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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.
// =============================================================================
// TreePredictions returns the per-class probabilities for each input by
// evaluating the given tree.
#include <algorithm>
#include "tensorflow/contrib/tensor_forest/core/ops/tree_utils.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/kernels/bounds_check.h"
namespace tensorflow {
using tensorforest::CHILDREN_INDEX;
using tensorforest::FEATURE_INDEX;
using tensorforest::LEAF_NODE;
using tensorforest::FREE_NODE;
using tensorforest::CheckTensorBounds;
using tensorforest::DecideNode;
using tensorforest::Sum;
REGISTER_OP("TreePredictions")
.Attr("valid_leaf_threshold: float")
.Input("input_data: float")
.Input("tree: int32")
.Input("tree_thresholds: float")
.Input("node_per_class_weights: float")
.Output("predictions: float")
.Doc(R"doc(
Returns the per-class probabilities for each input.
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:= A 2-d int32 tensor. `tree[i][0]` gives the index of the left child
of the i-th node, `tree[i][0] + 1` gives the index of the right child of
the i-th node, and `tree[i][1]` gives the index of the feature used to
split the i-th node.
tree_thresholds: `tree_thresholds[i]` is the value used to split the i-th
node.
node_per_class_weights: `node_per_class_weights[n][c]` records how many
training examples have class c and have ended up in node n.
predictions: `predictions[i][j]` is the probability that input i is class j.
valid_leaf_threshold: Minimum number of samples that have arrived to a leaf
to be considered a valid leaf, otherwise use the parent.
)doc");
class TreePredictions : public OpKernel {
public:
explicit TreePredictions(OpKernelConstruction* context)
: OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr(
"valid_leaf_threshold", &valid_leaf_threshold_));
}
void Compute(OpKernelContext* context) override {
const Tensor& input_data = context->input(0);
const Tensor& tree_tensor = context->input(1);
const Tensor& tree_thresholds = context->input(2);
const Tensor& node_per_class_weights = context->input(3);
OP_REQUIRES(context, tree_tensor.shape().dims() == 2,
errors::InvalidArgument(
"tree should be two-dimensional"));
OP_REQUIRES(context, tree_thresholds.shape().dims() == 1,
errors::InvalidArgument(
"tree_threhsolds should be one-dimensional"));
OP_REQUIRES(context, node_per_class_weights.shape().dims() == 2,
errors::InvalidArgument(
"node_pcw should be two-dimensional"));
if (input_data.shape().dim_size(0) > 0) {
OP_REQUIRES(context, input_data.shape().dims() == 2,
errors::InvalidArgument(
"input_data should be two-dimensional"));
}
OP_REQUIRES(
context,
tree_tensor.shape().dim_size(0) ==
tree_thresholds.shape().dim_size(0) &&
tree_tensor.shape().dim_size(0) ==
node_per_class_weights.shape().dim_size(0),
errors::InvalidArgument(
"Number of nodes should be the same in "
"tree, tree_thresholds and node_pcw."));
// Check tensor bounds.
if (!CheckTensorBounds(context, input_data)) return;
if (!CheckTensorBounds(context, tree_tensor)) return;
if (!CheckTensorBounds(context, tree_thresholds)) return;
if (!CheckTensorBounds(context, node_per_class_weights)) return;
const int32 num_classes = static_cast<int32>(
node_per_class_weights.shape().dim_size(1));
const int32 num_data = static_cast<int32>(
input_data.shape().dim_size(0));
const int32 num_nodes = static_cast<int32>(
tree_tensor.shape().dim_size(0));
Tensor* output_predictions = nullptr;
TensorShape output_shape;
output_shape.AddDim(num_data);
output_shape.AddDim(num_classes - 1);
OP_REQUIRES_OK(context,
context->allocate_output(0, output_shape,
&output_predictions));
auto out = output_predictions->tensor<float, 2>();
const auto node_pcw = node_per_class_weights.tensor<float, 2>();
const auto tree = tree_tensor.tensor<int32, 2>();
const auto thresholds = tree_thresholds.unaligned_flat<float>();
for (int i = 0; i < num_data; i++) {
const Tensor point = input_data.Slice(i, i+1);
int node_index = 0;
int parent = -1;
while (true) {
OP_REQUIRES(context, FastBoundsCheck(node_index, num_nodes),
errors::InvalidArgument("node_index not in valid range."))
const int32 left_child = tree(node_index, CHILDREN_INDEX);
if (left_child == LEAF_NODE) {
float sum = node_pcw(node_index, 0);
float parent_weight = 0.0;
if (sum < valid_leaf_threshold_ && parent >= 0) {
VLOG(1) << "not enough samples at leaf, including parent counts."
<< "child sum = " << sum;
float parent_sum = node_pcw(parent, 0);
// Weight the parent's counts just enough so that the new sum is
// valid_leaf_threshold_, but never give any counts a weight of
// more than 1.
parent_weight = std::min(1.0f,
(valid_leaf_threshold_ - sum) / parent_sum);
sum += parent_weight * parent_sum;
VLOG(1) << "Sum w/ parent included = " << sum;
}
for (int c = 1; c < num_classes; c++) {
float w = node_pcw(node_index, c);
if (parent_weight > 0.0) {
w += parent_weight * node_pcw(parent, c);
}
out(i, c - 1) = w / sum;
}
break;
} else if (left_child == FREE_NODE) {
LOG(ERROR) << "Reached a free node, not good.";
return;
}
parent = node_index;
node_index = left_child +
DecideNode(point, tree(node_index, FEATURE_INDEX),
thresholds(node_index));
}
}
VLOG(1) << "tree: " << tree;
VLOG(1) << "output: " << out;
}
private:
float valid_leaf_threshold_;
};
REGISTER_KERNEL_BUILDER(Name("TreePredictions").Device(DEVICE_CPU),
TreePredictions);
} // namespace tensorflow
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