def process_run(args, run_file_name, annotation, description, thresh):
'''
compute scores and generate output files for a single run
:returns dict: max_scores for this one run
'''
## Generate confusion matrices from a run for each target_id
## and for each step of the confidence cutoff
stats = build_confusion_matrix(
os.path.join(args.run_dir, run_file_name) + '.gz',
annotation, args.cutoff_step, args.unan_is_true, args.include_training,
thresh=thresh,
require_positives=args.require_positives,
debug=args.debug)
compile_and_average_performance_metrics(stats)
max_scores = find_max_scores(stats)
log(json.dumps(stats, indent=4, sort_keys=True))
base_output_filepath = os.path.join(
args.run_dir,
run_file_name + '-' + description)
output_filepath = base_output_filepath + '.csv'
write_performance_metrics(output_filepath, stats)
## Output a graph of the key performance statistics
graph_filepath = base_output_filepath + '.png'
write_graph(graph_filepath, stats)
return max_scores
def process_run(args, run_file_name, annotation, description, thresh):
'''
compute scores and generate output files for a single run
:returns dict: max_scores for this one run
'''
## Generate confusion matrices from a run for each target_id
## and for each step of the confidence cutoff
stats = build_confusion_matrix(os.path.join(args.run_dir, run_file_name) +
'.gz',
annotation,
args.cutoff_step,
args.unan_is_true,
args.include_training,
thresh=thresh,
require_positives=args.require_positives,
debug=args.debug)
compile_and_average_performance_metrics(stats)
max_scores = find_max_scores(stats)
log(json.dumps(stats, indent=4, sort_keys=True))
base_output_filepath = os.path.join(args.run_dir,
run_file_name + '-' + description)
output_filepath = base_output_filepath + '.csv'
write_performance_metrics(output_filepath, stats)
## Output a graph of the key performance statistics
graph_filepath = base_output_filepath + '.png'
write_graph(graph_filepath, stats)
return max_scores
def score_all_runs(args, description, reject):
'''
score all the runs in the specified runs dir using the various
filters and configuration settings
:param description: string used for file names
:param reject: callable to rejects truth data
'''
if args.include_neutral:
thresh = 0
elif args.include_useful:
thresh = 1
else:
thresh = 2
## Load in the annotation data
annotation = load_annotation(args.annotation, thresh,
args.min_len_clean_visible, reject,
require_positives=args.require_positives
)
log( 'This assumes that all run file names end in .gz' )
#import gc
#from guppy import hpy
#hp = hpy()
run_count = 0
team_scores = defaultdict(lambda: defaultdict(dict))
for run_file in os.listdir(args.run_dir):
if not run_file.endswith('.gz'):
continue
if args.run_name_filter and not run_file.startswith(args.run_name_filter):
continue
## take the name without the .gz
run_file_name = '.'.join(run_file.split('.')[:-1])
log( 'processing: %s.gz' % run_file_name )
max_scores = process_run(args, run_file_name, annotation, description, thresh)
## split into team name and create stats file
team_id, system_id = run_file_name.split('-')
team_scores[team_id][system_id] = max_scores
#gc.collect()
#log(str(hp.heap()))
run_count += 1
#if run_count > 2:
# break
## When folder is finished running output a high level summary of the scores to overview.csv
write_team_summary(description, team_scores)
def score_confusion_matrix_OVERLAP(CM, DOCS_TPs, annotation, positives,
cutoff_step_size=50, unannotated_is_TN=False,
debug=False):
'''
construct OVERLAP_TPs by excluding from DOCS_TPs those assertions
that do not overlap any string identified by an assessor
'''
cutoffs = range(0, 999, cutoff_step_size)
OVERLAP_TPs = list()
for rec in DOCS_TPs:
(stream_id, target_id, conf, rating, contains_mention,
date_hour, slot_type, runs_equiv_id, start_byte, end_byte) = rec
start_byte = int(start_byte)
end_byte = int(end_byte)
for true_equiv_id, equiv_class in annotation[stream_id][target_id][slot_type].items():
offsets = equiv_class['stream_ids'][stream_id][1]
overlaps = False
for offset in offsets:
assert isinstance(offset[0], int)
assert isinstance(offset[1], int)
if start_byte <= offset[1] and end_byte >= offset[0]:
overlaps = True
break
log('(%d, %d) compared to offsets %r\n' % (start_byte, end_byte, offsets))
if not overlaps:
increment_CM(False, conf, cutoffs, CM, OVERLAPS, unannotated_is_TN)
#log('found one!! system equiv_id (%r) --> assessors equiv_id (%r)'
# % (runs_equiv_id, true_equiv_id))
rec = list(rec)
rec[7] = (runs_equiv_id, true_equiv_id)
rec = tuple(rec)
OVERLAP_TPs.append(rec)
increment_CM(True, conf, cutoffs, CM, OVERLAPS, unannotated_is_TN)
correct_FN(CM, OVERLAPS, positives)
return CM, OVERLAP_TPs
def load_annotation(path_to_annotation_file, include_useful, include_neutral, min_len_clean_visible, reject):
'''
Loads the annotation file into a dict
path_to_annotation_file: string filesystem path to the annotation file
include_useful: true to include docs marked useful and vital
reject: callable that returns boolean given a target_id
'''
annotation_file = csv.reader(open(path_to_annotation_file, 'r'), delimiter='\t')
annotation = dict()
for row in annotation_file:
## Skip comments
if row[0][0] == "#":
continue
stream_id = row[2]
target_id = row[3]
rating = int(row[5])
if len(row) == 12:
## only the later versions of the truth data carried this
## twelve column for excluding documents with insufficient
## clean_visible to be judged. We use a default cutoff of
## 100 bytes which means removing these counts below:
# (stream_id, target_id) pairs: 34921 above, and 15767 below 100 bytes of clean_visible
# (assessor_id, stream_id, target_id) pairs: 47446 above, and 19948 below 100 bytes of clean_visible
len_clean_visible = int(row[11])
if len_clean_visible < min_len_clean_visible:
log('excluding stream_id=%s for len(clean_visible)=%d' % (stream_id, len_clean_visible))
continue
if reject(target_id):
log('excluding truth data for %s' % target_id)
continue
if include_neutral:
thresh = 0
elif include_useful:
thresh = 1
else:
thresh = 2
