def setUp(self):
_FileCorpusBaseTest.setUp(self)
self.directory = 'fctesthamcorpus'
self.cache_size = 100
self.factory = FileMessageFactory()
self.stuff_corpus()
self.corpus = FileCorpus(self.factory, self.directory, '?',
self.cache_size)
def main(argv):
opts, args = getopt.getopt(argv, "h", ["help"])
for opt, arg in opts:
if opt in ("-h", "--help"):
usage()
return
# Create the corpuses and the factory that reads the messages.
if options["pop3proxy", "cache_use_gzip"]:
messageFactory = GzipFileMessageFactory()
else:
messageFactory = FileMessageFactory()
sc = get_pathname_option("Storage", "spam_cache")
hc = get_pathname_option("Storage", "ham_cache")
spamCorpus = FileCorpus(messageFactory, sc)
hamCorpus = FileCorpus(messageFactory, hc)
# Read in all the trained messages.
allTrained = {}
for corpus, disposition in [(spamCorpus, 'Yes'), (hamCorpus, 'No')]:
for m in corpus:
message = mboxutils.get_message(m.getSubstance())
message._pop3CacheDisposition = disposition
allTrained[m.key()] = message
# Sort the messages into the order they arrived, then work out a scaling
# factor for the graph - 'limit' is the widest it can be in characters.
keys = allTrained.keys()
keys.sort()
limit = 70
if len(keys) < limit:
scale = 1
else:
scale = len(keys) // (limit // 2)
# Build the data - an array of cumulative success indexed by count.
count = successful = 0
successByCount = []
for key in keys:
message = allTrained[key]
disposition = message[options["Headers", "classification_header_name"]]
if (message._pop3CacheDisposition == disposition):
successful += 1
count += 1
if count % scale == (scale - 1):
successByCount.append(successful // scale)
# Build the graph, as a list of rows of characters.
size = count // scale
graph = [[" " for i in range(size + 3)] for j in range(size)]
for c in range(size):
graph[c][1] = "|"
graph[c][c + 3] = "."
graph[successByCount[c]][c + 3] = "*"
graph.reverse()
# Print the graph.
print "\n Success of the classifier over time:\n"
print " . - Number of messages over time"
print " * - Number of correctly classified messages over time\n\n"
for row in range(size):
line = ''.join(graph[row])
if row == 0:
print line + " %d" % count
elif row == (count - successful) // scale:
print line + " %d" % successful
else:
print line
print " " + "_" * (size + 2)
def test_filter(self):
self.assertEqual(len(self.corpus.msgs), 3)
# Try again, with all messages.
self.corpus = FileCorpus(self.factory, self.directory, '*',
self.cache_size)
self.assertEqual(len(self.corpus.msgs), 4)
