def _collect_parameters_and_results(self, start, stop, params):
self._result_pool = ResultPool()
self._result_pool.load_results(self._folder_name, start, stop, params)
if (self._result_pool.have_varying_datasets()
or self._result_pool.have_varying_leave_out()):
self._dataset_averaged_results = DatasetAveragedResults()
self._dataset_averaged_results.extract_results(self._result_pool)
def create_meaned_amp_pred_corrs(prefix='',
folder='data/models/paper/ours/cnt/deep4/car/',
params='default',
perturb_names=('no_dev', 'rand_mad', 'rand_std')):
"""This takes computed cov_vars and transforms them to corrs
and saves corrs."""
if params == 'default':
params = dict(cnt_preprocessors="$cz_zero_resample_car_demean",
trial_start=1500, trial_stop=4000)
res_pool = ResultPool()
res_pool.load_results(folder,
params=params)
result_file_names = res_pool.result_file_names()
results = res_pool.result_objects()
# sort by dataset filename
sort_order = np.argsort([r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
all_base_names = [name.replace('.result.pkl', '')
for name in result_file_names]
if prefix != '':
prefix = '.' + prefix
for i_file, base_name in enumerate(all_base_names):
log.info("Loading {:s}".format(results[i_file].parameters['dataset_filename']))
create_meaned_amp_pred_corrs_for_file(base_name, prefix, perturb_names)
def load_meaned_amp_pred_corrs(prefix='',
folder='data/models/paper/ours/cnt/deep4/car/',
params='default',
perturb_names=('no_dev','rand_mad', 'rand_std')):
if params == 'default':
params = dict(cnt_preprocessors="$cz_zero_resample_car_demean",
trial_start=1500, trial_stop=4000)
res_pool = ResultPool()
res_pool.load_results(folder,
params=params)
result_file_names = res_pool.result_file_names()
results = res_pool.result_objects()
if prefix != '':
prefix = '.' + prefix
# sort by dataset filename
sort_order = np.argsort([r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
all_base_names = [name.replace('.result.pkl', '')
for name in result_file_names]
clean_mask = []
all_corrs = dict()
for i_file, base_name in enumerate(all_base_names):
# Check that all perturbations exist
all_perturbations_exist = True
for perturb_name in perturb_names:
filename_end = '{:s}.{:s}.amp_cov_var_corrs.npy'.format(
prefix, perturb_name)
filename = base_name + filename_end
if not os.path.isfile(filename):
all_perturbations_exist = False
log.warn("{:s} does not exist".format(
filename))
if not all_perturbations_exist:
log.warn("Skipping {:s} since not all perturbations exist".format(
base_name))
continue
# Check that all exist for subject
if any(s in results[i_file].parameters['dataset_filename'] for s in unclean_sets):
clean_mask.append(False)
else:
clean_mask.append(True)
for perturb_name in perturb_names:
filename_end = '{:s}.{:s}.amp_cov_var_corrs.npy'.format(
prefix, perturb_name)
filename = base_name + filename_end
assert os.path.isfile(filename), ("Expect {:s} to exist".format(
filename))
this_arr = all_corrs.pop(perturb_name, [])
this_corrs = np.load(filename)
this_arr.append(this_corrs)
all_corrs[perturb_name] = this_arr
clean_mask = np.array(clean_mask)
return all_corrs, clean_mask
def create_unit_output_class_corrs_for_files(folder_name, params,
start, stop, i_all_layers):
res_pool = ResultPool()
res_pool.load_results(folder_name, params=params)
res_file_names = res_pool.result_file_names()
all_base_names = [name.replace('.result.pkl', '')
for name in res_file_names]
start = start or 0
stop = stop or len(all_base_names)
for i_file, basename in enumerate(all_base_names[start:stop]):
log.info("Running {:s} ({:d} of {:d})".format(
basename, i_file + start + 1, stop))
create_unit_output_class_corrs(basename, i_all_layers)
def create_all_amplitude_perturbation_corrs(folder_name,
params,
start,
stop,
with_square,
with_square_cov,
after_softmax,
n_samples,
perturbations='default'):
if perturbations == 'default':
perturbations = (
(
'no_dev',
FuncAndArgs(
rand_diff,
with_blocks=False, #just return 1
deviation_func=lambda arr, axis, keepdims: 1)),
('rand_mad',
FuncAndArgs(rand_diff,
with_blocks=False,
deviation_func=median_absolute_deviation)),
('rand_std',
FuncAndArgs(rand_diff, with_blocks=False, deviation_func=np.std)),
)
elif perturbations == 'simple_no_scale':
perturbations = (
(
'no_dev',
FuncAndArgs(
rand_diff,
with_blocks=False, #just return 1
deviation_func=lambda arr, axis, keepdims: 1)), )
assert not (with_square and with_square_cov)
res_pool = ResultPool()
res_pool.load_results(folder_name, params=params)
res_file_names = res_pool.result_file_names()
all_base_names = [
name.replace('.result.pkl', '') for name in res_file_names
]
start = start or 0
stop = stop or len(all_base_names)
for i_file, base_name in enumerate(all_base_names[start:stop]):
log.info("Running {:s} ({:d} of {:d})".format(base_name,
i_file + start + 1,
stop))
create_amplitude_perturbation_corrs(base_name,
with_square=with_square,
with_square_cov=with_square_cov,
after_softmax=after_softmax,
n_samples=n_samples,
perturbations=perturbations)
def create_unit_output_class_corrs_for_files(folder_name, params, start, stop,
i_all_layers):
res_pool = ResultPool()
res_pool.load_results(folder_name, params=params)
res_file_names = res_pool.result_file_names()
all_base_names = [
name.replace('.result.pkl', '') for name in res_file_names
]
start = start or 0
stop = stop or len(all_base_names)
for i_file, basename in enumerate(all_base_names[start:stop]):
log.info("Running {:s} ({:d} of {:d})".format(basename,
i_file + start + 1,
stop))