## Add the stream_id and target_id to a hashed dictionary
## 0 means that its not vital 1 means that it is vital
if (stream_id, target_id) in annotation:
## 2 means the annotators gave it a yes for vitality
if rating < thresh:
annotation[(stream_id, target_id)] = False
else:
## store bool values in the annotation index
annotation[(stream_id, target_id)] = rating >= thresh
num_true = sum(map(int, annotation.values()))
log('loaded annotation to create a dict of %d (stream_id, target_id) pairs with %d True' % (len(annotation), num_true))
if num_true == 0:
sys.exit('found no true positives given the filters')
return annotation
def score_confusion_matrix_DATE_HOUR(CM, FILL_TPs, annotation, positives,
cutoff_step_size=50, unannotated_is_TN=False,
debug=False):
'''
construct DATE_HOUR_TPs by excluding from FILL_TPs those
assertions that happen after the first one
'''
cutoffs = range(0, 999, cutoff_step_size)
## FILL_TPs are already in date_hour order, so we only have to
## count the first one for each equiv_id
seen = set()
DATE_HOUR_TPs = list()
log('considering %d unique DATE_HOUR assertions' % len(FILL_TPs))
for rec in FILL_TPs:
(stream_id, target_id, conf, rating, contains_mention,
date_hour, slot_type, equiv_id, start_byte, end_byte) = rec
if positives[DATE_HOUR].get(target_id, 0) == 0:
log('ignoring assertion on entity for which no DATE_HOUR positives are known: %s' % target_id)
continue
if equiv_id in seen:
increment_CM(False, conf=conf, cutoffs=cutoffs, CM=CM, mode=DATE_HOUR,
target_id=target_id,
unannotated_is_TN=unannotated_is_TN)
continue
## this way of filtering is inadequate -- should be giving
## partial credit for finding slot fill late
seen.add(equiv_id)
DATE_HOUR_TPs.append(rec)
increment_CM(True, conf=conf, cutoffs=cutoffs, CM=CM, mode=DATE_HOUR,
target_id=target_id,
unannotated_is_TN=unannotated_is_TN)
for cutoff in CM[DATE_HOUR][target_id]:
## Then subtract the number of TP at each cutoffs
## (since FN+TP==True things in annotation set)
CM[DATE_HOUR][target_id][cutoff]['FN'] = \
positives[DATE_HOUR][target_id] - CM[DATE_HOUR][target_id][cutoff]['TP']
log('considering %d DATE_HOUR_TPs' % len(DATE_HOUR_TPs))
return CM, DATE_HOUR_TPs
def load_annotation(path_to_annotation_file, reject, slot_type_filter=None):
'''
Loads the SSF truth data from its JSON format on disk
path_to_annotation_file: string file system path to the JSON annotation file
reject: callable that returns boolean given a target_id
'''
try:
native_annotation = json.load(open(path_to_annotation_file))
except Exception, exc:
sys.exit( 'failed to open %r:\n%s' % (path_to_annotation_file, traceback.format_exc(exc)) )
for target_id in native_annotation.keys():
if reject(target_id):
log('excluding truth data for %s' % target_id)
native_annotation.pop(target_id)
## invert the annotation file to have a stream_id index pointing
## to target_ids point to slot_types pointing to slot fills,
## instead of the reverse
annotation = defaultdict(lambda: defaultdict(lambda: defaultdict(dict)))
for target_id, slots in native_annotation.items():
for slot_type, fills in slots.items():
if slot_type_filter and slot_type != slot_type_filter:
log('excluding truth data for %s' % slot_type)
continue
elif slot_type in ['SignificantOther', 'Children']:
log('excluding truth data for %s because not part of official slot inventory. To score this, use --slot-type' % slot_type)
continue
def score_confusion_matrix(path_to_run_file, annotation, cutoff_step, unannotated_is_TN, include_training, debug):
'''
This function generates the confusion matrix (number of true/false positives
and true/false negatives.
path_to_run_file: str, a filesystem link to the run submission
annotation: dict, containing the annotation data
cutoff_step: int, increment between cutoffs
unannotated_is_TN: boolean, true to count unannotated as negatives
include_training: boolean, true to include training documents
returns a confusion matrix dictionary for each target_id
'''
## Open the run file
if path_to_run_file.endswith('.gz'):
run_file = gzip.open(path_to_run_file, 'r')
else:
run_file = open(path_to_run_file, 'r')
## Create a dictionary containing the confusion matrix (CM)
cutoffs = range(0, 999, cutoff_step)
CM = dict()
## count the total number of assertions per entity
num_assertions = {}
## Iterate through every row of the run
for onerow in run_file:
## Skip Comments
if onerow.startswith('#') or len(onerow.strip()) == 0:
continue
row = onerow.split()
stream_id = row[2]
timestamp = int(stream_id.split('-')[0])
target_id = row[3]
score = int(float(row[4]))
if target_id not in num_assertions:
num_assertions[target_id] = {'total': 0,
'in_TTR': 0,
'in_ETR': 0,
'in_annotation_set': 0}
## keep track of total number of assertions per entity
num_assertions[target_id]['total'] += 1
if timestamp <= END_OF_FEB_2012:
num_assertions[target_id]['in_TTR'] += 1
else:
num_assertions[target_id]['in_ETR'] += 1
## If the entity has been seen yet create a confusion matrix for it
if not target_id in CM:
CM[target_id] = dict()
for cutoff in cutoffs:
CM[target_id][cutoff] = dict(TP=0, FP=0, FN=0, TN=0)
if (not include_training) and (timestamp <= END_OF_FEB_2012):
continue
in_annotation_set = (stream_id, target_id) in annotation
if in_annotation_set:
num_assertions[target_id]['in_annotation_set'] += 1
## In the annotation set and useful
if in_annotation_set and annotation[(stream_id, target_id)]:
for cutoff in cutoffs:
if score > cutoff:
## If above the cutoff: true-positive
CM[target_id][cutoff]['TP'] += 1
## In the annotation set and non-useful
elif in_annotation_set and not annotation[(stream_id, target_id)]:
for cutoff in cutoffs:
if score > cutoff:
## Above the cutoff: false-positive
CM[target_id][cutoff]['FP'] += 1
else:
## Below the cutoff: true-negative
CM[target_id][cutoff]['TN'] += 1
## Not in the annotation set so its a negative (if flag is true)
elif unannotated_is_TN:
for cutoff in cutoffs:
if score > cutoff:
## Above the cutoff: false-positive
CM[target_id][cutoff]['FP'] += 1
else:
## Below the cutoff: true-negative
CM[target_id][cutoff]['TN'] += 1
## Correct FN for things in the annotation set that are NOT in the run
## First, calculate number of true things in the annotation set
annotation_positives = defaultdict(int)
