def weighted_initial(self, working_set, mislabeled):
print "Total Cluster Centroids Chosen: ", len(self.mislabeled_chosen)
print len(mislabeled), " mislabeled emails remaining as possible cluster centroids"
if len(mislabeled) == 0: #No more centers to select
return (None, None, 'NO_CENTROIDS')
else:
prob, mislabeled_point = mislabeled.pop(0) # Choose most potent mislabeled email
self.mislabeled_chosen.append(mislabeled_point)
print "Chose the mislabeled point with z = ", prob
data_y, data_x = h.compose_set(working_set)
vec_data_x = vectorize_set(data_x)
init_email = None
init_pos = None
label = None
if "frequency" in self.distance_opt:
min_distance = sys.maxint
for i,email_indices in enumerate(vec_data_x):
if None not in email_indices: # actual data
current_distance = distance(email_indices, mislabeled_point, self.distance_opt)
if current_distance < min_distance:
init_email = data_x[i]
init_pos = i
min_distance = current_distance
if init_email is None:
print "Training emails remaining: ", len(data_x)
else:
label = data_y[init_pos]
print "-> selected cluster centroid with label: ", label, " and distance: ", min_distance, " from mislabeled point"
return (label, init_pos, init_email)
def __init__(self, msg, size, active_unlearner, label, distance_opt,
working_set=None, separate=True):
# Clustroid specs
self.clustroid = msg[1] # index of msg
self.label = label
self.common_features = []
self.separate = separate
self.size = size # arbitrarily set to 100
self.active_unlearner = active_unlearner # point to calling au instance
self.opt = distance_opt
# The data
self.working_set = working_set
self.train_y = self.working_set[0]
self.train_x = self.working_set[1]
self.pol_y = self.working_set[2]
self.pol_x = self.working_set[3]
self.data_y, self.data_x = h.compose_set(self.working_set)
time_1 = time.time()
self.vec_data_x = vectorize_set(self.data_x)
print 'Vectorizing data_x took: ', h.sec_to_english(time.time() - time_1)
self.ham = set()
self.spam = set()
if 'frequency' in self.opt:
self.cluster_word_frequency = msg[0] # actual vector representation of msg
self.added = [] # keeps track of order emails are added
self.dist_list = self.distance_array(self.separate) # returns list containing dist from all emails in phantom space to center clustroid
self.cluster_set = self.make_cluster() # adds closest emails to cluster
self.divide() # adds cluster emails to ham and spam
def __init__(self,
msg,
size,
active_unlearner,
label,
distance_opt,
working_set=None,
separate=True):
# Clustroid specs
self.clustroid = msg[1] # index of msg
self.label = label
self.common_features = []
self.separate = separate
self.size = size # arbitrarily set to 100
self.active_unlearner = active_unlearner # point to calling au instance
self.opt = distance_opt
# The data
self.working_set = working_set
self.train_y = self.working_set[0]
self.train_x = self.working_set[1]
self.pol_y = self.working_set[2]
self.pol_x = self.working_set[3]
self.data_y, self.data_x = h.compose_set(self.working_set)
time_1 = time.time()
self.vec_data_x = vectorize_set(self.data_x)
print 'Vectorizing data_x took: ', h.sec_to_english(time.time() -
time_1)
self.ham = set()
self.spam = set()
if 'frequency' in self.opt:
self.cluster_word_frequency = msg[
0] # actual vector representation of msg
self.added = [] # keeps track of order emails are added
self.dist_list = self.distance_array(
self.separate
) # returns list containing dist from all emails in phantom space to center clustroid
self.cluster_set = self.make_cluster(
) # adds closest emails to cluster
self.divide() # adds cluster emails to ham and spam
def weighted_initial(self, working_set, mislabeled):
print "Total Cluster Centroids Chosen: ", len(self.mislabeled_chosen)
print len(
mislabeled
), " mislabeled emails remaining as possible cluster centroids"
if len(mislabeled) == 0: #No more centers to select
return (None, None, 'NO_CENTROIDS')
else:
prob, mislabeled_point = mislabeled.pop(
0) # Choose most potent mislabeled email
self.mislabeled_chosen.append(mislabeled_point)
print "Chose the mislabeled point with z = ", prob
data_y, data_x = h.compose_set(working_set)
vec_data_x = vectorize_set(data_x)
init_email = None
init_pos = None
label = None
if "frequency" in self.distance_opt:
min_distance = sys.maxint
for i, email_indices in enumerate(vec_data_x):
if None not in email_indices: # actual data
current_distance = distance(email_indices,
mislabeled_point,
self.distance_opt)
if current_distance < min_distance:
init_email = data_x[i]
init_pos = i
min_distance = current_distance
if init_email is None:
print "Training emails remaining: ", len(data_x)
else:
label = data_y[init_pos]
print "-> selected cluster centroid with label: ", label, " and distance: ", min_distance, " from mislabeled point"
return (label, init_pos, init_email)