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 apply_embedding_transformation(embeddings, positive_seeds, negative_seeds,
n_epochs=5, n_dim=10, force_orthogonal=False,
plot=False, plot_points=50, plot_seeds=False,
**kwargs):
print("Preparing to learn embedding tranformation")
dataset = DatasetMinibatchIterator(embeddings, positive_seeds, negative_seeds, **kwargs)
model = get_model(embeddings.m.shape[1], n_dim, **kwargs)
print("Learning embedding transformation")
# prog = util.Progbar(n_epochs)
for epoch in range(n_epochs):
dataset.shuffle()
loss = 0
for i, tup in enumerate(dataset):
X, y = tup[0], tup[1]
loss += model.train_on_batch(X, y)[0] * y.size
Q, b = model.get_weights()
if force_orthogonal:
Q = orthogonalize(Q)
model.set_weights([Q, np.zeros_like(b)])
# prog.update(epoch + 1, exact_values=[('loss', loss / dataset.y.size)])
Q, b = model.get_weights()
new_mat = embeddings.m.dot(Q)[:,0:n_dim]
# print "Orthogonality rmse", np.mean(np.sqrt(
# np.square(np.dot(Q, Q.T) - np.identity(Q.shape[0]))))
if plot and n_dim == 2:
plot_words = positive_seeds + negative_seeds if plot_seeds else \
[w for w in embeddings if w not in positive_seeds and w not in negative_seeds]
plot_words = set(random.sample(plot_words, plot_points))
to_plot = {w: embeddings[w] for w in embeddings if w in plot_words}
lexicon = lexicons.load_lexicon()
plt.figure(figsize=(10, 10))
for w, e in to_plot.items():
plt.text(e[0], e[1], w,
bbox=dict(facecolor='green' if lexicon[w] == 1 else 'red', alpha=0.1))
xmin, ymin = np.min(np.vstack(to_plot.values()), axis=0)
xmax, ymax = np.max(np.vstack(to_plot.values()), axis=0)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.show()
return Embedding(new_mat, embeddings.iw, normalize=n_dim!=1)
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)