def to_data_frame(file_names,
result_objs,
varying_params,
constant_params,
shorten_headers=True):
all_params = [merge_dicts(var, constant_params) for var in varying_params]
file_numbers = [int(f.split('/')[-1].split('.')[0]) for f in file_names]
# remove dollars
for param_dict in all_params:
for key, val in param_dict.iteritems():
param_dict[key] = remove_dollar(val)
param_keys = all_params[0].keys()
param_vals = [[v[key] for key in param_keys] for v in all_params]
# transform lists to tuples to make them hashable
param_vals = [[to_tuple_if_list(v) for v in var_list]
for var_list in param_vals]
param_vals = np.array(param_vals, dtype=object)
test_accs = (1 - get_final_misclasses(result_objs, 'test')) * 100
train_accs = (1 - get_final_misclasses(result_objs, 'train')) * 100
training_times = get_training_times(result_objs)
# try adding sample accuracies, might exist, might not
sample_accs_exist = (hasattr(result_objs[0], 'monitor_channels')
and 'test_sample_misclass'
in result_objs[0].monitor_channels)
if sample_accs_exist:
test_sample_accs = (
1 - get_final_misclasses(result_objs, 'test_sample')) * 100
train_sample_accs = (
1 - get_final_misclasses(result_objs, 'train_sample')) * 100
vals_and_misclasses = np.append(param_vals,
np.array([
training_times, test_accs,
test_sample_accs, train_accs,
train_sample_accs
]).T,
axis=1)
else:
vals_and_misclasses = np.append(
param_vals,
np.array([training_times, test_accs, train_accs]).T,
axis=1)
if shorten_headers:
param_keys = [prettify_word(key) for key in param_keys]
if sample_accs_exist:
all_keys = param_keys + [
'time', 'test', 'test_sample', 'train', 'train_sample'
]
else:
all_keys = param_keys + ['time', 'test', 'train']
data_frame = MetaDataFrame(vals_and_misclasses,
index=file_numbers,
columns=all_keys)
data_frame = to_numeric_where_possible(data_frame)
data_frame.time = pd.to_timedelta(np.round(data_frame.time), unit='s')
return data_frame
def print_stats(results, csp_results, n_diffs=None):
res_misclasses = get_final_misclasses(results)
csp_misclasses = get_final_misclasses(csp_results)
res_times = get_training_times(results)
csp_times = get_training_times(csp_results)
# make a the smaller misclass, b the larger misclass
if np.mean(res_misclasses) < np.mean(csp_misclasses):
a = res_misclasses
b = csp_misclasses
else:
a = csp_misclasses
b = res_misclasses
actual_diff = np.mean(a - b)
if n_diffs is None:
diffs = perm_mean_diffs(a, b)
else:
diffs = perm_mean_diffs_sampled(a, b, n_diffs=n_diffs)
res_to_csp_diff = np.mean(res_misclasses - csp_misclasses)
print("deep accuracy: {:.1f}".format(100 *
(1 - np.mean(res_misclasses))))
print("csp accuracy: {:.1f}".format(100 *
(1 - np.mean(csp_misclasses))))
print("diff accuracy: {:.1f}".format(100 * -res_to_csp_diff))
print("std : {:.1f}".format(
100 * np.std(res_misclasses - csp_misclasses)))
print("one sided perm {:.5f}".format(
np.sum(diffs <= actual_diff) / float(len(diffs))))
print("one sided wilcoxon {:.5f}".format(
scipy.stats.wilcoxon(res_misclasses, csp_misclasses)[1] / 2))
#print("two sided perm {:.5f}".format(np.sum(
# abs(diffs) >= abs(actual_diff)) / float(len(diffs))))
#print("two sided wilcoxon {:.5f}".format(scipy.stats.wilcoxon(
# res_misclasses, csp_misclasses)[1]))
print("deep time: {:s}".format(
str(datetime.timedelta(seconds=round(np.mean(res_times))))))
print("csp time: {:s}".format(
str(datetime.timedelta(seconds=round(np.mean(csp_times))))))
