreddit, cds=True)
print("Generating SVD vectors...")
makelowdim.run(const['INDICES'], const['PPMI'], const['VECS'])