def show_num_mention():
train, val = load_feature_and_split()
p_dict = get_perspective_dict()
claims = get_claims_from_ids(lmap(lambda x: x['cid'], train))
claim_d = claims_to_dict(claims)
grouped = group_by(train, lambda x: x['cid'])
for cid in grouped:
print("Claim:", claim_d[cid])
for dp in grouped[cid]:
p_text = p_dict[dp['pid']]
print(dp['label'], get_num_mention(dp), p_text)
def test_generative_model():
train, val = load_feature_and_split()
print("Training lm")
classifier = learn_lm(train)
stopwords = load_stopwords()
def fileter_fn(data_point: Dict):
remove_stopword_and_punct(stopwords, data_point['feature'])
foreach(fileter_fn, train)
def is_correct(data_point: Dict):
x = data_point['feature']
y = int(data_point['label'])
return classifier.predict(x) == int(y)
correctness = lmap(is_correct, val)
print("val acc: ", average(correctness))
def lm_contribution():
train, val = load_feature_and_split()
print("Training lm")
stopwords = load_stopwords()
def fileter_fn(data_point):
remove_stopword_and_punct(stopwords, data_point[0][0])
foreach(fileter_fn, train)
classifier = learn_lm(train)
acc_contrib = Counter()
for data_point in train:
(tf, num), y = data_point
contrib = classifier.counter_contribution(tf)
# print("{} {} {}".format(y, classifier.predict(tf), classifier.counter_odd(tf)))
# print("--------------")
for t, score in contrib.most_common(100):
acc_contrib[t] += score
for t, score in acc_contrib.most_common(100):
print(t, score, classifier.P_w_C_dict[t], classifier.P_w_NC_dict[t])
def test_logistic_regression():
train_and_val = load_feature_and_split()
train: List[Dict] = train_and_val[0]
val: List[Dict] = train_and_val[1]
valid_datapoint_list: List[Dict] = train + val
stopwords = load_stopwords()
def fileter_fn(data_point: Dict):
remove_stopword_and_punct(stopwords, data_point['feature'])
foreach(fileter_fn, train)
foreach(fileter_fn, val)
tf_list = lmap(lambda dp: dp['feature'], valid_datapoint_list)
tf_acc = Counter()
for tf in tf_list:
tf_acc.update(tf)
voca: List[str] = left(tf_acc.most_common(10000))
#voca = set(flatten(lmap(get_voca_from_datapoint, valid_datapoint_list)))
voca2idx: Dict[str, int] = dict(zip(list(voca), range(len(voca))))
idx2voca: Dict[int, str] = {v: k for k, v in voca2idx.items()}
print("Num voca:", len(voca))
feature_size = len(voca) + 1
def featurize(datapoint: Dict):
tf = datapoint['feature']
y = int(datapoint['label'])
v = np.zeros([feature_size])
for t, prob in tf.items():
if t in voca2idx:
v[voca2idx[t]] = prob
v[-1] = datapoint['num_mention']
return v, int(y)
x, y = zip(*lmap(featurize, train))
val_x, val_y = zip(*lmap(featurize, val))
model = LogisticRegression()
model.fit(x, y)
x_a = np.array(x)
print(x_a.shape)
avg_x = np.sum(x_a, axis=0)
def acc(y, pred_y):
return np.average(np.equal(y, pred_y))
pred_y = model.predict(x)
print("train acc", acc(y, pred_y))
print("val acc", acc(val_y, model.predict(val_x)))
t = np.multiply(avg_x, model.coef_)
contrib = t[0]
ranked_idx = np.argsort(contrib)
def print_feature_at(idx):
if idx == feature_size - 1:
print("[NUM_MENTION]", contrib[idx])
else:
print(idx2voca[idx], contrib[idx])
print("Top k features (POS)")
for i in range(30):
idx = ranked_idx[i]
print_feature_at(idx)
print("Top k features (NEG)")
for i in range(30):
j = len(voca) - 1 - i
idx = ranked_idx[j]
print_feature_at(idx)
print("In training data")
print("pred\tgold\tterms")
for i in range(100):
terms = left(train[i]['feature'].most_common(50))
terms = list(terms[25:])
print(pred_y[i], y[i], terms) #
def mention_num_based():
train, val = load_feature_and_split()
mention_num_classifier(train, val)