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num_features = feature_count.shape[1]
container.add_initializer(threshold_name, proto_type,
[1], [binarize])
container.add_initializer(
zero_tensor_name,
proto_type, [1, num_features],
np.zeros((1, num_features)).ravel())
container.add_node(
'Greater', [input_name, threshold_name],
condition_name, name=scope.get_unique_operator_name('Greater'),
op_version=9)
apply_cast(scope, condition_name, cast_values_name, container,
to=proto_type)
apply_add(scope, [zero_tensor_name, cast_values_name],
binarised_input_name, container, broadcast=1)
input_name = binarised_input_name
apply_exp(scope, feature_log_prob_name, exp_result_name, container)
apply_sub(scope, [constant_name, exp_result_name], sub_result_name,
container, broadcast=1)
apply_log(scope, sub_result_name, neg_prob_name, container)
container.add_node('ReduceSum', neg_prob_name,
sum_neg_prob_name, axes=[0],
name=scope.get_unique_operator_name('ReduceSum'))
apply_sub(scope, [feature_log_prob_name, neg_prob_name],
difference_matrix_name, container)
container.add_node(
'MatMul', [input_name, difference_matrix_name],
dot_prod_name, name=scope.get_unique_operator_name('MatMul'))
sigmoid_predict_result_name = scope.get_unique_variable_name(
'sigmoid_predict_result')
container.add_initializer(a_name, onnx_proto.TensorProto.FLOAT,
[], [model.calibrators_[k].a_])
container.add_initializer(b_name, onnx_proto.TensorProto.FLOAT,
[], [model.calibrators_[k].b_])
container.add_initializer(unity_name, onnx_proto.TensorProto.FLOAT,
[], [1])
apply_mul(scope, [a_name, df_col_name], a_df_prod_name, container,
broadcast=0)
apply_add(scope, [a_df_prod_name, b_name], exp_parameter_name,
container, broadcast=0)
apply_exp(scope, exp_parameter_name, exp_result_name, container)
apply_add(scope, [unity_name, exp_result_name], denominator_name,
container, broadcast=0)
apply_div(scope, [unity_name, denominator_name],
sigmoid_predict_result_name, container, broadcast=0)
return sigmoid_predict_result_name
desired_shape=[-1, 1, features])
apply_sub(scope, [reshaped_input_name, theta_name], subtracted_input_name,
container, broadcast=1)
apply_pow(scope, [subtracted_input_name, exponent_name], pow_result_name,
container, broadcast=1)
apply_div(scope, [pow_result_name, sigma_name], div_result_name,
container, broadcast=1)
container.add_node('ReduceSum', div_result_name,
reduced_sum_name, axes=[2], keepdims=0,
name=scope.get_unique_operator_name('ReduceSum'))
apply_mul(scope, [reduced_sum_name, prod_operand_name], mul_result_name,
container, broadcast=1)
apply_sub(scope, [sigma_sum_log_name, mul_result_name],
part_log_likelihood_name,
container, broadcast=1)
apply_add(scope, [jointi_name, part_log_likelihood_name],
sum_result_name, container, broadcast=1)
return sum_result_name
container.add_initializer(n_classes_name, onnx_proto.TensorProto.FLOAT,
[], [n_classes])
apply_cast(scope, reduced_prob_name, cast_prob_name, container,
to=onnx_proto.TensorProto.BOOL)
container.add_node('Not', cast_prob_name,
bool_not_cast_prob_name,
name=scope.get_unique_operator_name('Not'))
apply_cast(scope, bool_not_cast_prob_name, mask_name, container,
to=onnx_proto.TensorProto.FLOAT)
apply_add(scope, [concatenated_prob_name, mask_name],
masked_concatenated_prob_name, container, broadcast=1)
apply_mul(scope, [mask_name, n_classes_name], reduced_prob_mask_name,
container, broadcast=1)
apply_add(scope, [reduced_prob_name, reduced_prob_mask_name],
masked_reduced_prob_name, container, broadcast=0)
return masked_concatenated_prob_name, masked_reduced_prob_name
container.add_initializer(intercept_name, onnx_proto.TensorProto.FLOAT,
model.intercept_.shape, model.intercept_)
input_name = operator.inputs[0].full_name
if type(operator.inputs[0].type) == Int64TensorType:
cast_input_name = scope.get_unique_variable_name('cast_input')
apply_cast(scope, operator.input_full_names, cast_input_name,
container, to=onnx_proto.TensorProto.FLOAT)
input_name = cast_input_name
container.add_node(
'MatMul', [input_name, coef_name],
matmul_result_name,
name=scope.get_unique_operator_name('MatMul'))
apply_add(scope, [matmul_result_name, intercept_name],
score_name, container, broadcast=0)
return score_name
reduced_prob_mask_name = scope.get_unique_variable_name(
'reduced_prob_mask')
masked_reduced_prob_name = scope.get_unique_variable_name(
'masked_reduced_prob')
container.add_initializer(n_classes_name, onnx_proto.TensorProto.FLOAT,
[], [n_classes])
apply_cast(scope, reduced_prob_name, cast_prob_name, container,
to=onnx_proto.TensorProto.BOOL)
container.add_node('Not', cast_prob_name,
bool_not_cast_prob_name,
name=scope.get_unique_operator_name('Not'))
apply_cast(scope, bool_not_cast_prob_name, mask_name, container,
to=onnx_proto.TensorProto.FLOAT)
apply_add(scope, [concatenated_prob_name, mask_name],
masked_concatenated_prob_name, container, broadcast=1)
apply_mul(scope, [mask_name, n_classes_name], reduced_prob_mask_name,
container, broadcast=1)
apply_add(scope, [reduced_prob_name, reduced_prob_mask_name],
masked_reduced_prob_name, container, broadcast=0)
return masked_concatenated_prob_name, masked_reduced_prob_name
proba_updated_name = scope.get_unique_variable_name('proba_updated')
mask_name = scope.get_unique_variable_name('mask')
reduced_proba_updated_name = scope.get_unique_variable_name(
'reduced_proba_updated')
container.add_initializer(num_classes_name, onnx_proto.TensorProto.FLOAT,
[], [num_classes])
apply_cast(scope, reduced_proba, bool_reduced_proba_name, container,
to=onnx_proto.TensorProto.BOOL)
container.add_node('Not', bool_reduced_proba_name,
bool_not_reduced_proba_name,
name=scope.get_unique_operator_name('Not'))
apply_cast(scope, bool_not_reduced_proba_name, not_reduced_proba_name,
container, to=onnx_proto.TensorProto.FLOAT)
apply_add(scope, [proba, not_reduced_proba_name],
proba_updated_name, container, broadcast=1)
apply_mul(scope, [not_reduced_proba_name, num_classes_name],
mask_name, container, broadcast=1)
apply_add(scope, [reduced_proba, mask_name],
reduced_proba_updated_name, container, broadcast=0)
return proba_updated_name, reduced_proba_updated_name