def pairs_quality(weight_vec_dict, match_check_funct): """Pairs quality is the ratio of true matches divided by the total number of matches of the compared record pairs returned after blocking. It is measured as: pq = |TP| / all_matches with TP being the true positives, and all matches being the number of weight vectors given. The arguments that have to be set when this method is called are: weight_vec_dict A dictionary containing weight vectors. match_check_funct This has to be a function (or method), assumed to have as arguments the two record identifiers of a record pair and its weight vector, and returns True if the record pair is from a true match, or False otherwise. Thus, 'match_check_funct' is of the form: match_flag = match_check_funct(rec_id1, rec_id2, weight_vec) """ auxiliary.check_is_dictionary('weight_vec_dict', weight_vec_dict) auxiliary.check_is_function_or_method('match_check_funct', match_check_funct) total_num_rec_pairs = len(weight_vec_dict) logging.info('') logging.info('Calculate pairs quality:') logging.info(' Number of record pairs in weight vector dictionary: %d' % \ (total_num_rec_pairs)) # Get number of true matches in weight vector dictionary - - - - - - - - - - # num_true_matches = 0 num_false_matches = 0 for (rec_id_tuple, this_vec) in weight_vec_dict.iteritems(): if (match_check_funct(rec_id_tuple[0], rec_id_tuple[1], this_vec) == True): num_true_matches += 1 else: num_false_matches += 1 assert total_num_rec_pairs == (num_true_matches + num_false_matches) logging.info(' Number of true and false matches in weight vector ' + \ 'dictionary: %d / %d' % (num_true_matches, num_false_matches)) pq = float(num_true_matches) / total_num_rec_pairs logging.info(' Pairs quality: %.4f%%' % (100.0 * pq)) # As percentage assert pq <= 1.0 return pq
def pairs_quality(weight_vec_dict, match_check_funct): """Pairs quality is the ratio of true matches divided by the total number of matches of the compared record pairs returned after blocking. It is measured as: pq = |TP| / all_matches with TP being the true positives, and all matches being the number of weight vectors given. The arguments that have to be set when this method is called are: weight_vec_dict A dictionary containing weight vectors. match_check_funct This has to be a function (or method), assumed to have as arguments the two record identifiers of a record pair and its weight vector, and returns True if the record pair is from a true match, or False otherwise. Thus, 'match_check_funct' is of the form: match_flag = match_check_funct(rec_id1, rec_id2, weight_vec) """ auxiliary.check_is_dictionary('weight_vec_dict', weight_vec_dict) auxiliary.check_is_function_or_method('match_check_funct', match_check_funct) total_num_rec_pairs = len(weight_vec_dict) logging.info('') logging.info('Calculate pairs quality:') logging.info(' Number of record pairs in weight vector dictionary: %d' % \ (total_num_rec_pairs)) # Get number of true matches in weight vector dictionary - - - - - - - - - - # num_true_matches = 0 num_false_matches = 0 for (rec_id_tuple, this_vec) in weight_vec_dict.iteritems(): if (match_check_funct(rec_id_tuple[0], rec_id_tuple[1], this_vec) == True): num_true_matches += 1 else: num_false_matches += 1 assert total_num_rec_pairs == (num_true_matches + num_false_matches) logging.info(' Number of true and false matches in weight vector ' + \ 'dictionary: %d / %d' % (num_true_matches, num_false_matches)) pq = float(num_true_matches) / total_num_rec_pairs logging.info(' Pairs quality: %.4f%%' % (100.0*pq)) # As percentage assert pq <= 1.0 return pq
def testIsFunction(self): # - - - - - - - - - - - - - - - - - - - - - - - - """Test 'check_is_function_or_method' function.""" def f1(x): print x def f2(): print "hello" assert auxiliary.check_is_function_or_method("TestArgument", f1) == None assert auxiliary.check_is_function_or_method("TestArgument", f2) == None assert auxiliary.check_is_function_or_method("TestArgument", self.setUp) == None