for key in annotation:
stream_id = key[0]
timestamp = int(stream_id.split('-')[0])
if (not include_training) and (timestamp <= 1325375999):
continue
target_id = key[1]
annotation_positives[target_id] += annotation[(stream_id,target_id)]
for target_id in CM:
for cutoff in CM[target_id]:
## Then subtract the number of TP at each cutoffs
## (since FN+TP==True things in annotation set)
CM[target_id][cutoff]['FN'] = annotation_positives[target_id] - CM[target_id][cutoff]['TP']
if debug:
log( 'showing assertion counts:' )
log( json.dumps(num_assertions, indent=4, sort_keys=True) )
return CM
def score_all_runs(args, description, reject):
'''
score all the runs in the specified runs dir using the various
filters and configuration settings
:param description: string used for file names
:param reject: callable to rejects truth data
'''
if args.include_neutral:
thresh = 0
elif args.include_useful:
thresh = 1
else:
thresh = 2
## Load in the annotation data
annotation = load_annotation(args.annotation, thresh,
args.min_len_clean_visible, reject,
require_positives=args.require_positives
)
log( 'This assumes that all run file names end in .gz' )
annotationWriter = open('validassessments.csv', 'w')
for ((stream_id, target_id) , is_pos) in annotation.iteritems():
#'dde6ec 1332929640-c50cda6bee1564a599ae620d8918382e http://en.wikipedia.org/wiki/Atacocha 1000 1 1332929640'
timestamp = int(stream_id.split('-')[0])
assessment = 1 if is_pos else 0
annotationWriter.write('reserved\t%s\t%s\t1000\t%d\t%s\n'%(stream_id, target_id, assessment, timestamp))
annotationWriter.close()
#import gc
#from guppy import hpy
#hp = hpy()
run_count = 0
team_scores = defaultdict(lambda: defaultdict(dict))
for run_file in os.listdir(args.run_dir):
if not run_file.endswith('.gz'):
print 'ignoring %s because it does not end on *gz'%run_file
continue
if args.run_name_filter and not run_file.startswith(args.run_name_filter):
print 'filename filter set to %s, but does not match %s'%(args.run_name_filter, run_file)
continue
## take the name without the .gz
run_file_name = '.'.join(run_file.split('.')[:-1])
log( 'processing: %s.gz' % run_file_name )
print( 'processing: %s.gz' % run_file_name )
max_scores = process_run(args, run_file_name, annotation, description, thresh)
## split into team name and create stats file
team_id, system_id = run_file_name.split('-')
team_scores[team_id][system_id] = max_scores
#gc.collect()
#log(str(hp.heap()))
run_count += 1
#if run_count > 2:
# break
## When folder is finished running output a high level summary of the scores to overview.csv
write_team_summary(description, team_scores)
def build_confusion_matrix(path_to_run_file, annotation, cutoff_step, unannotated_is_TN, include_training, debug, thresh=2, require_positives=False):
'''
This function generates the confusion matrix (number of true/false positives
and true/false negatives.
path_to_run_file: str, a filesystem link to the run submission
annotation: dict, containing the annotation data
cutoff_step: int, increment between cutoffs
unannotated_is_TN: boolean, true to count unannotated as negatives
include_training: boolean, true to include training documents
returns a confusion matrix dictionary for each target_id
'''
## Open the run file
if path_to_run_file.endswith('.gz'):
run_file = gzip.open(path_to_run_file, 'r')
else:
run_file = open(path_to_run_file, 'r')
## Create a dictionary containing the confusion matrix (CM)
cutoffs = range(0, 999, cutoff_step)
CM = dict()
## count the total number of assertions per entity
num_assertions = {}
num_positives = defaultdict(int)
for (stream_id, target_id), is_positive in annotation.items():
## compute total counts of number of positives for each target_id
if is_positive:
num_positives[target_id] += 1
## make sure that the confusion matrix has entries for all entities
if target_id not in CM:
CM[target_id] = dict()
for cutoff in cutoffs:
CM[target_id][cutoff] = dict(TP=0, FP=0, FN=0, TN=0)
## Iterate through every row of the run and construct a
## de-duplicated run summary
run_set = dict()
for onerow in run_file:
## Skip Comments
if onerow.startswith('#') or len(onerow.strip()) == 0:
continue
row = onerow.split()
stream_id = row[2]
timestamp = int(stream_id.split('-')[0])
target_id = row[3]
conf = int(float(row[4]))
assert 0 < conf <= 1000
row[4] = conf
rating = int(row[5])
assert -1 <= rating <= 2
row[5] = rating
#log('ratings: %r <?> %r' % (rating, thresh))
if rating < thresh:
log('ignoring assertion below the rating threshold: %r < %r' % (rating, thresh))
continue
if require_positives and num_positives.get(target_id, 0) == 0:
log('ignoring assertion on entity for which no CCR positives are known: %s' % target_id)
continue
assertion_key = (stream_id, target_id)
if assertion_key in run_set:
other_row = run_set[assertion_key]
if other_row[4] > conf:
log('ignoring a duplicate row with lower conf: %d > %d'
% (other_row[4], conf))
continue
if other_row[4] == conf:
## compare rating level
if other_row[5] != rating:
log('same conf, different rating:\n%r\n%r\ntaking higher rating' % (row, other_row))
## accept higher rating
if other_row[5] > rating:
continue
#log('got a row: %r' % (row,))
run_set[assertion_key] = row
log('considering %d assertions' % len(run_set))
run_set = run_set.values()
while run_set:
row = run_set.pop()
stream_id = row[2]
timestamp = int(stream_id.split('-')[0])
target_id = row[3]
conf = row[4]
rating = row[5]
if target_id not in num_assertions:
num_assertions[target_id] = {'total': 0,
'in_TTR': 0,
'in_ETR': 0,
'in_annotation_set': 0}
## keep track of total number of assertions per entity
num_assertions[target_id]['total'] += 1
if timestamp <= END_OF_FEB_2012:
num_assertions[target_id]['in_TTR'] += 1
else:
num_assertions[target_id]['in_ETR'] += 1
if (not include_training) and (timestamp <= END_OF_FEB_2012):
continue
in_annotation_set = (stream_id, target_id) in annotation
if in_annotation_set:
num_assertions[target_id]['in_annotation_set'] += 1
## In the annotation set and useful
if in_annotation_set and annotation[(stream_id, target_id)]:
