Merge pull request #3747 from TirelessClock/loss_functions

Shifted sse loss to decision tree to streamline xgboost functionality and ease regularisation.

src/mlpack/methods/decision_tree/decision_tree.hpp

@@ -15,20 +15,9 @@
 
 #include <mlpack/core.hpp>
 
-#include "gini_gain.hpp"
-#include "information_gain.hpp"
-#include "mad_gain.hpp"
-#include "mse_gain.hpp"
-
-#include "best_binary_numeric_split.hpp"
-#include "random_binary_numeric_split.hpp"
-
-#include "best_binary_categorical_split.hpp"
-#include "all_categorical_split.hpp"
-
-#include "all_dimension_select.hpp"
-#include "random_dimension_select.hpp"
-#include "multiple_random_dimension_select.hpp"
+#include "fitness_functions/fitness_functions.hpp"
+#include "split_functions/split_functions.hpp"
+#include "select_functions/select_functions.hpp"
 
 namespace mlpack {
 

src/mlpack/methods/decision_tree/decision_tree_regressor.hpp

@@ -15,12 +15,9 @@
 
 #include <mlpack/core.hpp>
 
-#include "mad_gain.hpp"
-#include "mse_gain.hpp"
-#include "best_binary_numeric_split.hpp"
-#include "all_categorical_split.hpp"
-#include "random_binary_numeric_split.hpp"
-#include "all_dimension_select.hpp"
+#include "fitness_functions/fitness_functions.hpp"
+#include "split_functions/split_functions.hpp"
+#include "select_functions/select_functions.hpp"
 
 namespace mlpack {
 

src/mlpack/methods/decision_tree/fitness_functions/fitness_functions.hpp

@@ -0,0 +1,4 @@
+#include "gini_gain.hpp"
+#include "information_gain.hpp"
+#include "mad_gain.hpp"
+#include "mse_gain.hpp"

src/mlpack/methods/decision_tree/fitness_functions/gini_gain.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/gini_gain.hpp
+ * @file methods/decision_tree/fitness_functions/gini_gain.hpp
  * @author Ryan Curtin
  *
  * The GiniGain class, which is a fitness function (FitnessFunction) for

src/mlpack/methods/decision_tree/fitness_functions/information_gain.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/information_gain.hpp
+ * @file methods/decision_tree/fitness_functions/information_gain.hpp
  * @author Ryan Curtin
  *
  * An implementation of information gain, which can be used in place of Gini

src/mlpack/methods/decision_tree/fitness_functions/mad_gain.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/mad_gain.hpp
+ * @file methods/decision_tree/fitness_functions/mad_gain.hpp
  * @author Rishabh Garg
  *
  * The mean absolute deviation gain class, a fitness function for regression
@@ -15,7 +15,7 @@ n.
 #define MLPACK_METHODS_DECISION_TREE_MAD_GAIN_HPP
 
 #include <mlpack/prereqs.hpp>
-#include "utils.hpp"
+#include "mlpack/methods/decision_tree/utils.hpp"
 
 namespace mlpack {
 

src/mlpack/methods/decision_tree/fitness_functions/mse_gain.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/mse_gain.hpp
+ * @file methods/decision_tree/fitness_functions/mse_gain.hpp
  * @author Rishabh Garg
  *
  * The mean squared error gain class, which is a fitness funtion for
@@ -14,7 +14,7 @@
 #define MLPACK_METHODS_DECISION_TREE_MSE_GAIN_HPP
 
 #include <mlpack/prereqs.hpp>
-#include "utils.hpp"
+#include <mlpack/methods/decision_tree/utils.hpp>
 
 namespace mlpack {
 

src/mlpack/methods/decision_tree/select_functions/all_dimension_select.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/all_dimension_select.hpp
+ * @file methods/decision_tree/select_functions/all_dimension_select.hpp
  * @author Ryan Curtin
  *
  * Selects all dimensions for a split.

src/mlpack/methods/decision_tree/select_functions/multiple_random_dimension_select.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/multiple_random_dimension_select.hpp
+ * @file methods/decision_tree/select_functions/multiple_random_dimension_select.hpp
  * @author Ryan Curtin
  *
  * Select a number of random dimensions to pick from.

src/mlpack/methods/decision_tree/select_functions/random_dimension_select.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/random_dimension_select.hpp
+ * @file methods/decision_tree/select_functions/random_dimension_select.hpp
  * @author Ryan Curtin
  *
  * Selects one single random dimension to split on.

src/mlpack/methods/decision_tree/select_functions/select_functions.hpp

@@ -0,0 +1,3 @@
+#include "all_dimension_select.hpp"
+#include "multiple_random_dimension_select.hpp"
+#include "random_dimension_select.hpp"

src/mlpack/methods/decision_tree/split_functions/all_categorical_split.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/all_categorical_split.hpp
+ * @file methods/decision_tree/split_functions/all_categorical_split.hpp
  * @author Ryan Curtin
  *
  * This file defines a tree splitter that split a categorical feature into all

src/mlpack/methods/decision_tree/split_functions/all_categorical_split_impl.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/all_categorical_split_impl.hpp
+ * @file methods/decision_tree/split_functions/all_categorical_split_impl.hpp
  * @author Ryan Curtin
  *
  * Implementation of the AllCategoricalSplit categorical split class.

src/mlpack/methods/decision_tree/split_functions/best_binary_categorical_split_impl.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/all_categorical_split_impl.hpp
+ * @file methods/decision_tree/split_functions/all_categorical_split_impl.hpp
  * @author Nikolay Apanasov (nikolay@apanasov.org)
  *
  * Implementation of the BestBinaryCategoricalSplit categorical split class.

src/mlpack/methods/decision_tree/split_functions/best_binary_numeric_split.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/best_binary_numeric_split.hpp
+ * @file methods/decision_tree/split_functions/best_binary_numeric_split.hpp
  * @author Ryan Curtin
  *
  * A tree splitter that finds the best binary numeric split.
@@ -13,7 +13,7 @@
 #define MLPACK_METHODS_DECISION_TREE_BEST_BINARY_NUMERIC_SPLIT_HPP
 
 #include <mlpack/prereqs.hpp>
-#include "mse_gain.hpp"
+#include <mlpack/methods/decision_tree/fitness_functions/mse_gain.hpp>
 
 #include <mlpack/core/util/sfinae_utility.hpp>
 

src/mlpack/methods/decision_tree/split_functions/best_binary_numeric_split_impl.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/best_binary_numeric_split_impl.hpp
+ * @file methods/decision_tree/split_functions/best_binary_numeric_split_impl.hpp
  * @author Ryan Curtin
  *
  * Implementation of strategy that finds the best binary numeric split.

src/mlpack/methods/decision_tree/split_functions/random_binary_numeric_split.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/random_binary_numeric_split.hpp
+ * @file methods/decision_tree/split_functions/random_binary_numeric_split.hpp
  * @author Rishabh Garg
  *
  * A tree splitter that finds a random binary numeric split.

src/mlpack/methods/decision_tree/split_functions/random_binary_numeric_split_impl.hpp

@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/random_binary_numeric_split_impl.hpp
+ * @file methods/decision_tree/split_functions/random_binary_numeric_split_impl.hpp
  * @author Rishabh Garg
  *
  * Implementation of strategy that finds the random binary numeric split.

src/mlpack/methods/decision_tree/split_functions/split_functions.hpp

@@ -0,0 +1,4 @@
+#include "all_categorical_split.hpp"
+#include "best_binary_numeric_split.hpp"
+#include "random_binary_numeric_split.hpp"
+#include "best_binary_categorical_split.hpp"