def export_summary_results(
df_experiments,
path_out,
params=None,
name_txt=ImRegBenchmark.NAME_RESULTS_TXT,
name_csv=ImRegBenchmark.NAME_RESULTS_CSV,
):
""" export the summary as CSV and TXT
:param DF df_experiments: DataFrame with experiments
:param str path_out: path to the output folder
:param dict|None params: experiment parameters
:param str name_csv: results file name
:param str name_txt: results file name
>>> export_summary_results(pd.DataFrame(), '')
"""
costume_percentiles = np.arange(0., 1., 0.05)
if df_experiments.empty:
logging.error('No registration results found.')
return
if 'ID' in df_experiments.columns:
df_experiments.set_index('ID', inplace=True)
df_summary = df_experiments.describe(percentiles=costume_percentiles).T
df_summary['median'] = df_experiments.median()
nb_missing = np.sum(df_experiments['IRE Mean'].isnull())\
if 'IRE Mean' in df_experiments.columns else len(df_experiments)
df_summary['missing'] = nb_missing / float(len(df_experiments))
df_summary.sort_index(inplace=True)
path_csv = os.path.join(path_out, name_csv)
logging.debug('exporting CSV summary: %s', path_csv)
df_summary.to_csv(path_csv)
path_txt = os.path.join(path_out, name_txt)
logging.debug('exporting TXT summary: %s', path_txt)
pd.set_option('display.float_format', '{:10,.3f}'.format)
pd.set_option('expand_frame_repr', False)
with open(path_txt, 'w') as fp:
if params:
fp.write(string_dict(params, 'CONFIGURATION:'))
fp.write('\n' * 3 + 'RESULTS:\n')
fp.write('completed registration experiments: %i' %
len(df_experiments))
fp.write('\n' * 2)
fp.write(
repr(df_summary[[
'mean', 'std', 'median', 'min', 'max', 'missing', '5%', '25%',
'50%', '75%', '95%'
]]))
def __init__(self, exp_params, stamp_unique=True):
""" initialise the experiment, create experiment folder and set logger
:param dict exp_params: {str: value}
:param bool stamp_unique: add at the end of experiment folder unique
time stamp (actual date and time)
"""
self._main_thread = True
self.params = copy.deepcopy(exp_params)
self.params['class'] = self.__class__.__name__
self._check_required_params()
self.__check_exist_path()
self.__create_folder(stamp_unique)
set_experiment_logger(self.params['path_exp'], FILE_LOGS)
# set stream logging to info level
for lh in logging.getLogger().handlers:
if isinstance(lh, logging.StreamHandler) and \
not isinstance(lh, logging.FileHandler):
lh.setLevel(logging.INFO)
logging.info('initialise experiment...')
logging.info(string_dict(self.params, 'PARAMETERS:'))