def verify_classifiers(nagssifiers, fromfile, verbose=0):
nag_teach_file = open(fromfile, 'r')
slack_tag = r"""BREAK"""
correct_per_tag = defaultdict(int)
incorrect_per_tag = defaultdict(int)
for line in nag_teach_file.readlines():
parts = line.split('\t')
tags = literal_eval(parts[1])
tokens = list(tokenize_en(parts[2]))
if len(tags)==0:
# binary classificaiton result.
prob = 1.0
for tag in nagssifiers:
prob *= nagssifiers[tag].get_probability("", tokens)
if prob>=0.5:
correct_per_tag[""]+=1
else:
incorrect_per_tag[""]+=1
if verbose>1:
print("p(C|X)=%f, for C=%s and X=%s" % (prob, slack_tag, tokens))
for tag in tags:
# binary classificaiton result.
prob = nagssifiers[tag].get_probability(tag, tokens)
if prob>=0.5:
correct_per_tag[tag]+=1
else:
incorrect_per_tag[tag]+=1
if verbose>1:
print("p(C|X)=%f, for C=%s and X=%s" % (prob, tag, tokens))
for tag in correct_per_tag.keys():
right = correct_per_tag[tag]
wrong = incorrect_per_tag[tag]
tot = right+wrong
if verbose>0:
if tag=="":
tag = slack_tag
print("%s: %d %% right, %d %% wrong" %
(tag, right*100/tot, wrong*100/tot) )
if verbose==0:
print("They _do_ classify, up the verbiosity for more info.")
def test_if_should_nag(nagssifiers, requiredTags, onActivity, requireAll):
tokens = list(tokenize_en(onActivity))
isSuspicious = False
doingTheTask = False
for tag in requiredTags:
utag = tag.upper()
prob = nagssifiers[utag].get_probability(utag, tokens)
print utag, prob
if prob>=0.5:
# Probably doing a task with this tag
doingTheTask = True
else:
isSuspicious = True
isSuspicious = isSuspicious and requireAll
return True if not doingTheTask or isSuspicious else False
def teach_classifiers(fromfile):
nag_teach_file = open(fromfile, 'r')
# Collect teach data
teach_data = defaultdict(list)
for line in nag_teach_file.readlines():
# Tab delimeted list with format:
# <timestamp> <tokenizable string> <list of tags (labels)>
parts = line.strip().split('\t')
tags = literal_eval(parts[1])
tokens = list(tokenize_en(parts[2]))
if len(tags)==0:
teach_data[""]+=tokens
for tag in tags:
teach_data[tag]+=tokens
nag_teach_file.close()
# Teach new set of classifiers
nagssifiers = defaultdict(NaiveBayes)
for tag,tokens in teach_data.items():
# Shorthand for "not tagged work"
if tag=="":
continue
# Positive samples
nagssifiers[tag].teach_tokens(tag, tokens)
#TODO: Try out negative samples training with
# self.nagssifiers[""].teach_tokens("", teach_data[""])
# how does it affect the accuracy?
# Negative samples
for ntag,ntokens in teach_data.items():
if tag==ntag:
continue
nagssifiers[tag].teach_tokens("", ntokens)
return nagssifiers
