class KappaRater(object):
def __init__(self, S):
this_dir = os.path.dirname(os.path.realpath(__file__))
dir1 = os.path.join(this_dir, S, "G1")
dir2 = os.path.join(this_dir, S, "G2")
self.annotation_task = AnnotationTask(data=self.__readfile(dir1, dir2))
def __readfile(self, *args):
data = []
for i in xrange(len(args)):
lines = self.__get_lines(args[i])
coder = "c"+str(i+1)
for ind, line in enumerate(lines):
item, label = line
d = (coder, str(ind)+"_"+item, label)
# print d
data.append(d)
return data
def __get_lines(self, dir):
lines = []
for root, dirs, files in os.walk(dir):
for file in files:
if file.endswith(".txt"):
# f = open(os.path.join(root, file), 'r')
with io.open(os.path.join(root, file), 'r', newline='', encoding="utf-8") as f: # keep \r\n for .ann positioning
print f
lines += anntoconll.text_to_conll_lines(f)
f.close()
return lines
def kappa(self):
return self.annotation_task.kappa()
class KappaRater(object):
def __init__(self, S):
this_dir = os.path.dirname(os.path.realpath(__file__))
dir1 = os.path.join(this_dir, S, "G1")
dir2 = os.path.join(this_dir, S, "G2")
self.annotation_task = AnnotationTask(data=self.__readfile(dir1, dir2))
def __readfile(self, *args):
data = []
for i in xrange(len(args)):
lines = self.__get_lines(args[i])
coder = "c"+str(i+1)
for ind, line in enumerate(lines):
item, label = line
d = (coder, str(ind)+"_"+item, label)
data.append(d)
return data
def __get_lines(self, dir):
lines = []
for root, dirs, files in os.walk(dir):
for file in files:
if file.endswith(".txt"):
with io.open(os.path.join(root, file), 'r', newline='', encoding="utf-8") as f:
print f
lines += anntoconll.text_to_conll_lines(f)
f.close()
return lines
def kappa(self):
return self.annotation_task.kappa()
def __main__(argv):
if len(argv) != 2:
print("Specify cmd arg")
sys.exit(2)
else:
arg = argv[1]
if arg == 'img':
reliability_mat = getReliabilityMatImg("../data/imageGID_job_map_expt2_corrected.csv")
else:
reliability_mat = getReliabilityMatTurker()
t = AnnotationTask(data=reliability_mat)
print("Calculating the agreement scores")
alpha = t.alpha()
print("Alpha = %f" %alpha)
s = t.S()
print("S = %f" %s)
pi = t.pi()
print("Pi = %f" %pi)
kappa = t.kappa()
print("kappa = %f" %kappa)
def calculate_kappa(filename):
# save labels
label_list = []
with open('data/' + filename + '_data_result.json') as json_file:
tweets = json.load(json_file)
for row in tweets:
label_list.append(row['label'])
# Generate two fake labels to calculate kappa
man_1_label = change_some_values(label_list)
man_2_label = change_some_values(label_list)
# save the labels to a csv file
save_to_csv('data/label_1.csv', man_1_label)
save_to_csv('data/label_2.csv', man_2_label)
# calculate inter annotator agreement
civ_1 = ['c1'] * len(man_1_label)
civ_2 = ['c2'] * len(man_2_label)
item_num_list = range(0, len(man_1_label))
civ_1 = zip(civ_1, item_num_list, man_1_label)
civ_2 = zip(civ_2, item_num_list, man_2_label)
task_data = civ_1 + civ_2
task = AnnotationTask(data=task_data)
# observed disagreement for the weighted kappa coefficient
print 'kappa: ' + str(task.kappa())
def calc_agreements(nr_of_abstracts=150):
# Loop over the abstracts and calculate the kappa and alpha per abstract
aggregate = []
for i in range(0, nr_of_abstracts):
# try:
annotators = round_robin(i)
annotations_A = flatten(get_annotations(i, annotators[0]))
annotations_B = flatten(get_annotations(i, annotators[1]))
annotations = __str_combine_annotations(annotations_A, annotations_B)
a = AnnotationTask(annotations, agreement_fn)
aggregate.append({
"kappa" : a.kappa(),
"alpha" : a.alpha(),
"annotator_A" : annotators[0],
"annotator_B" : annotators[1] })
# except:
# print("Could not calculate kappa for abstract %i" % (i + 1))
# pass
# Summary statistics
kappa = describe([a['kappa'] for a in aggregate])
print("number of abstracts %i" % kappa[0])
print("[kappa] mean: " + str(kappa[2]))
print("[kappa] variance: " + str(kappa[3]))
alpha = describe([a['alpha'] for a in aggregate])
print("[alpha] mean: " + str(alpha[2]))
print("[alpha] variance: " + str(alpha[3]))
def calculate_kappa(filename):
# save labels
label_list = []
with open('data/' + filename + '_data_result.json') as json_file:
tweets = json.load(json_file)
for row in tweets:
label_list.append(row['label'])
# Generate two fake labels to calculate kappa
man_1_label = change_some_values(label_list)
man_2_label = change_some_values(label_list)
# save the labels to a csv file
save_to_csv('data/label_1.csv', man_1_label)
