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// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
// RoutingFunction returns the probability of reaching each leaf node
// in a soft decision tree.
#include <stdlib.h>
#include <time.h>
#include <algorithm>
#include <cmath>
#include <memory>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.h"
#include "tensorflow/contrib/tensor_forest/kernels/tree_utils.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/gtl/top_n.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/work_sharder.h"
namespace tensorflow {
using shape_inference::InferenceContext;
using shape_inference::ShapeHandle;
using tensorforest::CheckTensorBounds;
using tensorforest::LeftProbability;
// The term 'routing function' is synonymous with 'the probability
// that an instance is routed to each leaf node.' It is defined in
// 'Deep Neural Decision Forests' by Kontschieder et al.
REGISTER_OP("RoutingFunction")
.Attr("max_nodes: int")
.Input("input_data: float")
.Input("tree_parameters: float")
.Input("tree_biases: float")
.Output("probabilities: float")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle input, params;
TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &input));
TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, ¶ms));
c->set_output(0, c->Matrix(c->Dim(input, 0), c->Dim(params, 0)));
return Status::OK();
})
.Doc(R"doc(
Returns the probability that each input will reach each leaf node.
max_nodes: The number of nodes in the tree.
input_data: The training batch's features as a 2-d tensor; `input_data[i][j]`
gives the j-th feature of the i-th input.
tree_parameters: `tree_parameters[i]` gives the weight of
the logistic regression model that translates from node features to
probabilities.
tree_biases: `tree_biases[i]` gives the bias of the logistic
regression model that translates from node features to
probabilities.
probabilities: `probabilities[i][j]` is the probability that input i
will reach node j.
)doc");
class RoutingFunction : public OpKernel {
public:
explicit RoutingFunction(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("max_nodes", &max_nodes_));
}
void Compute(OpKernelContext* context) override {
const Tensor& input_data = context->input(0);
const Tensor& tree_parameters_tensor = context->input(1);
const Tensor& tree_biases_tensor = context->input(2);
if (input_data.shape().dim_size(0) > 0) {
OP_REQUIRES(context, input_data.shape().dims() == 2,
errors::InvalidArgument(
"input_data should be two-dimensional"));
}
// Check tensor bounds.
if (!CheckTensorBounds(context, input_data)) return;
const int32 num_data = static_cast<int32>(
input_data.shape().dim_size(0));
const int32 num_features = static_cast<int32>(
input_data.shape().dim_size(1));
Tensor* output_probabilities = nullptr;
TensorShape output_shape;
output_shape.AddDim(num_data);
output_shape.AddDim(max_nodes_);
OP_REQUIRES_OK(context,
context->allocate_output(0, output_shape,
&output_probabilities));
auto out_probs = output_probabilities->tensor<float, 2>();
const auto tree_biases = tree_biases_tensor.tensor<float, 1>();
// Iteratively compute the probability of reaching each leaf.
for (int i = 0; i < num_data; i++) {
const Tensor point = input_data.Slice(i, i+1);
out_probs(i, 0) = 1.0;
for (int j = 0; j < max_nodes_ / 2; j++) {
int32 left_child = 2*j + 1;
int32 right_child = left_child + 1;
float prob = out_probs(i, j);
float left_prob = LeftProbability(point,
tree_parameters_tensor.Slice(j, j+1),
tree_biases(j),
num_features);
out_probs(i, left_child) = prob * left_prob;
out_probs(i, right_child) = prob * (1.0 - left_prob);
}
}
}
private:
int32 max_nodes_;
};
REGISTER_KERNEL_BUILDER(Name("RoutingFunction").Device(DEVICE_CPU),
RoutingFunction);
} // namespace tensorflow
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