xref: /third_party/mindspore/mindspore/ccsrc/minddata/dataset/engine/gnn/local_node.cc

/**
 * Copyright 2020 Huawei Technologies Co., Ltd
 *
 * 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 "minddata/dataset/engine/gnn/local_node.h"

#include <algorithm>
#include <random>
#include <string>
#include <utility>

#include "minddata/dataset/engine/gnn/edge.h"
#include "minddata/dataset/util/random.h"

namespace mindspore {
namespace dataset {
namespace gnn {

LocalNode::LocalNode(NodeIdType id, NodeType type, WeightType weight)
    : Node(id, type, weight), rnd_(GetRandomDevice()) {
  rnd_.seed(GetSeed());
}

Status LocalNode::GetFeatures(FeatureType feature_type, std::shared_ptr<Feature> *out_feature) {
  auto itr = features_.find(feature_type);
  if (itr != features_.end()) {
    *out_feature = itr->second;
    return Status::OK();
  } else {
    std::string err_msg = "Invalid feature type:" + std::to_string(feature_type);
    RETURN_STATUS_UNEXPECTED(err_msg);
  }
}

Status LocalNode::GetAllNeighbors(NodeType neighbor_type, std::vector<NodeIdType> *out_neighbors, bool exclude_itself) {
  std::vector<NodeIdType> neighbors;
  auto itr = neighbor_nodes_.find(neighbor_type);
  if (itr != neighbor_nodes_.end()) {
    if (exclude_itself) {
      neighbors.resize(itr->second.first.size());
      std::transform(itr->second.first.begin(), itr->second.first.end(), neighbors.begin(),
                     [](const std::shared_ptr<Node> node) { return node->id(); });
    } else {
      neighbors.resize(itr->second.first.size() + 1);
      neighbors[0] = id_;
      std::transform(itr->second.first.begin(), itr->second.first.end(), neighbors.begin() + 1,
                     [](const std::shared_ptr<Node> node) { return node->id(); });
    }
  } else {
    MS_LOG(DEBUG) << "No neighbors. node_id:" << id_ << " neighbor_type:" << neighbor_type;
    if (!exclude_itself) {
      neighbors.emplace_back(id_);
    }
  }
  *out_neighbors = std::move(neighbors);
  return Status::OK();
}

Status LocalNode::GetRandomSampledNeighbors(const std::vector<std::shared_ptr<Node>> &neighbors, int32_t samples_num,
                                            std::vector<NodeIdType> *out) {
  std::vector<NodeIdType> shuffled_id(neighbors.size());
  std::iota(shuffled_id.begin(), shuffled_id.end(), 0);
  std::shuffle(shuffled_id.begin(), shuffled_id.end(), rnd_);
  int32_t num = std::min(samples_num, static_cast<int32_t>(neighbors.size()));
  for (int32_t i = 0; i < num; ++i) {
    out->emplace_back(neighbors[shuffled_id[i]]->id());
  }
  return Status::OK();
}

Status LocalNode::GetWeightSampledNeighbors(const std::vector<std::shared_ptr<Node>> &neighbors,
                                            const std::vector<WeightType> &weights, int32_t samples_num,
                                            std::vector<NodeIdType> *out) {
  CHECK_FAIL_RETURN_UNEXPECTED(neighbors.size() == weights.size(),
                               "The number of neighbors does not match the weight.");
  std::discrete_distribution<NodeIdType> discrete_dist(weights.begin(), weights.end());
  for (int32_t i = 0; i < samples_num; ++i) {
    NodeIdType index = discrete_dist(rnd_);
    out->emplace_back(neighbors[index]->id());
  }
  return Status::OK();
}

Status LocalNode::GetSampledNeighbors(NodeType neighbor_type, int32_t samples_num, SamplingStrategy strategy,
                                      std::vector<NodeIdType> *out_neighbors) {
  std::vector<NodeIdType> neighbors;
  neighbors.reserve(samples_num);
  auto itr = neighbor_nodes_.find(neighbor_type);
  if (itr != neighbor_nodes_.end()) {
    if (strategy == SamplingStrategy::kRandom) {
      while (neighbors.size() < samples_num) {
        RETURN_IF_NOT_OK(GetRandomSampledNeighbors(itr->second.first, samples_num - neighbors.size(), &neighbors));
      }
    } else if (strategy == SamplingStrategy::kEdgeWeight) {
      RETURN_IF_NOT_OK(GetWeightSampledNeighbors(itr->second.first, itr->second.second, samples_num, &neighbors));
    } else {
      RETURN_STATUS_UNEXPECTED("Invalid strategy");
    }
  } else {
    MS_LOG(DEBUG) << "There are no neighbors. node_id:" << id_ << " neighbor_type:" << neighbor_type;
    // If there are no neighbors, they are filled with kDefaultNodeId
    for (int32_t i = 0; i < samples_num; ++i) {
      neighbors.emplace_back(kDefaultNodeId);
    }
  }
  *out_neighbors = std::move(neighbors);
  return Status::OK();
}

Status LocalNode::AddNeighbor(const std::shared_ptr<Node> &node, const WeightType &weight) {
  auto itr = neighbor_nodes_.find(node->type());
  if (itr != neighbor_nodes_.end()) {
    itr->second.first.push_back(node);
    itr->second.second.push_back(weight);
  } else {
    std::vector<std::shared_ptr<Node>> nodes = {node};
    std::vector<WeightType> weights = {weight};
    neighbor_nodes_[node->type()] = std::make_pair(std::move(nodes), std::move(weights));
  }
  return Status::OK();
}

Status LocalNode::AddAdjacent(const std::shared_ptr<Node> &node, const std::shared_ptr<Edge> &edge) {
  auto node_id = node->id();
  auto edge_id = edge->id();
  adjacent_nodes_.insert({node_id, edge_id});
  return Status::OK();
}

Status LocalNode::GetEdgeByAdjNodeId(const NodeIdType &adj_node_id, EdgeIdType *out_edge_id) {
  auto itr = adjacent_nodes_.find(adj_node_id);
  if (itr != adjacent_nodes_.end()) {
    (*out_edge_id) = itr->second;
  } else {
    (*out_edge_id) = -1;
    MS_LOG(WARNING) << "Number " << adj_node_id << " node is not adjacent to number " << this->id() << " node.";
  }

  return Status::OK();
}

Status LocalNode::UpdateFeature(const std::shared_ptr<Feature> &feature) {
  auto itr = features_.find(feature->type());
  if (itr != features_.end()) {
    RETURN_STATUS_UNEXPECTED("Feature already exists");
  } else {
    features_[feature->type()] = feature;
    return Status::OK();
  }
}

}  // namespace gnn
}  // namespace dataset
}  // namespace mindspore