def ports_setup(self):
port_type = PortsSpecSchema.port_type
input_ports = {
'points_df_in': {
port_type: [cudf.DataFrame, dask_cudf.DataFrame]
}
}
output_ports = {
'distance_df': {
port_type: [cudf.DataFrame, dask_cudf.DataFrame]
},
'distance_abs_df': {
PortsSpecSchema.port_type: [cudf.DataFrame, dask_cudf.DataFrame]
}
}
input_connections = self.get_connected_inports()
if 'points_df_in' in input_connections:
types = input_connections['points_df_in']
# connected, use the types passed in from parent
return NodePorts(inports={'points_df_in': {port_type: types}},
outports={'distance_df': {port_type: types},
'distance_abs_df': {port_type: types},
})
else:
return NodePorts(inports=input_ports, outports=output_ports)
def ports_setup(self):
port_type = PortsSpecSchema.port_type
if self.instance is not None:
inports = self.instance.input_ports
outports = self.instance.output_ports
else:
try:
p_inports = self.instanceClass.input_ports
p_outports = self.instanceClass.output_ports
feeder = FeedProperty(self.conf)
inports = p_inports.fget(feeder)
outports = p_outports.fget(feeder)
except Exception:
inports = None
outports = None
o_inports = {}
o_outports = {}
if inports is not None:
for k in inports.keys():
o_inports[k] = {port_type: NmTensor}
if outports is not None:
for k in outports.keys():
o_outports[k] = {port_type: NmTensor}
if issubclass(self.instanceClass, TrainableNM):
# added the port for tying the weights
o_inports[self.INPUT_NM] = {port_type: TrainableNM}
o_outports[self.OUTPUT_NM] = {port_type: TrainableNM}
elif issubclass(self.instanceClass, LossNM):
o_outports[self.OUTPUT_NM] = {port_type: LossNM}
elif issubclass(self.instanceClass, DataLayerNM):
o_outports[self.OUTPUT_NM] = {port_type: DataLayerNM}
return NodePorts(inports=o_inports, outports=o_outports)
def ports_setup(self):
input_ports = {}
output_ports = {
'points_df_out': {
PortsSpecSchema.port_type: pd.DataFrame
}
}
return NodePorts(inports=input_ports, outports=output_ports)
def ports_setup(self):
port_type = PortsSpecSchema.port_type
dy = PortsSpecSchema.dynamic
o_inports = {}
o_inports[self.INPUT_PORT_NAME] = {port_type: str}
o_inports['input_tensor'] = {port_type: NmTensor, dy: True}
# if hasattr(self, 'inputs'):
# for inp in self.inputs:
# if inp['to_port'] in (self.INPUT_PORT_NAME,):
# continue
# # TODO: Move TaskGrah rewire logic here instead of in
# # chartEngine.tsx ChartEngine._fixNeMoPorts
# o_inports[inp['from_node'].uid+'@'+inp['from_port']] = {
# port_type: NmTensor}
o_outports = {}
o_outports[self.OUTPUT_PORT_NAME] = {port_type: list}
return NodePorts(inports=o_inports, outports=o_outports)
def ports_setup(self):
input_ports = {
'df1': {
PortsSpecSchema.port_type:
[cudf.DataFrame, dask_cudf.DataFrame]
},
'df2': {
PortsSpecSchema.port_type:
[cudf.DataFrame, dask_cudf.DataFrame]
}
}
output_ports = {'max_diff': {PortsSpecSchema.port_type: float}}
connections = self.get_connected_inports()
for key in input_ports:
if key in connections:
# connected
types = connections[key]
input_ports[key].update({PortsSpecSchema.port_type: types})
return NodePorts(inports=input_ports, outports=output_ports)
def ports_setup(self):
ptype = self.conf.get('port_type', list)
inports = {'inlist': {PortsSpecSchema.port_type: ptype}}
outports = {'sum': {PortsSpecSchema.port_type: float}}
return NodePorts(inports=inports, outports=outports)
def ports_setup(self):
ptype = self.conf.get('port_type', list)
output_ports = {'numlist': {PortsSpecSchema.port_type: ptype}}
return NodePorts(outports=output_ports)