for cutoff in cutoffs:
if conf > cutoff:
## If above the cutoff: true-positive
CM[target_id][cutoff]['TP'] += 1
## In the annotation set and non-useful
elif in_annotation_set and not annotation[(stream_id, target_id)]:
for cutoff in cutoffs:
if conf > cutoff:
## Above the cutoff: false-positive
CM[target_id][cutoff]['FP'] += 1
else:
## Below the cutoff: true-negative
CM[target_id][cutoff]['TN'] += 1
## Not in the annotation set so its a negative (if flag is true)
elif unannotated_is_TN:
for cutoff in cutoffs:
if conf > cutoff:
## Above the cutoff: false-positive
CM[target_id][cutoff]['FP'] += 1
else:
## Below the cutoff: true-negative
CM[target_id][cutoff]['TN'] += 1
## Correct FN for things in the annotation set that are NOT in the run
## First, calculate number of true things in the annotation set
annotation_positives = defaultdict(int)
for stream_id, target_id in annotation:
timestamp = int(stream_id.split('-')[0])
if (not include_training) and (timestamp <= END_OF_FEB_2012):
continue
annotation_positives[target_id] += int(annotation[(stream_id,target_id)])
for target_id in CM:
for cutoff in CM[target_id]:
## Then subtract the number of TP at each cutoffs
## (since FN+TP==True things in annotation set)
#log('annotation_positives[%s] = %d' % (target_id, annotation_positives[target_id]))
#log('CN[%s][cutoff=%d] = %r' % (target_id, cutoff, CM[target_id][cutoff]))
CM[target_id][cutoff]['FN'] = annotation_positives[target_id] - CM[target_id][cutoff]['TP']
#log('CN[%s][cutoff=%d] = %r' % (target_id, cutoff, CM[target_id][cutoff]))
assert annotation_positives[target_id] >= CM[target_id][cutoff]['TP'], \
"how did we get more TPs than available annotation_positives[target_id=%s] = %d >= %d = CM[target_id][cutoff=%f]['TP']" \
% (target_id, annotation_positives[target_id], CM[target_id][cutoff]['TP'], cutoff)
log( 'showing assertion counts:' )
log( json.dumps(num_assertions, indent=4, sort_keys=True) )
return CM
def load_annotation(path_to_annotation_file, thresh, min_len_clean_visible, reject, require_positives=False, any_up=False):
'''
Loads the annotation file into a dict
path_to_annotation_file: string filesystem path to the annotation file
include_useful: true to include docs marked useful and vital
:param min_len_clean_visible: minimum length of the clean_visible,
which is in the 12 column of the expanded truth data file
:param reject: callable that returns boolean given a target_id
:param require_positives: if set to True, reject any target entity
for which no true positives exist.
'''
assert -1 <= thresh <= 2, thresh
annotation_file = csv.reader(open(path_to_annotation_file, 'r'), delimiter='\t')
annotation = dict()
for row in annotation_file:
## Skip comments
if row[0][0] == "#":
continue
stream_id = row[2]
target_id = row[3]
rating = int(row[5])
assert -1 <= rating <=2, rating
if len(row) == 12:
## only the later versions of the truth data carried this
## twelve column for excluding documents with insufficient
## clean_visible to be judged. We use a default cutoff of
## 100 bytes which means removing these counts below:
# (stream_id, target_id) pairs: 34921 above, and 15767 below 100 bytes of clean_visible
# (assessor_id, stream_id, target_id) pairs: 47446 above, and 19948 below 100 bytes of clean_visible
len_clean_visible = int(row[11])
if len_clean_visible < min_len_clean_visible:
log('excluding stream_id=%s for len(clean_visible)=%d' % (stream_id, len_clean_visible))
continue
if reject(target_id):
log('excluding truth data for %s' % target_id)
continue
## Add the stream_id and target_id to a hashed dictionary
## 0 means that its not vital 1 means that it is vital
if (stream_id, target_id) in annotation:
## if rating is below threshold, then some assessor viewed
## it as not good enough, so be conservative and downgrade
if not any_up and rating < thresh:
## default any_up=False means that if *any* assessor
## voted *against* the assertion, then *exclude* it
annotation[(stream_id, target_id)] = False
elif any_up and rating >= thresh:
## any_up means that if *any* assessor voted *for* the
## assertion, then *include* it
annotation[(stream_id, target_id)] = True
else:
## store bool values in the annotation index
annotation[(stream_id, target_id)] = rating >= thresh
has_true = set()
for (stream_id, target_id), is_true in annotation.items():
if is_true:
has_true.add(target_id)
if require_positives:
for stream_id, target_id in annotation.keys():
if target_id not in has_true:
log('rejecting %s for lack of any true positives -- because require_positives=True' % target_id)
annotation.pop( (stream_id, target_id) )
log('%d target_ids have at least one true positive' % len(has_true))
num_true = sum(map(int, annotation.values()))
log('loaded annotation to create a dict of %d (stream_id, target_id) pairs with %d True' % (len(annotation), num_true))
if num_true == 0:
sys.exit('found no true positives given the filters')
return annotation
help='beginning of string of filename to filter runs that get considered')
args = parser.parse_args()
accepted_target_ids = set()
if args.group or args.entity_type:
if not args.topics_path:
sys.exit('must specify --topics-path to use --group')
targets = json.load(open(args.topics_path))['targets']
for targ in targets:
if targ['group'] == args.group or targ['entity_type'] == args.entity_type:
accepted_target_ids.add(targ['target_id'])
description = make_description(args)
## construct reject callable
def reject(target_id):
if args.reject_twitter and 'twitter.com' in target_id:
return True
if args.reject_wikipedia and 'wikipedia.org' in target_id:
return True
if args.group or args.entity_type:
if target_id not in accepted_target_ids:
return True ## i.e. reject it
return False
score_all_runs(args, description, reject)
elapsed = time.time() - start_time
log('finished after %d seconds at at %r'
% (elapsed, datetime.utcnow()))
def build_confusion_matrix(path_to_run_file,
annotation,
cutoff_step,
unannotated_is_TN,
include_training,
debug,
thresh=2,
require_positives=0):
'''
This function generates the confusion matrix (number of true/false positives
and true/false negatives.