save_to_csv('data/label_2.csv', man_2_label)
# calculate inter annotator agreement
civ_1 = ['c1'] * len(man_1_label)
civ_2 = ['c2'] * len(man_2_label)
item_num_list = range(0, len(man_1_label))
civ_1 = zip(civ_1, item_num_list, man_1_label)
civ_2 = zip(civ_2, item_num_list, man_2_label)
task_data = civ_1 + civ_2
task = AnnotationTask(data=task_data)
# observed disagreement for the weighted kappa coefficient
print 'kappa: ' + str(task.kappa())
def get_kappa(input):
head,body = fio.ReadMatrix(input, True)
data = []
for i,row in enumerate(body):
for coder, label in enumerate(row):
if label == 'a': label = '0'
data.append((head[coder], i, label))
task = AnnotationTask(data)
print head[0], head[1], task.kappa_pairwise(head[0], head[1])
print head[0], head[2], task.kappa_pairwise(head[0], head[2])
print head[1], head[2], task.kappa_pairwise(head[1], head[2])
return task.kappa()
def agree_tags(delta, column):
"""
egytokenes címkézési feladatokra számol egyetértést
:param delta: az összevetett adat
:param column: az az oszlop, amelyre egyetértést akarunk számolni
:return:
"""
by_field = reverse_tags(delta, column)
task = AnnotationTask(data=by_field)
oa = task.avg_Ao() # observed agreement
s = task.S() # Bennett, Albert and Goldstein S (1954) all categories are equally likely
pi = task.pi() # Scott pi (1955) single distribution
kappa = task.kappa() # Cohen kappa (1960) individual coder distribution
w_kappa = task.weighted_kappa()
alpha = task.alpha() # Krippendorff alpha (1980)
return oa, s, pi, kappa, w_kappa, alpha
def compute_annotator_agreement_nltkmetrics(data_array):
''' See http://nltk.org/api/nltk.metrics.html#nltk.metrics.agreement '''
print "####### Agreement coefficients according to NLTK metrics.agreement #######"
t = AnnotationTask(data=data_array)
print "Average observed agreement across all coders and items: "+str(t.avg_Ao())
print "Cohen's Kappa (Cohen 1960): "+str(t.kappa())
print "Weighted kappa (Cohen 1968): "+str(t.weighted_kappa())
print "Scott's pi (Scott 1955): "+str(t.pi())
#print "pi_avg: "+str(t.pi_avg())
print "alpha (Krippendorff 1980): "+str(t.alpha())
print "Observed disagreement for the alpha coefficient: "+str(t.Do_alpha())
print "S (Bennett, Albert and Goldstein 1954): "+str(t.S())
#print "n-notation used in Artstein and Poesio (2007): "+str(t.N(k=, ic???))
print "Observed disagreement for the weighted kappa coefficient averaged over all labelers: "+str(t.Do_Kw())
def kappa(self):
"""Data is a list of list.
Each element is a list :
[annotator, element, label]
"""
if not KAPPA: return 'Not installed'
#if self.last is None: return # must be specific to a feature
data = []
nb = 1
for elem in self.ano:
u1 = elem[1]
u2 = elem[2]
if u1 is None or u2 is None:
continue
else:
data.append([self.ano1.get_code(), nb, u1.get(self.last)])
data.append([self.ano2.get_code(), nb, u2.get(self.last)])
nb += 1
task = AnnotationTask(data)
return task.kappa()
def getagreement(tpl,datadir,task_type='all'):
"""Get agreement values for annotators in the :data:'tpl' list
Args:
tpl (list): combination group of annotators
datadir (str): Cache data directory used by joblib
Returns:
namedtuple defined as ``Agree = collections.namedtuple('Agree', ['kappa', 'alpha','avg_ao'], verbose=True)``
"""
mem = Memory(cachedir=datadir)
readjson=mem.cache(json2taskdata.readjson,mmap_mode='r')
create_task_data= mem.cache(json2taskdata.create_task_data)
count_occurrances=mem.cache(json2taskdata.count_occurrances)
count_labels=mem.cache(json2taskdata.count_labels)
annotators=set()
lectask=[]
#-------------------------------------------------------------------------------
# for each annotator in group tpl
#-------------------------------------------------------------------------------
for stditem in tpl:
aname=stditem.split('.')[0][3:][-2:]
annotators.add(aname)
lecdict=readjson(stditem)
newlectask= create_task_data(lecdict,task_type=task_type,annotator=aname)
label_data=json2taskdata.create_labels_list(newlectask)
abscount=count_occurrances(str(label_data))
yaml.dump(abscount,open(os.path.join( datadir,'abscount-'+aname+'.yaml'),'w'))
setcount=count_labels(newlectask)
yaml.dump(setcount,open(os.path.join( datadir,'setcount-'+aname+'.yaml'),'w'))
lectask=lectask+newlectask
task=AnnotationTask(data=lectask,distance=nltk.metrics.distance.masi_distance_mod)
return {frozenset(annotators): Agree(task.kappa(),task.alpha(),task.avg_Ao())}
def status_view(request, task_id=None):
"""
Renders the evaluation tasks status page for staff users.
