def test_pairs(self):
pairs = (
(("Testacular", "Testacular"), True),
(("Ass'n Managers v. Lissner", "Association Managers v. Lissner"), False),
)
for q, a in pairs:
self.assertEqual(case_name_in_candidate(*q), a)
def test_pairs(self):
pairs = (
(('Testacular', 'Testacular'), True),
(("Ass'n Managers v. Lissner", 'Association Managers v. Lissner'), False),
)
for q, a in pairs:
self.assertEqual(case_name_in_candidate(*q), a)
def find_duplicates(doc, case_path):
"""Return True if it should be saved, else False"""
log_print("Running duplicate checks...")
# 1. Is the item completely outside of the current corpus?
if not needs_dup_check(doc):
log_print(" - Not a duplicate: Outside of date range for selected court.")
return []
else:
log_print(" - Could be a duplicate: Inside of date range for selected court.")
# 2. Can we find any duplicates and information about them?
stats, candidates = dup_finder.get_dup_stats(doc)
if len(candidates) == 0:
log_print(" - Not a duplicate: No candidate matches found.")
return []
elif len(candidates) == 1:
if doc.citation.docket_number and candidates[0].get("docketNumber") is not None:
# One in the other or vice versa
if (
re.sub("(\D|0)", "", candidates[0]["docketNumber"]) in re.sub("(\D|0)", "", doc.citation.docket_number)
) or (
re.sub("(\D|0)", "", doc.citation.docket_number) in re.sub("(\D|0)", "", candidates[0]["docketNumber"])
):
log_print(" - Duplicate found: Only one candidate returned and docket number matches.")
return [candidates[0]["id"]]
else:
if doc.docket.court_id == "cit":
# CIT documents have neutral citations in the database. Look that up and compare against that.
candidate_doc = Document.objects.get(pk=candidates[0]["id"])
if doc.citation.neutral_cite and candidate_doc.citation.neutral_cite:
if candidate_doc.citation.neutral_cite in doc.citation.docket_number:
log_print(
" - Duplicate found: One candidate from CIT and its neutral citation matches the new document's docket number."
)
return [candidates[0]["id"]]
else:
log_print(" - Not a duplicate: Only one candidate but docket number differs.")
return []
else:
log_print(" - Skipping docket_number dup check.")
if doc.citation.case_name == candidates[0].get("caseName"):
log_print(" - Duplicate found: Only one candidate and case name is a perfect match.")
return [candidates[0]["id"]]
if dup_helpers.case_name_in_candidate(doc.citation.case_name, candidates[0].get("caseName")):
log_print(
" - Duplicate found: All words in new document's case name are in the candidate's case name (%s)"
% candidates[0].get("caseName")
)
return [candidates[0]["id"]]
else:
# More than one candidate.
if doc.citation.docket_number:
dups_by_docket_number = dup_helpers.find_same_docket_numbers(doc, candidates)
if len(dups_by_docket_number) > 1:
log_print(" - Duplicates found: %s candidates matched by docket number." % len(dups_by_docket_number))
return [can["id"] for can in dups_by_docket_number]
elif len(dups_by_docket_number) == 1:
log_print(" - Duplicate found: Multiple candidates returned, but one matched by docket number.")
return [dups_by_docket_number[0]["id"]]
else:
log_print(" - Could be a duplicate: Unable to find good match via docket number.")
else:
log_print(" - Skipping docket_number dup check.")
# 3. Filter out obviously bad cases and then pass remainder forward for manual review.
filtered_candidates, filtered_stats = dup_helpers.filter_by_stats(candidates, stats)
log_print(" - %s candidates before filtering. With stats: %s" % (stats["candidate_count"], stats["cos_sims"]))
log_print(
" - %s candidates after filtering. Using filtered stats: %s"
% (filtered_stats["candidate_count"], filtered_stats["cos_sims"])
)
if len(filtered_candidates) == 0:
log_print(" - Not a duplicate: After filtering no good candidates remained.")
return []
elif len(filtered_candidates) == 1 and filtered_stats["cos_sims"][0] > 0.93:
log_print(
" - Duplicate found: One candidate after filtering and cosine similarity is high (%s)"
% filtered_stats["cos_sims"][0]
)
return [filtered_candidates[0]["id"]]
else:
duplicates = []
high_sims_count = len([sim for sim in filtered_stats["cos_sims"] if sim > 0.98])
low_sims_count = len([sim for sim in filtered_stats["cos_sims"] if sim < 0.95])
for k in range(0, len(filtered_candidates)):
if all(
[
(high_sims_count == 1), # Only one high score
(low_sims_count == filtered_stats["candidate_count"] - 1), # All but one have low scores
]
):
# If only one of the items is very high, then we can ignore the others and assume it's right
if filtered_stats["cos_sims"][k] > 0.98:
duplicates.append(filtered_candidates[k]["id"])
break
else:
# ignore the others
continue
else:
# Have to determine by "hand"
log_print(" %s) Case name: %s" % (k + 1, doc.citation.case_name))
log_print(" %s" % filtered_candidates[k]["caseName"])
log_print(" Docket nums: %s" % doc.citation.docket_number)
log_print(" %s" % filtered_candidates[k].get("docketNumber", "None"))
log_print(" Cosine Similarity: %s" % filtered_stats["cos_sims"][k])
log_print(" Candidate URL: file://%s" % case_path)
log_print(" Match URL: https://www.courtlistener.com%s" % (filtered_candidates[k]["absolute_url"]))
choice = raw_input("Is this a duplicate? [Y/n]: ")
choice = choice or "y"
if choice == "y":
duplicates.append(filtered_candidates[k]["id"])
if len(duplicates) == 0:
log_print(" - Not a duplicate: Manual determination found no matches.")
return []
elif len(duplicates) == 1:
log_print(" - Duplicate found: Manual determination found one match.")
return [duplicates[0]]
elif len(duplicates) > 1:
log_print(" - Duplicates found: Manual determination found %s matches." % len(duplicates))
return duplicates
def find_duplicates(doc, case_path):
"""Return True if it should be saved, else False"""
log_print("Running duplicate checks...")
