[LEARNER] refactor learner

include/xgboost/learner.h

@@ -14,7 +14,7 @@
 #include <vector>
 #include "./base.h"
 #include "./gbm.h"
-#include "./meric.h"
+#include "./metric.h"
 #include "./objective.h"
 
 namespace xgboost {
@@ -36,6 +36,14 @@ namespace xgboost {
  */
 class Learner : public rabit::Serializable {
  public:
+  /*!
+   * \brief set configuration from pair iterators.
+   * \param begin The beginning iterator.
+   * \param end The end iterator.
+   * \tparam PairIter iterator<std::pair<std::string, std::string> >
+   */
+  template<typename PairIter>
+  inline void Configure(PairIter begin, PairIter end);
   /*!
    * \brief Set the configuration of gradient boosting.
    *  User must call configure once before InitModel and Training.
@@ -59,7 +67,7 @@ class Learner : public rabit::Serializable {
    * \param iter current iteration number
    * \param train reference to the data matrix.
    */
-  void UpdateOneIter(int iter, DMatrix* train);
+  virtual void UpdateOneIter(int iter, DMatrix* train) = 0;
   /*!
    * \brief Do customized gradient boosting with in_gpair.
    *  in_gair can be mutated after this call.
@@ -67,9 +75,9 @@ class Learner : public rabit::Serializable {
    * \param train reference to the data matrix.
    * \param in_gpair The input gradient statistics.
    */
-  void BoostOneIter(int iter,
-                    DMatrix* train,
-                    std::vector<bst_gpair>* in_gpair);
+  virtual void BoostOneIter(int iter,
+                            DMatrix* train,
+                            std::vector<bst_gpair>* in_gpair) = 0;
   /*!
    * \brief evaluate the model for specific iteration using the configured metrics.
    * \param iter iteration number
@@ -77,9 +85,9 @@ class Learner : public rabit::Serializable {
    * \param data_names name of each dataset
    * \return a string corresponding to the evaluation result
    */
-  std::string EvalOneIter(int iter,
-                          const std::vector<DMatrix*>& data_sets,
-                          const std::vector<std::string>& data_names);
+  virtual std::string EvalOneIter(int iter,
+                                  const std::vector<DMatrix*>& data_sets,
+                                  const std::vector<std::string>& data_names) = 0;
   /*!
    * \brief get prediction given the model.
    * \param data input data
@@ -89,11 +97,11 @@ class Learner : public rabit::Serializable {
    *   predictor, when it equals 0, this means we are using all the trees
    * \param pred_leaf whether to only predict the leaf index of each tree in a boosted tree predictor
    */
-  void Predict(DMatrix* data,
-               bool output_margin,
-               std::vector<float> *out_preds,
-               unsigned ntree_limit = 0,
-               bool pred_leaf = false) const;
+  virtual void Predict(DMatrix* data,
+                       bool output_margin,
+                       std::vector<float> *out_preds,
+                       unsigned ntree_limit = 0,
+                       bool pred_leaf = false) const = 0;
   /*!
    * \return whether the model allow lazy checkpoint in rabit.
    */
@@ -151,5 +159,13 @@ inline void Learner::Predict(const SparseBatch::Inst& inst,
     obj_->PredTransform(out_preds);
   }
 }
+
+// implementing configure.
+template<typename PairIter>
+inline void Learner::Configure(PairIter begin, PairIter end) {
+  std::vector<std::pair<std::string, std::string> > vec(begin, end);
+  this->Configure(vec);
+}
+
 }  // namespace xgboost
 #endif  // XGBOOST_LEARNER_H_