def custom_tranform_converter(scope, operator, container):
input = operator.inputs[0]
output = operator.outputs[0]
weights_name = scope.get_unique_variable_name("weights")
atype = onnx_proto.TensorProto.FLOAT
weights = [0.5, 0.1, 10]
shape = [len(weights), 1]
container.add_initializer(weights_name, atype, shape, weights)
apply_mul(scope, [input.full_name, weights_name], output.full_name,
container)
def convert_sklearn_ada_boost_regressor(scope, operator, container):
"""
Rewrites the converters implemented in
:epkg:`sklearn-onnx` to support an operator supported
doubles.
"""
op = operator.raw_operator
negate_name = scope.get_unique_variable_name('negate')
estimators_weights_name = scope.get_unique_variable_name(
'estimators_weights')
half_scalar_name = scope.get_unique_variable_name('half_scalar')
last_index_name = scope.get_unique_variable_name('last_index')
negated_labels_name = scope.get_unique_variable_name('negated_labels')
sorted_values_name = scope.get_unique_variable_name('sorted_values')
sorted_indices_name = scope.get_unique_variable_name('sorted_indices')
array_feat_extractor_output_name = scope.get_unique_variable_name(
'array_feat_extractor_output')
median_value_name = scope.get_unique_variable_name('median_value')
comp_value_name = scope.get_unique_variable_name('comp_value')
median_or_above_name = scope.get_unique_variable_name('median_or_above')
median_idx_name = scope.get_unique_variable_name('median_idx')
cast_result_name = scope.get_unique_variable_name('cast_result')
reshaped_weights_name = scope.get_unique_variable_name('reshaped_weights')
median_estimators_name = scope.get_unique_variable_name(
'median_estimators')
container.add_initializer(negate_name, container.proto_dtype, [], [-1])
container.add_initializer(estimators_weights_name, container.proto_dtype,
[len(op.estimator_weights_)],
op.estimator_weights_)
container.add_initializer(half_scalar_name, container.proto_dtype, [],
[0.5])
container.add_initializer(
last_index_name,
onnx_proto.TensorProto.INT64, # pylint: disable=E1101
[],
[len(op.estimators_) - 1])
concatenated_labels = _get_estimators_label(scope, operator, container, op)
apply_mul(scope, [concatenated_labels, negate_name],
negated_labels_name,
container,
broadcast=1)
apply_topk(scope,
negated_labels_name, [sorted_values_name, sorted_indices_name],
container,
k=len(op.estimators_))
container.add_node(
'ArrayFeatureExtractor',
[estimators_weights_name, sorted_indices_name],
array_feat_extractor_output_name,
op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
apply_reshape(scope,
array_feat_extractor_output_name,
reshaped_weights_name,
container,
desired_shape=(-1, len(op.estimators_)))
weights_cdf_name = cum_sum(scope, container, reshaped_weights_name,
len(op.estimators_))
container.add_node(
'ArrayFeatureExtractor', [weights_cdf_name, last_index_name],
median_value_name,
op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
apply_mul(scope, [median_value_name, half_scalar_name],
comp_value_name,
container,
broadcast=1)
container.add_node('Less', [weights_cdf_name, comp_value_name],
median_or_above_name,
name=scope.get_unique_operator_name('Less'))
apply_cast(scope,
median_or_above_name,
cast_result_name,
container,
to=container.proto_dtype)
container.add_node('ArgMin',
cast_result_name,
median_idx_name,
name=scope.get_unique_operator_name('ArgMin'),
axis=1)
container.add_node(
'ArrayFeatureExtractor', [sorted_indices_name, median_idx_name],
median_estimators_name,
op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
container.add_node(
'ArrayFeatureExtractor', [concatenated_labels, median_estimators_name],
operator.output_full_names,
op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))