def omega_partial_fit(self,
modelname,
Xname,
Yname=None,
pure_python=True,
**kwargs):
result = self.get_delegate(modelname).partial_fit(*self.delegate_args,
**self.delegate_kwargs)
return sanitized(result)
def omega_predict_proba(self,
modelname,
Xname,
rName=None,
pure_python=True,
**kwargs):
result = self.get_delegate(modelname).predict_proba(
*self.delegate_args, **self.delegate_kwargs)
return sanitized(result)
def omega_fit_transform(self,
modelname,
Xname,
Yname=None,
rName=None,
pure_python=True,
**kwargs):
result = self.get_delegate(modelname).fit_transform(
*self.delegate_args, **self.delegate_kwargs)
return sanitized(result)
def omega_score(self,
modelname,
Xname,
Yname,
rName=True,
pure_python=True,
**kwargs):
result = self.get_delegate(modelname).score(*self.delegate_args,
**self.delegate_kwargs)
return sanitized(result)
def schedule_omegaml_job(self, nb_file, **kwargs):
"""
schedules the running of omegaml job
"""
result = self.om.jobs.schedule(nb_file)
return sanitized(result)
def omega_gridsearch(self, modelname, Xname, Yname, parameters=None, **kwargs):
result = self.get_delegate(modelname).gridsearch(*self.delegate_args, **self.delegate_kwargs)
return sanitized(result)
def omega_decision_function(self, modelname, Xname, rName=None, **kwargs):
result = self.get_delegate(modelname).decision_function(*self.delegate_args, **self.delegate_kwargs)
return sanitized(result)
def omega_transform(self, modelname, Xname, rName=None, **kwargs):
result = self.get_delegate(modelname).transform(*self.delegate_args, **self.delegate_kwargs)
return sanitized(result)
def omega_reduce(self, results, modelName=None, rName=None, pure_python=True, **kwargs):
result = self.get_delegate(modelName).reduce(modelName, results, **self.delegate_kwargs)
return sanitized(result)
def run_omegaml_job(self, nb_file, event=None, **kwargs):
"""
runs omegaml job
"""
result = self.om.jobs.run_notebook(nb_file, event=event)
return sanitized(result)
