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)