"""
LOGGER.info('Rendering evaluation task overview for user "{0}".'.format(
request.user.username))
# Check if user is member in WMT13 group. If so, redirect to wmt13 app.
if request.user.groups.filter(name="WMT13").exists():
LOGGER.info('Redirecting user "{0}" to WMT13 overview.'.format(
request.user.username))
return redirect('appraise.wmt13.views.overview')
if task_id:
task = get_object_or_404(EvaluationTask, task_id=task_id)
headers = task.get_status_header()
status = []
for user in task.users.all():
status.append((user.username, task.get_status_for_user(user)))
scores = None
result_data = []
raw_result_data = Counter()
users = list(task.users.all())
for item in EvaluationItem.objects.filter(task=task):
results = []
for user in users:
qset = EvaluationResult.objects.filter(user=user, item=item)
if qset.exists():
category = str(qset[0].results)
results.append((user.id, item.id, category))
raw_result_data[qset[0].raw_result] += 1
if len(results) == len(users):
result_data.extend(results)
# todo for gisting, calculate - somehow - the percentage of answers against the number of different answers ->
# in that same gap, and also regroup them for readability
_raw_results = []
_keys = raw_result_data.keys()
_total_results = float(sum(raw_result_data.values()))
for key in sorted(_keys):
value = raw_result_data[key]
_raw_results.append((key, value, 100 * value / _total_results))
try:
# Computing inter-annotator agreement only makes sense for more
# than one coder -- otherwise, we only display result_data...
if len(users) > 1:
# Check if we can safely use NLTK's AnnotationTask class.
try:
from nltk.metrics.agreement import AnnotationTask
chk = AnnotationTask(data=[('b', '1', 'k'),
('a', '1', 'k')])
assert(chk == 1.0)
except AssertionError:
LOGGER.debug('Fixing outdated version of AnnotationTask.')
from appraise.utils import AnnotationTask
# We have to sort annotation data to prevent StopIterator errors.
result_data.sort()
annotation_task = AnnotationTask(result_data)
scores = (
annotation_task.alpha(),
annotation_task.kappa(),
annotation_task.S(),
annotation_task.pi()
)
except ZeroDivisionError:
scores = None
except ImportError:
scores = None
dictionary = {
'combined': task.get_status_for_users(),
'commit_tag': COMMIT_TAG,
'headers': headers,
'scores': scores,
'raw_results': _raw_results,
'status': status,
'task_id': task.task_id,
'task_name': task.task_name,
'title': 'Evaluation Task Status',
}
return render(request, 'evaluation/status_task.html', dictionary)
else:
evaluation_tasks = {}
for task_type_id, task_type in APPRAISE_TASK_TYPE_CHOICES:
# We collect a list of task descriptions for this task_type.
evaluation_tasks[task_type] = []
# Super users see all EvaluationTask items, even non-active ones.
if request.user.is_superuser:
_tasks = EvaluationTask.objects.filter(task_type=task_type_id)
else:
_tasks = EvaluationTask.objects.filter(task_type=task_type_id,
active=True)
# Loop over the QuerySet and compute task description data.
for _task in _tasks:
if not APPRAISE_TASK_CACHE.has_key(_task.task_id):
APPRAISE_TASK_CACHE[_task.task_id] = {}
_cache = APPRAISE_TASK_CACHE[_task.task_id]
if not _cache.has_key(request.user.username):
_update_task_cache(_task, request.user)
_task_data = _cache[request.user.username]
# Append new task description to current task_type list.
evaluation_tasks[task_type].append(_task_data)
