def predict_cmd(model, output_format=None):
'''
Instantiates a Predict object to run a prediction using the given input
file and model.
This method must be self-contained and suitable for being called in
cascade, by models which use the output of other models as input.
'''
from flame.predict import Predict
predict = Predict(model['endpoint'], model['version'], output_format)
ext_input, model_set = predict.get_model_set()
if ext_input:
success, model_res = get_external_input(predict, model_set,
model['infile'])
if not success:
return False, model_res
# now run the model using the data from the external sources
success, results = predict.run(model_res)
else:
# run the model with the input file
success, results = predict.run(model['infile'])
LOG.info('Prediction completed...')
return success, results
def predict_cmd(arguments, output_format=None):
'''
Instantiates a Predict object to run a prediction using the given input
file and model.
This method must be self-contained and suitable for being called in
cascade, by models which use the output of other models as input.
'''
from flame.predict import Predict
# safety check if model exists
repo_path = pathlib.Path(utils.model_repository_path())
model_list = os.listdir(repo_path)
if arguments['endpoint'] not in model_list:
LOG.error('Endpoint name not found in model repository.')
return False, 'Endpoint name not found in model repository.'
# ** DEPRECATE **
# this is a back-compatibility trick for older versions of APIs
# not supporting the label argument
if 'label' not in arguments:
arguments['label'] = 'temp'
predict = Predict(arguments['endpoint'],
version=arguments['version'],
output_format=output_format,
label=arguments['label'])
ensemble = predict.get_ensemble()
# ensemble[0] Boolean with True for ensemble models and False otherwyse
# ensemble[1] List of ensemble model model_names
# ensemble[2] List of ensemble model versions
if ensemble[0]:
if arguments['infile'] is None:
return False, 'ensemble models require allways an input file'
success, model_res = get_ensemble_input(predict, ensemble[1],
ensemble[2],
arguments['infile'])
if not success:
return False, model_res
# now run the model using the data from the external sources
success, results = predict.run(model_res)
else:
# run the model with the input file
success, results = predict.run(arguments['infile'])
LOG.info('Prediction completed...')
return success, results
def get_predictions(models_frame, name_col, version_col, file):
"""
Get prediction of a set of compounds for a given list of models.
Endpoint and version column names needed. Returns a dataframe
with predictions for each endpoint as columns
"""
model_predictions = pd.DataFrame()
for model, version in zip(models_frame[name_col],
models_frame[version_col]):
pred = Predict(model, version)
results = pred.run(file)
results = json.loads(results[1])
c0 = np.asarray(results['c0'])
c1 = np.asarray(results['c1'])
final = []
for val, val2 in zip(c0, c1):
if val and not val2:
final.append(0)
elif val2 and not val:
final.append(1)
else:
final.append(2)
model_predictions[model + '-' + str(version)] = final
return model_predictions
def predict_cmd(arguments, output_format=None):
'''
Instantiates a Predict object to run a prediction using the given input
file and model.
This method must be self-contained and suitable for being called in
cascade, by models which use the output of other models as input.
'''
from flame.predict import Predict
# safety check if model exists
endpoint_dir = utils.model_path(arguments['endpoint'], 0)
if not os.path.isdir(endpoint_dir):
return False, 'Endpoint name not found in model repository.'
# ** DEPRECATE **
# this is a back-compatibility trick for older versions of APIs
# not supporting the label argument
if 'label' not in arguments:
arguments['label'] = 'temp'
if 'output_format' in arguments:
output_format = arguments['output_format']
predict = Predict(arguments['endpoint'], version=arguments['version'], output_format=output_format, label=arguments['label'])
if utils.isSingleThread():
predict.set_single_CPU()
ensemble = predict.get_ensemble()
# ensemble[0] Boolean with True for ensemble models and False otherwyse
# ensemble[1] List of ensemble model model_names
# ensemble[2] List of ensemble model versions
if ensemble[0]:
if arguments['infile'] is None:
return False, 'ensemble models require allways an input file'
emodels = ensemble[1]
evers = ensemble[2]
success, model_res = get_ensemble_input(predict, emodels, evers, arguments['infile'])
if not success:
predict.conveyor.setError (model_res)
LOG.error (model_res)
# return False, model_res # TO-DO, comment this line and run prediction to allow odata to generate error info
# check the presence of changes in the inner models
modelID = predict.conveyor.getMeta('modelID')
for i in range(len (emodels)):
success, iID = utils.getModelID(emodels[i], evers[i], 'model')
if success:
if iID not in modelID:
predict.conveyor.setWarning (f'Inner model {emodels[i]}.{evers[i]} has been updated. Rebuilding of ensemble model is recommended')
LOG.warning (f'Inner model {emodels[i]}.{evers[i]} has been updated. Rebuilding of ensemble model is recommended')
# now run the model using the data from the external sources
success, results = predict.run(model_res)
else:
# run the model with the input file
success, results = predict.run(arguments['infile'])
LOG.info('Prediction completed...')
return success, results