def test_synthetic_add(self):
synthetic = SyntheticDatabase(10, 10, number_features=3) # 100 records, 10 entities, 10 records each
self.assertEqual(len(synthetic.database.records), 100)
synthetic.add(5, 10)
self.assertEqual(len(synthetic.database.records), 150)
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()