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// Copyright 2017 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.
// =============================================================================
#include "tensorflow/contrib/boosted_trees/lib/utils/batch_features.h"
#include "tensorflow/contrib/boosted_trees/lib/utils/macros.h"
#include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h"
#include "tensorflow/core/lib/core/errors.h"
namespace tensorflow {
namespace boosted_trees {
namespace utils {
Status BatchFeatures::Initialize(
std::vector<Tensor> dense_float_features_list,
std::vector<Tensor> sparse_float_feature_indices_list,
std::vector<Tensor> sparse_float_feature_values_list,
std::vector<Tensor> sparse_float_feature_shapes_list,
std::vector<Tensor> sparse_int_feature_indices_list,
std::vector<Tensor> sparse_int_feature_values_list,
std::vector<Tensor> sparse_int_feature_shapes_list) {
// Validate number of feature columns.
auto num_dense_float_features = dense_float_features_list.size();
auto num_sparse_float_features = sparse_float_feature_indices_list.size();
auto num_sparse_int_features = sparse_int_feature_indices_list.size();
QCHECK(num_dense_float_features + num_sparse_float_features +
num_sparse_int_features >
0)
<< "Must have at least one feature column.";
// Read dense float features.
dense_float_feature_columns_.reserve(num_dense_float_features);
for (uint32 dense_feat_idx = 0; dense_feat_idx < num_dense_float_features;
++dense_feat_idx) {
auto dense_float_feature = dense_float_features_list[dense_feat_idx];
TF_CHECK_AND_RETURN_IF_ERROR(
TensorShapeUtils::IsMatrix(dense_float_feature.shape()),
errors::InvalidArgument("Dense float feature must be a matrix."));
TF_CHECK_AND_RETURN_IF_ERROR(
dense_float_feature.dim_size(0) == batch_size_,
errors::InvalidArgument(
"Dense float vector must have batch_size rows: ", batch_size_,
" vs. ", dense_float_feature.dim_size(0)));
TF_CHECK_AND_RETURN_IF_ERROR(
dense_float_feature.dim_size(1) == 1,
errors::InvalidArgument(
"Dense float features may not be multivalent: dim_size(1) = ",
dense_float_feature.dim_size(1)));
dense_float_feature_columns_.emplace_back(dense_float_feature);
}
// Read sparse float features.
sparse_float_feature_columns_.reserve(num_sparse_float_features);
TF_CHECK_AND_RETURN_IF_ERROR(
sparse_float_feature_values_list.size() == num_sparse_float_features &&
sparse_float_feature_shapes_list.size() == num_sparse_float_features,
errors::InvalidArgument("Inconsistent number of sparse float features."));
for (uint32 sparse_feat_idx = 0; sparse_feat_idx < num_sparse_float_features;
++sparse_feat_idx) {
auto sparse_float_feature_indices =
sparse_float_feature_indices_list[sparse_feat_idx];
auto sparse_float_feature_values =
sparse_float_feature_values_list[sparse_feat_idx];
auto sparse_float_feature_shape =
sparse_float_feature_shapes_list[sparse_feat_idx];
TF_CHECK_AND_RETURN_IF_ERROR(
TensorShapeUtils::IsMatrix(sparse_float_feature_indices.shape()),
errors::InvalidArgument(
"Sparse float feature indices must be a matrix."));
TF_CHECK_AND_RETURN_IF_ERROR(
TensorShapeUtils::IsVector(sparse_float_feature_values.shape()),
errors::InvalidArgument(
"Sparse float feature values must be a vector."));
TF_CHECK_AND_RETURN_IF_ERROR(
TensorShapeUtils::IsVector(sparse_float_feature_shape.shape()),
errors::InvalidArgument(
"Sparse float feature shape must be a vector."));
auto shape_flat = sparse_float_feature_shape.flat<int64>();
TF_CHECK_AND_RETURN_IF_ERROR(
shape_flat.size() == 2,
errors::InvalidArgument(
"Sparse float feature column must be two-dimensional."));
TF_CHECK_AND_RETURN_IF_ERROR(
shape_flat(0) == batch_size_,
errors::InvalidArgument(
"Sparse float feature shape incompatible with batch size."));
auto tensor_shape = TensorShape({shape_flat(0), shape_flat(1)});
auto order_dims = sparse::SparseTensor::VarDimArray({0, 1});
sparse::SparseTensor sparse_tensor;
TF_RETURN_IF_ERROR(sparse::SparseTensor::Create(
sparse_float_feature_indices, sparse_float_feature_values, tensor_shape,
order_dims, &sparse_tensor));
sparse_float_feature_columns_.push_back(std::move(sparse_tensor));
}
// Read sparse int features.
sparse_int_feature_columns_.reserve(num_sparse_int_features);
TF_CHECK_AND_RETURN_IF_ERROR(
sparse_int_feature_values_list.size() == num_sparse_int_features &&
sparse_int_feature_shapes_list.size() == num_sparse_int_features,
errors::InvalidArgument("Inconsistent number of sparse int features."));
for (uint32 sparse_feat_idx = 0; sparse_feat_idx < num_sparse_int_features;
++sparse_feat_idx) {
auto sparse_int_feature_indices =
sparse_int_feature_indices_list[sparse_feat_idx];
auto sparse_int_feature_values =
sparse_int_feature_values_list[sparse_feat_idx];
auto sparse_int_feature_shape =
sparse_int_feature_shapes_list[sparse_feat_idx];
TF_CHECK_AND_RETURN_IF_ERROR(
TensorShapeUtils::IsMatrix(sparse_int_feature_indices.shape()),
errors::InvalidArgument(
"Sparse int feature indices must be a matrix."));
TF_CHECK_AND_RETURN_IF_ERROR(
TensorShapeUtils::IsVector(sparse_int_feature_values.shape()),
errors::InvalidArgument("Sparse int feature values must be a vector."));
TF_CHECK_AND_RETURN_IF_ERROR(
TensorShapeUtils::IsVector(sparse_int_feature_shape.shape()),
errors::InvalidArgument("Sparse int feature shape must be a vector."));
auto shape_flat = sparse_int_feature_shape.flat<int64>();
TF_CHECK_AND_RETURN_IF_ERROR(
shape_flat.size() == 2,
errors::InvalidArgument(
"Sparse int feature column must be two-dimensional."));
TF_CHECK_AND_RETURN_IF_ERROR(
shape_flat(0) == batch_size_,
errors::InvalidArgument(
"Sparse int feature shape incompatible with batch size."));
auto tensor_shape = TensorShape({shape_flat(0), shape_flat(1)});
auto order_dims = sparse::SparseTensor::VarDimArray({0, 1});
sparse::SparseTensor sparse_tensor;
TF_RETURN_IF_ERROR(sparse::SparseTensor::Create(
sparse_int_feature_indices, sparse_int_feature_values, tensor_shape,
order_dims, &sparse_tensor));
sparse_int_feature_columns_.push_back(std::move(sparse_tensor));
}
return Status::OK();
}
} // namespace utils
} // namespace boosted_trees
} // namespace tensorflow
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