class DictGBC(object):
def __init__(self, texts, classes, nlpdict=None):
# TODO: add list of smileys to texts/classes
self.gbc = GradientBoostingClassifier(max_depth=3, learn_rate=0.1)
if nlpdict:
self.dictionary = nlpdict
else:
self.dictionary = NLPDict(texts=texts)
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
self.gbc.fit(vectors, classes)
def _build_vector(self, text):
item = self.dictionary.tokenize(text)
vector = []
words = list(stemmed_curse_words)
words.extend(list(you_words))
words.sort()
for word in words:
freq = sum([1 for word2 in item['stemmed'] if word == word2])
vector.append(freq)
vector.append(item['ratio'])
vector.append(len(item['original']))
freq_x = sum([1 for ch in item['original'] if ch == '!'])
freq_q = sum([1 for ch in item['original'] if ch == '?'])
freq_a = sum([1 for ch in item['original'] if ch == '*'])
vector.append(freq_x)
vector.append(freq_q)
vector.append(freq_a)
return np.array(vector)
def classify(self, texts):
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
pred_prob = self.gbc.predict_proba(vectors)
predictions = []
for pair in pred_prob:
predictions.append(pair[1] - pair[0])
predictions = np.array(predictions)
predictions = (predictions + 1) / 2
predictions[predictions > 1] = 1
predictions[predictions < 0] = 0
return predictions
class DictRFC(object):
def __init__(self, texts, classes, nlpdict=None):
# TODO: add list of smileys to texts/classes
self.rfc = RandomForestClassifier(n_estimators=50)
if nlpdict:
self.dictionary = nlpdict
else:
self.dictionary = NLPDict(texts=texts)
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
self.rfc.fit(vectors, classes)
def _build_vector(self, text):
item = self.dictionary.tokenize(text)
vector = []
words = list(stemmed_curse_words)
words.extend(list(you_words))
words.sort()
for word in words:
freq = sum([1 for word2 in item['stemmed'] if word == word2])
vector.append(freq)
vector.append(item['ratio'])
vector.append(len(item['original']))
freq_x = sum([1 for ch in item['original'] if ch == '!'])
freq_q = sum([1 for ch in item['original'] if ch == '?'])
freq_a = sum([1 for ch in item['original'] if ch == '*'])
vector.append(freq_x)
vector.append(freq_q)
vector.append(freq_a)
return np.array(vector)
def classify(self, texts):
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
pred_prob = self.rfc.predict_proba(vectors)
predictions = []
for pair in pred_prob:
predictions.append(pair[1] - pair[0])
predictions = np.array(predictions)
predictions = (predictions + 1) / 2
predictions[predictions > 1] = 1
predictions[predictions < 0] = 0
return predictions
class DictKNN(object):
def __init__(self, texts, classes):
# TODO: add list of smileys to texts/classes
self.knn = KNeighborsClassifier(n_neighbors=7, weights='distance')
self.dictionary = NLPDict(texts=texts)
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
self.knn.fit(vectors, classes)
def _build_vector(self, text):
item = self.dictionary.tokenize(text)
vector = []
words = list(stemmed_curse_words)
words.extend(list(you_words))
words.sort()
for word in words:
freq = sum([1 for word2 in item['stemmed'] if word == word2])
vector.append(freq)
vector.append(item['ratio'])
vector.append(len(item['original']))
freq_x = sum([1 for ch in item['original'] if ch == '!'])
freq_q = sum([1 for ch in item['original'] if ch == '?'])