create_unit_output_class_corrs(basename, i_all_layers)
def dataset_to_env_file(wanted_dataset_filename):
""" For any dataset filename, give envelope filename
These experiments are, where envelopes were calculated from originally"""
res_pool= ResultPool()
res_pool.load_results('data/models-backup/paper/ours/cnt/deep4/car/',
params=dict(cnt_preprocessors="$cz_zero_resample_car_demean",
trial_start=1500, trial_stop=4000))
dataset_to_env_file_name = dict()
for result, res_file_name in zip(res_pool.result_objects(), res_pool.result_file_names()):
dataset_file_name = result.parameters['dataset_filename']
envelope_file_name = res_file_name.replace('.result.pkl', '.env.npy')
assert os.path.isfile(envelope_file_name)
dataset_to_env_file_name[dataset_file_name] = envelope_file_name
return dataset_to_env_file_name[wanted_dataset_filename]
def create_env_class_corrs(folder, params, start, stop):
res_pool = ResultPool()
res_pool.load_results(folder, params=params)
res_file_names = res_pool.result_file_names()
all_base_names = [
name.replace('.result.pkl', '') for name in res_file_names
]
start = start or 0
stop = stop or len(all_base_names)
with_square = True
for i_exp, base_name in enumerate(all_base_names[start:stop]):
log.info("Running {:s} ({:d} of {:d})".format(base_name,
i_exp + start + 1, stop))
create_env_class_corr_file(base_name, with_square)
def _collect_parameters_and_results(self, start, stop, params):
self._result_pool = ResultPool()
self._result_pool.load_results(self._folder_name, start, stop, params)
if (self._result_pool.have_varying_datasets() or
self._result_pool.have_varying_leave_out()):
self._dataset_averaged_results = DatasetAveragedResults()
self._dataset_averaged_results.extract_results(self._result_pool)
def create_env_class_corrs(folder, params,start,stop):
res_pool = ResultPool()
res_pool.load_results(folder, params=params)
res_file_names = res_pool.result_file_names()
all_base_names = [name.replace('.result.pkl', '')
for name in res_file_names]
start = start or 0
stop = stop or len(all_base_names)
with_square = True
for i_exp, base_name in enumerate(all_base_names[start:stop]):
log.info("Running {:s} ({:d} of {:d})".format(base_name,
i_exp + start + 1, stop))
create_env_class_corr_file(base_name, with_square)
def create_env_corrs(folder_name, params, start, stop):
from braindecode.analysis.create_env_class_corrs import create_env_class_corr_file
res_pool = ResultPool()
res_pool.load_results(folder_name, params=params)
res_file_names = res_pool.result_file_names()
all_base_names = [name.replace('.result.pkl', '')
for name in res_file_names]
start = start or 0
stop = stop or len(all_base_names)
# Hackhack hardcoded layers, since I know this is correct layers atm
i_all_layers = [8,14,20,26,28] #for shallow [3, 4, 5, 7]
for i_file, base_name in enumerate(all_base_names[start:stop]):
with_square = True
log.info("Running {:s} ({:d} of {:d})".format(
base_name, i_file+start+1, stop))
create_topo_env_corrs_files(base_name, i_all_layers, with_square)
create_env_class_corr_file(base_name, with_square)
def create_env_corrs(folder_name, params, start, stop):
from braindecode.analysis.create_env_class_corrs import create_env_class_corr_file
res_pool = ResultPool()
res_pool.load_results(folder_name, params=params)
res_file_names = res_pool.result_file_names()
all_base_names = [name.replace('.result.pkl', '')
for name in res_file_names]
start = start or 0
stop = stop or len(all_base_names)
# Hackhack hardcoded layers, since I know this is correct layers atm
i_all_layers = [8,14,20,26,28] #for shallow [3, 4, 5, 7]
for i_file, base_name in enumerate(all_base_names[start:stop]):
with_square = True
log.info("Running {:s} ({:d} of {:d})".format(
base_name, i_file+start+1, stop))
create_topo_env_corrs_files(base_name, i_all_layers, with_square)
create_env_class_corr_file(base_name, with_square)
def load_amp_corrs(with_square, with_square_corr, cov_or_corr):
assert not (with_square and with_square_corr)
assert cov_or_corr == 'cov' or cov_or_corr == 'corr'
res_pool = ResultPool()
res_pool.load_results('data/models/paper/ours/cnt/deep4/car/',
params=dict(sensor_names="$all_EEG_sensors", batch_modifier="null",
low_cut_off_hz="null", first_nonlin="$elu"))
result_file_names = res_pool.result_file_names()
results = res_pool.result_objects()
# sort by dataset filename
sort_order = np.argsort([r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
all_base_names = [name.replace('.result.pkl', '')
for name in result_file_names]
clean_mask = []
all_corrs = dict()
for i_file, base_name in enumerate(all_base_names):
if any(s in results[i_file].parameters['dataset_filename'] for s in unclean_sets):
clean_mask.append(False)
else:
clean_mask.append(True)
for perturb_name in ('rand_mad', 'rand_std', 'no_dev'):
file_name_end = '.{:s}.amp_{:s}s.npy'.format(perturb_name,
cov_or_corr)
if with_square:
file_name_end = '.square' + file_name_end
if with_square_corr:
file_name_end = ".corrtosquare" + file_name_end
file_name = base_name + file_name_end
assert os.path.isfile(file_name), "Expect {:s} to exist".format(
file_name)
this_arr = all_corrs.pop(perturb_name, [])
this_arr.append(np.load(file_name))
all_corrs[perturb_name] = this_arr
clean_mask = np.array(clean_mask)
return all_corrs, clean_mask
def create_meaned_amp_pred_corrs(
prefix='',
folder='data/models/paper/ours/cnt/deep4/car/',
params='default',
perturb_names=('no_dev', 'rand_mad', 'rand_std')):
"""This takes computed cov_vars and transforms them to corrs
and saves corrs."""
