def tests_for_all_extract(self):
search_path = os.path.join(
configs,
os.path.normpath('test_data/config.yml/'
'test_extract_*.config.yml'))
extract_config_files = glob.glob(search_path)
for extract_config_file in extract_config_files:
if os.getcwd() != homedir:
os.chdir(homedir)
hvc.extract(extract_config_file)
extract_config = hvc.parse_config(extract_config_file, 'extract')
for todo in extract_config['todo_list']:
# switch to test dir
os.chdir(todo['output_dir'])
extract_outputs = list(
filter(os.path.isdir, glob.glob('*extract_output*')))
extract_outputs.sort(key=os.path.getmtime)
os.chdir(extract_outputs[-1]) # most recent
ftr_files = glob.glob('features_from*')
ftr_dicts = []
for ftr_file in ftr_files:
ftr_dicts.append(joblib.load(ftr_file))
if any(['features' in ftr_dict for ftr_dict in ftr_dicts]):
assert all(
['features' in ftr_dict for ftr_dict in ftr_dicts])
for ftr_dict in ftr_dicts:
labels = ftr_dict['labels']
if 'features' in ftr_dict:
features = ftr_dict['features']
assert features.shape[0] == len(labels)
# make sure number of features i.e. columns is constant across feature matrices
ftr_cols = [
ftr_dict['features'].shape[1] for ftr_dict in ftr_dicts
]
assert np.unique(ftr_cols).shape[-1] == 1
if any([
'neuralnets_input_dict' in ftr_dict
for ftr_dict in ftr_dicts
]):
assert all([
'neuralnets_input_dict' in ftr_dict
for ftr_dict in ftr_dicts
])
# make sure rows in summary dict features == sum of rows of each ftr file features
summary_file = glob.glob('summary_feature_file_*')
# (should only be one summary file)
assert len(summary_file) == 1
summary_dict = joblib.load(summary_file[0])
def _yaml_config_asserts(self,
extract_yaml_config_file,
tmp_output_dir):
replace_dict = {'output_dir':
('replace with tmp_output_dir',
str(tmp_output_dir))}
# have to put tmp_output_dir into yaml file
extract_config_rewritten = rewrite_config(extract_yaml_config_file,
tmp_output_dir,
replace_dict)
# helper function that is called by tests below
hvc.extract(extract_config_rewritten)
extract_config = hvc.parse_config(extract_config_rewritten,
'extract')
for todo in extract_config['todo_list']:
os.chdir(todo['output_dir'])
extract_outputs = list(
filter(os.path.isdir, glob('*extract_output*')
)
)
extract_outputs.sort(key=os.path.getmtime)
os.chdir(extract_outputs[-1]) # most recent
ftr_files = glob('features_from*')
ftr_dicts = []
for ftr_file in ftr_files:
ftr_dicts.append(joblib.load(ftr_file))
if any(['features' in ftr_dict for ftr_dict in ftr_dicts]):
assert all(['features' in ftr_dict for ftr_dict in ftr_dicts])
for ftr_dict in ftr_dicts:
labels = ftr_dict['labels']
if 'features' in ftr_dict:
features = ftr_dict['features']
assert features.shape[0] == len(labels)
# make sure number of features i.e. columns is constant across feature matrices
ftr_cols = [ftr_dict['features'].shape[1] for ftr_dict in ftr_dicts]
assert np.unique(ftr_cols).shape[-1] == 1
if any(['neuralnets_input_dict' in ftr_dict for ftr_dict in ftr_dicts]):
assert all(['neuralnets_input_dict' in ftr_dict for ftr_dict in ftr_dicts])
def _yaml_config_asserts(self, select_yaml_config_path, tmp_output_dir,
feature_file):
select_config_rewritten = rewrite_config(
select_yaml_config_path,
tmp_output_dir,
replace_dict={
'feature_file': ('replace with feature_file', feature_file),
'output_dir':
('replace with tmp_output_dir', str(tmp_output_dir))
})
select_outputs_before = glob(
os.path.join(str(tmp_output_dir), 'select_output*',
'summary_model_select_file*'))
hvc.select(select_config_rewritten)
# helper function with assertions shared by all
# tests for hvc.select run with config.yml files
select_outputs_after = glob(
os.path.join(str(tmp_output_dir), 'select_output*',
'summary_model_select_file*'))
select_output = [
after for after in select_outputs_after
if after not in select_outputs_before
]
# should only be one summary output file
assert len(select_output) == 1
# now check for every model in config
# if there is corresponding folder with model files etc
select_config = hvc.parse_config(select_config_rewritten, 'select')
select_output_dir = os.path.dirname(select_output[0])
select_model_dirs = next(
os.walk(select_output_dir))[1] # [1] to return just dir names
select_model_folder_names = [
determine_model_output_folder_name(model_dict)
for model_dict in select_config['models']
]
for folder_name in select_model_folder_names:
assert folder_name in select_model_dirs
return True
def check_select_output(config_path, output_dir):
"""
"""
select_output = glob(
os.path.join(str(output_dir), 'summary_model_select_file*'))
# should only be one summary output file
assert len(select_output) == 1
# now check for every model in config
# if there is corresponding folder with model files etc
select_config = hvc.parse_config(config_path, 'select')
select_model_dirs = next(
os.walk(output_dir))[1] # [1] to return just dir names
select_model_folder_names = [
determine_model_output_folder_name(model_dict)
for model_dict in select_config['models']
]
for folder_name in select_model_folder_names:
assert folder_name in select_model_dirs
return True