def testIsFunction(self): # - - - - - - - - - - - - - - - - - - - - - - - - """Test 'check_is_function_or_method' function.""" def f1(x): print x def f2(): print 'hello' assert (auxiliary.check_is_function_or_method('TestArgument', f1) == None) assert (auxiliary.check_is_function_or_method('TestArgument', f2) == None) assert (auxiliary.check_is_function_or_method('TestArgument', self.setUp) == None)
def testIsFunction( self): # - - - - - - - - - - - - - - - - - - - - - - - - """Test 'check_is_function_or_method' function.""" def f1(x): print(x) def f2(): print("hello") assert auxiliary.check_is_function_or_method("TestArgument", f1) assert auxiliary.check_is_function_or_method("TestArgument", f2) assert auxiliary.check_is_function_or_method("TestArgument", self.setUp)
def pairs_completeness(weight_vec_dict, dataset1, dataset2, get_id_funct, match_check_funct): """Pairs completeness is measured as pc = Nm / M with Nm (<= M) being the number of correctly classified truly matched record pairs in the blocked comparison space, and M the total number of true matches. If both data sets are the same a deduplication is assumed, otherwise a linkage. The arguments that have to be set when this method is called are: weight_vec_dict A dictionary containing weight vectors. dataset1 The initialised first data set object. dataset2 The initialised second data set object. get_id_funct This has to be a function (or method), assumed to have argument a record (assumed to be a list fo field values), and returns the record identifier from that record. match_check_funct This has to be a function (or method), assumed to have as arguments the two record identifiers of a record pair and its weight vector, and returns True if the record pair is from a true match, or False otherwise. Thus, 'match_check_funct' is of the form: match_flag = match_check_funct(rec_id1, rec_id2, weight_vec) """ auxiliary.check_is_dictionary('weight_vec_dict', weight_vec_dict) auxiliary.check_is_not_none('dataset1', dataset1) auxiliary.check_is_not_none('dataset2', dataset2) auxiliary.check_is_function_or_method('get_id_funct', get_id_funct) auxiliary.check_is_function_or_method('match_check_funct', match_check_funct) # Check if a deduplication will be done or a linkage - - - - - - - - - - - - # if (dataset1 == dataset2): do_dedup = True else: do_dedup = False logging.info('') logging.info('Calculate pairs completeness:') logging.info(' Data set 1: %s (containing %d records)' % \ (dataset1.description, dataset1.num_records)) if (do_dedup == True): logging.info(' Data sets are the same: Deduplication') else: logging.info(' Data set 2: %s (containing %d records)' % \ (dataset2.description, dataset2.num_records)) logging.info(' Data sets differ: Linkage') logging.info(' Number of record pairs in weight vector dictionary: %d' % \ (len(weight_vec_dict))) num_all_true_matches = 0 # Count the total number of all true matches # For a deduplication only process data set 1 - - - - - - - - - - - - - - - - # if (do_dedup == True): # Build a dictionary with entity identifiers as keys and a list of their # record identifier (rec_ident) as values # entity_ident_dict = {} for (rec_ident, rec) in dataset1.readall(): ent_id = get_id_funct(rec) this_rec_list = entity_ident_dict.get(ent_id, []) this_rec_list.append(rec_ident) entity_ident_dict[ent_id] = this_rec_list logging.info(' Number of unique entity identifiers in data set 1: %d' % \ (len(entity_ident_dict))) for (ent_id, rec_list) in entity_ident_dict.iteritems(): num_this_rec = len(rec_list) if (num_this_rec > 1): num_all_true_matches += num_this_rec * (num_this_rec - 1) / 2 # More efficent version: Only count number of matches ber record don't # store them # entity_ident_dict2 = {} for (rec_ident, rec) in dataset1.readall(): ent_id = get_id_funct(rec) ent_id_count = entity_ident_dict2.get(ent_id, 0) + 1 entity_ident_dict2[ent_id] = ent_id_count assert sum(entity_ident_dict2.values()) == dataset1.num_records tm = 0 # Total number of true matches (without indexing) for (ent_id, ent_count) in entity_ident_dict2.iteritems(): tm += ent_count * (ent_count - 1) / 2 assert num_all_true_matches == tm else: # For a linkage - - - - - - - - - - - - - - - - - - - - - - - - - - - # Build two dictionaries with entity identifiers as keys and a list of # their record identifier (rec_ident) as values # entity_ident_dict1 = {} entity_ident_dict2 = {} for (rec_ident, rec) in dataset1.readall(): ent_id = get_id_funct(rec) this_rec_list = entity_ident_dict1.get(ent_id, []) this_rec_list.append(rec_ident) entity_ident_dict1[ent_id] = this_rec_list logging.info(' Number of unique entity identifiers in data set 1: %d' % \ (len(entity_ident_dict1))) for (rec_ident, rec) in dataset2.readall(): ent_id = get_id_funct(rec) this_rec_list = entity_ident_dict2.get(ent_id, []) this_rec_list.append(rec_ident) entity_ident_dict2[ent_id] = this_rec_list logging.info(' Number of unique entity identifiers in data set 2: %d' % \ (len(entity_ident_dict2))) # Now calculate total true match number (loop over smaller dict) # if (len(entity_ident_dict1) < len(entity_ident_dict2)): for (ent_id1, rec_list1) in entity_ident_dict1.iteritems(): if (ent_id1 in entity_ident_dict2): rec_list2 = entity_ident_dict2[ent_id1] num_all_true_matches += len(rec_list1) * len(rec_list2) else: for (ent_id2, rec_list2) in entity_ident_dict2.iteritems(): if (ent_id2 in entity_ident_dict1): rec_list1 = entity_ident_dict1[ent_id2] num_all_true_matches += len(rec_list1) * len(rec_list2) # More efficent version: Only count number of matches ber record don't # store them # entity_ident_dict3 = {} entity_ident_dict4 = {} for (rec_ident, rec) in dataset1.readall(): ent_id = get_id_funct(rec) ent_id_count = entity_ident_dict3.get(ent_id, 0) + 1 entity_ident_dict3[ent_id] = ent_id_count for (rec_ident, rec) in dataset2.readall(): ent_id = get_id_funct(rec) ent_id_count = entity_ident_dict4.get(ent_id, 0) + 1 entity_ident_dict4[ent_id] = ent_id_count assert sum(entity_ident_dict3.values()) == dataset1.num_records assert sum(entity_ident_dict4.values()) == dataset2.num_records tm = 0 # Total number of true matches (without indexing) if (len(entity_ident_dict3) < len(entity_ident_dict4)): for (ent_id, ent_count) in entity_ident_dict3.iteritems(): if ent_id in entity_ident_dict4: tm += ent_count * entity_ident_dict4[ent_id] else: for (ent_id, ent_count) in entity_ident_dict4.iteritems(): if ent_id in entity_ident_dict3: tm += ent_count * entity_ident_dict3[ent_id] assert num_all_true_matches == tm logging.info(' Number of all true matches: %d' % (num_all_true_matches)) # Get number of true matches in weight vector dictionary - - - - - - - - - - # num_true_matches = 0 num_false_matches = 0 for (rec_id_tuple, this_vec) in weight_vec_dict.iteritems(): if (match_check_funct(rec_id_tuple[0], rec_id_tuple[1], this_vec) == True): num_true_matches += 1 else: num_false_matches += 1 assert len(weight_vec_dict) == num_true_matches + num_false_matches logging.info(' Number of true and false matches in weight vector ' + \ 'dictionary: %d / %d' % (num_true_matches,num_false_matches)) if (num_all_true_matches > 0): pc = float(num_true_matches) / float(num_all_true_matches) logging.info(' Pairs completeness: %.4f%%' % (100.0 * pc)) # As percentage else: pc = 0.0 logging.info(' No true matches - cannot calculate pairs completeness') assert pc <= 1.0, pc return pc