if params == 'default':
params = dict(cnt_preprocessors="$cz_zero_resample_car_demean",
trial_start=1500,
trial_stop=4000)
res_pool = ResultPool()
res_pool.load_results(folder, params=params)
result_file_names = res_pool.result_file_names()
results = res_pool.result_objects()
# sort by dataset filename
sort_order = np.argsort(
[r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
all_base_names = [
name.replace('.result.pkl', '') for name in result_file_names
]
if prefix != '':
prefix = '.' + prefix
for i_file, base_name in enumerate(all_base_names):
log.info("Loading {:s}".format(
results[i_file].parameters['dataset_filename']))
create_meaned_amp_pred_corrs_for_file(base_name, prefix, perturb_names)
def load_data_frame(folder, params=None, shorten_headers=True):
res_pool = ResultPool()
res_pool.load_results(folder, params=params)
result_objs = res_pool.result_objects()
varying_params = res_pool.varying_params()
constant_params = res_pool.constant_params()
file_names = res_pool.result_file_names()
data_frame = to_data_frame(file_names,
result_objs,
varying_params,
constant_params,
shorten_headers=shorten_headers)
data_frame.attrs = {'folder': folder, 'params': params}
return data_frame
def load_data_frame(folder, params=None, shorten_headers=True):
res_pool = ResultPool()
res_pool.load_results(folder, params=params)
result_objs = res_pool.result_objects()
varying_params = res_pool.varying_params()
constant_params = res_pool.constant_params()
file_names = res_pool.result_file_names()
data_frame = to_data_frame(file_names, result_objs, varying_params,
constant_params, shorten_headers=shorten_headers)
data_frame.attrs = {'folder': folder, 'params': params}
return data_frame
def load_analysis_results(folder, params, file_name_end):
result_pool = ResultPool()
result_pool.load_results(folder, params=params)
result_file_names = result_pool.result_file_names()
results = result_pool.result_objects() # sort by dataset filename
sort_order = np.argsort([r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
analysis_result_per_person = []
clean_mask = []
for file_name, result in zip(result_file_names, results):
analysis_result_file = file_name.replace('.result.pkl',
file_name_end)
assert os.path.isfile(analysis_result_file)
analysis_result = np.load(analysis_result_file)
if any(s in result.parameters['dataset_filename'] for s in unclean_sets):
clean_mask.append(False)
else:
clean_mask.append(True)
analysis_result_per_person.append(analysis_result)
analysis_result_per_person = np.array(analysis_result_per_person)
clean_mask = np.array(clean_mask)
return analysis_result_per_person, clean_mask
def create_envelopes(folder_name, params, start, stop):
res_pool = ResultPool()
res_pool.load_results(folder_name, params=params)
res_file_names = res_pool.result_file_names()
log.info("{:d} files found.".format(len(res_file_names)))
yaml_file_names = [name.replace('.result.pkl', '.yaml')
for name in res_file_names]
stop = stop or len(yaml_file_names)
i_file = start
for i_file in xrange(start, stop):
file_name = yaml_file_names[i_file]
log.info("Running {:s} ({:d} of {:d})".format(
file_name, i_file+1, stop))
log.info("Parameters {:s}".format(
str(res_pool.result_objects()[i_file].parameters)))
create_envelopes_for_experiment(file_name)
def dataset_to_env_file(wanted_dataset_filename):
""" For any dataset filename, give envelope filename
These experiments are, where envelopes were calculated from originally"""
res_pool= ResultPool()
res_pool.load_results('data/models-backup/paper/ours/cnt/deep4/car/',
params=dict(cnt_preprocessors="$cz_zero_resample_car_demean",
trial_start=1500, trial_stop=4000))
dataset_to_env_file_name = dict()
for result, res_file_name in zip(res_pool.result_objects(), res_pool.result_file_names()):
dataset_file_name = result.parameters['dataset_filename']
envelope_file_name = res_file_name.replace('.result.pkl', '.env.npy')
assert os.path.isfile(envelope_file_name)
dataset_to_env_file_name[dataset_file_name] = envelope_file_name
return dataset_to_env_file_name[wanted_dataset_filename]
def load_amp_corrs(with_square, with_square_corr, cov_or_corr):
assert not (with_square and with_square_corr)
assert cov_or_corr == 'cov' or cov_or_corr == 'corr'
res_pool = ResultPool()
res_pool.load_results('data/models/paper/ours/cnt/deep4/car/',
params=dict(sensor_names="$all_EEG_sensors",
batch_modifier="null",
low_cut_off_hz="null",
first_nonlin="$elu"))
result_file_names = res_pool.result_file_names()
results = res_pool.result_objects()
# sort by dataset filename
sort_order = np.argsort(
[r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
all_base_names = [
name.replace('.result.pkl', '') for name in result_file_names
]
clean_mask = []
all_corrs = dict()
for i_file, base_name in enumerate(all_base_names):
if any(s in results[i_file].parameters['dataset_filename']
for s in unclean_sets):
clean_mask.append(False)
else:
clean_mask.append(True)
for perturb_name in ('rand_mad', 'rand_std', 'no_dev'):
file_name_end = '.{:s}.amp_{:s}s.npy'.format(
perturb_name, cov_or_corr)
if with_square:
file_name_end = '.square' + file_name_end
if with_square_corr:
file_name_end = ".corrtosquare" + file_name_end
file_name = base_name + file_name_end
assert os.path.isfile(file_name), "Expect {:s} to exist".format(
file_name)
this_arr = all_corrs.pop(perturb_name, [])
this_arr.append(np.load(file_name))
all_corrs[perturb_name] = this_arr
clean_mask = np.array(clean_mask)
return all_corrs, clean_mask
def load_patterns(folder='data/models/paper/ours/csp/car/'):
res_pool = ResultPool()
res_pool.load_results(folder)
result_file_names = res_pool.result_file_names()
results = res_pool.result_objects()
# sort by dataset filename
sort_order = np.argsort([r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
# now sorted
dataset_names = [r.parameters['dataset_filename'] for r in results]
all_patterns = []
clean_mask = []
all_exps = []
for file_name, dataset in zip(result_file_names, dataset_names):
log.info("Loading for {:s}".format(dataset))
model_file_name = file_name.replace('.result.pkl', '.pkl')
csp_exp = np.load(model_file_name)
patterns = csp_exp.binary_csp.patterns
pattern_arr = patterns_to_single_array(patterns)
pattern_arr = pattern_arr.squeeze()
assert not np.any(np.isnan(pattern_arr))
all_patterns.append(pattern_arr)
all_exps.append(csp_exp)
if any([s in dataset for s in unclean_sets]):
clean_mask.append(False)
else:
clean_mask.append(True)
all_patterns = np.array(all_patterns)
clean_mask = np.array(clean_mask)
return all_patterns, clean_mask, all_exps
def load_analysis_results(folder, params, file_name_end):
result_pool = ResultPool()
result_pool.load_results(folder, params=params)
result_file_names = result_pool.result_file_names()
results = result_pool.result_objects() # sort by dataset filename
sort_order = np.argsort(
[r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
analysis_result_per_person = []
clean_mask = []
for file_name, result in zip(result_file_names, results):
analysis_result_file = file_name.replace('.result.pkl', file_name_end)
assert os.path.isfile(analysis_result_file)
analysis_result = np.load(analysis_result_file)
if any(s in result.parameters['dataset_filename']
for s in unclean_sets):
clean_mask.append(False)
else:
clean_mask.append(True)
analysis_result_per_person.append(analysis_result)
analysis_result_per_person = np.array(analysis_result_per_person)
clean_mask = np.array(clean_mask)
return analysis_result_per_person, clean_mask
class ResultPrinter:
def __init__(self, folder_name):
self._folder_name = folder_name
def print_results(self,
templates=False,
constants=False,
sets=False,
start=None,
stop=None,
params=None,
shorten=True,
ignore=(),
markdown=False):
print("Printing results in {:s}:".format(self._folder_name))
self._collect_parameters_and_results(start, stop, params)
self._format_results()
self._print(templates, constants, sets, shorten, ignore, markdown)
def _collect_parameters_and_results(self, start, stop, params):
self._result_pool = ResultPool()
self._result_pool.load_results(self._folder_name, start, stop, params)
if (self._result_pool.have_varying_datasets()
or self._result_pool.have_varying_leave_out()):
self._dataset_averaged_results = DatasetAveragedResults()
self._dataset_averaged_results.extract_results(self._result_pool)
def _format_results(self):
self._formatted_results = []
misclasses_per_experiment = self._result_pool.get_misclasses()
for misclasses in misclasses_per_experiment:
formatted_result = self._format_result(misclasses)
self._formatted_results.append(formatted_result)
def _format_result(self, misclasses):
""" Format result for one experiment. """
formatted_result = {}
self._add_misclasses(formatted_result, misclasses)
self._add_best_and_best_epoch(formatted_result, misclasses)
return formatted_result
def _add_misclasses(self, formatted_result, misclasses):
""" Add misclasses for one experiment"""
for key in misclasses:
