def test_each_combination_works():
"""Ensures each of combination of feature selection and classifier works."""
nrep = 10
nproc = 1
gsl = 'none' # to speed up the process
failed_combos = list()
for clf_name in cfg.classifier_choices:
for fs_name in cfg.all_dim_red_methods:
# ensure a fresh start
remove_neuropredict_results(out_dir)
try:
cli_str = 'np_classify -y {} -t {} -n {} -c {} -o {} ' \
' -e {} -dr {} -g {} ' \
''.format(out_path1, train_perc, nrep, nproc, out_dir,
clf_name, fs_name, gsl)
sys.argv = shlex.split(cli_str)
cli()
except:
failed_combos.append('{:35} {:35}'.format(clf_name, fs_name))
traceback.print_exc()
print('\nCombinations failed:\n{}'.format('\n'.join(failed_combos)))
if len(failed_combos) > 4:
print(
'\n -----> 5 or more combinations of DR and CLF failed! Fix them')
def test_separable_100perc():
"""Test to ensure fully separable classes lead to close to perfect prediction!
"""
separable_ds = make_fully_separable_classes(max_class_size=100,
max_dim=np.random.randint(2,
max_dim))
separable_ds.description = 'fully_separable_dataset'
out_path_sep = os.path.join(out_dir, 'two_separable_classes.pkl')
out_dir_sep = os.path.join(out_dir, 'fully_separable_test')
os.makedirs(out_dir_sep, exist_ok=True)
separable_ds.save(out_path_sep)
nrep = 10
gsl = 'none' # to speed up the process
for clf_name in cfg.classifier_choices:
for fs_name in cfg.all_dim_red_methods:
cli_str = 'neuropredict -y {} -t {} -n {} -c {} -g {} -o {} -e {} -dr ' \
'{}' \
''.format(out_path_sep, train_perc, nrep, 1, gsl, out_dir_sep,
clf_name, fs_name)
sys.argv = shlex.split(cli_str)
cli()
cv_results = neuropredict.reports.load_results_from_folder(out_dir_sep)
for sg, result in cv_results.items():
raise_if_mean_differs_from(result['accuracy_balanced'],
result['target_sizes'],
reference_level=1.0,
# comparing to perfect
eps_chance_acc=0.5,
method_descr='{} {}'.format(fs_name,
clf_name))
def test_basic_run():
sys.argv = shlex.split(
'np_classify -y {} {} -t {} -n {} -c {} -g {} -o {} '
'-e {} -dr {} -k {} --sub_groups {} -p {} -cl {} -cm {}'
''.format(out_path1, out_path2, train_perc, num_rep_cv, num_procs,
gs_level, out_dir, estimator, dr_method, dr_size, sg_list,
positive_class, covar_arg, deconf_method))
cli()
def test_arff():
arff_path = realpath(pjoin(dirname(dirname(dirname(__file__))), # 3 levels up
'example_datasets', 'arff', 'iris.arff'))
sys.argv = shlex.split('neuropredict -a {} -t {} -n {} -c {} -g {} -o {} '
'-e {} -dr {}'.format(arff_path, train_perc,
num_repetitions, num_procs,
gs_level, out_dir, classifier,
fs_method))
cli()
def test_print_options():
" ensures the CLI works. "
known_out_dir = out_dir
options_path = pjoin(out_dir, cfg.file_name_options)
if pexists(options_path):
with raises(SystemExit):
sys.argv = shlex.split('neuropredict --print_options {}'
''.format(known_out_dir))
cli()
known_nonexisting_dir = known_out_dir + '_43_34563$#*$@)'
with raises(IOError):
sys.argv = shlex.split('neuropredict --po {}'
''.format(known_nonexisting_dir))
cli()
def test_vis():
" ensures the CLI works. "
res_path = pjoin(out_dir, 'rhst_results.pkl')
if pexists(res_path):
with raises(SystemExit):
sys.argv = shlex.split('neuropredict --make_vis {}'.format(out_dir))
cli()
expected_results = ['balanced_accuracy.pdf',
'compare_misclf_rates.pdf',
'feature_importance.pdf']
for rpath in expected_results:
if not pexists(rpath):
raise ValueError('an expected result {} not produced'
''.format(rpath))
else:
print('previously computed results not found in \n {}'.format(out_dir))
def test_each_combination_works():
"Ensures each of combination of feature selection and classifier works."
nrep = 10
gsl = 'none' # to speed up the process
for clf_name in cfg.classifier_choices:
for fs_name in cfg.all_dim_red_methods:
try:
cli_str = 'neuropredict -y {} -t {} -n {} -c {} -o {} ' \
' -e {} -dr {} -g {} ' \
''.format(out_path, train_perc, nrep, num_procs, out_dir,
clf_name, fs_name, gsl)
sys.argv = shlex.split(cli_str)
cli()
except:
print('\n ---> combination failed: {} {}'.format(clf_name, fs_name))
raise
def test_chance_clf_binary_svm():
global ds_path_list, method_names, out_dir, num_repetitions, \
gs_level, train_perc, num_procs
sys.argv = shlex.split('neuropredict -y {} {} -t {} -n {} -c {} -g {} -o {} '
'-e {} -dr {}'.format(out_path, out_path2, train_perc,
min_rep_per_class *
rand_two_class.num_targets,
num_procs, gs_level, out_dir,
classifier, fs_method))
cli()
cv_results = neuropredict.reports.load_results_from_folder(out_dir)
for sg, result in cv_results.items():
raise_if_mean_differs_from(result['accuracy_balanced'],
result['target_sizes'],
eps_chance_acc=eps_chance_acc_binary)
def test_chance_multiclass():
global ds_path_list, method_names, out_dir, num_repetitions, \
gs_level, train_perc, num_procs
clf = 'randomforestclassifier'
fs_method = 'variancethreshold'
nrep = total_num_classes * min_rep_per_class
gsl = 'none' # to speed up the process
sys.argv = shlex.split('neuropredict -y {} -t {} -n {} -c {} -g {} '
'-o {} -e {} -dr {}'
''.format(out_path_multiclass, train_perc, nrep,
num_procs, gsl, out_dir, clf, fs_method))
cli()
cv_results = neuropredict.reports.load_results_from_folder(out_dir)
for sg, result in cv_results.items():
raise_if_mean_differs_from(result['accuracy_balanced'],
result['target_sizes'],
eps_chance_acc,
method_descr='{} {} gsl {}'
''.format(fs_method, clf, gsl))
def test_chance_clf_binary_svm():
sys.argv = shlex.split(
'neuropredict -y {} {} -t {} -n {} -c {} -g {} -o {} '
'-e {} -dr {}'
''.format(out_path1, out_path2, train_perc,
min_rep_per_class * ds_two.num_targets, num_procs, gs_level,
out_dir, estimator, dr_method))
result_paths = cli()
import pickle
for sg_id, res_path in result_paths.items():
with open(res_path, 'rb') as res_fid:
result = pickle.load(res_fid)
perf = result['results']
bal_acc_all_dsets = list(
perf.metric_val['balanced_accuracy_score'].values())
raise_if_mean_differs_from(np.column_stack(bal_acc_all_dsets),
result['_target_sizes'],
eps_chance_acc=eps_chance_acc_binary)
def main():
"Entry point."
classify.cli()
def test_versioning():
" ensures the CLI works. "
with raises(SystemExit):
sys.argv = shlex.split('neuropredict -v')
cli()