# If there are no tasks descriptions for this task_type, we skip it.
if len(evaluation_tasks[task_type]) == 0:
evaluation_tasks.pop(task_type)
dictionary = {
'active_page': "STATUS",
'commit_tag': COMMIT_TAG,
'evaluation_tasks': evaluation_tasks,
'title': 'Evaluation Task Status',
}
return render(request, 'evaluation/status.html', dictionary)
data = []
sentiment_r1_5_scale = []
sentiment_r2_5_scale = []
for r1, r2 in zip(sentences_r1, sentences_r2):
sentiment_r1_5_scale.append(int(r1[5]))
data.append((6, r1[0], r1[5]))
sentiment_r2_5_scale.append(int(r2[5]))
data.append((7, r2[0], r2[5]))
if (r1[0] != r2[0]):
print r1[0]
except Exception, e:
print e
# disconnect from server
db.close()
print i
print skll.kappa(sentiment_r1_5_scale, sentiment_r2_5_scale)
annotation = AnnotationTask(data=data)
print annotation.kappa()
print annotation.alpha()
def status_view(request, task_id=None):
"""
Renders the evaluation tasks status page for staff users.
"""
LOGGER.info('Rendering evaluation task overview for user "{0}".'.format(
request.user.username))
# Check if user is member in WMT13 group. If so, redirect to wmt13 app.
if request.user.groups.filter(name="WMT13").exists():
LOGGER.info('Redirecting user "{0}" to WMT13 overview.'.format(
request.user.username))
return redirect('appraise.wmt13.views.overview')
if task_id:
task = get_object_or_404(EvaluationTask, task_id=task_id)
headers = task.get_status_header()
status = []
for user in task.users.all():
status.append((user.username, task.get_status_for_user(user)))
scores = None
result_data = []
raw_result_data = Counter()
users = list(task.users.all())
for item in EvaluationItem.objects.filter(task=task):
results = []
for user in users:
qset = EvaluationResult.objects.filter(user=user, item=item)
if qset.exists():
category = str(qset[0].results)
results.append((user.id, item.id, category))
raw_result_data[qset[0].raw_result] += 1
if len(results) == len(users):
result_data.extend(results)
_raw_results = []
_keys = raw_result_data.keys()
_total_results = float(sum(raw_result_data.values()))
for key in sorted(_keys):
value = raw_result_data[key]
_raw_results.append((key, value, 100 * value / _total_results))
try:
# Computing inter-annotator agreement only makes sense for more
# than one coder -- otherwise, we only display result_data...
if len(users) > 1:
# Check if we can safely use NLTK's AnnotationTask class.
try:
from nltk.metrics.agreement import AnnotationTask
chk = AnnotationTask(data=[('b', '1', 'k'), ('a', '1',
'k')])
assert (chk == 1.0)
except AssertionError:
LOGGER.debug('Fixing outdated version of AnnotationTask.')
from appraise.utils import AnnotationTask
# We have to sort annotation data to prevent StopIterator errors.
result_data.sort()
annotation_task = AnnotationTask(result_data)
scores = (annotation_task.alpha(), annotation_task.kappa(),
annotation_task.S(), annotation_task.pi())
except ZeroDivisionError:
scores = None
except ImportError:
scores = None
dictionary = {
'combined': task.get_status_for_users(),
'commit_tag': COMMIT_TAG,
'headers': headers,
'scores': scores,
'raw_results': _raw_results,
'status': status,
'task_id': task.task_id,
'task_name': task.task_name,
'title': 'Evaluation Task Status',
}
return render(request, 'evaluation/status_task.html', dictionary)
else:
evaluation_tasks = {}
for task_type_id, task_type in APPRAISE_TASK_TYPE_CHOICES:
# We collect a list of task descriptions for this task_type.
evaluation_tasks[task_type] = []
# Super users see all EvaluationTask items, even non-active ones.
if request.user.is_superuser:
_tasks = EvaluationTask.objects.filter(task_type=task_type_id)
else:
_tasks = EvaluationTask.objects.filter(task_type=task_type_id,
active=True)
# Loop over the QuerySet and compute task description data.
for _task in _tasks:
if not APPRAISE_TASK_CACHE.has_key(_task.task_id):
APPRAISE_TASK_CACHE[_task.task_id] = {}
_cache = APPRAISE_TASK_CACHE[_task.task_id]
if not _cache.has_key(request.user.username):
_update_task_cache(_task, request.user)
_task_data = _cache[request.user.username]
# Append new task description to current task_type list.
evaluation_tasks[task_type].append(_task_data)
