update

Koukyosyumei · Aug 18, 2023 · f7842b5 · f7842b5
1 parent 2f70bee
commit f7842b5
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Showing 7 changed files with 10 additions and 20 deletions.
diff --git a/src/aijack/collaborative/tree/a.cpp b/src/aijack/collaborative/tree/a.cpp
@@ -1,5 +1,4 @@
 #include "secureboost/secureboost.h"
 #include "xgboost/xgboost.h"
 
-int main() { // XGBoostClassifier clf(2);
-}
+int main() { XGBoostClassifier clf(2); }
diff --git a/src/aijack/collaborative/tree/a.out b/src/aijack/collaborative/tree/a.out
diff --git a/src/aijack/collaborative/tree/core/model.h b/src/aijack/collaborative/tree/core/model.h
@@ -20,7 +20,7 @@ template <typename PartyName> struct TreeModelBase {
    * @param parties The vector of parties.
    * @param y The vector of ground-truth vectors
    */
-  virtual void fit(vector<float> &y) = 0;
+  virtual void fit(vector<PartyName> &parties, vector<float> &y) = 0;
 
   /**
    * @brief Function to return the predicted scores of the given data.

diff --git a/src/aijack/collaborative/tree/secureboost/node.h b/src/aijack/collaborative/tree/secureboost/node.h
@@ -14,7 +14,7 @@ struct SecureBoostNode : Node<SecureBoostParty> {
   int num_classes;
 
   // SecureBoostNode() {}
-  SecureBoostNode(vector<SecureBoostParty> &parties_, vector<float> &y_,
+  SecureBoostNode(vector<SecureBoostParty> parties_, vector<float> &y_,
                   int num_classes_,
                   vector<vector<PaillierCipherText>> &gradient_,
                   vector<vector<PaillierCipherText>> &hessian_,

diff --git a/src/aijack/collaborative/tree/secureboost/secureboost.h b/src/aijack/collaborative/tree/secureboost/secureboost.h
@@ -34,18 +34,14 @@ struct SecureBoostBase : TreeModelBase<SecureBoostParty> {
   vector<SecureBoostTree> estimators;
   vector<float> logging_loss;
 
-  vector<SecureBoostParty> &parties;
-
-  SecureBoostBase(vector<SecureBoostParty> &parties_, int num_classes_,
-                  float subsample_cols_ = 0.8,
+  SecureBoostBase(int num_classes_, float subsample_cols_ = 0.8,
                   float min_child_weight_ = -1 *
                                             numeric_limits<float>::infinity(),
                   int depth_ = 5, int min_leaf_ = 5, float learning_rate_ = 0.4,
                   int boosting_rounds_ = 5, float lam_ = 1.5, float gamma_ = 1,
                   float eps_ = 0.1, int active_party_id_ = -1,
                   int completelly_secure_round_ = 0, float init_value_ = 1.0,
-                  int n_job_ = 1, bool save_loss_ = true)
-      : parties(parties_) {
+                  int n_job_ = 1, bool save_loss_ = true) {
     num_classes = num_classes_;
     subsample_cols = subsample_cols_;
     min_child_weight = min_child_weight_;
@@ -81,9 +77,8 @@ struct SecureBoostBase : TreeModelBase<SecureBoostParty> {
   }
 
   vector<SecureBoostTree> get_estimators() { return estimators; }
-  vector<SecureBoostParty> get_parties() { return parties; }
 
-  void fit(vector<float> &y) {
+  void fit(vector<SecureBoostParty> &parties, vector<float> &y) {
     try {
       if ((active_party_id < 0) || (active_party_id > parties.size())) {
         throw invalid_argument("invalid active_party_id");

diff --git a/src/aijack/collaborative/tree/xgboost/node.h b/src/aijack/collaborative/tree/xgboost/node.h
@@ -35,7 +35,7 @@ struct XGBoostNode : public Node<XGBoostParty> {
   vector<float> entire_class_cnt;
 
   // XGBoostNode() {}
-  XGBoostNode(vector<XGBoostParty> &parties_, vector<float> &y_,
+  XGBoostNode(vector<XGBoostParty> parties_, vector<float> &y_,
               int num_classes_, vector<vector<float>> &gradient_,
               vector<vector<float>> &hessian_, vector<int> &idxs_,
               float min_child_weight_, float lam_, float gamma_, float eps_,

diff --git a/src/aijack/collaborative/tree/xgboost/xgboost.h b/src/aijack/collaborative/tree/xgboost/xgboost.h
@@ -29,22 +29,19 @@ struct XGBoostBase : TreeModelBase<XGBoostParty> {
 
   float upsilon_Y;
 
-  vector<XGBoostParty> &parties;
   LossFunc *lossfunc_obj;
 
   vector<vector<float>> init_pred;
   vector<XGBoostTree> estimators;
   vector<float> logging_loss;
 
-  XGBoostBase(vector<XGBoostParty> &parties_, int num_classes_,
-              float subsample_cols_ = 0.8,
+  XGBoostBase(int num_classes_, float subsample_cols_ = 0.8,
               float min_child_weight_ = -1 * numeric_limits<float>::infinity(),
               int depth_ = 5, int min_leaf_ = 5, float learning_rate_ = 0.4,
               int boosting_rounds_ = 5, float lam_ = 1.5, float gamma_ = 1,
               float eps_ = 0.1, int active_party_id_ = -1,
               int completelly_secure_round_ = 0, float init_value_ = 1.0,
-              int n_job_ = 1, bool save_loss_ = true)
-      : parties(parties_) {
+              int n_job_ = 1, bool save_loss_ = true) {
     num_classes = num_classes_;
     subsample_cols = subsample_cols_;
     min_child_weight = min_child_weight_;
@@ -80,9 +77,8 @@ struct XGBoostBase : TreeModelBase<XGBoostParty> {
   }
 
   vector<XGBoostTree> get_estimators() { return estimators; }
-  vector<XGBoostParty> get_parties() { return parties; }
 
-  void fit(vector<float> &y) {
+  void fit(vector<XGBoostParty> &parties, vector<float> &y) {
     int row_count = y.size();
 
     vector<vector<float>> base_pred;