path_to_run_file: str, a filesystem link to the run submission
annotation: dict, containing the annotation data from *after* the cutoff
cutoff_step: int, increment between cutoffs
unannotated_is_TN: boolean, true to count unannotated as negatives
include_training: boolean, true to include training documents
returns a confusion matrix dictionary for each target_id
'''
## Open the run file
if path_to_run_file.endswith('.gz'):
run_file = gzip.open(path_to_run_file, 'r')
else:
run_file = open(path_to_run_file, 'r')
## Create a dictionary containing the confusion matrix (CM)
cutoffs = range(0, 999, cutoff_step)
CM = dict()
## count the total number of assertions per entity
num_assertions = {}
num_positives = defaultdict(int)
for (stream_id, target_id), is_positive in annotation.items():
## compute total counts of number of positives for each target_id
if is_positive:
num_positives[target_id] += 1
## make sure that the confusion matrix has entries for all entities
if target_id not in CM:
CM[target_id] = dict()
for cutoff in cutoffs:
CM[target_id][cutoff] = dict(TP=0, FP=0, FN=0, TN=0)
## Iterate through every row of the run and construct a
## de-duplicated run summary
run_set = dict()
for onerow in run_file:
## Skip Comments
if onerow.startswith('#') or len(onerow.strip()) == 0:
continue
row = onerow.split()
stream_id = row[2]
timestamp = int(stream_id.split('-')[0])
target_id = row[3]
conf = int(float(row[4]))
assert 0 < conf <= 1000
row[4] = conf
rating = int(row[5])
assert -1 <= rating <= 2
row[5] = rating
#log('ratings: %r <?> %r' % (rating, thresh))
if rating < thresh:
log('ignoring assertion below the rating threshold: %r < %r' %
(rating, thresh))
continue
if num_positives.get(target_id, 0) < require_positives:
log('ignoring assertion on entity for which no CCR positives are known: %s'
% target_id)
continue
assertion_key = (stream_id, target_id)
if assertion_key in run_set:
other_row = run_set[assertion_key]
if other_row[4] > conf:
log('ignoring a duplicate row with lower conf: %d > %d' %
(other_row[4], conf))
continue
if other_row[4] == conf:
## compare rating level
if other_row[5] != rating:
log('same conf, different rating:\n%r\n%r\ntaking higher rating'
% (row, other_row))
## accept higher rating
if other_row[5] > rating:
continue
#log('got a row: %r' % (row,))
run_set[assertion_key] = row
log('considering %d assertions' % len(run_set))
run_set = run_set.values()
while run_set:
row = run_set.pop()
stream_id = row[2]
timestamp = int(stream_id.split('-')[0])
target_id = row[3]
conf = row[4]
rating = row[5]
if target_id not in num_assertions:
num_assertions[target_id] = {
'total': 0,
'in_ETR': 0,
'in_annotation_set': 0
}
## keep track of total number of assertions per entity
num_assertions[target_id]['total'] += 1
num_assertions[target_id]['in_ETR'] += 1
in_annotation_set = (stream_id, target_id) in annotation
if in_annotation_set:
num_assertions[target_id]['in_annotation_set'] += 1
## In the annotation set and useful
if in_annotation_set and annotation[(stream_id, target_id)]:
for cutoff in cutoffs:
if conf > cutoff:
## If above the cutoff: true-positive
CM[target_id][cutoff]['TP'] += 1
## In the annotation set and non-useful
elif in_annotation_set and not annotation[(stream_id, target_id)]:
for cutoff in cutoffs:
if conf > cutoff:
## Above the cutoff: false-positive
CM[target_id][cutoff]['FP'] += 1
else:
## Below the cutoff: true-negative
CM[target_id][cutoff]['TN'] += 1
## Not in the annotation set so its a negative (if flag is true)
elif unannotated_is_TN:
for cutoff in cutoffs:
if conf > cutoff:
## Above the cutoff: false-positive
CM[target_id][cutoff]['FP'] += 1
else:
## Below the cutoff: true-negative
CM[target_id][cutoff]['TN'] += 1
## Correct FN for things in the annotation set that are NOT in the run
## First, calculate number of true things in the annotation set
annotation_positives = defaultdict(int)
for stream_id, target_id in annotation:
timestamp = int(stream_id.split('-')[0])
annotation_positives[target_id] += int(annotation[(stream_id,
target_id)])
for target_id in CM:
for cutoff in CM[target_id]:
## Then subtract the number of TP at each cutoffs
## (since FN+TP==True things in annotation set)
#log('annotation_positives[%s] = %d' % (target_id, annotation_positives[target_id]))
#log('CN[%s][cutoff=%d] = %r' % (target_id, cutoff, CM[target_id][cutoff]))
CM[target_id][cutoff]['FN'] = annotation_positives[target_id] - CM[
target_id][cutoff]['TP']
#log('CN[%s][cutoff=%d] = %r' % (target_id, cutoff, CM[target_id][cutoff]))
assert annotation_positives[target_id] >= CM[target_id][cutoff]['TP'], \
"how did we get more TPs than available annotation_positives[target_id=%s] = %d >= %d = CM[target_id][cutoff=%f]['TP']" \
% (target_id, annotation_positives[target_id], CM[target_id][cutoff]['TP'], cutoff)
log('showing assertion counts:')
log(json.dumps(num_assertions, indent=4, sort_keys=True))
return CM
def score_confusion_matrix_FILL(CM, OVERLAP_TPs, annotation, positives,
unannotated_is_TN=False,
cutoff_step_size=50, debug=False):
'''
construct FILL_TPs by excluding from OVERLAP_TPs those assertions
that either:
1) re-use an earlier (run)equiv_id that was not associated with
the same (truth)equiv_id from the truth set
1) fail to re-use an earlier (run)equiv_id that _was_
associated with a (truth)equiv_id from the truth set
'''
cutoffs = range(0, 999, cutoff_step_size)
FILL_TPs = dict()
runs_to_true = dict()
true_to_runs = dict()
log('considering %d unique FILL assertions' % len(OVERLAP_TPs))
for rec in OVERLAP_TPs:
(stream_id, target_id, conf, rating, contains_mention, date_hour,
slot_type, (runs_equiv_id, true_equiv_id), start_byte, end_byte) = rec
if positives[FILL].get(target_id, 0) == 0:
log('ignoring assertion on entity for which no FILL positives are known: %s' % target_id)
continue
## this is a tri-state variable
FILL_correct = None
if runs_equiv_id not in runs_to_true and true_equiv_id not in true_to_runs:
runs_to_true[runs_equiv_id] = true_equiv_id
true_to_runs[true_equiv_id] = runs_equiv_id
else:
## check failure mode #1 in __doc__ string
if runs_equiv_id in runs_to_true:
## run has previously asserted this equiv_id
if true_equiv_id == runs_to_true[runs_equiv_id]:
FILL_correct = True
else:
FILL_correct = False
## check failure mode #2 in __doc__ string
if true_equiv_id in true_to_runs:
if runs_equiv_id == true_to_runs[true_equiv_id]:
if FILL_correct is not False:
FILL_correct = True
else:
FILL_correct = False
if FILL_correct in [True, None]:
assertion_key = (stream_id, target_id, slot_type, true_equiv_id)
if assertion_key in FILL_TPs:
other_row = FILL_TPs[assertion_key]
if other_row[4] > conf:
log('ignoring a duplicate row with lower conf: %d > %d'
% (other_row[4], conf))
continue
FILL_TPs[assertion_key] = rec
increment_CM(FILL_correct, conf=conf, cutoffs=cutoffs, CM=CM, mode=FILL,
target_id=target_id,
unannotated_is_TN=unannotated_is_TN)
correct_FN(CM, FILL, positives)
FILL_TPs = FILL_TPs.values()
return CM, FILL_TPs
def score_confusion_matrix_DOCS(run_file_handle, annotation, positives,
cutoff_step_size=50, unannotated_is_TN=False, debug=False):
'''
read a run submission and generate a confusion matrix (number of
true/false positives and true/false negatives) for DOCS mode
evaluation. Generate a confusion matrix for each cutoff step and
each mode.
