0.5, 0.5, 0.5, 1.0, 0.5, 200)
time_end = time.time()
sys.stdout.flush()
outlier_size = float(len(outliers))
time_used = (time_end - time_begin) / outlier_size
rms_list.append((rms, data_size))
precision_list.append((precision, data_size))
recall_list.append((recall, data_size))
accuracy_list.append((accuracy, data_size))
time_list.append((time_used, outlier_size))
data_size_list.append(data_size)
repair_distance_list.append(repair_distance)
rms = [
UtilFunc.rms_average(rms_lists[name]) for name, _ in exp_methods
]
precision = [
UtilFunc.plat_average(precision_lists[name])
for name, _ in exp_methods
]
recall = [
UtilFunc.plat_average(recall_lists[name])
for name, _ in exp_methods
]
accuracy = [
UtilFunc.plat_average(accuracy_lists[name])
for name, _ in exp_methods
]
time_used = [
UtilFunc.plat_average(time_lists[name]) for name, _ in exp_methods
else:
jaccard, precision, recall, f1, accuracy, errorcount, data_size = (0.5, 0.5, 0.5, 0.5, 0.5, 0, 200)
time_end = time.time()
outlier_size = float(len(outliers))
time_used = (time_end - time_begin) / outlier_size
jaccard_list.append((jaccard, data_size))
precision_list.append((precision, data_size))
recall_list.append((recall, data_size))
f1_list.append((f1, data_size))
accuracy_list.append((accuracy, data_size))
errorcount_list.append((errorcount, data_size))
time_list.append((time_used, outlier_size))
datasize_list.append(data_size)
jaccard = [UtilFunc.plat_average(jaccard_lists[name]) for name, _ in exp_methods]
precision = [UtilFunc.plat_average(precision_lists[name]) for name, _ in exp_methods]
recall = [UtilFunc.plat_average(recall_lists[name]) for name, _ in exp_methods]
f1 = [UtilFunc.plat_average(f1_lists[name]) for name, _ in exp_methods]
accuracy = [UtilFunc.plat_average(accuracy_lists[name]) for name, _ in exp_methods]
errorcount = [UtilFunc.plat_average(errorcount_lists[name]) for name, _ in exp_methods]
time_used = [UtilFunc.plat_average(time_lists[name]) for name, _ in exp_methods]
output_content_jaccard += str(error_distance) + '\t%f\t%f\t%f\t%f\t%f\r\n' % tuple(jaccard)
output_content_precision += str(error_distance) + '\t%f\t%f\t%f\t%f\t%f\r\n' % tuple(precision)
output_content_recall += str(error_distance) + '\t%f\t%f\t%f\t%f\t%f\r\n' % tuple(recall)
output_content_f1 += str(error_distance) + '\t%f\t%f\t%f\t%f\t%f\r\n' % tuple(f1)
output_content_accuracy += str(error_distance) + '\t%f\t%f\t%f\t%f\t%f\r\n' % tuple(accuracy)
output_content_error_count += str(error_distance) + '\t%f\t%f\t%f\t%f\t%f\r\n' % tuple(errorcount)
output_content_time += str(error_distance) + '\t%f\t%f\t%f\t%f\t%f\r\n' % tuple(time_used)
if exp_func is not None:
errorcount, data_size = exp_func(schema,
epsilon=epsilon,
neighbor_k=neighbor_k,
filenames=filenames)
else:
errorcount, data_size = (0, 200)
time_end = time.time()
outlier_size = float(len(outliers))
errorcount_list.append((errorcount, data_size))
datasize_list.append(data_size)
errorcount = [
UtilFunc.plat_average(errorcount_lists[name])
for name, _ in exp_methods
]
#time_used = [UtilFunc.plat_average(time_lists[name]) for name, _ in exp_methods]
output_content_error_count += str(
k) + '\t%f\t%f\t%f\t%f\r\n' % tuple(errorcount)
#output_content_time += str(k) + '\t%f\t%f\r\n' % tuple(time_used)
#print 'Average time(s): %s' % time_used
print 'Error Count: %s' % errorcount
print output_content_error_count
#print output_content_time
with open('result/subspace_k_error_count_magic.dat', 'w') as f:
f.write(output_content_error_count)
0.5, 0.5, 0.5, 0.5, 0.5, 0, 200)
time_end = time.time()
outlier_size = float(len(outliers))
time_used = (time_end - time_begin) / outlier_size
jaccard_list.append((jaccard, data_size))
precision_list.append((precision, data_size))
recall_list.append((recall, data_size))
f1_list.append((f1, data_size))
accuracy_list.append((accuracy, data_size))
errorcount_list.append((errorcount, data_size))
time_list.append((time_used, outlier_size))
datasize_list.append(data_size)
jaccard = [
UtilFunc.plat_average(jaccard_lists[name])
for name, _ in exp_methods
]
precision = [
UtilFunc.plat_average(precision_lists[name])
for name, _ in exp_methods
]
recall = [
UtilFunc.plat_average(recall_lists[name])
for name, _ in exp_methods
]
f1 = [UtilFunc.plat_average(f1_lists[name]) for name, _ in exp_methods]
accuracy = [
UtilFunc.plat_average(accuracy_lists[name])
for name, _ in exp_methods
]