def quality_measures(weight_vec_dict, match_set, non_match_set, match_check_funct): """Calculate several quality measures based on the number of true positives, true negatives, false positives and false negatives in the given match and non-match sets and weight vector dictionary using the given match check function. The function calculates and returns: - Accuracy: (|TP|+|TN|) --------------------- (|TP|+|TN|+|FP|+|FN|) - Precision: |TP| ----------- (|TP|+|FP|) - Recall: |TP| ----------- (|TP|+|FN|) - F-Measure: 2 * (Precision * Recall) -------------------------- (Precision + Recall) With TP the True Positives, TN the True negatives, FP the False Positives and FN the False Negatives. For a discussion about measuring data linkage and deduplication quality please refer to: Quality and Complexity Measures for Data Linkage and Deduplication Peter Christen and Karl Goiser Book chapter in "Quality Measures in Data Mining" Studies in Computational Intelligence, Vol. 43 F. Guillet and H. Hamilton (eds), Springer March 2007. """ auxiliary.check_is_dictionary('weight_vec_dict', weight_vec_dict) auxiliary.check_is_set('match set', match_set) auxiliary.check_is_set('non match set', non_match_set) auxiliary.check_is_function_or_method('match_check_funct', match_check_funct) if ((len(match_set) + len(non_match_set)) != len(weight_vec_dict)): logging.exception('Match and non-match set are not of same length as ' + \ 'weight vector dictionary: %d, %d / %d' % \ (len(match_set),len(non_match_set),len(weight_vec_dict))) raise Exception tp = 0.0 fp = 0.0 tn = 0.0 fn = 0.0 for rec_id_tuple in match_set: w_vec = weight_vec_dict[rec_id_tuple] if (match_check_funct(rec_id_tuple[0], rec_id_tuple[1], w_vec) == True): tp += 1 else: fp += 1 for rec_id_tuple in non_match_set: w_vec = weight_vec_dict[rec_id_tuple] if (match_check_funct(rec_id_tuple[0], rec_id_tuple[1], w_vec) == False): tn += 1 else: fn += 1 logging.info('') logging.info('Classification results: TP=%d, FP=%d / TN=%d, FN=%d' % \ (tp, fp, tn, fn)) if ((tp != 0) or (fp != 0) or (tn != 0) or (fn != 0)): acc = (tp + tn) / (tp + fp + tn + fn) else: acc = 0.0 if ((tp != 0) or (fp != 0)): prec = tp / (tp + fp) else: prec = 0.0 if ((tp != 0) or (fn != 0)): reca = tp / (tp + fn) else: reca = 0.0 if ((prec != 0.0) or (reca != 0.0)): fmeas = 2 * (prec * reca) / (prec + reca) else: fmeas = 0.0 logging.info('Quality measures:') logging.info(' Accuracy: %.6f Precision:%.4f Recall: %.4f ' % \ (acc, prec, reca)+'F-measure: %.4f' % (fmeas)) return acc, prec, reca, fmeas
def pairs_completeness(weight_vec_dict, dataset1, dataset2, get_id_funct, match_check_funct): """Pairs completeness is measured as pc = Nm / M with Nm (<= M) being the number of correctly classified truly matched record pairs in the blocked comparison space, and M the total number of true matches. If both data sets are the same a deduplication is assumed, otherwise a linkage. The arguments that have to be set when this method is called are: weight_vec_dict A dictionary containing weight vectors. dataset1 The initialised first data set object. dataset2 The initialised second data set object. get_id_funct This has to be a function (or method), assumed to have argument a record (assumed to be a list fo field values), and returns the record identifier from that record. match_check_funct This has to be a function (or method), assumed to have as arguments the two record identifiers of a record pair and its weight vector, and returns True if the record pair is from a true match, or False otherwise. Thus, 'match_check_funct' is of the form: match_flag = match_check_funct(rec_id1, rec_id2, weight_vec) """ auxiliary.check_is_dictionary('weight_vec_dict', weight_vec_dict) auxiliary.check_is_not_none('dataset1', dataset1) auxiliary.check_is_not_none('dataset2', dataset2) auxiliary.check_is_function_or_method('get_id_funct', get_id_funct) auxiliary.check_is_function_or_method('match_check_funct', match_check_funct) # Check if a