run_file_handle: str, a filesystem link to the run submission
annotation: dict, containing the annotation data
cutoff_step_size: int, increment between cutoffs
unannotated_is_TN: boolean, true to count unannotated as negatives
returns a confusion matrix dictionary for each target_id
'''
## Create a dictionary containing the confusion matrix (CM)
cutoffs = range(0, 999, cutoff_step_size)
def init_confusion_matrix():
return dict(TP=0, FP=0, FN=0, TN=0)
## confusion matrix is mode-->target_id-->cutoff-->2-by-2 matrix
CM = {mode: defaultdict(lambda: defaultdict(init_confusion_matrix))
for mode in MODES}
for stream_id in annotation:
for target_id in annotation[stream_id]:
for mode in MODES:
## make sure that the confusion matrix has entries for all entities
if target_id not in CM[mode]:
CM[mode][target_id] = dict()
for cutoff in cutoffs:
CM[mode][target_id][cutoff] = dict(TP=0, FP=0, FN=0, TN=0)
## count the total number of assertions per entity
num_assertions = {}
## keep assertions that are in the annotation set, because this is
## much smaller than the entire run submission. We will pass this
## to the four evaluation steps beyond DOCS.
DOCS_TPs = list()
## Iterate through every row of the run and construct a
## de-duplicated run summary
run_set = dict()
for assertion_key, row in assertions(run_file_handle):
conf = row[4]
stream_id, target_id, slot_type = assertion_key
if positives[DOCS].get(target_id, 0) == 0:
#log('ignoring assertion on entity for which no DOCS positives are known: %s' % target_id)
continue
if assertion_key in run_set:
other_row = run_set[assertion_key]
if other_row[4] > conf:
log('ignoring a duplicate row with lower conf: %d > %d'
% (other_row[4], conf))
continue
#log('got a row: %r' % (row,))
run_set[assertion_key] = row
log('considering %d unique DOCS assertions' % len(run_set))
for row in run_set.values():
stream_id = row[2]
timestamp = int(stream_id.split('-')[0])
target_id = row[3]
conf = row[4]
rating = row[5]
contains_mention = int(row[6])
date_hour = row[7]
slot_type = row[8]
equiv_id = row[9]
start_byte, end_byte = row[10].split('-')
start_byte = int(start_byte)
end_byte = int(end_byte)
if target_id not in num_assertions:
num_assertions[target_id] = {'total': 0,
'is_annotated_TP': 0}
## keep track of total number of assertions per entity
num_assertions[target_id]['total'] += 1
## all modes start with DOCS, so is_annotated_TP means that
## the system has a DOCS-TP above some conf threshold
is_annotated_TP = False
if stream_id in annotation:
if target_id in annotation[stream_id]:
if slot_type in annotation[stream_id][target_id]:
is_annotated_TP = True
rec = (stream_id, target_id, conf, rating, contains_mention, date_hour, slot_type, equiv_id, start_byte, end_byte)
DOCS_TPs.append( rec )
#log('TP: %r' % (rec,))
if is_annotated_TP:
num_assertions[target_id]['is_annotated_TP'] += 1
increment_CM(is_annotated_TP, conf=conf, cutoffs=cutoffs, CM=CM,
mode=DOCS,
target_id=target_id, unannotated_is_TN=unannotated_is_TN)
correct_FN(CM, DOCS, positives)
if debug:
print 'showing assertion counts:'
print json.dumps(num_assertions, indent=4, sort_keys=True)
## sort by date_hour
DOCS_TPs.sort(key=itemgetter(5))
return CM, DOCS_TPs
def score_all_runs(args, description, reject):
'''
score all the runs in the specified runs dir using the various
filters and configuration settings
:param description: string used for file names
:param reject: callable to rejects truth data
'''
## Load in the annotation data
annotation = load_annotation(args.annotation, args.include_useful, args.include_neutral,
args.min_len_clean_visible, reject)
log( 'This assumes that all run file names end in .gz' )
team_scores = defaultdict(lambda: defaultdict(dict))
for run_file in os.listdir(args.run_dir):
if not run_file.endswith('.gz'):
continue
## take the name without the .gz
run_file_name = '.'.join(run_file.split('.')[:-1])
log( 'processing: %s.gz' % run_file_name )
## Generate the confusion matrix for a run
CM = score_confusion_matrix(
os.path.join(args.run_dir, run_file),
annotation, args.cutoff_step, args.unan_is_true, args.include_training,
debug=args.debug)
## Generate performance metrics for a run
Scores = performance_metrics(CM)
## Generate the average metrics
(CM['average'], Scores['average']) = full_run_metrics(CM, Scores, args.use_micro_averaging)
max_scores = find_max_scores(Scores)
## split into team name and create stats file
team_id, system_id = run_file_name.split('-')
team_scores[team_id][system_id] = max_scores
## Print the top F-Score
log( ' max(avg(F_1)): %.3f' % max_scores['average']['F'] )
log( ' max(F_1(avg(P), avg(R))): %.3f' % max_scores['average']['F_recomputed'] )
log( ' max(avg(SU)): %.3f' % max_scores['average']['SU'] )
base_output_filepath = os.path.join(
args.run_dir,
run_file_name + '-' + description)
output_filepath = base_output_filepath + '.csv'
write_performance_metrics(output_filepath, CM, Scores)
log( ' wrote metrics table to %s' % output_filepath )
if not plt:
log( ' not generating plot, because could not import matplotlib' )
else:
## Output a graph of the key performance statistics
graph_filepath = base_output_filepath + '.png'
write_graph(graph_filepath, Scores['average'])
log( ' wrote plot image to %s' % graph_filepath )
## When folder is finished running output a high level summary of the scores to overview.csv
write_team_summary(description, team_scores)
pooled_assertion_keys=None):
'''
Loads the SSF truth data from its JSON format on disk
path_to_annotation_file: string file system path to the JSON annotation file
reject: callable that returns boolean given a target_id
'''
try:
native_annotation = json.load(open(path_to_annotation_file))
except Exception, exc:
sys.exit( 'failed to open %r:\n%s' % (path_to_annotation_file, traceback.format_exc(exc)) )
for target_id in native_annotation.keys():
if reject(target_id):
log('excluding truth data for %s' % target_id)
native_annotation.pop(target_id)
## invert the annotation file to have a stream_id index pointing
## to target_ids point to slot_types pointing to slot fills,
## instead of the reverse
# stream_id --> target_id --> slot_types
annotation = defaultdict(lambda: defaultdict(lambda: defaultdict(dict)))
