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 main():
"""
Runs a single entity resolution on data (real or synthetic) using a match function (logistic regression, decision
tree, or random forest)
"""
data_type = 'real'
decision_threshold = 0.7
train_class_balance = 0.5
max_block_size = 1000
cores = 2
if data_type == 'synthetic':
database_train = SyntheticDatabase(100, 10, 10)
corruption = 0.1
corruption_array = corruption*np.random.normal(loc=0.0, scale=1.0, size=[1000,
database_train.database.feature_descriptor.number])
database_train.corrupt(corruption_array)
database_validation = SyntheticDatabase(100, 10, 10)
corruption_array = corruption*np.random.normal(loc=0.0, scale=1.0, size=[1000,
database_validation.database.feature_descriptor.number])
database_validation.corrupt(corruption_array)
database_test = SyntheticDatabase(10, 10, 10)
corruption_array = corruption*np.random.normal(loc=0.0, scale=1.0, size=[1000,
database_test.database.feature_descriptor.number])
database_test.corrupt(corruption_array)
labels_train = database_train.labels
labels_validation = database_validation.labels
labels_test = database_test.labels
database_train = database_train.database
database_validation = database_validation.database
database_test = database_test.database
single_block = True
elif data_type == 'real':
# Uncomment to use all features (annotations and LM)
#database_train = Database('../data/trafficking/cluster_subsample0_10000.csv', header_path='../data/trafficking/cluster_subsample_header_all.csv')
#database_validation = Database('../data/trafficking/cluster_subsample1_10000.csv', header_path='../data/trafficking/cluster_subsample_header_all.csv')
#database_test = Database('../data/trafficking/cluster_subsample2_10000.csv', header_path='../data/trafficking/cluster_subsample_header_all.csv')
# Uncomment to only use annotation features
#database_train = Database('../data/trafficking/cluster_subsample0_10000.csv', header_path='../data/trafficking/cluster_subsample_header_annotations.csv')
#database_validation = Database('../data/trafficking/cluster_subsample1_10000.csv', header_path='../data/trafficking/cluster_subsample_header_annotations.csv')
#database_test = Database('../data/trafficking/cluster_subsample2_10000.csv', header_path='../data/trafficking/cluster_subsample_header_annotations.csv')
# Uncomment to only use LM features
database_train = Database('../data/trafficking/cluster_subsample0_10000.csv', header_path='../data/trafficking/cluster_subsample_header_LM.csv')
database_validation = Database('../data/trafficking/cluster_subsample1_10000.csv', header_path='../data/trafficking/cluster_subsample_header_LM.csv')
database_test = Database('../data/trafficking/cluster_subsample2_10000.csv', header_path='../data/trafficking/cluster_subsample_header_LM.csv')
labels_train = fast_strong_cluster(database_train)
labels_validation = fast_strong_cluster(database_validation)
labels_test = fast_strong_cluster(database_test)
single_block = False
else:
Exception('Invalid experiment type'+data_type)
entities = deepcopy(database_test)
blocking_scheme = BlockingScheme(entities, max_block_size, single_block=single_block)
train_seed = generate_pair_seed(database_train, labels_train, train_class_balance, require_direct_match=True, max_minor_class=5000)
validation_seed = generate_pair_seed(database_validation, labels_validation, 0.5, require_direct_match=True, max_minor_class=5000)
# forest_all = ForestMatchFunction(database_all_train, labels_train, train_seed, decision_threshold)
# forest_all.test(database_all_validation, labels_validation, validation_seed)
# tree_all = TreeMatchFunction(database_all_train, labels_train, train_seed, decision_threshold)
# tree_all.test(database_all_validation, labels_validation, validation_seed)
# logistic_all = LogisticMatchFunction(database_all_train, labels_train, train_seed, decision_threshold)
# logistic_all.test(database_all_validation, labels_validation, validation_seed)
forest_annotations = ForestMatchFunction(database_train, labels_train, train_seed, decision_threshold)
roc = forest_annotations.test(database_validation, labels_validation, validation_seed)
#roc.make_plot()
#plt.show()
# tree_annotations = TreeMatchFunction(database_annotations_train, labels_train, train_seed, decision_threshold)
# tree_annotations.test(database_annotations_validation, labels_validation, validation_seed)
# logistic_annotations = LogisticMatchFunction(database_annotations_train, labels_train, train_seed, decision_threshold)
# logistic_annotations.test(database_annotations_validation, labels_validation, validation_seed)
# forest_LM = ForestMatchFunction(database_LM_train, labels_train, train_seed, decision_threshold)
# forest_LM.test(database_LM_validation, labels_validation, validation_seed)
# tree_LM = TreeMatchFunction(database_LM_train, labels_train, train_seed, decision_threshold)
# tree_LM.test(database_LM_validation, labels_validation, validation_seed)
# logistic_LM = LogisticMatchFunction(database_LM_train, labels_train, train_seed, decision_threshold)
# logistic_LM.test(database_LM_validation, labels_validation, validation_seed)
# forest_all.roc.write_rates('match_forest_all.csv')
# tree_all.roc.write_rates('match_tree_all.csv')
# logistic_all.roc.write_rates('match_logistic_all.csv')
#
# forest_annotations.roc.write_rates('match_forest_annotations.csv')
# tree_annotations.roc.write_rates('match_tree_annotations.csv')
# logistic_annotations.roc.write_rates('match_logistic_annotations.csv')
#
# forest_LM.roc.write_rates('match_forest_LM.csv')
# tree_LM.roc.write_rates('match_tree_LM.csv')
# logistic_LM.roc.write_rates('match_logistic_LM.csv')
# ax = forest_all.roc.make_plot()
# _ = tree_all.roc.make_plot(ax=ax)
# _ = logistic_all.roc.make_plot(ax=ax)
# plt.show()
#forest_annotations.roc.make_plot()
#plt.show()
#entities.merge(strong_labels)
#er = EntityResolution()
#weak_labels = er.run(entities, match_function, blocking_scheme, cores=cores)
weak_labels = weak_connected_components(database_test, forest_annotations, blocking_scheme)
entities.merge(weak_labels)
#strong_labels = fast_strong_cluster(entities)
#entities.merge(strong_labels)
# out = open('ER.csv', 'w')
# out.write('phone,cluster_id\n')
# for cluster_counter, (entity_id, entity) in enumerate(entities.records.iteritems()):
# phone_index = 21
# for phone in entity.features[phone_index]:
# out.write(str(phone)+','+str(cluster_counter)+'\n')
# out.close()
print 'Metrics using strong features as surrogate label. Entity resolution run using weak and strong features'
metrics = Metrics(labels_test, weak_labels)
# estimated_test_class_balance = count_pairwise_class_balance(labels_test)
# new_metrics = NewMetrics(database_all_test, weak_labels, forest_all, estimated_test_class_balance)
metrics.display()