# get last epoch from all folds
# need transform in case we just have one experiment(?)
this_misclasses = self._atleast_2d_or_1d_of_arr(misclasses[key])
final_misclasses = []
for misclass_fold in this_misclasses:
final_misclasses.append(misclass_fold[-1])
this_mean = np.mean(final_misclasses)
formatted_result[key] = "{:5.2f}%".format((1 - this_mean) * 100)
if (len(final_misclasses) > 1): # only for crossval
this_std = np.std(final_misclasses)
formatted_result[key + '_std'] = "{:4.2f}%".format(this_std *
100)
def _add_best_and_best_epoch(self, formatted_result, misclasses):
if 'test' in misclasses:
# Determine minimal number of epochs and
# only take misclass rates until that epoch
test_misclasses = deepcopy(misclasses['test'])
# transform to list of one list in case of only one experiment
test_misclasses = self._atleast_2d_or_1d_of_arr(test_misclasses)
min_epoch_num = np.min([len(a) for a in test_misclasses])
same_epochs_misclasses = [
a[0:min_epoch_num] for a in test_misclasses
]
same_epochs_misclasses = np.array(same_epochs_misclasses)
average_misclass = np.mean(same_epochs_misclasses, axis=0)
best_epoch = np.argmin(average_misclass)
best_misclass = average_misclass[best_epoch]
formatted_result['best'] = "{:5.2f}%".format(
(1 - best_misclass) * 100)
# +1 for 1-based indexing
formatted_result['best_epoch'] = "{:3d}".format(best_epoch + 1)
def _atleast_2d_or_1d_of_arr(self, arr):
if not isinstance(arr[0], list) and arr.ndim == 1:
return np.atleast_2d(arr)
else:
return arr
def _print(self, templates, constants, individual_datasets, shorten,
ignore, markdown):
if (templates):
self._print_templates()
if (constants):
self._print_constant_parameters()
if (individual_datasets):
self._print_experiments_result_table(shorten, ignore, markdown)
if (self._result_pool.have_varying_datasets()
or self._result_pool.have_varying_leave_out()):
self._print_experiments_averaged_datasets_result_table(
shorten, ignore, markdown)
def _print_templates(self):
# templates should all be same so just print first one
print("Templates ...")
for name, template in self._result_pool.template().iteritems():
print(name + ":")
# substitute object tags that cannot be loaded properly
#value = re.sub(r"!obj:[a-zA-Z0-9_.]*\.([A-Za-z_]*)", r"\1:",
# template)
# remove anchors...
value = re.sub(r"\&([A-Za-z_0-9]*)", "", template)
value = re.sub(r"!obj:[a-zA-Z0-9_.]*\.([A-Za-z_]*)", r"&\1", value)
# substitute stars => references to other objects
# which may be somehwere in the actual yaml-training file...
value = value.replace("*", "")
#yaml.dump(yaml.load(value), sys.stdout, default_flow_style=False)
print value
def _print_constant_parameters(self):
print("Parameters...")
yaml.dump(self._result_pool.constant_params(),
sys.stdout,
default_flow_style=False)
print('')
def _print_experiments_result_table(self,
shorten,
ignore_headers=(),
markdown=False):
table_headers, table_rows = self._create_experiments_headers_and_rows()
if shorten:
table_headers, table_rows = self._prettify_headers_rows(
table_headers, table_rows)
if len(ignore_headers) > 0:
keep_mask = np.array(
[h not in ignore_headers for h in table_headers])
table_headers = np.array(table_headers)[keep_mask]
table_rows = np.array(table_rows)[keep_mask]
if markdown:
self._print_markdown_table(table_headers, table_rows)
#self._print_csv_table(table_headers, table_rows)
self._print_table(table_headers, table_rows)
def _create_experiments_headers_and_rows(self):
''' Create table rows and headers for result table'''
param_headers, param_rows = self._create_experiment_param_headers_and_rows(
)
result_headers, result_rows = self._create_result_headers_and_rows()
# Check that results and params have same amount of rows
# and that each row has same size
assert (len(param_rows) == len(result_rows))
for i in range(len(param_rows)):
assert (len(param_rows[i]) == len(param_rows[0]))
assert (len(result_rows[i]) == len(result_rows[0]))
table_headers = param_headers + result_headers
# merge rows together...
table_rows = [
param_rows[i] + result_rows[i] for i in range(len(param_rows))
]
# some hacky header_substitutions for nicer printing
header_substitutions = {
'frequency_start': 'freq_start',
'frequency_stop': 'freq_stop',
'updates_per_batch': 'batch_updates',
'dataset_filename': 'filename',
'max_increasing_epochs': '>epochs'
}
table_headers = [header if not header_substitutions.has_key(header) \
else header_substitutions[header] \
for header in table_headers]
return table_headers, table_rows
def _create_experiment_param_headers_and_rows(self):
varying_param_keys = self._result_pool.varying_params()[0].keys()