unofficial_slots = ['SignificantOther', 'Children']
for target_id, slots in native_annotation.items():
for slot_type, fills in slots.items():
if slot_type_filter and slot_type != slot_type_filter:
log('excluding truth data for %s' % slot_type)
continue
def score_all_runs(args, description, reject):
'''
score all the runs in the specified runs dir using the various
filters and configuration settings
:param description: string used for file names
:param reject: callable to rejects truth data
'''
if args.include_neutral:
thresh = 0
elif args.include_useful:
thresh = 1
else:
thresh = 2
## Load in the annotation data
annotation = load_annotation(
args.annotation,
thresh,
args.min_len_clean_visible,
reject,
require_positives=args.require_positives,
any_up=args.any_up,
restricted_entity_list=args.restricted_entity_list,
)
log('This assumes that all run file names end in .gz')
#import gc
#from guppy import hpy
#hp = hpy()
run_count = 0
team_scores = defaultdict(lambda: defaultdict(dict))
for run_file in os.listdir(args.run_dir):
if not run_file.endswith('.gz'):
continue
if args.run_name_filter and not run_file.startswith(
args.run_name_filter):
continue
## take the name without the .gz
run_file_name = '.'.join(run_file.split('.')[:-1])
log('processing: %s.gz' % run_file_name)
try:
max_scores = process_run(args, run_file_name, annotation,
description, thresh)
## split into team name and create stats file
team_id, system_id = run_file_name.split('-')
team_scores[team_id][system_id] = max_scores
except Exception, exc:
logger.critical('died on %s', run_file_name, exc_info=True)
sys.exit(str(exc))
#gc.collect()
#log(str(hp.heap()))
run_count += 1
accepted_target_ids = set()
if args.group or args.entity_type:
if not args.topics_path:
sys.exit('must specify --topics-path to use --group')
targets = json.load(open(args.topics_path))['targets']
for targ in targets:
if ('group' in targ and targ.get('group')
== args.group) or targ['entity_type'] == args.entity_type:
accepted_target_ids.add(targ['target_id'])
if args.restricted_entity_list:
args.restricted_entity_list = set(
open(args.restricted_entity_list).read().splitlines())
log('loaded %d entities into restricted_entity_list:\n%s' %
(len(args.restricted_entity_list), '\n'.join(
args.restricted_entity_list)))
description = make_description(args)
## construct reject callable
def reject(target_id):
if args.reject_twitter and 'twitter.com' in target_id:
return True
if args.reject_wikipedia and 'wikipedia.org' in target_id:
return True
if args.group or args.entity_type:
if target_id not in accepted_target_ids:
return True ## i.e. reject it
return False
def score_confusion_matrix_DOCS(run_file_handle, annotation, positives,
cutoff_step_size=50, unannotated_is_TN=False, debug=False):
'''
read a run submission and generate a confusion matrix (number of
true/false positives and true/false negatives) for DOCS mode
evaluation. Generate a confusion matrix for each cutoff step and
each mode.
run_file_handle: str, a filesystem link to the run submission
annotation: dict, containing the annotation data
cutoff_step_size: int, increment between cutoffs
unannotated_is_TN: boolean, true to count unannotated as negatives
returns a confusion matrix dictionary for each target_id
'''
## Create a dictionary containing the confusion matrix (CM)
cutoffs = range(0, 999, cutoff_step_size)
def init_confusion_matrix():
return dict(TP=0, FP=0, FN=0, TN=0)
## confusion matrix is mode-->target_id-->cutoff-->2-by-2 matrix
CM = {mode: defaultdict(lambda: defaultdict(init_confusion_matrix))
for mode in MODES}
## count the total number of assertions per entity
num_assertions = {}
## keep assertions that are in the annotation set, because this is
## much smaller than the entire run submission. We will pass this
## to the four evaluation steps beyond DOCS.
DOCS_TPs = list()
## Iterate through every row of the run
for onerow in run_file_handle:
## Skip Comments
if onerow.startswith('#') or len(onerow.strip()) == 0:
continue
row = onerow.split()
assert len(row) == 11, row
try:
stream_id = row[2]
timestamp = int(stream_id.split('-')[0])
target_id = row[3]
conf = int(float(row[4]))
rating = int(row[5])
contains_mention = int(row[6])
date_hour = row[7]
slot_type = row[8]
equiv_id = row[9]
start_byte, end_byte = row[10].split('-')
start_byte = int(start_byte)
end_byte = int(end_byte)
except Exception, exc:
print repr(row)
sys.exit(traceback.format_exc(exc))
if target_id not in num_assertions:
num_assertions[target_id] = {'total': 0,
'is_annotated_TP': 0}
## keep track of total number of assertions per entity
num_assertions[target_id]['total'] += 1
## all modes start with DOCS, so is_annotated_TP means that
## the system has a DOCS-TP above some conf threshold
is_annotated_TP = False
if stream_id in annotation:
if target_id in annotation[stream_id]:
if slot_type in annotation[stream_id][target_id]:
is_annotated_TP = True
rec = (stream_id, target_id, conf, rating, contains_mention, date_hour, slot_type, equiv_id, start_byte, end_byte)
DOCS_TPs.append( rec )
log('TP: %r' % (rec,))
if is_annotated_TP:
num_assertions[target_id]['is_annotated_TP'] += 1
increment_CM(is_annotated_TP, conf, cutoffs, CM, DOCS, target_id, unannotated_is_TN)
def ssf_runs(args):
'''
yield file handles for all of the SSF runs
'''
log( 'This assumes that all run file names end in .gz' )
run_count = 0
for run_file_name in os.listdir(args.run_dir):
if not run_file_name.endswith('.gz'):
log( 'ignoring: %s' % run_file_name )
continue
if args.run_name_filter and not run_file_name.startswith(args.run_name_filter):
log( 'ignoring: %s' % run_file_name)
continue
## Open the run file
run_file_path = os.path.join(args.run_dir, run_file_name)
if run_file_path.endswith('.gz'):
run_file_handle = gzip.open(run_file_path, 'r')
else:
run_file_handle = open(run_file_path, 'r')
first_line = run_file_handle.readline()
assert first_line.startswith('#')
try:
filter_run = json.loads(first_line[1:])
except:
sys.exit('failed to get JSON out of: %r' % first_line[1:])
