def validator_classifier_converter(scope, operator, container):
outputs = operator.outputs # outputs in ONNX graph
op = operator.raw_operator # scikit-learn model (mmust be fitted)
# We reuse existing converter and declare it
# as a local operator.
model = op.estimator_
alias = get_model_alias(type(model))
val_op = scope.declare_local_operator(alias, model)
val_op.inputs = operator.inputs
# We add an intermediate outputs.
val_label = scope.declare_local_variable('val_label', Int64TensorType())
val_prob = scope.declare_local_variable('val_prob', FloatTensorType())
val_op.outputs.append(val_label)
val_op.outputs.append(val_prob)
# We adjust the output of the submodel.
shape_calc = get_shape_calculator(alias)
shape_calc(val_op)
# We now handle the validation.
val_max = scope.get_unique_variable_name('val_max')
container.add_node('ReduceMax',
val_prob.full_name,
val_max,
name=scope.get_unique_operator_name('ReduceMax'),
axes=[1],
keepdims=0)
th_name = scope.get_unique_variable_name('threshold')
container.add_initializer(th_name, onnx_proto.TensorProto.FLOAT, [1],
[op.threshold])
val_bin = scope.get_unique_variable_name('val_bin')
apply_greater(scope, [val_max, th_name], val_bin, container)
val_val = scope.get_unique_variable_name('validate')
apply_cast(scope,
val_bin,
val_val,
container,
to=onnx_proto.TensorProto.INT64)
# We finally link the intermediate output to the shared converter.
apply_identity(scope, val_label.full_name, outputs[0].full_name, container)
apply_identity(scope, val_prob.full_name, outputs[1].full_name, container)
apply_identity(scope, val_val, outputs[2].full_name, container)
def discretizer_transformer_converter(scope, operator, container):
op = operator.raw_operator
# We convert the discretizer into a tree.
model = op.astree()
# We add a placeholder to call the converter for
# this model.
alias = get_model_alias(type(model))
op = scope.declare_local_operator(alias)
op.inputs = operator.inputs
op.raw_operator = model
tree_out = scope.declare_local_variable(
'treeout', operator.inputs[0].type.__class__())
op.outputs.append(tree_out)
out_name = operator.outputs[0].full_name
apply_cast(scope,
tree_out.full_name,
out_name,
container,
to=onnx_proto.TensorProto.INT64)