def __init__(self):
super().__init__()
yes_node, no_node = (self.start().add(DummyAction()).add(
Decision(MyModel())).add(yes=YesAction(), no=NoAction()))
no_node.add(DummyAction())
self.end()
def create_graph():
"""Create a graph which prints hello for each even number x in the input stream,
using a conditional RuleBasedModel node and a HelloPrinter h1.Action."""
graph = Graph()
graph.start() \
.add(Decision(SimpleRuleBasedModel(), result_field="predictions")) \
.add(yes=HelloPrinter(), no=NoOp())
graph.end()
return graph
def __init__(self):
super().__init__()
yes_node, no_node = (self.start().add(Decision(MyModel())).add(
yes=YesAction(), no=NoAction()))
self.end()
self.nodes.end.transform_output = lambda inputs: {
'result': inputs['results'] + inputs['no_results'] * 1000
}
def __init__(self):
super().__init__()
yes_node, no_node = (self.start().add(DummyAction()).add(
Decision(MyModel())).add(yes=YesAction(), no=NoAction()))
no_node.add(DummyAction())
self.end()
self.nodes.YesAction.transform_output = lambda inputs: {
'yes_sum': inputs['results']
}
self.nodes.NoAction.transform_output = lambda inputs: {
'no_sum': inputs['results']
}
def test_render_graph(self):
class DummyModel(MLModel):
pass
g = Graph()
g.start()
g.add(DummyModel(), id='m1')\
.add(Decision(DummyModel(), id='m2'))\
.add(
yes=Action(DummyModel(), id='m3'),
no=Action(DummyModel(), id='m4'),
)
g.end()
g.visualize().to_dot()