def one_or_more_timeref_redblk(note):
note = n2vals(note)
timeCount = ca.daysofweek(note)['daysofweek'] if type(
ca.daysofweek(note)) == dict else ca.daysofweek(note)[1]
timeCount += ca.months(note)['months'] if type(
ca.months(note)) == dict else ca.months(note)[1]
return 'red' if timeCount > 0 else 'black'
def one_or_more_daysofweek_redblk(note):
note = n2vals(note)
lines = ca.daysofweek(note)
if type(lines) == dict:
val = lines['daysofweek']
else:
val = lines[1]
return 'red' if lines > 0 else 'black'
def one_or_more_verbs_redblk(note):
note = n2vals(note)
lines = ca.note_verbs(note)
if type(lines) == dict:
val = lines['note_verbs']
else:
val = lines[1]
return 'red' if val > 0 else 'black'
def one_or_more_verbs_redblk(note):
note = n2vals(note)
lines = ca.note_verbs(note)
if type(lines) == dict:
val = lines['note_verbs']
else:
val = lines[1]
return 'red' if val > 0 else 'black'
def one_or_more_daysofweek_redblk(note):
note = n2vals(note)
lines = ca.daysofweek(note)
if type(lines) == dict:
val = lines['daysofweek']
else:
val = lines[1]
return 'red' if lines > 0 else 'black'
def one_or_more_lines_multicolor(note):
note = n2vals(note)
lines = ca.note_lines(note)
if type(lines) == dict:
val = lines['note_lines']
else:
val = lines[1]
if val == 1: return 'black'
if val in [2, 3, 4, 5]: return 'blue'
if val in range(6, 12): return 'orange'
return 'red'
def one_or_more_lines_multicolor(note):
note = n2vals(note)
lines = ca.note_lines(note)
if type(lines) == dict:
val = lines['note_lines']
else:
val = lines[1]
if val == 1: return 'black'
if val in [2,3,4,5]: return 'blue'
if val in range(6,12): return 'orange'
return 'red'
def user_note_rank_spearman(u):
try:
print u
import jv3.study.thesis_figures as tfigs
ff = [note_rank(tfigs.n2vals(n)) for n in u.note_owner.order_by('created')]
ff = [x['note_rank'] for x in ff if x['note_rank'] is not None]
reverse_standard = range(len(ff))
reverse_standard.reverse()
return r('cor.test')(c(ff),c(reverse_standard),method='spearman')[3][0]
except :
import sys
print sys.exc_info()
return None
def user_note_rank_spearman(u):
try:
print u
import jv3.study.thesis_figures as tfigs
ff = [
note_rank(tfigs.n2vals(n))
for n in u.note_owner.order_by('created')
]
ff = [x['note_rank'] for x in ff if x['note_rank'] is not None]
reverse_standard = range(len(ff))
reverse_standard.reverse()
return r('cor.test')(c(ff), c(reverse_standard),
method='spearman')[3][0]
except:
import sys
print sys.exc_info()
return None
def two_or_more_sentences_redblk(note): note = n2vals(note) senCount = ca.note_sentences(note)['note_sentences'] if type(ca.note_sentences(note)) == dict else ca.note_sentences(note)[1] return 'red' if senCount > 1 else 'black'
def two_or_more_sentences_redblk(note):
note = n2vals(note)
senCount = ca.note_sentences(note)['note_sentences'] if type(
ca.note_sentences(note)) == dict else ca.note_sentences(note)[1]
return 'red' if senCount > 1 else 'black'
def one_or_more_timeref_redblk(note): note = n2vals(note) timeCount = ca.daysofweek(note)['daysofweek'] if type(ca.daysofweek(note)) == dict else ca.daysofweek(note)[1] timeCount += ca.months(note)['months'] if type(ca.months(note)) == dict else ca.months(note)[1] return 'red' if timeCount > 0 else 'black'
def one_or_more_todoref_redblk(note): note = n2vals(note) todoCount = ca.note_todos(note)['note_todos'] if type(ca.note_todos(note) ) == dict else ca.note_todos(note)[1] return 'red' if todoCount > 0 else 'black'
def one_or_no_url_redblk(note):
note = n2vals(note)
urls = ca.note_urls(note)
if type(urls) == dict:
return 'red' if urls['note_urls'] > 0 else 'black'
return 'red' if urls[1] > 0 else 'black'
def initMaxEntModel(encoding, weights):
maxEntModel = nltk.MaxentClassifier(encoding, weights)
return maxEntModel
def refClassifier():
## Ref and nonRef thingies
refNotes, nonRefNotes = splitNotes('reference')
labels = ['reference', 'nonref']
mapping, weights = getStdMapping()
encoding = nltk.BinaryMaxentFeatureEncoding(labels, mapping)
maxEntModel = initMaxEntModel(encoding, weights)
nvals = lambda note: tfigs.n2vals(note)
pos_word = lambda word: nltk.pos_tag([word])[0][1]
def pos_word(word):
return nltk.pos_tag([word])[0][1]
def word_is_verb(word):
return pos_word(word) in ["VB", "VBD", "VBG", "VBN", "VBP", "VBZ"]
def word_is_noun(word):
return pos_word(word) in ["NN", "NNS", "NNP", "NNPS"]
def one_or_no_email_redblk(note):
note = n2vals(note)
emails = ca.note_emails(note)
if type(emails) == dict:
return 'red' if emails['email_addrs'] > 0 else 'black'
return 'red' if emails[1] > 0 else 'black'
def one_or_no_email_redblk(note):
note = n2vals(note)
emails = ca.note_emails(note)
if type(emails) == dict:
return 'red' if emails['email_addrs'] > 0 else 'black'
return 'red' if emails[1] > 0 else 'black'
def compute_feature_named(feature_name,n):
if getattr(ca,feature_name,None):
return getattr(ca,feature_name)(n2vals(n))[feature_name] ## compute the feature given the note!
return None
notCat.append(Note.objects.filter(id=k)[0])
return (cat, notCat)
def initMaxEntModel(encoding, weights):
maxEntModel = nltk.MaxentClassifier(encoding, weights)
return maxEntModel
def refClassifier():
## Ref and nonRef thingies
refNotes, nonRefNotes = splitNotes('reference')
labels = ['reference', 'nonref']
mapping, weights = getStdMapping()
encoding = nltk.BinaryMaxentFeatureEncoding(labels, mapping)
maxEntModel = initMaxEntModel(encoding, weights)
nvals = lambda note : tfigs.n2vals(note)
pos_word = lambda word: nltk.pos_tag([word])[0][1]
def pos_word(word):
return nltk.pos_tag([word])[0][1]
def word_is_verb(word):
return pos_word(word) in ["VB","VBD","VBG","VBN","VBP","VBZ"]
def word_is_noun(word):
return pos_word(word) in ["NN","NNS","NNP","NNPS"]
try:
pass
#mem = mem
#if mem == None:
def one_or_more_todoref_redblk(note):
note = n2vals(note)
todoCount = ca.note_todos(note)['note_todos'] if type(
ca.note_todos(note)) == dict else ca.note_todos(note)[1]
return 'red' if todoCount > 0 else 'black'
def one_or_no_url_redblk(note):
note = n2vals(note)
urls = ca.note_urls(note)
if type(urls) == dict:
return 'red' if urls['note_urls'] > 0 else 'black'
return 'red' if urls[1] > 0 else 'black'
def compute_feature_named(feature_name, n):
if getattr(ca, feature_name, None):
return getattr(ca, feature_name)(
n2vals(n))[feature_name] ## compute the feature given the note!
return None