deduplication will be done or a linkage - - - - - - - - - - - - # if (dataset1 == dataset2): do_dedup = True else: do_dedup = False logging.info('') logging.info('Calculate pairs completeness:') logging.info(' Data set 1: %s (containing %d records)' % \ (dataset1.description, dataset1.num_records)) if (do_dedup == True): logging.info(' Data sets are the same: Deduplication') else: logging.info(' Data set 2: %s (containing %d records)' % \ (dataset2.description, dataset2.num_records)) logging.info(' Data sets differ: Linkage') logging.info(' Number of record pairs in weight vector dictionary: %d' % \ (len(weight_vec_dict))) num_all_true_matches = 0 # Count the total number of all true matches # For a deduplication only process data set 1 - - - - - - - - - - - - - - - - # if (do_dedup == True): # Build a dictionary with entity identifiers as keys and a list of their # record identifier (rec_ident) as values # entity_ident_dict = {} for (rec_ident, rec) in dataset1.readall(): ent_id = get_id_funct(rec) this_rec_list = entity_ident_dict.get(ent_id, []) this_rec_list.append(rec_ident) entity_ident_dict[ent_id] = this_rec_list logging.info(' Number of unique entity identifiers in data set 1: %d' % \ (len(entity_ident_dict))) for (ent_id, rec_list) in entity_ident_dict.iteritems(): num_this_rec = len(rec_list) if (num_this_rec > 1): num_all_true_matches += num_this_rec*(num_this_rec-1)/2 # More efficent version: Only count number of matches ber record don't # store them # entity_ident_dict2 = {} for (rec_ident, rec) in dataset1.readall(): ent_id = get_id_funct(rec) ent_id_count = entity_ident_dict2.get(ent_id, 0) + 1 entity_ident_dict2[ent_id] = ent_id_count assert sum(entity_ident_dict2.values()) == dataset1.num_records tm = 0 # Total number of true matches (without indexing) for (ent_id, ent_count) in entity_ident_dict2.iteritems(): tm += ent_count*(ent_count-1)/2 assert num_all_true_matches == tm else: # For a linkage - - - - - - - - - - - - - - - - - - - - - - - - - - - # Build two dictionaries with entity identifiers as keys and a list of # their record identifier (rec_ident) as values # entity_ident_dict1 = {} entity_ident_dict2 = {} for (rec_ident, rec) in dataset1.readall(): ent_id = get_id_funct(rec) this_rec_list = entity_ident_dict1.get(ent_id, []) this_rec_list.append(rec_ident) entity_ident_dict1[ent_id] = this_rec_list logging.info(' Number of unique entity identifiers in data set 1: %d' % \ (len(entity_ident_dict1))) for (rec_ident, rec) in dataset2.readall(): ent_id = get_id_funct(rec) this_rec_list = entity_ident_dict2.get(ent_id, []) this_rec_list.append(rec_ident) entity_ident_dict2[ent_id] = this_rec_list logging.info(' Number of unique entity identifiers in data set 2: %d' % \ (len(entity_ident_dict2))) # Now calculate total true match number (loop over smaller dict) # if (len(entity_ident_dict1) < len(entity_ident_dict2)): for (ent_id1, rec_list1) in entity_ident_dict1.iteritems(): if (ent_id1 in entity_ident_dict2): rec_list2 = entity_ident_dict2[ent_id1] num_all_true_matches += len(rec_list1) * len(rec_list2) else: for (ent_id2, rec_list2) in entity_ident_dict2.iteritems(): if (ent_id2 in entity_ident_dict1): rec_list1 = entity_ident_dict1[ent_id2] num_all_true_matches += len(rec_list1) * len(rec_list2) # More efficent version: Only count number of matches ber record don't # store them # entity_ident_dict3 = {} entity_ident_dict4 = {} for (rec_ident, rec) in dataset1.readall(): ent_id = get_id_funct(rec) ent_id_count = entity_ident_dict3.get(ent_id, 0) + 1 entity_ident_dict3[ent_id] = ent_id_count for (rec_ident, rec) in dataset2.readall(): ent_id = get_id_funct(rec) ent_id_count = entity_ident_dict4.get(ent_id, 0) + 1 entity_ident_dict4[ent_id] = ent_id_count assert sum(entity_ident_dict3.values()) == dataset1.num_records assert sum(entity_ident_dict4.values()) == dataset2.num_records