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Version: 0.6

Listener

  • apply a model to compute outputs on a query
  • outputs are refreshed every-time new data are added
  • outputs are saved to the db.databackend

dependencies

usage pattern

(learn how to build a model here)

from superduper import Listener
m = ...  # build a model
q = ... # build a select query

listener = Listener(
    model=m,
    select=q,
    key='x',
)

db.apply(listener)
info

how do i choose the key parameter? key refers to the field, or fields which will be fed into the model as *args and **kwargs

the following forms are possible:

  • key='x',
  • key=('x','y'),
  • key={'x': 'x', 'y': 'y'},
  • key=(('x',), {'y': 'y'}),
danger

refactor the rest, also mention eager mode

Configuring models to ingest features from other models

There are two ways to connect models in Superduper:

  • via interdependent Listeners
  • via the Graph component

In both cases, the first step is define the computation graph using a simple formalism.

Building a computation graph​

Here is an example of building a graph with 3 members:

from superduper.components.graph import document_node
from superduper import ObjectModel

m1 = ObjectModel('m1', object=lambda x: x + 1)
m2 = ObjectModel('m2', object=lambda x: x + 2)
m3 = ObjectModel('m3', object=lambda x, y: x * y)

input = document_node('x1', 'x2')

# `outputs` specifies in which field the outputs will be cached/ saved
out1 = m1(x=input['x1'], outputs='o1')
out2 = m2(x=input['x2'], outputs='o2')
out3 = m3(x=out1, y=out2, outputs='o3')

The variable out3 now contains the computation graph in out3.parent_graph.

In order to use this graph, developers may choose between creating a Model instance which passes inputs recursively through the graph:

>>> graph_model = out3.to_graph('my_graph_model')
>>> graph_model.predict({'x1': 1, 'x2': 2})
6

and creating an Application which bundles several Listener instances which can be applied with db.apply where intermediate outputs are cached in db.databackend. The order in which these listeners are applied respects the graph topology.

q = db['my_documents'].find()
stack = out3.to_listeners(q, 'my_stack')
db.apply(stack)