freq_a = sum([1 for ch in item['original'] if ch == '*'])
vector.append(freq_x)
vector.append(freq_q)
vector.append(freq_a)
return np.array(vector)
def classify(self, texts):
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
pred_prob = self.knn.predict_proba(vectors)
predictions = []
for pair in pred_prob:
predictions.append(pair[1] - pair[0])
predictions = np.array(predictions)
predictions = (predictions + 1) / 2
predictions[predictions > 1] = 1
predictions[predictions < 0] = 0
return predictions
class EnsembleSVM(object):
def __init__(self, texts, classes, params, models):
self.models = models
self.params = params
self.texts = texts
self.classes = classes
self.dictionary = NLPDict(texts=texts)
classes = np.array(classes)
texts = np.array(texts)
net_inputs = []
expected_outputs = np.array([])
print 'get training data'
# get some training data
for train, test in cross_validation.StratifiedKFold(classes, 3):
texts_train = texts[train]
classes_train = classes[train]
# classes_train = list(classes_train)
texts_test = texts[test]
classes_test = classes[test]
net_inputs_batch = []
for model, params in self.models:
m = model(texts_train, classes_train, *params)
p = np.array(m.classify(texts_test))[np.newaxis]
if len(net_inputs_batch):
net_inputs_batch = np.vstack([net_inputs_batch, p])
else:
net_inputs_batch = p
net_inputs_batch = net_inputs_batch.T
vectors = self._build_vector(texts_test[0])[np.newaxis]
for i in xrange(1, len(texts_test)):
vectors = np.vstack((vectors, self._build_vector(texts_test[i])))
net_inputs_batch = np.hstack((net_inputs_batch, vectors))
expected_outputs = \
np.concatenate((expected_outputs, classes_test), axis=0)
if len(net_inputs):
net_inputs = \
np.vstack((net_inputs, net_inputs_batch))
else:
net_inputs = net_inputs_batch
self.svm = svm.LinearSVC(C=1, class_weight='auto')
print 'train network'
# train network
self.svm.fit(net_inputs, expected_outputs)
def _build_vector(self, text):
item = self.dictionary.tokenize(text)
vector = []
words = list(stemmed_curse_words)
words.extend(list(you_words))
words.sort()
freq = 0
for word in words:
freq += sum([1 for word2 in item['stemmed'] if word == word2])
vector.append(min(freq / 10.0, 1))
vector.append(item['ratio'])
vector.append(min(len(item['original']) / 500.0, 1))
freq_x = sum([1 for ch in item['original'] if ch == '!'])
freq_q = sum([1 for ch in item['original'] if ch == '?'])
freq_a = sum([1 for ch in item['original'] if ch == '*'])
vector.append(min(1, freq_x / 10.0))
vector.append(min(1, freq_q / 10.0))
vector.append(min(1, freq_a / 10.0))
return np.array(vector)
def classify(self, texts):
print 'classify'
net_inputs = []
for model, params in self.models:
m = model(self.texts, self.classes, *params)
p = np.array(m.classify(texts))[np.newaxis]
if len(net_inputs):
net_inputs = np.vstack([net_inputs, p])
else:
net_inputs = p
net_inputs = net_inputs.T
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
net_inputs = np.hstack((net_inputs, vectors))
predictions = self.svm.decision_function(net_inputs)
predictions = np.transpose(predictions)[0]
predictions = predictions / 2 + 0.5
predictions[predictions > 1] = 1
predictions[predictions < 0] = 0
return predictions
class EnsembleSVM(object):
def __init__(self, texts, classes, params, models):
self.models = models
self.params = params
self.texts = texts
self.classes = classes
self.dictionary = NLPDict(texts=texts)
classes = np.array(classes)
texts = np.array(texts)
net_inputs = []
expected_outputs = np.array([])
print 'get training data'
# get some training data
for train, test in cross_validation.StratifiedKFold(classes, 3):
texts_train = texts[train]
classes_train = classes[train]
# classes_train = list(classes_train)
texts_test = texts[test]
classes_test = classes[test]
net_inputs_batch = []
for model, params in self.models:
m = model(texts_train, classes_train, *params)
p = np.array(m.classify(texts_test))[np.newaxis]
if len(net_inputs_batch):
net_inputs_batch = np.vstack([net_inputs_batch, p])
else:
net_inputs_batch = p
net_inputs_batch = net_inputs_batch.T
vectors = self._build_vector(texts_test[0])[np.newaxis]
for i in xrange(1, len(texts_test)):
vectors = np.vstack(
(vectors, self._build_vector(texts_test[i])))
net_inputs_batch = np.hstack((net_inputs_batch, vectors))
expected_outputs = \
np.concatenate((expected_outputs, classes_test), axis=0)
if len(net_inputs):
net_inputs = \
np.vstack((net_inputs, net_inputs_batch))
else:
net_inputs = net_inputs_batch
self.svm = svm.LinearSVC(C=1, class_weight='auto')
print 'train network'
# train network
self.svm.fit(net_inputs, expected_outputs)
def _build_vector(self, text):
item = self.dictionary.tokenize(text)
vector = []
words = list(stemmed_curse_words)
words.extend(list(you_words))
words.sort()
freq = 0
for word in words:
freq += sum([1 for word2 in item['stemmed'] if word == word2])
vector.append(min(freq / 10.0, 1))
vector.append(item['ratio'])
vector.append(min(len(item['original']) / 500.0, 1))
freq_x = sum([1 for ch in item['original'] if ch == '!'])