# If there are no tasks descriptions for this task_type, we skip it.
if len(evaluation_tasks[task_type]) == 0:
evaluation_tasks.pop(task_type)
dictionary = {
'active_page': "STATUS",
'commit_tag': COMMIT_TAG,
'evaluation_tasks': evaluation_tasks,
'title': 'Evaluation Task Status',
}
return render(request, 'evaluation/status.html', dictionary)
def annotation(output):
t = AnnotationTask(data=[x.split() for x in open(output)])
print "\nAverage observed agreement: " + str(t.avg_Ao())
print "\nKappa: " + str(t.kappa())
def dispatch(self, request, *args, **kwargs):
self.task = get_object_or_404(Task, pk=self.kwargs['pk'])
self.array = []
self.kappa = []
self.kappa1 = []
self.kappa_name = "/media/csvfileFinal.csv"
self.eval_name = "/media/csvfileP.csv"
self.kappa_nameLong = "/media/csvfileFinal.csv"
self.lblr = []
self.head = []
self.coder_emails = PostResponse.objects.filter(
task=self.task.pk).values_list(
'responder__email',
flat=True).distinct().order_by('responder__email')
post_list = self.task.post_list.all()
if os.path.exists(
'/home/salae001/new/LabelingSystem-master/labelingsystem/media/csvfileFinal.csv'
):
print('existsssss')
os.remove(
'/home/salae001/new/LabelingSystem-master/labelingsystem/media/csvfileFinal.csv'
)
if os.path.exists(
'/home/salae001/new/LabelingSystem-master/labelingsystem/media/csvfileP.csv'
):
os.remove(
'/home/salae001/new/LabelingSystem-master/labelingsystem/media/csvfileP.csv'
)
if os.path.exists(
'/home/salae001/new/LabelingSystem-master/labelingsystem/media/csvfile.csv'
):
os.remove(
'/home/salae001/new/LabelingSystem-master/labelingsystem/media/csvfile.csv'
)
name = "media/csvfileP.csv" # + str(self.task.pk)
self.eval_name = "/" + name
# print (name)
#dateVal =datetime.datetime.now()
filepp = open(str(name), "w+")
filepp.write(',')
for coder_email in self.coder_emails:
filepp.write(coder_email)
filepp.write(',')
filepp.write('Majority Vote')
filepp.write('\n')
voteList = {}
listTemp = []
cpr = 0
for post in post_list:
row = []
cpr = cpr + 1
#if cpr > 6:
# row.append('...')
# break
row.append(post.content)
filepp.write(post.content)
filepp.write(',')
i = 0
if len(self.coder_emails) > 5:
self.coder_emails_temp = self.coder_emails[0:5]
temp_emails = self.coder_emails
self.coder_emails_temp.append("(List continues...)")
#for coder_email in temp_emails:
# filepp.write(coder_email)
# filepp.write(';')
#filepp.write('\n')
voteList = {}
listTemp = []
for coder_email in temp_emails:
# if len(self.coder_emails) > 5 and coder_email == "(List continues...)":
# label = '...'
# else :
# print ('/....N?A////')
label = 'N/A'
try:
post_response = PostResponse.objects.filter(
task=self.task.pk,
post=post.pk,
responder__email=coder_email).last()
label = post_response.label
#print('label...',label)
#filepp.write(coder_email)
# filepp.write(';')
filepp.write(str(label))
# myMap = {}
listTemp.append(str(label))
# maximum = ( '', 0 ) # (occurring element, occurrences)
# for n in :
# if n in voteList:voteList[n] += 1
# else: voteList[n] = 1
# # Keep track of maximum on the go
# if voteList[n] > maximum[1]: maximum = (n,voteList[n])
filepp.write(',')
except:
filepp.write('N/A')
listTemp.append('N/A')
filepp.write(',')
pass
#if len(self.coder_emails) > 5:
# label = '...'
# row.append(label)
# filepp
maximum = ('', 0) # (occurring element, occurrences)
for n in listTemp:
if n in voteList:
voteList[n] += 1
else:
voteList[n] = 1
# Keep track of maximum on the go
if voteList[n] > maximum[1]:
maximum = (n, voteList[n])
# filepp.write(';')
#print('maximum', maximum)
# filepp.write(maximum[0])
# filepp.write('\n')
else:
self.coder_emails_temp = self.coder_emails
voteList = {}
listTemp = []
i = 0
for coder_email in self.coder_emails_temp:
#i = i+1
#if i>6: #self.coder_emails) > 5 and coder_email == "(List continues...)":
# break
if len(self.coder_emails
) > 5 and coder_email == "(List continues...)":
# print ('coder email-----------')
label = '...'