# Try to put dataset filename in second column after id...
# TODELAY: remove testfilename from this
if (self._result_pool.have_varying_datasets()):
if 'dataset_filename' in varying_param_keys:
filenamekey = 'dataset_filename'
else:
filenamekey = 'filename'
varying_param_keys.remove(filenamekey)
param_headers = ['id'] + [filenamekey] + varying_param_keys
else:
param_headers = ['id'] + varying_param_keys
param_rows = []
for result_obj_id in range(self._result_pool.num_experiments()):
param_row = []
# Put result_obj id nr first
file_name = self._result_pool.result_file_names()[result_obj_id]
result_obj_file_nr = int(
file_name.split('.result.pkl')[0].split('/')[-1])
param_row.append(result_obj_file_nr)
varying_params_this_result_obj = self._result_pool.varying_params(
)[result_obj_id]
# Put dataset filename second if exist
if (self._result_pool.have_varying_datasets()):
filename = varying_params_this_result_obj[filenamekey]
# remove unnecessary details of filename
filename = re.sub(r"(./)?data/[^/]*/", '', str(filename))
filename = re.sub(r"MoS[Cc][0-9]*S[0-9]*R[0-9]*_ds10_", '',
filename)
filename = re.sub(r"_autoclean_.*", '', filename)
filename = re.sub(".BBCI.mat", '', filename)
param_row.append(filename)
# Put rest of parameters
for param_key in varying_param_keys:
param_value = varying_params_this_result_obj[param_key]
# TODELAY: remove again this if
if param_key == 'test_filename':
param_value = re.sub(r"(./)?data/[^/]*/", '',
str(param_value))
param_value = re.sub(r"MoS[Cc][0-9]*S[0-9]*R[0-9]*_ds10_",
'', param_value)
param_value = re.sub(r"_autoclean_.*", '', param_value)
param_value = re.sub("BBCI.mat", '', param_value)
param_row.append(param_value)
param_rows.append(param_row)
return param_headers, param_rows
def _create_result_headers_and_rows(self):
result_headers = []
result_headers.append('time')
with_standard_deviation = self._formatted_results[0].has_key(
'train_std')
# just put 'epoch' not 'best_epoch' so that header is not so wide
result_type_headers = [
'test', 'test_sample', 'best', 'epoch', 'train', 'valid'
]
for result_type in result_type_headers:
# check if result exists, if yes add it
if (result_type in self._formatted_results[0]
or 'best_' + result_type in self._formatted_results[0]):
result_headers.append(result_type)
if (with_standard_deviation
and result_type in ['test', 'train', 'valid']):
result_headers.append('std')
result_rows = []
for result_obj_id in range(self._result_pool.num_experiments()):
result_row = []
results = self._formatted_results[result_obj_id]
#append training time
training_time = self._result_pool.training_times()[result_obj_id]
result_row.append(
str(datetime.timedelta(seconds=round(training_time))))
for result_type in [
'test', 'test_sample', 'best', 'best_epoch', 'train',
'valid'
]:
if result_type in results:
result_row.append(results[result_type])
if (with_standard_deviation
and result_type in ['test', 'train', 'valid']):
result_row.append(results[result_type + "_std"])
result_rows.append(result_row)
return result_headers, result_rows
def _print_experiments_averaged_datasets_result_table(
self, shorten, ignore_headers, markdown):
print("\n\nDataset-Averaged Results:")
table_headers, table_rows = \
self._create_averaged_dataset_headers_and_rows()
if shorten:
table_headers, table_rows = self._prettify_headers_rows(
table_headers, table_rows)
if len(ignore_headers) > 0:
keep_mask = np.array(
[h not in ignore_headers for h in table_headers])
table_headers = np.array(table_headers)[keep_mask]
table_rows = np.array(table_rows)[:, keep_mask]
self._print_table(table_headers, table_rows)
if markdown:
self._print_markdown_table(table_headers, table_rows)
def _create_averaged_dataset_headers_and_rows(self):
''' Create table rows and headers for result table averaged over
different datasets.
So in the end have a table for each unique parameter combination.'''
# TODELAY: don't use this weird hack to find out if there is
# a cross validation ;)
all_result_lists = self._dataset_averaged_results.results()
headers = self._create_dataset_averaged_headers(all_result_lists)
rows = []
for i, result_list in enumerate(all_result_lists):
row = [str(i),
str(len(result_list))] # id in table, number of files
parameter_row = self._create_parameter_row(result_list, headers)
row += parameter_row
misclasses = self._compute_final_misclasses(result_list)
training_times = [r['training_time'] for r in result_list]
result_row = self._create_result_row(headers, misclasses,
training_times)
row += result_row
rows.append(row)
return headers, rows
@staticmethod
def _create_dataset_averaged_headers(all_result_list):
params = deepcopy(all_result_list[0][0]['parameters'])
misclass_keys = all_result_list[0][0]['misclasses'].keys()
# sort to show test before train before valid
misclass_keys = sorted(misclass_keys,
key=lambda x: ('test' in x) * 1 +
('train' in x) * 2 + ('valid' in x) * 3)
result_keys = ["time", "std"]
for key in misclass_keys:
result_keys.append(key)
result_keys.append('std')
params.pop('filename', None)
params.pop('dataset_filename', None)
params.pop('transfer_leave_out', None)
params.pop('test_filename', None)
params.pop('trainer_filename', None)
return ["id", "files"] + params.keys() + result_keys
@staticmethod
def _create_result_row(headers, misclasses, training_times):
row = []
# Add training times
row += [
str(datetime.timedelta(seconds=round(np.mean(training_times))))
]
row += [str(datetime.timedelta(seconds=round(np.std(training_times))))]
# Add misclass for valid test train etc.
assert len(set(misclasses.keys()) -
set(headers)) == 0, ("All misclass"
"keys should be in headers")
# Add results in order they are defined in headers(!)
for key in headers:
if key in misclasses.keys():
row += [
"{:4.2f}%".format(100 - np.mean(misclasses[key] * 100))
]
row += ["{:4.2f}%".format(np.std(misclasses[key]) * 100)]
return row
@staticmethod
def _create_parameter_row(result_list, headers):
parameters = deepcopy(result_list[0]['parameters'])
parameters.pop('dataset_filename', None)
parameters.pop('filename', None)
sorted_params = []
for header in headers:
if header in parameters:
sorted_params.append(parameters[header])
return sorted_params
def _compute_final_misclasses(self, result_list):
""" Compute final fold-averaged misclasses for all experiments.