# 1. Is the item completely outside of the current corpus?
if not needs_dup_check(doc):
log_print(
" - Not a duplicate: Outside of date range for selected court.")
return []
else:
log_print(
" - Could be a duplicate: Inside of date range for selected court."
)
# 2. Can we find any duplicates and information about them?
stats, candidates = dup_finder.get_dup_stats(doc)
if len(candidates) == 0:
log_print(" - Not a duplicate: No candidate matches found.")
return []
elif len(candidates) == 1:
if doc.citation.docket_number and candidates[0].get(
'docketNumber') is not None:
# One in the other or vice versa
if (re.sub("(\D|0)", "", candidates[0]['docketNumber']) in
re.sub("(\D|0)", "", doc.citation.docket_number)) or \
(re.sub("(\D|0)", "", doc.citation.docket_number) in
re.sub("(\D|0)", "", candidates[0]['docketNumber'])):
log_print(
" - Duplicate found: Only one candidate returned and docket number matches."
)
return [candidates[0]['id']]
else:
if doc.docket.court_id == 'cit':
# CIT documents have neutral citations in the database. Look that up and compare against that.
candidate_doc = Document.objects.get(
pk=candidates[0]['id'])
if doc.citation.neutral_cite and candidate_doc.citation.neutral_cite:
if candidate_doc.citation.neutral_cite in doc.citation.docket_number:
log_print(
' - Duplicate found: One candidate from CIT and its neutral citation matches the new document\'s docket number.'
)
return [candidates[0]['id']]
else:
log_print(
" - Not a duplicate: Only one candidate but docket number differs."
)
return []
else:
log_print(" - Skipping docket_number dup check.")
if doc.citation.case_name == candidates[0].get('caseName'):
log_print(
" - Duplicate found: Only one candidate and case name is a perfect match."
)
return [candidates[0]['id']]
if dup_helpers.case_name_in_candidate(doc.citation.case_name,
candidates[0].get('caseName')):
log_print(
" - Duplicate found: All words in new document's case name are in the candidate's case name (%s)"
% candidates[0].get('caseName'))
return [candidates[0]['id']]
else:
# More than one candidate.
if doc.citation.docket_number:
dups_by_docket_number = dup_helpers.find_same_docket_numbers(
doc, candidates)
if len(dups_by_docket_number) > 1:
log_print(
" - Duplicates found: %s candidates matched by docket number."
% len(dups_by_docket_number))
return [can['id'] for can in dups_by_docket_number]
elif len(dups_by_docket_number) == 1:
log_print(
" - Duplicate found: Multiple candidates returned, but one matched by docket number."
)
return [dups_by_docket_number[0]['id']]
else:
log_print(
" - Could be a duplicate: Unable to find good match via docket number."
)
else:
log_print(" - Skipping docket_number dup check.")
# 3. Filter out obviously bad cases and then pass remainder forward for manual review.
filtered_candidates, filtered_stats = dup_helpers.filter_by_stats(
candidates, stats)
log_print(" - %s candidates before filtering. With stats: %s" %
(stats['candidate_count'], stats['cos_sims']))
log_print(" - %s candidates after filtering. Using filtered stats: %s" %
(filtered_stats['candidate_count'], filtered_stats['cos_sims']))
if len(filtered_candidates) == 0:
log_print(
" - Not a duplicate: After filtering no good candidates remained."
)
return []
elif len(
filtered_candidates) == 1 and filtered_stats['cos_sims'][0] > 0.93:
log_print(
" - Duplicate found: One candidate after filtering and cosine similarity is high (%s)"
% filtered_stats['cos_sims'][0])
return [filtered_candidates[0]['id']]
else:
duplicates = []
high_sims_count = len(
[sim for sim in filtered_stats['cos_sims'] if sim > 0.98])
low_sims_count = len(
[sim for sim in filtered_stats['cos_sims'] if sim < 0.95])
for k in range(0, len(filtered_candidates)):
if all([
(high_sims_count == 1), # Only one high score
(low_sims_count == filtered_stats['candidate_count'] - 1
) # All but one have low scores
]):
# If only one of the items is very high, then we can ignore the others and assume it's right
if filtered_stats['cos_sims'][k] > 0.98:
duplicates.append(filtered_candidates[k]['id'])
break
else:
# ignore the others
continue
else:
# Have to determine by "hand"
log_print(" %s) Case name: %s" %
(k + 1, doc.citation.case_name))
log_print(" %s" %
filtered_candidates[k]['caseName'])
log_print(" Docket nums: %s" % doc.citation.docket_number)
log_print(" %s" %
filtered_candidates[k].get('docketNumber', 'None'))
log_print(" Cosine Similarity: %s" %
filtered_stats['cos_sims'][k])
log_print(" Candidate URL: file://%s" % case_path)
log_print(" Match URL: https://www.courtlistener.com%s" %
(filtered_candidates[k]['absolute_url']))
choice = raw_input("Is this a duplicate? [Y/n]: ")
choice = choice or "y"
if choice == 'y':
duplicates.append(filtered_candidates[k]['id'])
if len(duplicates) == 0:
log_print(
" - Not a duplicate: Manual determination found no matches.")
return []
elif len(duplicates) == 1:
log_print(
" - Duplicate found: Manual determination found one match.")
return [duplicates[0]]
elif len(duplicates) > 1:
log_print(
" - Duplicates found: Manual determination found %s matches."
% len(duplicates))
return duplicates
