def select_best_fitting_record(main_zbl_record, candidates, meaning_fields = ['ti', 'au', 'jt', 'py']):
"""From list of candidate records select the most similar
(the one with the smallest edit distance on meaning_fields) to main_zbl_record.
>>> select_best_fitting_record({'ti': 'kot', 'jt': 'Koty', 'py': '1991'}, [{'ti': 'koty', 'jt': 'Koty'}, {'ti': 'kota', 'jt': 'Kozy', 'py': '1991'}]) == {'ti': 'koty', 'jt': 'Koty'}
True
"""
if len(candidates) == 1:
return candidates[0]
#generate subset of fields present in all records:
fields = set(main_zbl_record.keys())
for candidate in candidates:
fields = fields.intersection(candidate.keys())
#keep relevant fields:
fields = fields.intersection(set(meaning_fields))
#estimate edit distances for all candidates:
edit_distances = []
for candidate in candidates:
total_edit_distance = 0
for field in fields:
edit_distance = text_analysis.lev(candidate[field].lower(), main_zbl_record[field].lower())
total_edit_distance = total_edit_distance + edit_distance
edit_distances.append(total_edit_distance)
#select most similar:
most_similar_ix = edit_distances.index(min(edit_distances))
return candidates[most_similar_ix]
def are_elements_almost_equal(a1, a2, max_hist_dist = 4, max_edit_dist = 2):
""" Returns true iff a1, a2 fulfill following conditions:
- distance between histograms <= max_hist_dist
- edit distance <= max_edit_dist
Sample use:
>>> are_elements_almost_equal('andrzej', 'endrzaj', 4, 2)
True
>>> are_elements_almost_equal('andrzej', 'endrzaje', 4, 2)
False
>>> are_elements_almost_equal('andrzej', 'endzraj', 4, 2)
False
"""
hist_dist = text_analysis.word_hist_diff_total(a1, a2)
if hist_dist > max_hist_dist:
#print "a1=",a1,"a2=",a2,"hist_dist=",hist_dist
return False
edit_dist = text_analysis.lev(a1, a2)
if edit_dist > max_edit_dist:
#print "a1=",a1,"a2=",a2,"edit_dist=",edit_dist
return False
return True
