def action_searches_result(label):
'''
try to retrieve the searches result with the label used as argument
returns
- (False, Null) if it there is no directory or the search
pickle file cannot be found
- (True, JSON) with the results otherwyse
'''
opath = tempfile.gettempdir()
if not os.path.isdir(opath):
return False, f'directory {opath} not found'
# default in case label was not provided
if label is None:
label = 'temp'
iconveyor = Conveyor()
search_pkl_path = os.path.join(opath, 'similars-' + label + '.pkl')
if not os.path.isfile(search_pkl_path):
return False, f'file {search_pkl_path} not found'
with open(search_pkl_path, 'rb') as handle:
success, message = iconveyor.load(handle)
if not success:
print(f'error reading prediction results with message {message}')
return False, None
if not iconveyor.isKey('search_results'):
return False, 'search results not found'
results = iconveyor.getVal('search_results')
names = iconveyor.getVal('obj_nam')
if iconveyor.isKey('SMILES'):
smiles = iconveyor.getVal('SMILES')
if len(results) != len(names):
return False, 'results length does not match names'
for i in range(len(results)):
if iconveyor.isKey('SMILES'):
print(f'similars to {names[i]} [{smiles[i]}]')
else:
print(f'similars to {names[i]}')
iresult = results[i]
for j in range(len(iresult['distances'])):
dist = iresult['distances'][j]
name = iresult['names'][j]
smil = iresult['SMILES'][j]
print(f' {dist:.3f} : {name} [{smil}]')
# return a JSON generated by iconveyor
return True, iconveyor.getJSON()
def action_predictions_result (label):
'''
try to retrieve the prediction result with the label used as argument
returns
- (False, Null) if it there is no directory or the predictions
pickle files cannot be found
- (True, JSON) with the results otherwyse
'''
# get de model repo path
predictions_path = pathlib.Path(utils.predictions_repository_path())
label_path = predictions_path.joinpath(label)
if not label_path.is_dir():
print (f'directory {label_path} not found')
return False, None
result_path = label_path.joinpath('prediction-results.pkl')
if not result_path.is_file():
print (f'predictions not found for {label} directory')
return False, None
iconveyor = Conveyor()
with open(result_path, 'rb') as handle:
success, message = iconveyor.load(handle)
if not success:
print (f'error reading prediction results with message {message}')
return False, None
# console output
print_prediction_result(('obj_num','number of objects',iconveyor.getVal('obj_num')))
if iconveyor.isKey('external-validation'):
for val in iconveyor.getVal('external-validation'):
print_prediction_result (val)
if iconveyor.isKey('values'):
for i in range (iconveyor.getVal('obj_num')):
print (iconveyor.getVal('obj_nam')[i], '\t', float("{0:.4f}".format(iconveyor.getVal('values')[i])))
# return a JSON generated by iconveyor
input_type = iconveyor.getMeta('input_type')
return True, iconveyor.getJSON(xdata=(input_type == 'model_ensemble'))
def action_results(model, version=None, ouput_variables=False):
''' Returns a JSON with whole results info for a given model and version '''
if model is None:
return False, 'Empty model label'
rdir = utils.model_path(model, version)
if not os.path.isfile(os.path.join(rdir, 'results.pkl')):
return False, 'results not found'
from flame.conveyor import Conveyor
conveyor = Conveyor()
with open(os.path.join(rdir, 'results.pkl'), 'rb') as handle:
conveyor.load(handle)
return True, conveyor.getJSON()