Also works if there are no folds(train-test case)"""
final_misclasses = {}
misclasses = [r['misclasses'] for r in result_list]
misclass_keys = misclasses[0].keys() # train,test etc
get_last_elem = np.vectorize(lambda a: a[-1])
for key in misclass_keys:
this_misclasses = [m[key] for m in misclasses]
if np.array(this_misclasses[0][0]).shape != ():
this_final_misclasses = get_last_elem(this_misclasses)
else:
# can only happen in case all experiments have same number of
# epochs
this_final_misclasses = np.array(this_misclasses)[:, -1:]
# avg over folds if necessary
this_final_avg_misclasses = np.mean(this_final_misclasses, axis=1)
final_misclasses[key] = this_final_avg_misclasses
return final_misclasses
def _prettify_headers_rows(self, table_headers, table_rows):
""" Shorten names for nicer printing """
pretty_headers = []
for header in table_headers:
pretty_header = prettify_word(header)
pretty_headers.append(pretty_header)
pretty_rows = []
for row in table_rows:
pretty_row = []
# go through all parameter values and prettify
for value in row:
pretty_value = prettify_word(str(value))
pretty_row.append(pretty_value)
pretty_rows.append(pretty_row)
return pretty_headers, pretty_rows
def _print_table(self, headers, rows):
print("\nTerminal Table\n")
# first determine maximum length for any column
# two spaces padding
max_col_lengths = [0] * len(headers)
for column in range(len(headers)):
if (len(str(headers[column])) > max_col_lengths[column]):
max_col_lengths[column] = len(str(headers[column]))
for row in range(len(rows)):
if (len(str(rows[row][column])) > max_col_lengths[column]):
max_col_lengths[column] = len(str(rows[row][column]))
for index, header in enumerate(headers):
length = max_col_lengths[index] + 2
sys.stdout.write(("{:<" + str(length) + "}").format(header))
print('')
for row in rows:
for column, value in enumerate(row):
length = max_col_lengths[column] + 2
sys.stdout.write(("{:<" + str(length) + "}").format(value))
print('')
def _print_csv_table(self, headers, rows):
print("\nCSV Table\n")
wr = csv.writer(sys.stdout)
wr.writerow(headers)
for row in rows:
rowstrings = [str(value) for value in row]
rowstrings = [rowstr.replace("$", "") for rowstr in rowstrings]
wr.writerow(rowstrings)
def _print_markdown_table(self, headers, rows):
print("\nMarkdown Table\n")
headerline = "|".join(headers)
headerline = "|" + headerline + "|"
print headerline
# make seperatorline for table
seperatorline = "|".join(["-" for _ in headers])
seperatorline = "|" + seperatorline + "|"
print seperatorline
for row in rows:
rowstrings = [str(value) for value in row]
rowstrings = [rowstr.replace("$", "") for rowstr in rowstrings]
rowline = "|".join(rowstrings)
rowline = "|" + rowline + "|"
print rowline
def load_meaned_amp_pred_corrs(prefix='',
folder='data/models/paper/ours/cnt/deep4/car/',
params='default',
perturb_names=('no_dev', 'rand_mad',
'rand_std')):
if params == 'default':
params = dict(cnt_preprocessors="$cz_zero_resample_car_demean",
trial_start=1500,
trial_stop=4000)
res_pool = ResultPool()
res_pool.load_results(folder, params=params)
result_file_names = res_pool.result_file_names()
results = res_pool.result_objects()
if prefix != '':
prefix = '.' + prefix
# sort by dataset filename
sort_order = np.argsort(
[r.parameters['dataset_filename'] for r in results])
result_file_names = np.array(result_file_names)[sort_order]
results = np.array(results)[sort_order]
all_base_names = [
name.replace('.result.pkl', '') for name in result_file_names
]
clean_mask = []
all_corrs = dict()
for i_file, base_name in enumerate(all_base_names):
# Check that all perturbations exist
all_perturbations_exist = True
for perturb_name in perturb_names:
filename_end = '{:s}.{:s}.amp_cov_var_corrs.npy'.format(
prefix, perturb_name)
filename = base_name + filename_end
if not os.path.isfile(filename):
all_perturbations_exist = False
log.warn("{:s} does not exist".format(filename))
if not all_perturbations_exist:
log.warn("Skipping {:s} since not all perturbations exist".format(
base_name))
continue
# Check that all exist for subject
if any(s in results[i_file].parameters['dataset_filename']
for s in unclean_sets):
clean_mask.append(False)
else:
clean_mask.append(True)
for perturb_name in perturb_names:
filename_end = '{:s}.{:s}.amp_cov_var_corrs.npy'.format(
prefix, perturb_name)
filename = base_name + filename_end
assert os.path.isfile(filename), (
"Expect {:s} to exist".format(filename))
this_arr = all_corrs.pop(perturb_name, [])
this_corrs = np.load(filename)
this_arr.append(this_corrs)
all_corrs[perturb_name] = this_arr
clean_mask = np.array(clean_mask)
return all_corrs, clean_mask
class ResultPrinter:
def __init__(self, folder_name):
self._folder_name = folder_name
def print_results(self, templates=False,
constants=False,
sets=False,
start=None, stop=None,
params=None, shorten=True,
ignore=(),
markdown=False):
print ("Printing results in {:s}:".format(self._folder_name))
self._collect_parameters_and_results(start, stop, params)
self._format_results()
self._print(templates, constants, sets, shorten, ignore, markdown)
def _collect_parameters_and_results(self, start, stop, params):
self._result_pool = ResultPool()
self._result_pool.load_results(self._folder_name, start, stop, params)
if (self._result_pool.have_varying_datasets() or
self._result_pool.have_varying_leave_out()):
self._dataset_averaged_results = DatasetAveragedResults()
self._dataset_averaged_results.extract_results(self._result_pool)
def _format_results(self):
self._formatted_results = []
misclasses_per_experiment = self._result_pool.get_misclasses()
for misclasses in misclasses_per_experiment:
formatted_result = self._format_result(misclasses)
self._formatted_results.append(formatted_result)
def _format_result(self, misclasses):
""" Format result for one experiment. """
formatted_result = {}
self._add_misclasses(formatted_result, misclasses)
self._add_best_and_best_epoch(formatted_result, misclasses)
return formatted_result
def _add_misclasses(self, formatted_result, misclasses):
""" Add misclasses for one experiment"""
for key in misclasses:
