def _cache_locally(self, df: pd.DataFrame, name: str) -> str:
def clean_cache():
LOGGER.info('Cleaning cache. This may take some while...')
# For complete local execution do not clean cache!
exit(-1)
shutil.rmtree(self.work_dir)
Path(self.work_dir).mkdir(parents=True, exist_ok=True)
self.cache_total, self.cache_used, free = shutil.disk_usage(
self.work_dir)
LOGGER.info('Deleted local cache. Using {} of cache'.format(
self.cache_used / self.cache_total))
try:
local_file = store_data(df, self.work_dir, name)
except IOError:
clean_cache()
local_file = store_data(df, self.work_dir, name)
self.cache_used += os.stat(local_file).st_size
if self.cache_used / self.cache_total > self.cache_percentage:
clean_cache()
return local_file
def store_output_files(): data.store_data(cname_dir + cname_dict_filename, cnames) data.store_data(cname_dir + auth_dict_filename, auths) list_data.store_data(cname_dir + cname_filename, list(cname_list)) list_data.store_data(cname_dir + auth_filename, list(auth_list)) data.store_data(cname_dir + fail_cname_dict_filename, fail_cnames) data.store_data(cname_dir + fail_auth_dict_filename, fail_auths) list_data.store_data(cname_dir + fail_cname_filename, list(fail_cname_list)) list_data.store_data(cname_dir + fail_auth_filename, list(fail_auth_list))
def store_output_files():
data.store_data(output_dir + cname_dict_filename, cnames)
data.store_data(output_dir + auth_dict_filename, auths)
list_data.store_data(output_dir + cname_filename, list(cname_list))
list_data.store_data(output_dir + auth_filename, list(auth_list))
data.store_data(output_dir + fail_cname_dict_filename, fail_cnames)
data.store_data(output_dir + fail_auth_dict_filename, fail_auths)
list_data.store_data(output_dir + fail_cname_filename,
list(fail_cname_list))
list_data.store_data(output_dir + fail_auth_filename, list(fail_auth_list))
def store_output_files(): data.store_data(ips_dir + ip_dict_filename, ips) ip_list = dict_ips_to_list(ips) list_data.store_data(ips_dir + ip_filename, ip_list)
def store_output_files():
data.store_data(geo_dir + geo_dict_filename, geo)
geo_list = geo_dict_to_csv(geo)
list_data.store_data(geo_dir + geo_filename, geo_list)
def store_output_files(): data.store_data(geo_db_dir + ipinfo_db_filename, located_ips)
###################
## Read IP to Geolocation Database
###################
if DEBUG2: print "Read IP to Geolocation Database"
if os.path.exists(geo_db_dir + my_geo_db_filename):
if DEBUG3: print " read from my data structure"
geo_db = data.load_data(geo_db_dir + my_geo_db_filename)
else:
if DEBUG3: print " read from orig db"
geo_db = read_ip2geo_db(geo_db_dir + geo_db_filename)
data.store_data(geo_db_dir + my_geo_db_filename, geo_db)
located_ips = data.load_data(geo_dir + exist_ip_filename)
if DEBUG3: print " # records=%d" % (len(geo_db))
# try:
# address = search_in_db("8.8.8.8", geo_db)
# print address
# except Exception as e:
# print " [1] Search Exception " + type(e) + ": " + e
###################
## Locate IP
###################
if DEBUG2: print "Locate IP"
def store_output_files(): data.store_data(geo_dir + geo_dict_filename, geo) geo_list = geo_dict_to_csv(geo) list_data.store_data(geo_dir + geo_filename, geo_list) data.store_data(geo_dir + exist_ip_filename, located_ips)
def store_output_files(): data.store_data(geo_dir + geo_dict_filename, geo) geo_list = geo_dict_to_csv(geo) list_data.store_data(geo_dir + geo_filename, geo_list)
def store_output_files():
data.store_data(geo_db_dir + ipinfo_db_filename, located_ips)
def store_output_files():
data.store_data(ips_dir + ip_dict_filename, ips)
ip_list = dict_ips_to_list(ips)
list_data.store_data(ips_dir + ip_filename, ip_list)
def store_output_files(): # ips = merge_ips(ips, data.load_data(ips_dir + ip_dict_filename)) data.store_data(ips_dir + ip_dict_filename, ips) ip_list = dict_ips_to_list(ips) list_data.store_data(ips_dir + ip_filename, ip_list)
plt.savefig(os.path.join(args.evalf, 'cor_raw_%d.pdf' % idx_rel))
plt.show()
# store data for plotting
if args.env in ['Ball']:
# edge_acc_over_time: n_roll x n_timestep
record_names = ['edge_acc_over_time', 'edge_cor_over_time', 'fwd_loss_mse']
if args.baseline == 1:
record_path = os.path.join(args.evalf,
'rec_%d_baseline.h5' % args.n_kp)
else:
record_path = os.path.join(args.evalf, 'rec_%d.h5' % args.n_kp)
store_data(
record_names,
[edge_acc_over_time_record, edge_cor_over_time_record, fwd_loss_mse],
record_path)
print()
print('Edge Accuracy')
print('%.2f%%, std: %.6f' %
(np.mean(edge_acc_over_time_record[:, -1]) * 100.,
np.std(edge_acc_over_time_record[:, -1])))
print()
print('Correlation on Attributes')
for i in range(len(edge_cor_over_time_record)):
print('#%d:' % i, edge_cor_over_time_record[i][-1])
elif args.env in ['Cloth']:
