def cluster_more(self, n):
"""Expands the cluster to include n more emails and returns these additional emails.
If n more is not available, cluster size is simply truncated to include all remaining
emails."""
if 'frequency' in self.opt:
if n >= len(self.dist_list):
n = len(self.dist_list)
print "Adding ", n, " more emails to cluster of size ", self.size, " via ", self.opt, " method"
self.size += n
new_elements = []
added = 0
while added < n:
nearest = self.dist_list[0][1] # get nearest email
new_elements.append(nearest) # add to new list
self.added.append(nearest)
self.cluster_set.add(nearest) # add to original cluster set
self.cluster_word_frequency = helpers.update_word_frequencies(self.cluster_word_frequency, nearest) # update word frequencies
# self.dist_list = self.distance_array(self.separate) # update distance list w/ new frequency list
del self.dist_list[0]
self.update_dist_list()
added += 1
assert(len(new_elements) == n), str(len(new_elements)) + " " + str(n)
assert(len(self.cluster_set) == self.size), str(len(self.cluster_set)) + " " + str(self.size)
for msg in new_elements:
if msg.train == 1 or msg.train == 3:
self.ham.add(msg)
elif msg.train == 0 or msg.train == 2:
self.spam.add(msg)
return new_elements
old_cluster_set = self.cluster_set
if self.size + n <= len(self.dist_list):
self.size += n
else:
print "\nTruncating cluster size...\n"
if len(self.dist_list) > 0:
self.size = len(self.dist_list)
if self.sort_first:
new_cluster_set = set(item[1] for item in self.dist_list[:self.size])
else:
k_smallest = quickselect.k_smallest
new_cluster_set = set(item[1] for item in k_smallest(self.dist_list, self.size))
new_elements = list(item for item in new_cluster_set if item not in old_cluster_set)
self.cluster_set = new_cluster_set
assert(len(self.cluster_set) == self.size), len(self.cluster_set)
for msg in new_elements:
if msg.train == 1 or msg.train == 3:
self.ham.add(msg)
elif msg.train == 0 or msg.train == 2:
self.spam.add(msg)
return new_elements
def make_cluster(self):
"""Constructs the initial cluster of emails."""
# self.dist_list = [t for t in self.dist_list if t is not None]
if self.size > len(self.dist_list):
print "\nTruncating cluster size...\n"
self.size = len(self.dist_list)
if self.sort_first:
if 'frequency' in self.opt:
emails = [self.clustroid] # list of added emails
for d, e in self.dist_list: # Remove the duplicate clustroid in self.dist_list
if e.tag == self.clustroid.tag:
self.dist_list.remove((d, e))
# self.working_set.remove(e)
print "-> removed duplicate clustroid ", e.tag
break
current_size = 1
while current_size < self.size:
nearest = self.dist_list[0][1] # get nearest email
assert (nearest.tag != self.clustroid.tag), str(
nearest.tag) + " " + str(self.clustroid.tag)
emails.append(nearest) # add to list
self.added.append(
nearest) # track order in which emails are added
# self.working_set.remove(nearest) # remove from working set so email doesn't show up again when we recreate dist_list
self.cluster_word_frequency = helpers.update_word_frequencies(
self.cluster_word_frequency,
nearest) # update word frequencies
del self.dist_list[0] # so we don't add the email twice
self.update_dist_list(
) # new cluster_word_frequency, so need to resort closest emails
# self.dist_list = self.distance_array(self.separate) # update distance list w/ new frequency list
current_size += 1
print "-> cluster initialized with size", len(emails)
return set(emails)
else:
return set(item[1] for item in self.dist_list[:self.size])
else:
k_smallest = quickselect.k_smallest
return set(item[1]
for item in k_smallest(self.dist_list, self.size))
def make_cluster(self):
"""Constructs the initial cluster of emails."""
# self.dist_list = [t for t in self.dist_list if t is not None]
if self.size > len(self.dist_list):
print "\nTruncating cluster size...\n"
self.size = len(self.dist_list)
if self.sort_first:
if 'frequency' in self.opt:
emails = [self.clustroid] # list of added emails
for d,e in self.dist_list: # Remove the duplicate clustroid in self.dist_list
if e.tag == self.clustroid.tag:
self.dist_list.remove((d,e))
# self.working_set.remove(e)
print "-> removed duplicate clustroid ", e.tag
break
current_size = 1
while current_size < self.size:
nearest = self.dist_list[0][1] # get nearest email
assert(nearest.tag != self.clustroid.tag), str(nearest.tag) + " " + str(self.clustroid.tag)
emails.append(nearest) # add to list
self.added.append(nearest) # track order in which emails are added
# self.working_set.remove(nearest) # remove from working set so email doesn't show up again when we recreate dist_list
self.cluster_word_frequency = helpers.update_word_frequencies(self.cluster_word_frequency, nearest) # update word frequencies
del self.dist_list[0] # so we don't add the email twice
self.update_dist_list() # new cluster_word_frequency, so need to resort closest emails
# self.dist_list = self.distance_array(self.separate) # update distance list w/ new frequency list
current_size += 1
print "-> cluster initialized with size", len(emails)
return set(emails)
else:
return set(item[1] for item in self.dist_list[:self.size])
else:
k_smallest = quickselect.k_smallest
return set(item[1] for item in k_smallest(self.dist_list, self.size))
def cluster_more(self, n):
"""Expands the cluster to include n more emails and returns these additional emails.
If n more is not available, cluster size is simply truncated to include all remaining
emails."""
if 'frequency' in self.opt:
if n >= len(self.dist_list):
n = len(self.dist_list)
print "Adding ", n, " more emails to cluster of size ", self.size, " via ", self.opt, " method"
self.size += n
new_elements = []
added = 0
while added < n:
nearest = self.dist_list[0][1] # get nearest email
new_elements.append(nearest) # add to new list
self.added.append(nearest)
self.cluster_set.add(nearest) # add to original cluster set
self.cluster_word_frequency = helpers.update_word_frequencies(
self.cluster_word_frequency,
nearest) # update word frequencies
# self.dist_list = self.distance_array(self.separate) # update distance list w/ new frequency list
del self.dist_list[0]
self.update_dist_list()
added += 1
assert (
len(new_elements) == n), str(len(new_elements)) + " " + str(n)
assert (len(self.cluster_set) == self.size), str(
len(self.cluster_set)) + " " + str(self.size)
for msg in new_elements:
if msg.train == 1 or msg.train == 3:
self.ham.add(msg)
elif msg.train == 0 or msg.train == 2:
self.spam.add(msg)
return new_elements
old_cluster_set = self.cluster_set
if self.size + n <= len(self.dist_list):
self.size += n
else:
print "\nTruncating cluster size...\n"
if len(self.dist_list) > 0:
self.size = len(self.dist_list)
if self.sort_first:
new_cluster_set = set(item[1]
for item in self.dist_list[:self.size])
else:
k_smallest = quickselect.k_smallest
new_cluster_set = set(
item[1] for item in k_smallest(self.dist_list, self.size))
new_elements = list(item for item in new_cluster_set
if item not in old_cluster_set)
self.cluster_set = new_cluster_set
assert (len(self.cluster_set) == self.size), len(self.cluster_set)
for msg in new_elements:
if msg.train == 1 or msg.train == 3:
self.ham.add(msg)
elif msg.train == 0 or msg.train == 2:
self.spam.add(msg)
return new_elements