tm = 0 # Total number of true matches (without indexing) if (len(entity_ident_dict3) < len(entity_ident_dict4)): for (ent_id, ent_count) in entity_ident_dict3.iteritems(): if ent_id in entity_ident_dict4: tm += ent_count*entity_ident_dict4[ent_id] else: for (ent_id, ent_count) in entity_ident_dict4.iteritems(): if ent_id in entity_ident_dict3: tm += ent_count*entity_ident_dict3[ent_id] assert num_all_true_matches == tm logging.info(' Number of all true matches: %d' % (num_all_true_matches)) # Get number of true matches in weight vector dictionary - - - - - - - - - - # num_true_matches = 0 num_false_matches = 0 for (rec_id_tuple, this_vec) in weight_vec_dict.iteritems(): if (match_check_funct(rec_id_tuple[0], rec_id_tuple[1], this_vec) == True): num_true_matches += 1 else: num_false_matches += 1 assert len(weight_vec_dict) == num_true_matches+num_false_matches logging.info(' Number of true and false matches in weight vector ' + \ 'dictionary: %d / %d' % (num_true_matches,num_false_matches)) if (num_all_true_matches > 0): pc = float(num_true_matches) / float(num_all_true_matches) logging.info(' Pairs completeness: %.4f%%' % (100.0*pc)) # As percentage else: pc = 0.0 logging.info(' No true matches - cannot calculate pairs completeness') assert pc <= 1.0, pc return pc
def quality_measures(weight_vec_dict, match_set, non_match_set, match_check_funct): """Calculate several quality measures based on the number of true positives, true negatives, false positives and false negatives in the given match and non-match sets and weight vector dictionary using the given match check function. The function calculates and returns: - Accuracy: (|TP|+|TN|) --------------------- (|TP|+|TN|+|FP|+|FN|) - Precision: |TP| ----------- (|TP|+|FP|) - Recall: |TP| ----------- (|TP|+|FN|) - F-Measure: 2 * (Precision * Recall) -------------------------- (Precision + Recall) With TP the True Positives, TN the True negatives, FP the False Positives and FN the False Negatives. For a discussion about measuring data linkage and deduplication quality please refer to: Quality and Complexity Measures for Data Linkage and Deduplication Peter Christen and Karl Goiser Book chapter in "Quality Measures in Data Mining" Studies in Computational Intelligence, Vol. 43 F. Guillet and H. Hamilton (eds), Springer March 2007. """ auxiliary.check_is_dictionary('weight_vec_dict', weight_vec_dict) auxiliary.check_is_set('match set', match_set) auxiliary.check_is_set('non match set', non_match_set) auxiliary.check_is_function_or_method('match_check_funct', match_check_funct) if ((len(match_set) + len(non_match_set)) != len(weight_vec_dict)): logging.exception('Match and non-match set are not of same length as ' + \ 'weight vector dictionary: %d, %d / %d' % \ (len(match_set),len(non_match_set),len(weight_vec_dict))) raise Exception tp = 0.0 fp = 0.0 tn = 0.0 fn = 0.0 for rec_id_tuple in match_set: w_vec = weight_vec_dict[rec_id_tuple] if (match_check_funct(rec_id_tuple[0], rec_id_tuple[1], w_vec) == True): tp += 1 else: fp += 1 for rec_id_tuple in non_match_set: w_vec = weight_vec_dict[rec_id_tuple] if (match_check_funct(rec_id_tuple[0], rec_id_tuple[1], w_vec) == False): tn += 1 else: fn += 1 logging.info('') logging.info('Classification results: TP=%d, FP=%d / TN=%d, FN=%d' % \ (tp, fp, tn, fn)) if ((tp != 0) or (fp != 0) or (tn != 0) or (fn != 0)): acc = (tp + tn) / (tp + fp + tn + fn) else: acc = 0.0 if ((tp != 0) or (fp != 0)): prec = tp / (tp + fp) else: prec = 0.0 if ((tp != 0) or (fn != 0)): reca = tp / (tp + fn) else: reca = 0.0 if ((prec != 0.0) or (reca != 0.0)): fmeas = 2*(prec*reca) / (prec+reca) else: fmeas = 0.0 logging.info('Quality measures:') logging.info(' Accuracy: %.6f Precision:%.4f Recall: %.4f ' % \ (acc, prec, reca)+'F-measure: %.4f' % (fmeas)) return acc, prec, reca, fmeas