def run(self):
"""
Runs ER at all thresholds
:return predicted_labels: List of lists of predicted labels.
predicted_labels[threshold_index] = dict [identifier, cluster label]
:return metrics: List of lists of metric objects.
metrics[threshold_index] = Metrics object
:return er_objects: List of EntityResolution objects.
er_objects[threshold_index] = EntityResolution
:return new_metrics_objects: List of NewMetrics objects.
new_metrics_objects[threshold_index] = NewMetrics
"""
er = EntityResolution()
#weak_match_function = LogisticMatchFunction(self._database_train, self._labels_train, self._train_pair_seed, 0.5)
weak_match_function = ForestMatchFunction(self._database_train, self._labels_train, self._train_pair_seed, 0.5)
print 'Testing pairwise match function on test database'
ROC = weak_match_function.test(self._database_validation, self._labels_validation, self._validation_seed)
#ROC.make_plot()
metrics_list = list()
labels_list = list()
new_metrics_list = list()
class_balance_test = count_pairwise_class_balance(self._labels_test)
blocks = BlockingScheme(self._database_test, single_block=True)
for threshold in self.thresholds:
print 'Running entity resolution at threshold =', threshold
weak_match_function.set_decision_threshold(threshold)
labels_pred = weak_connected_components(self._database_test, weak_match_function, blocks)
#labels_pred = er.run(self._database_test, weak_match_function, single_block=True, max_block_size=np.Inf,
# cores=1)
metrics_list.append(Metrics(self._labels_test, labels_pred))
new_metrics_list.append(NewMetrics(self._database_test, labels_pred, weak_match_function, class_balance_test))
labels_list.append(labels_pred)
return labels_list, metrics_list, new_metrics_list
def setUp(self):
self._test_path = 'test_annotations_cleaned.csv'
self._database = Database(self._test_path)
self._labels = fast_strong_cluster(self._database)
self._blocking = BlockingScheme(self._database, single_block=True)
self._er = EntityResolution()
decision_threshold = 1.0
pair_seed = generate_pair_seed(self._database, self._labels, 0.5)
self._match_function = LogisticMatchFunction(self._database, self._labels, pair_seed, decision_threshold)
def test_completeness(self):
database = Database('test_annotations_10000_cleaned.csv', max_records=1000, header_path='test_annotations_10000_cleaned_header.csv')
database_train = database.sample_and_remove(800)
database_test = database
labels_train = fast_strong_cluster(database_train)
labels_test = fast_strong_cluster(database_test)
er = EntityResolution()
pair_seed = generate_pair_seed(database_train, labels_train, 0.5)
match_function = LogisticMatchFunction(database_train, labels_train, pair_seed, 0.99)
blocking_scheme = BlockingScheme(database_test)
labels_pred = er.run(database_test, match_function, blocking_scheme, cores=2)
number_fast_strong_records = len(labels_train) + len(labels_test)
self.assertEqual(number_fast_strong_records, 1000)
self.assertEqual(sorted((labels_train.keys() + labels_test.keys())), range(0, 1000))
number_swoosh_records = len(get_ids(database_test.records))
self.assertEqual(number_swoosh_records, len(database_test.records))
self.assertEqual(get_ids(database_test.records), sorted(labels_test.keys()))
self.assertEqual(get_ids(database_test.records), sorted(labels_pred.keys()))
def setUp(self):
self._test_path = 'test_annotations_cleaned.csv'
self._database = Database(self._test_path)
self._blocking = BlockingScheme(self._database)
self._er = EntityResolution()
def synthetic_sizes():
"""
Sizes experiment here
"""
resolution = 88
number_features = 10
number_entities = np.linspace(10, 100, num=resolution)
number_entities = number_entities.astype(int)
records_per_entity = 10
#train_database_size = 100
train_class_balance = 0.5
#validation_database_size = 100
corruption_multiplier = .001
databases = list()
db = SyntheticDatabase(number_entities[0], records_per_entity, number_features=number_features)
databases.append(deepcopy(db))