#continue
#try:
# post_response = PostResponse.objects.filter(task=self.task.pk, post=post.pk, responder__email=coder_email).last()
#print (post_response)
#label = post_response.label
#filepp.write(str(label))
#filepp.write(';')
# listTemp.append(str(label))
#except:
# filepp.write('N/A')#listTemp.append('N/A')
#filepp.write(';')
# listTemp.append(str(label))
#pass
else:
label = 'N/A'
try:
post_response = PostResponse.objects.filter(
task=self.task.pk,
post=post.pk,
responder__email=coder_email).last()
print(post_response)
label = post_response.label
if len(self.coder_emails) <= 5:
filepp.write(str(label))
filepp.write(',')
listTemp.append(str(label))
except:
if len(self.coder_emails) <= 5:
filepp.write('N/A') #listTemp.append('N/A')
filepp.write(',')
listTemp.append(str(label))
pass
row.append(label)
maximum = ('', 0)
for n in listTemp:
if n in voteList:
voteList[n] += 1
else:
voteList[n] = 1
# Keep track of maximum on the go
if voteList[n] > maximum[1]:
maximum = (n, voteList[n])
#filepp.write(';')
filepp.write(maximum[0])
filepp.write('\n')
# i = i+1
# maximum = ( '', 0 ) # (occurring element, occurrences)
# for n in listTemp:
# if n in voteList:
# voteList[n] += 1
# else:
# voteList[n] = 1
# Keep track of maximum on the go
# if voteList[n] > maximum[1]:
# maximum = (n,voteList[n])
#filepp.write(';')
# print('maximum', maximum)
# filepp.write(maximum[0])
# filepp.write('\n')
# row.append(maximum[0])
#row.append(label)
# self.coder_emails_temp.append("(List continues...)")
# row.append(maximum[0])
# self.array.append(row)
#maximum = ( '', 0 ) # (occurring element, occurrences)
#for n in listTemp:
# if n in voteList:
# voteList[n] += 1
# else:
# voteList[n] = 1
# # Keep track of maximum on the go
# if voteList[n] > maximum[1]:
# maximum = (n,voteList[n])
# #filepp.write(';')
#print('maximum', maximum)
#row.append(label)
maximum = ('', 0) # (occurring element, occurrences)
for n in listTemp:
if n in voteList:
voteList[n] += 1
else:
voteList[n] = 1
# Keep track of maximum on the go
if voteList[n] > maximum[1]:
maximum = (n, voteList[n])
#filepp.write(';')
print('maximum', maximum)
#filepp.write(maximum[0])
#filepp.write('\n')
row.append(maximum[0])
self.array.append(row)
try:
annotation_triplet_list = []
post_response_list = PostResponse.objects.filter(task=self.task.pk)
#rint (post_response_list)
post_response_t = [
part.encode("utf8") for part in PostResponse.objects.filter(
task=self.task.pk).values_list('responder__email',
flat=True).distinct()
]
lst_rp = []
triple_list = []
ctr = 0
# for post_response in post_response_t:
# post_response = str(post_response).replace('b\'', '')
# post_response = post_response.replace('\'', '')
# lst_rp.append(post_response)
# print (post_response)
#triple_list.append([])
#triple_list[ctr].append(post_response)
#triple_list[ctr].append(post_response)
#triple_list[ctr].append('0')
#ctr = ctr + 1
#print (triple_list)
# Get all combinations of [1, 2, 3]
# and length 2
# print (post_response)
#post_response = post_response.replace('b', '\'')
# print(post_response)
#print ('here')
#print("post_reposne", post_response_t)
if len(post_response_t) > 5:
post_response_t_temp = post_response_t[0:5]
for post_response in post_response_t_temp:
post_response = str(post_response).replace('b\'', '')
post_response = post_response.replace('\'', '')
lst_rp.append(post_response)
lst_rp.append("(List continues...)")
comb_temp = combinations(post_response_t, 2)
for i in list(comb_temp):
#print ("in the comb")
annotation_triplet_list = []
ip = []
sp = ""
# ct = ctr + 1
#print (ctr)
temp = str(i[0]).replace('b\'', '')
temp = temp.replace('\'', '')
if ([temp, temp, '0'] not in triple_list):
triple_list.append([])
triple_list[ctr].append(temp)
triple_list[ctr].append(temp)
triple_list[ctr].append('0')
ctr = ctr + 1
triple_list.append([])
for s in i:
st = str(s).replace('b\'', '')
st = st.replace('\'', '')
ip.append(st)
triple_list[ctr].append(st)
#triple_list[ctr].append(i[0])
#triple_list[ctr].append(i[0])
#triple_list[ctr].append(0)
print(triple_list[ctr])
for post_response in post_response_list:
# print(post_response)
# print(ip, post_response.responder.email)
if (post_response.responder.email in ip):
annotation_triplet = (
post_response.responder.email,
