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 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 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 main(args):
print('Loading data...')
train_sents, train_labels = [], []
with open(args.TRAIN, 'rt') as f:
lines = f.readlines()
for l in lines:
cols = l.split('\t')
train_labels.append(1 if cols[0] == 'Positive' else 0)
train_sents.append(cols[1].strip())
test_sents, test_labels = [], []
with open(args.TEST, 'rt') as f:
lines = f.readlines()
for l in lines:
cols = l.split('\t')
test_labels.append(1 if cols[0] == 'Positive' else 0)
test_sents.append(cols[1].strip())
pos_seeds, neg_seeds = seeds.review_seeds()
print('Creating word vectors...')
embeddings = create_representation("FULL", args.EMBED, 100, limit=30000)
print('Calculating polarities...')
polarities = random_walk(embeddings,
pos_seeds,
neg_seeds,
beta=0.99,
nn=10,
sym=True,
arccos=True)
print('Storing polarities...')
dict2csv(polarities, path='./data/polarities/default.csv')
print('Creating training sentence representations...')
train_reps = []
for i, sent in enumerate(train_sents):
print('\t%d/%d' % (i + 1, len(train_sents)), end='\r')
rep = sent2rep(sent, polarities)
train_reps.append(rep)
print()
print('Creating testing sentence representations...')
test_reps = []
for i, sent in enumerate(test_sents):
print('\t%d/%d' % (i + 1, len(test_sents)), end='\r')
rep = sent2rep(sent, polarities)
test_reps.append(rep)
print()
km = KMeans(n_clusters=2, verbose=1, max_iter=10000)
train_preds = km.fit_predict(train_reps)
test_preds = km.predict(test_reps)
print('\nMetrics on train set:')
evaluate(train_labels, train_preds)
print('\nMetrics on test set:')
evaluate(test_labels, test_preds)
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")
from socialsent3 import seeds
from socialsent3 import lexicons
from socialsent3.polarity_induction_methods import random_walk
from socialsent3.evaluate_methods import binary_metrics
from socialsent3.representations.representation_factory import create_representation
if __name__ == "__main__":
# print("Evaluting SentProp with 100 dimensional GloVe embeddings")
print("Evaluting SentProp with 300 dimensional fastText embeddings")
print("Evaluting only binary classification performance on General Inquirer lexicon")
lexicon = lexicons.load_lexicon("inquirer", remove_neutral=True)
pos_seeds, neg_seeds = seeds.hist_seeds()
# embeddings = create_representation("GIGA", "socialsent3/data/example_embeddings/glove.6B.100d.txt",
# set(lexicon.keys()).union(pos_seeds).union(neg_seeds))
embeddings = create_representation("GIGA", "socialsent3/data/example_embeddings/imdb.en.vec",
set(lexicon.keys()).union(pos_seeds).union(neg_seeds))
eval_words = [word for word in embeddings.iw
if word not in pos_seeds
and word not in neg_seeds]
# Using SentProp with 10 neighbors and beta=0.99
polarities = random_walk(embeddings, pos_seeds, neg_seeds, beta=0.99, nn=10,
sym=True, arccos=True)
acc, auc, avg_per = binary_metrics(polarities, lexicon, eval_words)
print("Accuracy with best threshold: {:0.2f}".format(acc))
print("ROC AUC: {:0.2f}".format(auc))
print("Average precision score: {:0.2f}".format(avg_per))
def hyperparam_eval():
print("Getting evaluation words and embeddings")
lexicon = lexicons.load_lexicon("bingliu", remove_neutral=False)
eval_words = set(lexicon.keys())
positive_seeds, negative_seeds = seeds.hist_seeds()
common_embed = create_representation(
"GIGA", constants.COMMON_EMBEDDINGS,
eval_words.union(positive_seeds).union(negative_seeds))
common_words = set(common_embed.iw)
eval_words = eval_words.intersection(common_words)
hist_embed = create_representation("SVD",
constants.SVD_EMBEDDINGS + "1990")
hist_words = set(hist_embed.iw)
eval_words = eval_words.intersection(hist_words)
eval_words = [
word for word in eval_words
if not word in positive_seeds and not word in negative_seeds
]
print("SentProp...")
