def check_extract_bow_feature_vectors():
ex_name = "Extract bow feature vectors"
texts = ["He loves her ", "He really really loves her"]
keys = ["he", "loves", "her", "really"]
dictionary = {k: i for i, k in enumerate(keys)}
exp_res = np.array([[1, 1, 1, 0], [1, 1, 1, 1]])
non_bin_res = np.array([[1, 1, 1, 0], [1, 1, 1, 2]])
try:
res = p1.extract_bow_feature_vectors(texts, dictionary)
except NotImplementedError:
log(red("FAIL"), ex_name, ": not implemented")
return
if not type(res) == np.ndarray:
log(red("FAIL"), ex_name, ": does not return a numpy array, type: ",
type(res))
return
if not len(res) == len(exp_res):
log(red("FAIL"), ex_name, ": expected an array of shape ",
exp_res.shape, " but got array of shape", res.shape)
return
log(green("PASS"), ex_name)
if (res == exp_res).all():
log(yellow("WARN"), ex_name, ": uses binary indicators as features")
elif (res == non_bin_res).all():
log(green("PASS"), ex_name, ": correct non binary features")
else:
log(red("FAIL"), ex_name, ": unexpected feature matrix")
return
#-------------------------------------------------------------------------------
train_data = utils.load_data('reviews_train.tsv')
val_data = utils.load_data('reviews_val.tsv')
test_data = utils.load_data('reviews_test.tsv')
train_texts, train_labels = zip(*((sample['text'], sample['sentiment'])
for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment'])
for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment'])
for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#-------------------------------------------------------------------------------
# Problem 5
#-------------------------------------------------------------------------------
# toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
# #
# T = 200
# L = 0.2
# #
# thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
# thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
# thetas_pegasos = p1.pegasos(toy_features, toy_labels, T, L)
#-------------------------------------------------------------------------------
train_data = utils.load_data('data/reviews_train.tsv')
val_data = utils.load_data('data/reviews_val.tsv')
test_data = utils.load_data('data/reviews_test.tsv')
train_texts, train_labels = zip(*((sample['text'], sample['sentiment'])
for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment'])
for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment'])
for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#-------------------------------------------------------------------------------
# Problem 5
#-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('data/toy_data.tsv')
T = 10
L = 0.2
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_pegasos = p1.pegasos(toy_features, toy_labels, T, L)
import numpy as np
#-------------------------------------------------------------------------------
# Data loading. There is no need to edit code in this section.
#-------------------------------------------------------------------------------
train_data = utils.load_data('reviews_train.tsv')
Arquivo = open('stopwords.txt')
stopwords_data = Arquivo.read()
stopwords_data = str(stopwords_data).split()
Arquivo.close()
train_texts, train_labels = zip(*((sample['text'], sample['sentiment']) for sample in train_data))
dictionary = p1.bag_of_words(train_texts,stopwords_data)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
T=25
L=0.01
thetas_pegasos = p1.pegasos(train_bow_features, train_labels, T, L,)
run=1
while(1):
input_texts=input('Input your review: ')
input_bow_features = p1.extract_bow_feature_vectors(['blah',input_texts],dictionary)
output=p1.classify(input_bow_features,thetas_pegasos[0],thetas_pegasos[1])
if (output[-1])==1:
print('_______________________________________________________________')
print('This is a possitive review!')
else:
print('_______________________________________________________________')
print('This is a negative review')
#-------------------------------------------------------------------------------
# Data loading. There is no need to edit code in this section.
#-------------------------------------------------------------------------------
train_data = utils.load_data('reviews_train.tsv')
val_data = utils.load_data('reviews_val.tsv')
test_data = utils.load_data('reviews_test.tsv')
train_texts, train_labels = zip(*((sample['text'], sample['sentiment']) for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment']) for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment']) for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#
#-------------------------------------------------------------------------------
# Section 1.7
#-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
T = 5
L = 10
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_avg_pa = p1.average_passive_aggressive(toy_features, toy_labels, T, L)
#-------------------------------------------------------------------------------
train_data = utils.load_data('reviews_train.tsv')
val_data = utils.load_data('reviews_val.tsv')
test_data = utils.load_data('reviews_test.tsv')
train_texts, train_labels = zip(*((sample['text'], sample['sentiment'])
for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment'])
for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment'])
for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#-------------------------------------------------------------------------------
# Problem 5
#-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
T = 10
L = 0.2
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_pegasos = p1.pegasos(toy_features, toy_labels, T, L)
# -------------------------------------------------------------------------------
# Data loading. There is no need to edit code in this section.
# -------------------------------------------------------------------------------
train_data = utils.load_data('reviews_train.tsv')
val_data = utils.load_data('reviews_val.tsv')
test_data = utils.load_data('reviews_test.tsv')
train_texts, train_labels = zip(*((sample['text'], sample['sentiment']) for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment']) for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment']) for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary, count=True)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary, count=True)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary, count=True)
class TestAlgoComparison(unittest.TestCase):
def test_algorithm_compare(self):
# -------------------------------------------------------------------------------
# # Problem 5
# #-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
T = 100
L = 0.2