def print_stats(results, csp_results, n_diffs=None):
res_misclasses = get_final_misclasses(results)
csp_misclasses = get_final_misclasses(csp_results)
res_times = get_training_times(results)
csp_times = get_training_times(csp_results)
# make a the smaller misclass, b the larger misclass
if np.mean(res_misclasses) < np.mean(csp_misclasses):
a = res_misclasses
b = csp_misclasses
else:
a = csp_misclasses
b = res_misclasses
actual_diff = np.mean(a - b)
if n_diffs is None:
diffs = perm_mean_diffs(a, b)
else:
diffs = perm_mean_diffs_sampled(a,b,n_diffs=n_diffs)
res_to_csp_diff = np.mean(res_misclasses - csp_misclasses)
print ("deep accuracy: {:.1f}".format( 100 * (1 - np.mean(res_misclasses))))
print ("csp accuracy: {:.1f}".format( 100 * (1 - np.mean(csp_misclasses))))
print ("diff accuracy: {:.1f}".format( 100 * -res_to_csp_diff))
print ("std : {:.1f}".format( 100 * np.std(res_misclasses -
csp_misclasses)))
print("one sided perm {:.5f}".format(np.sum(diffs <= actual_diff)
/ float(len(diffs))))
print("one sided wilcoxon {:.5f}".format(scipy.stats.wilcoxon(
res_misclasses, csp_misclasses)[1] / 2))
#print("two sided perm {:.5f}".format(np.sum(
# abs(diffs) >= abs(actual_diff)) / float(len(diffs))))
#print("two sided wilcoxon {:.5f}".format(scipy.stats.wilcoxon(
# res_misclasses, csp_misclasses)[1]))
print ("deep time: {:s}".format(str(datetime.timedelta(
seconds=round(np.mean(res_times))))))
print ("csp time: {:s}".format(str(datetime.timedelta(
seconds=round(np.mean(csp_times))))))
def to_data_frame(file_names, result_objs, varying_params, constant_params,
shorten_headers=True):
all_params = [merge_dicts(var, constant_params) for var in varying_params]
file_numbers = [int(f.split('/')[-1].split('.')[0]) for f in file_names]
# remove dollars
for param_dict in all_params:
for key, val in param_dict.iteritems():
param_dict[key] = remove_dollar(val)
param_keys = all_params[0].keys()
param_vals = [[v[key] for key in param_keys] for v in all_params]
# transform lists to tuples to make them hashable
param_vals = [[to_tuple_if_list(v) for v in var_list] for var_list in param_vals]
param_vals = np.array(param_vals, dtype=object)
test_accs = (1 - get_final_misclasses(result_objs, 'test')) * 100
train_accs = (1 - get_final_misclasses(result_objs, 'train')) * 100
training_times = get_training_times(result_objs)
# try adding sample accuracies, might exist, might not
sample_accs_exist = (hasattr(result_objs[0], 'monitor_channels') and
'test_sample_misclass' in result_objs[0].monitor_channels)
if sample_accs_exist:
test_sample_accs = (1 - get_final_misclasses(result_objs, 'test_sample')) * 100
train_sample_accs = (1 - get_final_misclasses(result_objs, 'train_sample')) * 100
vals_and_misclasses = np.append(param_vals,
np.array([training_times, test_accs, test_sample_accs,
train_accs, train_sample_accs]).T,
axis=1)
else:
vals_and_misclasses = np.append(param_vals,
np.array([training_times, test_accs, train_accs]).T,
axis=1)
if shorten_headers:
param_keys = [prettify_word(key) for key in param_keys]
if sample_accs_exist:
all_keys = param_keys + ['time', 'test', 'test_sample', 'train',
'train_sample']
else:
all_keys = param_keys + ['time', 'test', 'train']
data_frame = MetaDataFrame(vals_and_misclasses, index=file_numbers,
columns=all_keys)
data_frame = to_numeric_where_possible(data_frame)
data_frame.time = pd.to_timedelta(np.round(data_frame.time), unit='s')
return data_frame