for nn in [5, 10, 25, 50]:
for beta in [0.8, 0.9, 0.95, 0.99]:
print("Common")
polarities = run_method(
positive_seeds,
negative_seeds,
common_embed.get_subembed(
set(eval_words).union(negative_seeds).union(
positive_seeds)),
method=polarity_induction_methods.random_walk,
nn=nn,
beta=beta,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words)
print("Hist")
polarities = run_method(
positive_seeds,
negative_seeds,
hist_embed.get_subembed(
set(eval_words).union(negative_seeds).union(
positive_seeds)),
method=polarity_induction_methods.random_walk,
nn=nn,
beta=beta,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words)
print("Densify...")
for lr in [0.001, 0.01, 0.1, 0.5]:
for reg in [0.001, 0.01, 0.1, 0.5]:
print("LR : ", lr, "Reg: ", reg)
print("Common")
polarities = run_method(
positive_seeds,
negative_seeds,
common_embed.get_subembed(
set(eval_words).union(negative_seeds).union(
positive_seeds)),
method=polarity_induction_methods.densify,
lr=lr,
regularization_strength=reg,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tern=False)
print("Hist")
polarities = run_method(
positive_seeds,
negative_seeds,
hist_embed.get_subembed(
set(eval_words).union(negative_seeds).union(
positive_seeds)),
method=polarity_induction_methods.densify,
lr=lr,
regularization_strength=reg,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tern=False)
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)
def evaluate_finance_methods():
np.random.seed(0)
print("Getting evalution words and embeddings..")
gi = lexicons.load_lexicon("inquirer", remove_neutral=False)
lexicon = lexicons.load_lexicon("finance", remove_neutral=True)
### padding in neutrals from GI lexicon
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.finance_seeds()
stock_embed = create_representation("SVD", constants.STOCK_EMBEDDINGS)
stock_counts = create_representation("Explicit", constants.STOCK_COUNTS)
common_embed = create_representation(
"GIGA", constants.COMMON_EMBEDDINGS,
set(lexicon.keys()).union(positive_seeds).union(negative_seeds))
stock_words = set(stock_embed.iw)
common_words = set(common_embed)
eval_words = [
word for word in lexicon
if word in stock_words and word in common_words
and not word in positive_seeds and not word in negative_seeds
]
stock_counts = stock_counts.get_subembed(
set(eval_words).union(positive_seeds).union(negative_seeds),
restrict_context=False)
print("Evaluating with ", len(eval_words), "out of", len(lexicon))
print("Velikovich with 1990s Fic embeddings")
stock_counts.normalize()
polarities = run_method(
positive_seeds,
negative_seeds,
stock_counts,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.graph_propagate,
T=3,
boot_size=6,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=None)
print()
print("PMI")
polarities = run_method(positive_seeds,
negative_seeds,
stock_counts,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.pmi,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words)
print()
print("SentProp with stock embeddings")
polarities = run_method(
positive_seeds,
negative_seeds,
stock_embed.get_subembed(
set(eval_words).union(negative_seeds).union(positive_seeds)),
method=polarity_induction_methods.bootstrap,
beta=0.9,
nn=25,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words)
print("Densifier with stock embeddings")
polarities = run_method(
positive_seeds,
negative_seeds,
stock_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)
def evaluate_adj_methods():
"""
Evaluate different methods on standard English,
but restrict to words that are present in the 1990s portion of historical data.
"""
print("Getting evalution words and embeddings..")
