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 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 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 socialsent3.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 run(in_file, out_path, dim=300, keep_words=None):
base_embed = Explicit.load(in_file, normalize=False)
if keep_words is not 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 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 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("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 evaluation 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)