add_entities = [x - number_entities[i - 1] for i, x in enumerate(number_entities)][1:]
for add in add_entities:
db.add(add, records_per_entity)
databases.append(deepcopy(db))
corruption = np.random.normal(loc=0.0, scale=1.0, size=[number_entities[-1]*records_per_entity, number_features])
train = deepcopy(databases[0])
validation = deepcopy(databases[0])
train.corrupt(corruption_multiplier*np.random.normal(loc=0.0, scale=1.0, size=[len(train.database.records), number_features]))
validation.corrupt(corruption_multiplier*np.random.normal(loc=0.0, scale=1.0, size=[len(train.database.records), number_features]))
for db in databases:
db.corrupt(corruption_multiplier*corruption[:len(db.database.records), :])
er = EntityResolution()
train_pair_seed = generate_pair_seed(train.database, train.labels, train_class_balance)
weak_match_function = LogisticMatchFunction(train.database, train.labels, train_pair_seed, 0.5)
ROC = weak_match_function.test(validation.database, validation.labels, 0.5)
#ROC.make_plot()
## Optimize ER on small dataset
thresholds = np.linspace(0, 1.0, 10)
metrics_list = list()
#new_metrics_list = list()
pairwise_precision = list()
pairwise_recall = list()
pairwise_f1 = list()
for threshold in thresholds:
weak_match_function.set_decision_threshold(threshold)
labels_pred = er.run(deepcopy(databases[0].database), weak_match_function, single_block=True,
max_block_size=np.Inf, cores=1)
met = Metrics(databases[0].labels, labels_pred)
metrics_list.append(met)
pairwise_precision.append(met.pairwise_precision)
pairwise_recall.append(met.pairwise_recall)
pairwise_f1.append(met.pairwise_f1)
#class_balance_test = get_pairwise_class_balance(databases[0].labels)
#new_metrics_list.append(NewMetrics(databases[0].database, er, class_balance_test))
plt.plot(thresholds, pairwise_precision, label='Precision')
plt.plot(thresholds, pairwise_recall, label='Recall')
plt.plot(thresholds, pairwise_f1, label='F1')
plt.xlabel('Threshold')
plt.legend()
plt.ylabel('Score')
plt.title('Optimizing ER on small dataset')
#i = np.argmax(np.array(pairwise_f1))
#small_optimal_threshold = thresholds[i] # optimize this
small_optimal_threshold = 0.6
print 'Optimal small threshold set at =', small_optimal_threshold
plt.show()
## Possible score by optimizing on larger dataset
metrics_list = list()
pairwise_precision = list()
pairwise_recall = list()
pairwise_f1 = list()
thresholds_largedataset = np.linspace(0.6, 1.0, 8)
precision_lower_bound = list()
recall_lower_bound = list()
f1_lower_bound = list()
for threshold in thresholds_largedataset:
weak_match_function.set_decision_threshold(threshold)
labels_pred = er.run(deepcopy(databases[-1].database), weak_match_function, single_block=True,
max_block_size=np.Inf, cores=1)
met = Metrics(databases[-1].labels, labels_pred)
metrics_list.append(met)
pairwise_precision.append(met.pairwise_precision)
pairwise_recall.append(met.pairwise_recall)
pairwise_f1.append(met.pairwise_f1)
class_balance_test = count_pairwise_class_balance(databases[-1].labels)
new_metric = NewMetrics(databases[-1].database, labels_pred, weak_match_function, class_balance_test)
precision_lower_bound.append(new_metric.precision_lower_bound)
recall_lower_bound.append(new_metric.recall_lower_bound)
f1_lower_bound.append(new_metric.f1_lower_bound)
plt.plot(thresholds_largedataset, pairwise_precision, label='Precision', color='r')
plt.plot(thresholds_largedataset, pairwise_recall, label='Recall', color='b')
plt.plot(thresholds_largedataset, pairwise_f1, label='F1', color='g')
plt.plot(thresholds_largedataset, precision_lower_bound, label='Precision Bound', color='r', linestyle=':')
plt.plot(thresholds_largedataset, recall_lower_bound, label='Recall Bound', color='b', linestyle=':')
plt.plot(thresholds_largedataset, f1_lower_bound, label='F1 Bound', color='g', linestyle=':')
i = np.argmax(np.array(f1_lower_bound))
large_optimal_threshold = thresholds_largedataset[i]
print 'Optimal large threshold automatically set at =', large_optimal_threshold
print 'If not correct: debug.'