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())
adjs = vocab.pos_words("1990", "ADJ")
# 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.adj_seeds()
common_embed = create_representation(
"GIGA", constants.COMMON_EMBEDDINGS,
eval_words.union(positive_seeds).union(negative_seeds))
common_words = set(common_embed.iw)
eval_words = eval_words.intersection(common_words)
hist_embed = create_representation("SVD",
constants.COHA_EMBEDDINGS + "2000")
hist_counts = create_representation("Explicit",
constants.COUNTS + "1990",
normalize=False)
hist_words = set(hist_embed.iw)
eval_words = eval_words.intersection(hist_words)
embed_words = [
word for word in adjs if word in hist_words and word in common_words
]
eval_words = [
word for word in eval_words if word in embed_words
and not word in positive_seeds and not word in negative_seeds
]
hist_counts = hist_counts.get_subembed(
set(eval_words).union(positive_seeds).union(negative_seeds),
restrict_context=False)
print("Evaluating with ", len(eval_words), "out of", len(lexicon))
print("Embeddings with ", len(embed_words))
print("PMI")
polarities = run_method(positive_seeds,
negative_seeds,
hist_counts,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.pmi,
boot_size=6,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
print()
evaluate(qwn, lexicon, eval_words, tau_lexicon=kuperman)
print("Dist with 1990s Fic embeddings")
polarities = run_method(
positive_seeds,
negative_seeds,
hist_embed.get_subembed(
set(embed_words).union(negative_seeds).union(positive_seeds)),
method=polarity_induction_methods.dist,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
print()
print("Densifier with 1990s Fic embeddings")
polarities = run_method(
positive_seeds,
negative_seeds,
hist_embed.get_subembed(
set(embed_words).union(negative_seeds).union(positive_seeds)),
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.densify,
boot_size=6,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
print()
print("SentProp with 1990s Fic embeddings")
polarities = run_method(
positive_seeds,
negative_seeds,
hist_embed.get_subembed(
set(embed_words).union(negative_seeds).union(positive_seeds)),
method=polarity_induction_methods.bootstrap,
nn=25,
beta=0.9,
boot_size=6,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
print()
print("Velikovich with 1990s Fic embeddings")
hist_counts.normalize()
polarities = run_method(
positive_seeds,
negative_seeds,
hist_counts,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.graph_propagate,
T=3,
boot_size=6,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
print()
print("SentProp with CC")
polarities = run_method(
positive_seeds,
negative_seeds,
common_embed.get_subembed(
set(embed_words).union(negative_seeds).union(positive_seeds)),
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.random_walk,
beta=0.99,
nn=10,
boot_size=6,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
print("Densifier with CC")
polarities = run_method(
positive_seeds,
negative_seeds,
common_embed.get_subembed(
set(embed_words).union(negative_seeds).union(positive_seeds)),
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.densify,
boot_size=6,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=kuperman)
def main(args):
print('Loading data...')
train_sents, train_labels = [], []
with open(args.TRAIN, 'rt') as f:
lines = f.readlines()
for l in lines:
cols = l.split('\t')
train_labels.append(1 if cols[0] == 'Positive' else 0)
train_sents.append(cols[1].strip())
test_sents, test_labels = [], []
with open(args.TEST, 'rt') as f:
lines = f.readlines()
for l in lines:
cols = l.split('\t')
test_labels.append(1 if cols[0] == 'Positive' else 0)
test_sents.append(cols[1].strip())
pos_seeds, neg_seeds = seeds.review_seeds()
print('Creating word vectors...')
embeddings = create_representation("FULL", args.EMBED, 100,
limit=50000)
print('Calculating polarities...')
polarities = random_walk(embeddings, pos_seeds, neg_seeds, beta=0.99, nn=10,
sym=True, arccos=True)
print('Filtering polarities...')
polarities = filter_polarities(polarities, args.CUTOFF)
print('Storing polarities...')
dict2csv(polarities, path='./data/polarities/filtered.csv')
word_list = list(polarities.keys())
train_reps, test_reps = [], []
if args.ALGO == 'tf-idf':
tfidf = TfidfVectorizer(vocabulary=word_list, tokenizer=nltk.word_tokenize)
print('Creating training sentence representations...')
train_reps = tfidf.fit_transform(train_sents)
print('Creating testing sentence representations...')
test_reps = tfidf.fit_transform(test_sents)
else:
print('Creating training sentence representations...')
for i, sent in enumerate(train_sents):
print('\t%d/%d' % (i + 1, len(train_sents)), end='\r')
rep = sent2rep(sent, word_list=word_list)
train_reps.append(rep)
print()
print('Creating testing sentence representations...')
for i, sent in enumerate(test_sents):
print('\t%d/%d' % (i + 1, len(test_sents)), end='\r')
rep = sent2rep(sent, word_list=word_list)
test_reps.append(rep)
print()
if args.LSA != 0:
print('Transforming w/ LSA...')
svd = TruncatedSVD(args.LSA)
normalizer = Normalizer(copy=False)
lsa = make_pipeline(svd, normalizer)
train_reps = lsa.fit_transform(train_reps)
test_reps = lsa.fit_transform(test_reps)
km = KMeans(n_clusters=2, verbose=1, max_iter=10000)
train_preds = km.fit_predict(train_reps)
test_preds = km.predict(test_reps)
print('\nMetrics on train set:')
evaluate(train_labels, train_preds)
print('\nMetrics on test set:')
evaluate(test_labels, test_preds)