def create_prediction(self, model, input_data=None,
args=None, wait_time=3, retries=10, by_name=True):
"""Creates a new prediction.
The model parameter can be:
- a simple tree model
- a simple logistic regression model
- an ensemble
The by_name argument is now deprecated. It will be removed.
"""
logistic_regression_id = None
ensemble_id = None
model_id = None
resource_type = get_resource_type(model)
if resource_type == ENSEMBLE_PATH:
ensemble_id = get_ensemble_id(model)
if ensemble_id is not None:
check_resource(ensemble_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
elif resource_type == MODEL_PATH:
model_id = get_model_id(model)
check_resource(model_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
elif resource_type == LOGISTIC_REGRESSION_PATH:
logistic_regression_id = get_logistic_regression_id(model)
check_resource(logistic_regression_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
else:
raise Exception("A model or ensemble id is needed to create a"
" prediction. %s found." % resource_type)
if input_data is None:
input_data = {}
create_args = {}
if args is not None:
create_args.update(args)
create_args.update({
"input_data": input_data})
if model_id is not None:
create_args.update({
"model": model_id})
elif ensemble_id is not None:
create_args.update({
"ensemble": ensemble_id})
elif logistic_regression_id is not None:
create_args.update({
"logisticregression": logistic_regression_id})
body = json.dumps(create_args)
return self._create(self.prediction_url, body,
verify=self.verify_prediction)
def create_prediction(self, model, input_data=None,
args=None, wait_time=3, retries=10, by_name=True):
"""Creates a new prediction.
The model parameter can be:
- a simple tree model
- a simple logistic regression model
- an ensemble
The by_name argument is now deprecated. It will be removed.
"""
logistic_regression_id = None
ensemble_id = None
model_id = None
resource_type = get_resource_type(model)
if resource_type == ENSEMBLE_PATH:
ensemble_id = get_ensemble_id(model)
if ensemble_id is not None:
check_resource(ensemble_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
elif resource_type == MODEL_PATH:
model_id = get_model_id(model)
check_resource(model_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
elif resource_type == LOGISTIC_REGRESSION_PATH:
logistic_regression_id = get_logistic_regression_id(model)
check_resource(logistic_regression_id,
query_string=TINY_RESOURCE,
wait_time=wait_time, retries=retries,
raise_on_error=True, api=self)
else:
raise Exception("A model or ensemble id is needed to create a"
" prediction. %s found." % resource_type)
if input_data is None:
input_data = {}
create_args = {}
if args is not None:
create_args.update(args)
create_args.update({
"input_data": input_data})
if model_id is not None:
create_args.update({
"model": model_id})
elif ensemble_id is not None:
create_args.update({
"ensemble": ensemble_id})
elif logistic_regression_id is not None:
create_args.update({
"logisticregression": logistic_regression_id})
body = json.dumps(create_args)
return self._create(self.prediction_url, body,
verify=self.verify_prediction)
def delete_ensemble(self, ensemble):
"""Deletes a ensemble.
"""
check_resource_type(ensemble, ENSEMBLE_PATH,
message="An ensemble id is needed.")
ensemble_id = get_ensemble_id(ensemble)
if ensemble_id:
return self._delete("%s%s" % (self.url, ensemble_id))
def delete_ensemble(self, ensemble):
"""Deletes a ensemble.
"""
check_resource_type(ensemble, ENSEMBLE_PATH,
message="An ensemble id is needed.")
ensemble_id = get_ensemble_id(ensemble)
if ensemble_id:
return self._delete("%s%s" % (self.url, ensemble_id))
def update_ensemble(self, ensemble, changes):
"""Updates a ensemble.
"""
check_resource_type(ensemble, ENSEMBLE_PATH,
message="An ensemble id is needed.")
ensemble_id = get_ensemble_id(ensemble)
if ensemble_id:
body = json.dumps(changes)
return self._update("%s%s" % (self.url, ensemble_id), body)
def update_ensemble(self, ensemble, changes):
"""Updates a ensemble.
"""
check_resource_type(ensemble, ENSEMBLE_PATH,
message="An ensemble id is needed.")
ensemble_id = get_ensemble_id(ensemble)
if ensemble_id:
body = json.dumps(changes)
return self._update("%s%s" % (self.url, ensemble_id), body)
def get_ensemble(self, ensemble, query_string=''):
"""Retrieves an ensemble.
The ensemble parameter should be a string containing the
ensemble id or the dict returned by create_ensemble.
As an ensemble is an evolving object that is processed
until it reaches the FINISHED or FAULTY state, the function will
return a dict that encloses the ensemble values and state info
available at the time it is called.
"""
check_resource_type(ensemble, ENSEMBLE_PATH,
message="An ensemble id is needed.")
ensemble_id = get_ensemble_id(ensemble)
if ensemble_id:
return self._get("%s%s" % (self.url, ensemble_id),
query_string=query_string)
def get_ensemble(self, ensemble, query_string=''):
"""Retrieves an ensemble.
The ensemble parameter should be a string containing the
ensemble id or the dict returned by create_ensemble.
As an ensemble is an evolving object that is processed
until it reaches the FINISHED or FAULTY state, the function will
return a dict that encloses the ensemble values and state info
available at the time it is called.
"""
check_resource_type(ensemble, ENSEMBLE_PATH,
message="An ensemble id is needed.")
ensemble_id = get_ensemble_id(ensemble)
if ensemble_id:
return self._get("%s%s" % (self.url, ensemble_id),
query_string=query_string)