def predictable_tsne_converter(scope, operator, container):
output = operator.outputs[0]
op = operator.raw_operator
model = op.estimator_
alias = _get_sklearn_operator_name(type(model))
knn_op = scope.declare_local_operator(alias, model)
knn_op.inputs = operator.inputs
knn_output = scope.declare_local_variable('knn_output', FloatTensorType())
knn_op.outputs.append(knn_output)
shape_calc = get_shape_calculator(alias)
shape_calc(knn_op)
name = scope.get_unique_operator_name('Scaler')
attrs = dict(name=name,
scale=op.inv_std_.ravel().astype(float),
offset=op.mean_.ravel().astype(float))
container.add_node('Scaler', [knn_output.onnx_name], [output.full_name],
op_domain='ai.onnx.ml',
**attrs)
def predictable_tsne_converter(scope, operator, container):
"""
:param scope: name space, where to keep node names, get unused new names
:param operator: operator to converter, same object as sent to
*predictable_tsne_shape_calculator*
:param container: contains the ONNX graph
"""
# input = operator.inputs[0] # input in ONNX graph
output = operator.outputs[0] # output in ONNX graph
op = operator.raw_operator # scikit-learn model (mmust be fitted)
# First step is the k nearest-neighbours,
# we reuse existing converter and declare it as local
# operator
model = op.estimator_
alias = _get_sklearn_operator_name(type(model))
knn_op = scope.declare_local_operator(alias, model)
knn_op.inputs = operator.inputs
# We add an intermediate outputs.
knn_output = scope.declare_local_variable('knn_output', FloatTensorType())
knn_op.outputs.append(knn_output)
# We adjust the output of the submodel.
shape_calc = get_shape_calculator(alias)
shape_calc(knn_op)
# We add the normalizer which needs a unique node name.
name = scope.get_unique_operator_name('Scaler')
# The parameter follows the specifications of ONNX
# https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md#ai.onnx.ml.Scaler
attrs = dict(name=name,
scale=op.inv_std_.ravel().astype(float),
offset=op.mean_.ravel().astype(float))
# Let's finally add the scaler which connects the output
# of the k-nearest neighbours model to output of the whole model
# declared in ONNX graph
container.add_node('Scaler', [knn_output.onnx_name], [output.full_name],
op_domain='ai.onnx.ml',
**attrs)