def get_fit_solution_results(self, request_id, max_results=None):
request = core_pb2.GetFitSolutionResultsRequest(
request_id=request_id, )
LOGGER.debug("%s: %s", request.__class__.__name__, request)
fitted_solutions = []
for solution in self.stub.GetFitSolutionResults(request):
LOGGER.debug("%s: %s", solution.__class__.__name__, solution)
fitted_solutions.append(solution)
if max_results and len(fitted_solutions) >= max_results:
break
return fitted_solutions
def get_fit_solution_results(self, rid):
logger.info("Getting fit solution results for request with id: %s" %
rid)
msg = core_pb2.GetFitSolutionResultsRequest(request_id=rid)
results = None
for reply in self.serv.GetFitSolutionResults(msg):
if reply.progress.state == core_pb2.PENDING:
logger.debug(
"Fitting model to solution is still pending and hasn't begin"
)
elif reply.progress.state == core_pb2.RUNNING:
logger.debug(
"Fitting model to solution is currently running and has not completed: %s"
% reply.progress.status)
elif reply.progress.state == core_pb2.COMPLETED:
logger.info(
"Fitting model to solution has completed successfully: %s"
% reply.progress.status)
# logger.debug("Got reply: %s" % str(reply))
results = reply
elif reply.progress.state == core_pb2.ERRORED:
logger.error(
"Fitting model to solution has completed in an error state: %s"
% reply.progress.status)
else:
logger.warning(
"Fittin model to solution is in an unknown state: %s" %
str(reply.progress))
request = self.fitted_solution_requests.pop(rid, None)
for i in results.exposed_outputs:
if i == 'predictions':
logger.debug(results.exposed_outputs[i])
if results.exposed_outputs[i].HasField("csv_uri"):
# logger.debug(results.exposed_outputs[i].csv_uri)
result_data = pd.read_csv(
results.exposed_outputs[i].csv_uri)
# logger.debug(result_data.head())
else:
logger.debug("Output label: %s" % i)
# logger.debug(results.exposed_outputs[value_pb2.CSV_URI])
return results.fitted_solution_id, result_data
def do_train(self, solution_id, dataset_path):
fitted_solution = None
try:
response = self.core.FitSolution(
pb_core.FitSolutionRequest(
solution_id=solution_id,
inputs=[pb_value.Value(dataset_uri=dataset_path, )],
expose_outputs=[],
expose_value_types=['CSV_URI'],
users=[self.user],
))
# Results
results = self.core.GetFitSolutionResults(
pb_core.GetFitSolutionResultsRequest(
request_id=response.request_id, ))
for result in results:
if result.progress.state == pb_core.COMPLETED:
fitted_solution = result.fitted_solution_id
except:
logger.exception("Exception training %r", solution_id)
return fitted_solution
def get_fit_solution_results_request(request_id):
request = core_pb2.GetFitSolutionResultsRequest(request_id=request_id)
return request