post_response.post.content,
post_response.label.content)
# print (post_response.responder.email)
# print(annotation_triplet)
annotation_triplet_list.append(annotation_triplet)
t = AnnotationTask(annotation_triplet_list)
#print("kappa " +str(t.kappa()))
triple_list[ctr].append(str(t.kappa()))
#str(t.kappa()))
self.lblr.append(triple_list)
ctr = ctr + 1
self.alpha1 = t.alpha()
# print (triple_list)
self.kappa1.append(triple_list)
#print ('before EXPORT')
# exportCSV(triple_list, self.alpha1, self.coder_emails)
# print ('in export CSV')
#with open('result.csv','w') as file:
#print(self.task)
name = "media/csvfile.csv" #+ str(self.task.pk)
self.kappa_nameLong = "/" + name
print(name)
#dateVal =datetime.datetime.now()
filep = open(str(name), "w+")
#print ('here in csv')
#print (filep)
i = 0
ct = 1
filep.write(',')
prev_email = 's'
#if 's' is not '*****@*****.**':
# print (True)
for email in triple_list:
# print (email)
if email[0] != prev_email:
#print ('in email 0', email[0])
prev_email = email[0]
filep.write(email[0])
filep.write(',')
filep.write(email[1])
filep.write('\n')
for row in triple_list:
# print (row[0], row[1], row[2])
#filep.write(row[0])
#filep.write(row[0])
# for i in range(0, len(self.coder_emails)):
# filep.write(row[0])
# filep.write(';')
# print (i)
if i == 0 or i == ct - 1:
filep.write(row[0])
filep.write(',')
# print('row 0', row[0])
for k in range(0, i):
filep.write('--,')
# filep.write(row[0])
# filep.write(';')
if i == len(self.coder_emails) - 1:
i = ct
filep.write(row[2])
# filep.write(row[0])
# print (ct)
# print (range(0,ct))
# for k in range(0,ct) :
# filep.write('--;')
filep.write('\n')
ct = ct + 1
else:
i = i + 1
# filep.write('--')
filep.write(row[2])
filep.write(',')
#filep.write('\n')
filep.close()
#or col in row:
#file.write(col)
#print (triple_list)
# for rowp in triple_list:
# print (rowp)
# if forloop.counter != forloop.parentloop.counter:
# if col in rowp:
# file.write(rowp)
# file.close()
#self.kappa.append(triple_list)
else:
post_response_t_temp = post_response_t
for post_response in post_response_t_temp:
post_response = str(post_response).replace('b\'', '')
post_response = post_response.replace('\'', '')
lst_rp.append(post_response)
print(post_response)
self.head.append(lst_rp)
comb = combinations(post_response_t_temp, 2)
# print (comb)
#ctr = 0
#triple_list = []
ip = []
lst_rp = []
triple_list = []
ctr = 0
#triple_list.append([])
#triple_list.append(post_response_t)
# Print the obtained combinations
# for iv in list(comb):
# annotation_triplet_list = []
# triple_list.append([])
#print (i[0])
# temp = str(iv[0]).replace('b\'', '')
# temp = temp.replace('\'', '')
# if ([temp,temp,'0'] not in triple_list) :
# triple_list.append([])
# triple_list[ctr].append(temp)
# triple_list[ctr].append(temp)
# triple_list[ctr].append('0')
# ctr = ctr + 1
# print (triple_list)
# print (triple_list)
# comb = combinations(post_response_t, 2)
for i in list(comb):
# print ("in the comb")
annotation_triplet_list = []
ip = []
sp = ""
# ct = ctr + 1
print(ctr)
temp = str(i[0]).replace('b\'', '')
temp = temp.replace('\'', '')
if ([temp, temp, '0'] not in triple_list):
triple_list.append([])
triple_list[ctr].append(temp)
triple_list[ctr].append(temp)
triple_list[ctr].append('0')
ctr = ctr + 1
triple_list.append([])
for s in i:
# print (s)
#print (sp)
#p.join(s)
#p.join(" , ")
# print ("sds"+s)
#print (s)
# sp = sp+s+" , "
st = str(s).replace('b\'', '')
st = st.replace('\'', '')
print(st)
ip.append(st)
triple_list[ctr].append(st)
#triple_list[ctr].append(i[0])
#triple_list[ctr].append(i[0])
#triple_list[ctr].append(0)
print(triple_list[ctr])
#triple_list.append(sp)
# print(triple_list)
#print(post_response_list)
for post_response in post_response_list:
# print(post_response)
# print(ip, post_response.responder.email)
if (post_response.responder.email in ip):
annotation_triplet = (post_response.responder.email,
post_response.post.content,
post_response.label.content)
# print (post_response.responder.email)
# print(annotation_triplet)
annotation_triplet_list.append(annotation_triplet)
t = AnnotationTask(annotation_triplet_list)
#print("kappa " +str(t.kappa()))
triple_list[ctr].append(str(t.kappa()))
#str(t.kappa()))
self.lblr.append(triple_list)
ctr = ctr + 1