freq_q = sum([1 for ch in item['original'] if ch == '?'])
freq_a = sum([1 for ch in item['original'] if ch == '*'])
vector.append(min(1, freq_x / 10.0))
vector.append(min(1, freq_q / 10.0))
vector.append(min(1, freq_a / 10.0))
return np.array(vector)
def classify(self, texts):
print 'classify'
net_inputs = []
for model, params in self.models:
m = model(self.texts, self.classes, *params)
p = np.array(m.classify(texts))[np.newaxis]
if len(net_inputs):
net_inputs = np.vstack([net_inputs, p])
else:
net_inputs = p
net_inputs = net_inputs.T
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
net_inputs = np.hstack((net_inputs, vectors))
predictions = self.svm.decision_function(net_inputs)
predictions = np.transpose(predictions)[0]
predictions = predictions / 2 + 0.5
predictions[predictions > 1] = 1
predictions[predictions < 0] = 0
return predictions
class Ensemble(object):
'''
neural network using several clasifiers as input
'''
def __init__(self, texts, classes, params, models):
self.models = models
self.params = params
self.texts = texts
self.classes = classes
self.dictionary = NLPDict(texts=texts)
classes = np.array(classes)
texts = np.array(texts)
net_inputs = []
expected_outputs = np.array([])
print 'get training data'
# get some training data
for train, test in cross_validation.StratifiedKFold(classes, 5):
texts_train = texts[train]
classes_train = classes[train]
# classes_train = list(classes_train)
texts_test = texts[test]
classes_test = classes[test]
net_inputs_batch = []
for model, params in self.models:
m = model(texts_train, classes_train, *params)
p = np.array(m.classify(texts_test))[np.newaxis]
if len(net_inputs_batch):
net_inputs_batch = np.vstack([net_inputs_batch, p])
else:
net_inputs_batch = p
net_inputs_batch = net_inputs_batch.T
vectors = self._build_vector(texts_test[0])[np.newaxis]
for i in xrange(1, len(texts_test)):
vectors = np.vstack((vectors, self._build_vector(texts_test[i])))
net_inputs_batch = np.hstack((net_inputs_batch, vectors))
expected_outputs = \
np.concatenate((expected_outputs, classes_test), axis=0)
if len(net_inputs):
net_inputs = \
np.vstack((net_inputs, net_inputs_batch))
else:
net_inputs = net_inputs_batch
# init network
self.net = nl.net.newff( \
[[0, 1] for i in range(len(self.models) + 6)], \
self.params['structure'] \
)
print 'train network'
# train network
expected_outputs = expected_outputs[np.newaxis].T
nl.train.train_rprop( \
self.net, \
net_inputs, \
expected_outputs, \
epochs=self.params['epochs'] \
)
def _build_vector(self, text):
item = self.dictionary.tokenize(text)
vector = []
words = list(stemmed_curse_words)
words.extend(list(you_words))
words.sort()
freq = 0
for word in words:
freq += sum([1 for word2 in item['stemmed'] if word == word2])
vector.append(min(freq / 10.0, 1))
vector.append(item['ratio'])
vector.append(min(len(item['original']) / 500.0, 1))
freq_x = sum([1 for ch in item['original'] if ch == '!'])
freq_q = sum([1 for ch in item['original'] if ch == '?'])
freq_a = sum([1 for ch in item['original'] if ch == '*'])
vector.append(min(1, freq_x / 10.0))
vector.append(min(1, freq_q / 10.0))
vector.append(min(1, freq_a / 10.0))
return np.array(vector)
def classify(self, texts):
print 'classify'
net_inputs = []
for model, params in self.models:
m = model(self.texts, self.classes, *params)
p = np.array(m.classify(texts))[np.newaxis]
if len(net_inputs):
net_inputs = np.vstack([net_inputs, p])
else:
net_inputs = p
net_inputs = net_inputs.T
vectors = self._build_vector(texts[0])[np.newaxis]
for i in xrange(1, len(texts)):
vectors = np.vstack((vectors, self._build_vector(texts[i])))
net_inputs = np.hstack((net_inputs, vectors))
results = self.net.sim(net_inputs)
results = results.T[0]
return results
