class APAProject(object):
def __init__(self):
self.data_reader = DataReader('data/training_data/training.data', 'data/stopwords/stopwords.txt', True, 1000)
self.perceptron = Perceptron()
self.softmax = Softmax()
# Let's create 5 classifiers
universe_size = len(self.data_reader.universe)
self.perceptron_classifiers = [np.zeros((universe_size + 1)) for i in range(5)]
self.softmax_classifier = np.ones((5, universe_size + 1))
def file_to_data_set(self, file):
data_set = []
with open(file) as data:
for line in data:
_, score, sentence = line.split('|')
score = float(score)
# Calculating train target:
# 0 if 0 < score <= 0.2, 1 if 0.2 < score <= 0.4, etc...
class_number = math.floor(score * 5)
sentence_vector = self.data_reader.get_sentence_coordinates(sentence)
data_set.append((sentence_vector, class_number))
return data_set
def train_perceptron(self):
start_time = time.time()
print "Starting training session ..."
# We need to read data from datasmall and train the perceptron
training_data_set = self.file_to_data_set('data/training_data/training.data')
PERIODS = 5
for i in range(PERIODS):
# For each period, reshuffle
random.shuffle(training_data_set)
# We train every classfier
for (classifier_index, classifier) in enumerate(self.perceptron_classifiers):
self.perceptron_classifiers[classifier_index], updates = self.perceptron.train_epoch(training_data_set, classifier_index, classifier)
self.test_perceptron_multiclass()
training_end_time = time.time()
training_duration = training_end_time - start_time
print "Training session finished: duration %s seconds" % training_duration
def test_perceptron(self):
print "Starting testing session..."
test_data_set = self.file_to_data_set('data/test_data/test.data')
for (classifier_index, classifier) in enumerate(self.perceptron_classifiers):
error_count, success_count = self.perceptron.test_classifier(test_data_set, classifier, classifier_index)
print "Classifier %s just finished. %s%% results are good" % ((classifier_index + 1), success_count * 100 / (success_count + error_count))
def test_perceptron_multiclass(self):
print "Starting testing session..."
test_data_set = self.file_to_data_set('data/test_data/test.data')
success_count = 0
error_count = 0
for (sentence_vector, class_number) in test_data_set:
results_classifiers = []
test_class = -1
for (classifier_index, classifier) in enumerate(self.perceptron_classifiers):
results_classifiers.append(np.dot(classifier, sentence_vector))
if results_classifiers.index(max(results_classifiers)) == class_number:
success_count += 1
else:
error_count += 1
print "Classifier just finished. %s/%s ~= %s%% results are good" % (success_count, (error_count + success_count), success_count * 100 / (success_count + error_count))
def train_softmax(self):
start_time = time.time()
print "Starting softmax training session..."
# We need to read data from datasmall and train the perceptron
training_data_set = self.file_to_data_set('data/training_data/training.data')
PERIODS = 10
for i in range(PERIODS):
random.shuffle(training_data_set)
# On apprend PERIODS fois et a chaque passage on test le classifier pour etudier l'evolution
# Rappel : self.softmax_classifier = np.ones((5, universe_size))
self.softmax_classifier = self.softmax.train_epoch(self.softmax_classifier, training_data_set)
self.test_softmax()
training_end_time = time.time()
training_duration = training_end_time - start_time
print "Training session finished: duration %s seconds" % training_duration
def test_softmax(self):
print "Starting softmax testing session..."
test_data_set = self.file_to_data_set('data/test_data/test.data')
#test_data_set = self.file_to_data_set('data/training_data/training.data')
error_count, success_count = self.softmax.test_classifier(self.softmax_classifier, test_data_set)
print "Classifier just finished. %s/%s ~= %s%% results are good" % (success_count, (error_count + success_count), success_count * 100 / (success_count + error_count))