if len(post_response_t) > 5:
self.alpha = self.alpha1
else:
self.alpha = t.alpha()
print(triple_list)
self.kappa.append(triple_list)
name = "media/csvfileFinal.csv" #+ str(self.task.pk)
self.kappa_name = "/" + name
#print (name)
#dateVal =datetime.datetime.now()
filep = open(str(name), "w+")
#print ('here in csv')
#print (filep)
i = 0
ct = 1
filep.write(',')
prev_email = 's'
#if 's' is not '*****@*****.**':
# print (True)
for email in triple_list:
# print (email)
if email[0] != prev_email:
prev_email = email[0]
filep.write(email[0])
filep.write(',')
filep.write(email[1])
filep.write('\n')
for row in triple_list:
# print (row[0], row[1], row[2])
#filep.write(row[0])
#filep.write(row[0])
# for i in range(0, len(self.coder_emails)):
# filep.write(row[0])
# filep.write(';')
# print (i)
if i == 0 or i == ct - 1:
filep.write(row[0])
filep.write(',')
# print('row 0', row[0])
for k in range(0, i):
filep.write('--,')
# filep.write(row[0])
# filep.write(';')
if i == len(self.coder_emails) - 1:
i = ct
filep.write(row[2])
# filep.write(row[0])
# print (ct)
# print (range(0,ct))
# for k in range(0,ct) :
# filep.write('--;')
filep.write('\n')
ct = ct + 1
else:
i = i + 1
# filep.write('--')
filep.write(row[2])
filep.write(',')
#filep.write('\n')
filep.close()
#self.kappa.append(triple_list)
# print (self.kappa)
except:
self.alpha = 'N/A'
name = "media/csvfileFinal.csv" #+ str(self.task.pk)
self.kappa_name = "/" + name
filep = open(str(name), "w+")
return super(TaskEvaluationDetailView,
self).dispatch(request, *args, **kwargs)
'''
if ('0' in line):
print((annotator, word, '0'))
data.append((annotator, '0', word))
if ('1' in line):
data.append((annotator, '1', word))
if ('2' in line):
data.append((annotator, '2', word))
with open("annotd") as d:
content = d.readlines()
for line in content:
appender('d', line)
with open("annots") as d:
content = d.readlines()
for line in content:
appender('s', line)
with open("annots") as d:
content = d.readlines()
for line in content:
appender('k', line)
task = AnnotationTask(data)
#print(task.avg_Ao())
print(task.kappa())
"""
Compute the inter-annotator agreement
"""
import nltk
from nltk.metrics.agreement import AnnotationTask
t1 = AnnotationTask(data=[x.split() for x in open("1.txt")])
print t1.kappa()
t2 = AnnotationTask(data=[x.split() for x in open("2.txt")])
print t2.kappa()
def annotation(output): t = AnnotationTask(data=[x.split() for x in open(output)]) print "\nAverage observed agreement: " + str(t.avg_Ao()) print "\nKappa: " + str(t.kappa());
data = pd.read_csv('../input_data/labels-C.csv', sep=';', index_col=0)
allcoders = data.columns
experts = ['KEY', 'MG', 'MS', 'TM']
novices = ['KEY', 'CK', 'GK', 'RM']
cols = novices
# Total values
taskdata = []
for coder in cols:
for i in data[coder].index:
taskdata.append([coder, i, data[coder][i]])
ratingtask = AnnotationTask(data=taskdata)
print("kappa " + str(ratingtask.kappa()))
print("fleiss " + str(ratingtask.multi_kappa()))
print("alpha " + str(ratingtask.alpha()))
print("scotts " + str(ratingtask.pi()))
# Pairwise values
similarities = []
for coders in itertools.product(cols, repeat=2):
if coders[0] == coders[1]:
similarities.append(1)
else:
taskdata = []
for coder in coders:
for i in data[coder].index:
taskdata.append([coder, i, data[coder][i]])
from nltk.metrics.agreement import AnnotationTask
from nltk.metrics import ConfusionMatrix
# Here we have four items, each labeled by two different annotators. In two cases, the annotators agree. In two cases they don't.
toy_data = [
# annotators, element, label
['1', 5723, 'ORG'],
['2', 5723, 'ORG'],
['1', 55829, 'LOC'],
['2', 55829, 'LOC'],
['1', 259742, 'PER'],
['2', 259742, 'LOC'],
['1', 269340, 'PER'],
['2', 269340, 'LOC']
]
task = AnnotationTask(data=toy_data)
print(task.kappa())
print(task.alpha())
# 16h52 : Yes ! ça marche !
# L'annotateur est remplacé par une division en deux variables
# L'élément est remplacé par la position dans la liste
toy1 = ['ORG', 'LOC', 'PER', 'PER']
toy2 = ['ORG', 'LOC', 'LOC', 'LOC']
cm = ConfusionMatrix(toy1, toy2)
print(cm)
# multilabel pour une classe (un but)
# only 2 utilisateurs
rater1 = ['no', 'no', 'no', 'no', 'no', 'yes', 'no', 'no', 'no', 'no']
rater2 = ['yes', 'no', 'no', 'yes', 'yes', 'no', 'yes', 'yes', 'yes', 'yes']