### many CCR runs, including some from organizers have task_id
### set to SSF :-(, so we must detect this.
## read to first non-comment line
second_line = None
while not second_line:
second_line = run_file_handle.readline()
if second_line.strip().startswith('#'):
second_line = None
if 'NULL' in second_line or filter_run['task_id'] != 'kba-ssf-2013':
log( 'ignoring non-SSF run: %s' % run_file_name )
continue
## Open run file again now that we verified it is SSF
run_file_path = os.path.join(args.run_dir, run_file_name)
if run_file_path.endswith('.gz'):
run_file_handle = gzip.open(run_file_path, 'r')
else:
run_file_handle = open(run_file_path, 'r')
log( 'processing: %s' % run_file_name )
log( json.dumps(filter_run, indent=4, sort_keys=True) )
yield run_file_name, run_file_handle
def score_confusion_matrix_OVERLAP(CM, DOCS_TPs, annotation, positives,
cutoff_step_size=50, unannotated_is_TN=False,
debug=False):
'''
construct OVERLAP_TPs by excluding from DOCS_TPs those assertions
that do not overlap any string identified by an assessor
'''
cutoffs = range(0, 999, cutoff_step_size)
OVERLAP_TPs = dict()
log('considering %d unique OVERLAP assertions' % len(DOCS_TPs))
for rec in DOCS_TPs:
(stream_id, target_id, conf, rating, contains_mention,
date_hour, slot_type, runs_equiv_id, start_byte, end_byte) = rec
if positives[OVERLAP].get(target_id, 0) == 0:
log('ignoring assertion on entity for which no OVERLAP positives are known: %s' % target_id)
continue
start_byte = int(start_byte)
end_byte = int(end_byte)
for true_equiv_id, equiv_class in annotation[stream_id][target_id][slot_type].items():
offsets = equiv_class['stream_ids'][stream_id][1]
overlaps = False
for offset in offsets:
assert isinstance(offset[0], int)
assert isinstance(offset[1], int)
## we could/should be much stricter here, 10x is a big window
true_len = offset[1] - offset[0]
runs_len = end_byte - start_byte
if start_byte <= offset[1] and end_byte >= offset[0] and runs_len < 10 * true_len:
overlaps = True
break
#log('(%d, %d) compared to offsets %r\n' % (start_byte, end_byte, offsets))
if not overlaps:
increment_CM(False, conf=conf, cutoffs=cutoffs, CM=CM,
mode=OVERLAP,
target_id=target_id, unannotated_is_TN=unannotated_is_TN)
#log('found one!! system equiv_id (%r) --> assessors equiv_id (%r)'
# % (runs_equiv_id, true_equiv_id))
rec = list(rec)
rec[7] = (runs_equiv_id, true_equiv_id)
rec = tuple(rec)
assertion_key = (stream_id, target_id, slot_type, start_byte, end_byte)
if assertion_key in OVERLAP_TPs:
other_row = OVERLAP_TPs[assertion_key]
if other_row[4] > conf:
log('ignoring a duplicate row with lower conf: %d > %d'
% (other_row[4], conf))
continue
OVERLAP_TPs[assertion_key] = rec
increment_CM(True, conf=conf, cutoffs=cutoffs, CM=CM,
mode=OVERLAP,
target_id=target_id, unannotated_is_TN=unannotated_is_TN)
correct_FN(CM, OVERLAP, positives)
if OVERLAP_TPs:
assert CM[OVERLAP]
OVERLAP_TPs = OVERLAP_TPs.values()
return CM, OVERLAP_TPs
def load_annotation(path_to_annotation_file,
thresh,
min_len_clean_visible,
reject,
require_positives=False,
any_up=False,
restricted_entity_list=None):
'''Loads the annotation file into a dict
path_to_annotation_file: string filesystem path to the annotation file
include_useful: true to include docs marked useful and vital
:param min_len_clean_visible: minimum length of the clean_visible,
which is in the 12 column of the expanded truth data file
:param reject: callable that returns boolean given a target_id
:param require_positives: if set to True, reject any target entity
for which no true positives exist.
:param restricted_entity_list: a list of target_id strings that
are the only ones allowed in the annotation.
'''
assert -1 <= thresh <= 2, thresh
annotation_file = csv.reader(open(path_to_annotation_file, 'r'),
delimiter='\t')
annotation = dict()
for row in annotation_file:
## Skip comments
if row[0][0] == "#":
continue
stream_id = row[2]
target_id = row[3]
rating = int(row[5])
assert -1 <= rating <= 2, rating
if len(row) == 12:
## only the later versions of the truth data carried this
## twelve column for excluding documents with insufficient
## clean_visible to be judged. We use a default cutoff of
## 100 bytes which means removing these counts below:
# (stream_id, target_id) pairs: 34921 above, and 15767 below 100 bytes of clean_visible
# (assessor_id, stream_id, target_id) pairs: 47446 above, and 19948 below 100 bytes of clean_visible
len_clean_visible = int(row[11])
if len_clean_visible < min_len_clean_visible:
log('excluding stream_id=%s for len(clean_visible)=%d' %
(stream_id, len_clean_visible))
continue
if reject(target_id):
log('excluding truth data for %s' % target_id)
continue
if restricted_entity_list and target_id not in restricted_entity_list:
log('not in restricted_entity_list: %s' % target_id)
continue
## Add the stream_id and target_id to a hashed dictionary
## 0 means that its not vital 1 means that it is vital
if (stream_id, target_id) in annotation:
## if rating is below threshold, then some assessor viewed
## it as not good enough, so be conservative and downgrade
if not any_up and rating < thresh:
## default any_up=False means that if *any* assessor
## voted *against* the assertion, then *exclude* it
annotation[(stream_id, target_id)] = False
elif any_up and rating >= thresh:
## any_up means that if *any* assessor voted *for* the
## assertion, then *include* it
annotation[(stream_id, target_id)] = True
else:
## store bool values in the annotation index
annotation[(stream_id, target_id)] = rating >= thresh
has_true = Counter()
for (stream_id, target_id), is_true in annotation.items():
if is_true:
has_true[target_id] += 1
if require_positives:
for stream_id, target_id in annotation.keys():
if has_true[target_id] < require_positives:
log('rejecting %s for too few true positives: %d < %d = require_positives'\
% (target_id, has_true[target_id], require_positives))
annotation.pop((stream_id, target_id))
log('%d target_ids have at least one true positive' % len(has_true))
num_true = sum(map(int, annotation.values()))
log('loaded annotation to create a dict of %d (stream_id, target_id) pairs with %d True'
% (len(annotation), num_true))
if num_true == 0:
sys.exit('found no true positives given the filters')
return annotation