plt.xlabel('Threshold')
plt.legend()
plt.ylabel('Score')
plt.title('Optimizing ER on large dataset')
plt.show()
## Run on all dataset sizes
#new_metrics_list = list()
database_sizes = list()
small_pairwise_precision = list()
small_pairwise_recall = list()
small_pairwise_f1 = list()
large_precision_bound = list()
large_precision_bound_lower_ci = list()
large_precision_bound_upper_ci = list()
large_precision = list()
large_recall_bound = list()
large_recall_bound_lower_ci = list()
large_recall_bound_upper_ci = list()
large_recall = list()
large_f1 = list()
large_f1_bound = list()
for db in databases:
print 'Analyzing synthetic database with', len(db.database.records), 'records'
database_sizes.append(len(db.database.records))
weak_match_function.set_decision_threshold(small_optimal_threshold)
labels_pred = er.run(db.database, weak_match_function, single_block=True, max_block_size=np.Inf, cores=1)
met = Metrics(db.labels, labels_pred)
small_pairwise_precision.append(met.pairwise_precision)
small_pairwise_recall.append(met.pairwise_recall)
small_pairwise_f1.append(met.pairwise_f1)
weak_match_function.set_decision_threshold(large_optimal_threshold)
labels_pred = er.run(db.database, weak_match_function, single_block=True, max_block_size=np.Inf, cores=1)
met = Metrics(db.labels, labels_pred)
large_precision.append(met.pairwise_precision)
large_recall.append(met.pairwise_recall)
large_f1.append(met.pairwise_f1)
class_balance_test = count_pairwise_class_balance(db.labels)
new_metric = NewMetrics(db.database, labels_pred, weak_match_function, class_balance_test)
large_precision_bound.append(new_metric.precision_lower_bound)
large_recall_bound.append(new_metric.recall_lower_bound)
large_f1_bound.append(new_metric.f1_lower_bound)
large_precision_bound_lower_ci.append(new_metric.precision_lower_bound_lower_ci)
large_precision_bound_upper_ci.append(new_metric.precision_lower_bound_upper_ci)
large_recall_bound_lower_ci.append(new_metric.recall_lower_bound_lower_ci)
large_recall_bound_upper_ci.append(new_metric.recall_lower_bound_upper_ci)
with open('synthetic_sizes_temp.csv', 'wb') as f:
f.write('Database size, Precision (small opt), Recall (small opt), F1 (small opt), Precision (large opt), Precision bound (large opt), Lower CI, Upper CI, Recall (large opt), Recall bound (large opt), Lower CI, Upper CI, F1 (large opt), F1 bound (large opt)\n')
writer = csv.writer(f)
writer.writerows(izip(database_sizes, small_pairwise_precision, small_pairwise_recall, small_pairwise_f1, large_precision, large_precision_bound, large_precision_bound_lower_ci, large_precision_bound_upper_ci, large_recall, large_recall_bound, large_recall_bound_lower_ci, large_recall_bound_upper_ci, large_f1, large_f1_bound))
f.close()
plt.figure()
plt.plot(database_sizes, pairwise_precision, label='Precision', color='#4477AA', linewidth=3)
plt.plot(database_sizes, pairwise_recall, label='Recall', color='#CC6677', linewidth=3)
#plt.plot(database_sizes, pairwise_f1, label='F1', color='#DDCC77', linewidth=2)
plt.ylim([0, 1.05])
plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
plt.legend(title='Pairwise:', loc='lower left')
plt.xlabel('Number of Records')
plt.ylabel('Pairwise Score')
plt.title('Performance Degredation')
plt.show()