def subspace_main(schema, sigma_k=1.0, outliers=None, epsilon=None, filenames=None, neighbor_k=3, data_size=None,
used_attrs=None, alpha=0.2, trainRatio=0.2, axis='epsilon'):
with open('result/DCs.pkl', 'r') as f:
dcs = pickle.load(f)
if used_attrs is not None:
schema = used_attrs + ['class']
error_table_filename = filenames.get('error', '../dataset/letter/data_error')
error_table = read_table(error_table_filename, schema)
error_instance = Instance(schema)
error_instance.data = error_table.instance
repair = HolisticRepair(error_table, dcs, threshold=float('inf'))
try:
repair.repair()
except Exception:
import traceback;
traceback.print_exc()
repaired_instance = Instance(schema)
repaired_instance.data = error_table.get_data()
origin_table_filename = filenames.get('origin', '../dataset/letter/data_origin')
origin_table = read_table(origin_table_filename, schema)
origin_instance = Instance(schema)
origin_instance.data = origin_table.instance
violated_tuples = set()
for i in xrange(len(repair.violations)):
violated_tuples.add(repair.violations[i].tid1)
print len(violated_tuples)
jaccard, precision, recall, f1, accuracy, error_count = repair_accuracy_for_subspace(origin_instance, error_instance,
repaired_instance)
return jaccard, precision, recall, f1, accuracy, error_count, origin_instance.size()
def main(schema, sigma_k=1.0, outliers=None, epsilon=None, filenames=None, neighbor_k=3, data_size=None,
used_attrs=None):
with open('result/DCs-gps.pkl', 'r') as f:
dcs = pickle.load(f)
if used_attrs is not None:
schema = used_attrs + ['class']
error_table_filename = filenames.get('error', '../dataset/wisconsin/data_error')
error_table = read_table(error_table_filename, schema)
error_instance = Instance(schema)
error_instance.data = error_table.instance
repair = HolisticRepair(error_table, dcs, threshold=float('inf'))
try:
repair.repair()
except Exception:
import traceback
traceback.print_exc()
repaired_instance = Instance(schema)
repaired_instance.data = error_table.get_data()
origin_table_filename = filenames.get('origin', '../dataset/wisconsin/data_origin')
origin_table = read_table(origin_table_filename, schema)
origin_instance = Instance(schema)
origin_instance.data = origin_table.instance
rms, precision, recall, accuracy, repair_distance = repair_accuracy(origin_instance,
error_instance, repaired_instance)
return rms, precision, recall, accuracy, repair_distance, repaired_instance.size()