# get last epoch from all folds
# need transform in case we just have one experiment(?)
this_misclasses = self._atleast_2d_or_1d_of_arr(misclasses[key])
final_misclasses = []
for misclass_fold in this_misclasses:
final_misclasses.append(misclass_fold[-1])
this_mean = np.mean(final_misclasses)
formatted_result[key] = "{:5.2f}%".format((1 - this_mean) * 100)
if (len(final_misclasses) > 1): # only for crossval
this_std = np.std(final_misclasses)
formatted_result[key + '_std'] = "{:4.2f}%".format(
this_std * 100)
def _add_best_and_best_epoch(self, formatted_result, misclasses):
if 'test' in misclasses:
# Determine minimal number of epochs and
# only take misclass rates until that epoch
test_misclasses = deepcopy(misclasses['test'])
# transform to list of one list in case of only one experiment
test_misclasses = self._atleast_2d_or_1d_of_arr(test_misclasses)
min_epoch_num = np.min([len(a) for a in test_misclasses])
same_epochs_misclasses = [a[0:min_epoch_num] for a in test_misclasses]
same_epochs_misclasses = np.array(same_epochs_misclasses)
average_misclass = np.mean(same_epochs_misclasses, axis=0)
best_epoch = np.argmin(average_misclass)
best_misclass = average_misclass[best_epoch]
formatted_result['best'] = "{:5.2f}%".format((1 - best_misclass) * 100)
# +1 for 1-based indexing
formatted_result['best_epoch'] = "{:3d}".format(best_epoch + 1)
def _atleast_2d_or_1d_of_arr(self, arr):
if not isinstance(arr[0], list) and arr.ndim == 1:
return np.atleast_2d(arr)
else:
return arr
def _print(self, templates, constants, individual_datasets, shorten, ignore,
markdown):
if (templates):
self._print_templates()
if (constants):
self._print_constant_parameters()
if (individual_datasets):
self._print_experiments_result_table(shorten, ignore, markdown)
if (self._result_pool.have_varying_datasets() or
self._result_pool.have_varying_leave_out()):
self._print_experiments_averaged_datasets_result_table(shorten,
ignore, markdown)
def _print_templates(self):
# templates should all be same so just print first one
print("Templates ...")
for name, template in self._result_pool.template().iteritems():
print(name + ":")
# substitute object tags that cannot be loaded properly
#value = re.sub(r"!obj:[a-zA-Z0-9_.]*\.([A-Za-z_]*)", r"\1:",
# template)
# remove anchors...
value = re.sub(r"\&([A-Za-z_0-9]*)", "",
template)
value = re.sub(r"!obj:[a-zA-Z0-9_.]*\.([A-Za-z_]*)", r"&\1",
value)
# substitute stars => references to other objects
# which may be somehwere in the actual yaml-training file...
value = value.replace("*", "")
#yaml.dump(yaml.load(value), sys.stdout, default_flow_style=False)
print value
def _print_constant_parameters(self):
print("Parameters...")
yaml.dump(self._result_pool.constant_params(), sys.stdout,
default_flow_style=False)
print('')
def _print_experiments_result_table(self, shorten, ignore_headers=(),
markdown=False):
table_headers, table_rows = self._create_experiments_headers_and_rows()
if shorten:
table_headers, table_rows = self._prettify_headers_rows(table_headers,
table_rows)
if len(ignore_headers)> 0:
keep_mask = np.array([h not in ignore_headers
for h in table_headers])
table_headers = np.array(table_headers)[keep_mask]
table_rows = np.array(table_rows)[keep_mask]
if markdown:
self._print_markdown_table(table_headers, table_rows)
#self._print_csv_table(table_headers, table_rows)
self._print_table(table_headers, table_rows)
def _create_experiments_headers_and_rows(self):
''' Create table rows and headers for result table'''
param_headers, param_rows = self._create_experiment_param_headers_and_rows()
result_headers, result_rows = self._create_result_headers_and_rows()
# Check that results and params have same amount of rows
# and that each row has same size
assert(len(param_rows) == len(result_rows))
for i in range(len(param_rows)):
assert(len(param_rows[i]) == len(param_rows[0]))
assert(len(result_rows[i]) == len(result_rows[0]))
table_headers = param_headers + result_headers
# merge rows together...
table_rows = [param_rows[i] + result_rows[i] for i in range(len(param_rows))]
# some hacky header_substitutions for nicer printing
header_substitutions = {
'frequency_start': 'freq_start',
'frequency_stop': 'freq_stop',
'updates_per_batch': 'batch_updates',
'dataset_filename': 'filename',
'max_increasing_epochs': '>epochs'}
table_headers = [header if not header_substitutions.has_key(header) \
else header_substitutions[header] \
for header in table_headers]
return table_headers, table_rows
def _create_experiment_param_headers_and_rows(self):
varying_param_keys = self._result_pool.varying_params()[0].keys()
# Try to put dataset filename in second column after id...
# TODELAY: remove testfilename from this
if (self._result_pool.have_varying_datasets()):
if 'dataset_filename' in varying_param_keys:
filenamekey = 'dataset_filename'
else:
filenamekey = 'filename'
varying_param_keys.remove(filenamekey)
param_headers = ['id'] + [filenamekey] + varying_param_keys
else:
param_headers = ['id'] + varying_param_keys
param_rows = []
for result_obj_id in range(self._result_pool.num_experiments()):
param_row = []
# Put result_obj id nr first
file_name = self._result_pool.result_file_names()[result_obj_id]
result_obj_file_nr = int(file_name.split('.result.pkl')[0].split('/')[-1])
param_row.append(result_obj_file_nr)
varying_params_this_result_obj = self._result_pool.varying_params()[result_obj_id]
# Put dataset filename second if exist
if (self._result_pool.have_varying_datasets()):
filename = varying_params_this_result_obj[filenamekey]
# remove unnecessary details of filename
filename = re.sub(r"(./)?data/[^/]*/", '', str(filename))
filename = re.sub(r"MoS[Cc][0-9]*S[0-9]*R[0-9]*_ds10_", '',
filename)
filename = re.sub(r"_autoclean_.*", '', filename)
filename = re.sub(".BBCI.mat", '', filename)
param_row.append(filename)
# Put rest of parameters
for param_key in varying_param_keys:
param_value = varying_params_this_result_obj[param_key]
# TODELAY: remove again this if
if param_key == 'test_filename':
param_value = re.sub(r"(./)?data/[^/]*/", '', str(param_value))
param_value = re.sub(r"MoS[Cc][0-9]*S[0-9]*R[0-9]*_ds10_", '',
param_value)
param_value = re.sub(r"_autoclean_.*", '', param_value)
param_value = re.sub("BBCI.mat", '', param_value)
param_row.append(param_value)
param_rows.append(param_row)
return param_headers, param_rows
def _create_result_headers_and_rows(self):
result_headers = []
result_headers.append('time')
with_standard_deviation = self._formatted_results[0].has_key('train_std')
# just put 'epoch' not 'best_epoch' so that header is not so wide
result_type_headers = ['test', 'test_sample', 'best', 'epoch', 'train', 'valid']
for result_type in result_type_headers:
# check if result exists, if yes add it
if (result_type in self._formatted_results[0] or
'best_' + result_type in self._formatted_results[0]):
result_headers.append(result_type)
if (with_standard_deviation and
result_type in ['test', 'train', 'valid']):
result_headers.append('std')
result_rows = []
for result_obj_id in range(self._result_pool.num_experiments()):
result_row = []
results = self._formatted_results[result_obj_id]
#append training time
training_time = self._result_pool.training_times()[result_obj_id]
result_row.append(str(datetime.timedelta(
seconds=round(training_time))))
for result_type in ['test', 'test_sample', 'best', 'best_epoch', 'train', 'valid']:
if result_type in results:
result_row.append(results[result_type])
if (with_standard_deviation and
result_type in ['test', 'train', 'valid']):
result_row.append(results[result_type + "_std"])
result_rows.append(result_row)
return result_headers, result_rows
def _print_experiments_averaged_datasets_result_table(self, shorten,
ignore_headers, markdown):
print ("\n\nDataset-Averaged Results:")
table_headers, table_rows = \
self._create_averaged_dataset_headers_and_rows()
if shorten:
table_headers, table_rows = self._prettify_headers_rows(table_headers,
table_rows)
if len(ignore_headers)> 0:
keep_mask = np.array([h not in ignore_headers
for h in table_headers])
table_headers = np.array(table_headers)[keep_mask]
table_rows = np.array(table_rows)[:, keep_mask]
self._print_table(table_headers, table_rows)
if markdown:
self._print_markdown_table(table_headers, table_rows)
def _create_averaged_dataset_headers_and_rows(self):
''' Create table rows and headers for result table averaged over
different datasets.
