def GetExecutePipelineResults(self, request, context):
session = Session.get(request.context.session_id)
if session is not None:
for pipeline_id in request.pipeline_ids:
result = session.test_results.get(pipeline_id, None)
if result is not None:
yield result
def ExecutePipeline(self, request, context):
"""Predict step - multiple results messages returned via GRPC streaming.
"""
session = Session.get(request.context.session_id)
if session is None:
yield None
return
pipeline = session.get_pipeline(request.pipeline_id)
if pipeline is None:
yield None
return
datafile = None
if request.dataset_uri is not None:
datafile = self._create_path_from_uri(request.dataset_uri)
# Get test data directories
handler = GRPC_PlannerEventHandler(session)
handler.StartExecutingPipeline(pipeline)
session.controller.initialize_from_features(datafile,
session.train_features,
session.target_features,
session.outputdir,
view='TEST')
# Change this to be a yield too.. Save should happen within the handler
for result in session.controller.test(pipeline, handler):
if result is not None:
yield result
def ListPipelines(self, request, context):
pipeline_ids = []
session = Session.get(request.context.session_id)
if session is not None:
for pipeline_id in session.pipelines.keys():
pipeline_ids.append(pipeline_id)
response = self._create_response("Listing pipelines")
return core.PipelineListResult(response_info=response,
pipeline_ids=pipeline_ids)
def DeletePipelines(self, request, context):
pipeline_ids = []
session = Session.get(request.context.session_id)
if session is not None:
for pipeline_id in request.delete_pipeline_ids:
session.delete_pipeline(pipeline_id)
pipeline_ids.append(pipeline_id)
response = self._create_response("Deleted pipelines")
return core.PipelineListResult(response_info=response,
pipeline_ids=pipeline_ids)
def StartSession(self, request, context):
"""Session management
"""
session = Session.new()
session_context = core.SessionContext(session_id=session.id)
response = self._create_response("Session started")
session_response = core.SessionResponse(response_info=response,
user_agent=request.user_agent,
version=request.version,
context=session_context)
return session_response
def DescribeDataflow(self, request, context):
ok = True
response = self._create_response("Dataflow description")
session = Session.get(request.context.session_id)
if session is None:
response = self._create_response("Dataflow description",
code="SESSION_UNKNOWN")
ok = False
pipeline = session.get_pipeline(request.pipeline_id)
if pipeline is None:
response = self._create_response("Dataflow description",
code="INTERNAL")
ok = False
modules = []
connections = []
if ok:
for i in range(0, len(pipeline.primitives)):
primitive = pipeline.primitives[i]
inputs = [
dfext.DataflowDescription.Input(name="input_data",
type="pandas.DataFrame")
]
outputs = [
dfext.DataflowDescription.Output(name="output_data",
type="pandas.DataFrame")
]
if primitive.task == "Modeling":
inputs.append(
dfext.DataflowDescription.Input(
name="input_labels", type="pandas.DataFrame"))
modules.append(
dfext.DataflowDescription.Module(id=primitive.cls,
type=primitive.type,
label=primitive.name,
inputs=inputs,
outputs=outputs))
if i > 0:
prev_primitive = pipeline.primitives[i - 1]
connections.append(
dfext.DataflowDescription.Connection(
from_module_id=prev_primitive.cls,
from_output_name="output_data",
to_module_id=primitive.cls,
to_input_name="input_data"))
return dfext.DataflowDescription(pipeline_id=request.pipeline_id,
response_info=response,
modules=modules,
connections=connections)
def SetProblemDoc(self, request, context):
session = Session.get(request.context.session_id)
if session is not None:
c = session.controller
for update in request.updates:
if update.task_type:
# Get Problem details
c.problem.task_type = TaskType[core.TaskType.Name(update.task_type)]
if update.task_subtype:
c.problem.task_subtype = TaskSubType[core.TaskSubtype.Name(update.task_subtype)]
#if update.output_type:
# c.problem.output_type = core.OutputType.Name(update.output_type)
if update.metric:
metric = Metric[core.PerformanceMetric.Name(update.metric)]
c.problem.set_metrics([metric])
session.controller = c
return self._create_response("Updated Problem Doc")
def ExportPipeline(self, request, context):
session = Session.get(request.context.session_id)
if session is None:
return self._create_response("No such session", code="SESSION_UNKNOWN")
pipeline = session.get_pipeline(request.pipeline_id)
if pipeline is None:
return self._create_response("Invalid pipeline id", code="INVALID_ARGUMENT")
if request.pipeline_exec_uri is not None:
ex = session.controller.execution_helper
ex.create_pipeline_executable(pipeline, session.config)
exefile = self._create_path_from_uri(request.pipeline_exec_uri)
origfile = os.path.join(session.execdir, pipeline.id)
shutil.copy(origfile, exefile)
os.chmod(exefile, 0o755)
return self._create_response("Pipeline Exported")
else:
return self._create_response("No pipeline export uri", code="INVALID_ARGUMENT")
def GetDataflowResults(self, request, context):
session = Session.get(request.context.session_id)
ok = True
if session is None:
response = self._create_response("Dataflow results",
code="SESSION_UNKNOWN")
ok = False
pipeline = session.get_pipeline(request.pipeline_id)
if pipeline is None:
response = self._create_response("Dataflow results",
code="INTERNAL")
ok = False
if ok:
if pipeline.finished:
for result in self._get_pipeline_results(pipeline):
yield result
else:
while not pipeline.finished:
pipeline.waitForChanges()
for result in self._get_pipeline_results(pipeline):
yield result
else:
yield dfext.ModuleResult(response_info=response)
def CreatePipelines(self, request, context):
session = Session.get(request.context.session_id)
if session is None:
yield None
return
# Get training and target features
train_features = []
target_features = []
if len(request.predict_features) > 0:
for tfeature in request.predict_features:
resource_id = tfeature.resource_id
feature_name = tfeature.feature_name
train_features.append(Feature(resource_id, feature_name))
if len(request.target_features) > 0:
for tfeature in request.target_features:
resource_id = tfeature.resource_id
feature_name = tfeature.feature_name
target_features.append(Feature(resource_id, feature_name))
datafile = None
if request.dataset_uri is not None:
datafile = self._create_path_from_uri(request.dataset_uri)
session.train_features = train_features
session.target_features = target_features
# Create the planning controller if not already present
c = session.controller
if c is None:
c = Controller(self.libdir)
c.initialize_from_features(datafile,
train_features,
target_features,
session.outputdir,
view='TRAIN')
# Set Problem schema
if request.task > 0:
c.problem.task_type = TaskType[core.TaskType.Name(request.task)]
if request.task_subtype > 0:
c.problem.task_subtype = TaskSubType[core.TaskSubtype.Name(
request.task_subtype)]
#if request.output > 0:
# c.problem.output_type = core.OutputType.Name(request.output)
# Load metrics
metrics = []
if len(request.metrics) > 0:
for rm in request.metrics:
metrics.append(Metric[core.PerformanceMetric.Name(rm)])
c.problem.set_metrics(metrics)
# Set the max pipelines cutoff
cutoff = None
if request.max_pipelines is not None:
cutoff = request.max_pipelines
# Start planning / training
session.controller = c
if session.controller.l1_planner is None:
session.controller.initialize_planners()
for result in session.controller.train(
GRPC_PlannerEventHandler(session), cutoff=cutoff):
if result is not None:
yield result
def EndSession(self, request, context):
Session.delete(request.session_id)
return self._create_response("Session ended")