So in the end have a table for each unique parameter combination.'''
# TODELAY: don't use this weird hack to find out if there is
# a cross validation ;)
all_result_lists = self._dataset_averaged_results.results()
headers = self._create_dataset_averaged_headers(all_result_lists)
rows = []
for i, result_list in enumerate(all_result_lists):
row = [str(i), str(len(result_list))] # id in table, number of files
parameter_row = self._create_parameter_row(result_list, headers)
row += parameter_row
misclasses = self._compute_final_misclasses(result_list)
training_times = [r['training_time'] for r in result_list]
result_row = self._create_result_row(headers, misclasses,
training_times)
row += result_row
rows.append(row)
return headers, rows
@ staticmethod
def _create_dataset_averaged_headers(all_result_list):
params = deepcopy(all_result_list[0][0]['parameters'])
misclass_keys = all_result_list[0][0]['misclasses'].keys()
# sort to show test before train before valid
misclass_keys = sorted(misclass_keys,
key=lambda x: ('test' in x) * 1 + ('train' in x ) * 2 +
('valid' in x ) * 3)
result_keys = ["time", "std"]
for key in misclass_keys:
result_keys.append(key)
result_keys.append('std')
params.pop('filename', None)
params.pop('dataset_filename', None)
params.pop('transfer_leave_out', None)
params.pop('test_filename', None)
params.pop('trainer_filename', None)
return ["id", "files"] + params.keys() + result_keys
@staticmethod
def _create_result_row(headers, misclasses, training_times):
row = []
# Add training times
row += [str(datetime.timedelta(
seconds=round(np.mean(training_times))))]
row += [str(datetime.timedelta(
seconds=round(np.std(training_times))))]
# Add misclass for valid test train etc.
assert len(set(misclasses.keys()) - set(headers)) == 0, ("All misclass"
"keys should be in headers")
# Add results in order they are defined in headers(!)
for key in headers:
if key in misclasses.keys():
row += ["{:4.2f}%".format(100 - np.mean(misclasses[key] * 100))]
row += ["{:4.2f}%".format(np.std(misclasses[key]) * 100)]
return row
@staticmethod
def _create_parameter_row(result_list, headers):
parameters = deepcopy(result_list[0]['parameters'])
parameters.pop('dataset_filename', None)
parameters.pop('filename', None)
sorted_params = []
for header in headers:
if header in parameters:
sorted_params.append(parameters[header])
return sorted_params
def _compute_final_misclasses(self, result_list):
""" Compute final fold-averaged misclasses for all experiments.
Also works if there are no folds(train-test case)"""
final_misclasses = {}
misclasses = [r['misclasses'] for r in result_list]
misclass_keys = misclasses[0].keys() # train,test etc
get_last_elem = np.vectorize(lambda a : a[-1])
for key in misclass_keys:
this_misclasses = [m[key] for m in misclasses]
if np.array(this_misclasses[0][0]).shape != ():
this_final_misclasses = get_last_elem(this_misclasses)
else:
# can only happen in case all experiments have same number of
# epochs
this_final_misclasses = np.array(this_misclasses)[:,-1:]
# avg over folds if necessary
this_final_avg_misclasses = np.mean(this_final_misclasses, axis=1)
final_misclasses[key] = this_final_avg_misclasses
return final_misclasses
def _prettify_headers_rows(self, table_headers, table_rows):
""" Shorten names for nicer printing """
pretty_headers = []
for header in table_headers:
pretty_header = prettify_word(header)
pretty_headers.append(pretty_header)
pretty_rows = []
for row in table_rows:
pretty_row = []
# go through all parameter values and prettify
for value in row:
pretty_value = prettify_word(str(value))
pretty_row.append(pretty_value)
pretty_rows.append(pretty_row)
return pretty_headers, pretty_rows
def _print_table(self, headers, rows):
print("\nTerminal Table\n")
# first determine maximum length for any column
# two spaces padding
max_col_lengths = [0] * len(headers)
for column in range(len(headers)):
if (len(str(headers[column])) > max_col_lengths[column]):
max_col_lengths[column] = len(str(headers[column]))
for row in range(len(rows)):
if(len(str(rows[row][column])) > max_col_lengths[column]):
max_col_lengths[column] = len(str(rows[row][column]))
for index, header in enumerate(headers):
length = max_col_lengths[index] + 2
sys.stdout.write(("{:<" + str(length) + "}").format(header))
print('')
for row in rows:
for column, value in enumerate(row):
length = max_col_lengths[column] + 2
sys.stdout.write(("{:<" + str(length) + "}").format(value))
print('')
def _print_csv_table(self, headers, rows):
print("\nCSV Table\n")
wr = csv.writer(sys.stdout)
wr.writerow(headers)
for row in rows:
rowstrings = [str(value) for value in row]
rowstrings = [rowstr.replace("$", "") for rowstr in rowstrings]
wr.writerow(rowstrings)
def _print_markdown_table(self, headers, rows):
print("\nMarkdown Table\n")
headerline = "|".join(headers)
headerline = "|" + headerline + "|"
print headerline
# make seperatorline for table
seperatorline = "|".join(["-" for _ in headers])
seperatorline = "|" + seperatorline + "|"
print seperatorline
for row in rows:
rowstrings = [str(value) for value in row]
rowstrings = [rowstr.replace("$", "") for rowstr in rowstrings]
rowline = "|".join(rowstrings)
rowline = "|" + rowline + "|"
print rowline
