Added Train overloads

src/mlpack/methods/decision_tree/decision_tree_regressor.hpp

@@ -257,6 +257,122 @@ class DecisionTreeRegressor :
    * Clean up memory.
    */
   ~DecisionTreeRegressor();
+
+  /**
+   * Train the decision tree on the given data.  This will overwrite the
+   * existing model.  The data may have numeric and categorical types, specified
+   * by the datasetInfo parameter.  Setting minimumLeafSize and
+   * minimumGainSplit too small may cause the tree to overfit, but setting them
+   * too large may cause it to underfit.
+   *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
+   * @param data Dataset to train on.
+   * @param datasetInfo Type information for each dimension.
+   * @param labels Labels for each training point.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   * @return The final entropy of decision tree.
+   */
+  template<typename MatType, typename LabelsType>
+  double Train(MatType data,
+               const data::DatasetInfo& datasetInfo,
+               LabelsType labels,
+               const size_t minimumLeafSize = 10,
+               const double minimumGainSplit = 1e-7,
+               const size_t maximumDepth = 0,
+               DimensionSelectionType dimensionSelector =
+                   DimensionSelectionType());
+
+  /**
+   * Train the decision tree on the given data, assuming that all dimensions are
+   * numeric.  This will overwrite the given model. Setting minimumLeafSize and
+   * minimumGainSplit too small may cause the tree to overfit, but setting them
+   * too large may cause it to underfit.
+   *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
+   * @param data Dataset to train on.
+   * @param labels Labels for each training point.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   * @return The final entropy of decision tree.
+   */
+  template<typename MatType, typename LabelsType>
+  double Train(MatType data,
+               LabelsType labels,
+               const size_t minimumLeafSize = 10,
+               const double minimumGainSplit = 1e-7,
+               const size_t maximumDepth = 0,
+               DimensionSelectionType dimensionSelector =
+                   DimensionSelectionType());
+
+  /**
+   * Train the decision tree on the given weighted data.  This will overwrite
+   * the existing model.  The data may have numeric and categorical types,
+   * specified by the datasetInfo parameter.  Setting minimumLeafSize and
+   * minimumGainSplit too small may cause the tree to overfit, but setting them
+   * too large may cause it to underfit.
+   *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
+   * @param data Dataset to train on.
+   * @param datasetInfo Type information for each dimension.
+   * @param labels Labels for each training point.
+   * @param weights Weights of all the labels
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   * @return The final entropy of decision tree.
+   */
+  template<typename MatType, typename LabelsType, typename WeightsType>
+  double Train(MatType data,
+               const data::DatasetInfo& datasetInfo,
+               LabelsType labels,
+               WeightsType weights,
+               const size_t minimumLeafSize = 10,
+               const double minimumGainSplit = 1e-7,
+               const size_t maximumDepth = 0,
+               DimensionSelectionType dimensionSelector =
+                   DimensionSelectionType(),
+               const std::enable_if_t<arma::is_arma_type<typename
+                   std::remove_reference<WeightsType>::type>::value>* = 0);
+
+  /**
+   * Train the decision tree on the given weighted data, assuming that all
+   * dimensions are numeric.  This will overwrite the given model. Setting
+   * minimumLeafSize and minimumGainSplit too small may cause the tree to
+   * overfit, but setting them too large may cause it to underfit.
+   *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
+   * @param data Dataset to train on.
+   * @param labels Labels for each training point.
+   * @param weights Weights of all the labels
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   * @return The final entropy of decision tree.
+   */
+  template<typename MatType, typename LabelsType, typename WeightsType>
+  double Train(MatType data,
+               LabelsType labels,
+               WeightsType weights,
+               const size_t minimumLeafSize = 10,
+               const double minimumGainSplit = 1e-7,
+               const size_t maximumDepth = 0,
+               DimensionSelectionType dimensionSelector =
+                   DimensionSelectionType(),
+               const std::enable_if_t<arma::is_arma_type<typename
+                   std::remove_reference<WeightsType>::type>::value>* = 0);
 };
 
 

src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp

@@ -420,6 +420,176 @@ DecisionTreeRegressor<FitnessFunction,
     delete children[i];
 }
 
+//! Train on the given data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector)
+{
+  // Sanity check on data.
+  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  arma::rowvec weights; // Fake weights, not used.
+  return Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
+      numClasses, weights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Train on the given data, assuming all dimensions are numeric.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType data,
+    LabelsType labels,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector)
+{
+  // Sanity check on data.
+  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  arma::rowvec weights; // Fake weights, not used.
+  return Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses,
+      weights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Train on the given weighted data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<
+        arma::is_arma_type<
+        typename std::remove_reference<
+        WeightsType>::type>::value>*)
+{
+  // Sanity check on data.
+  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  return Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
+      numClasses, tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Train on the given weighted all numeric data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType data,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<
+        arma::is_arma_type<
+        typename std::remove_reference<
+        WeightsType>::type>::value>*)
+{
+  // Sanity check on data.
+  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  return Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses,
+      tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
 
 } // namespace tree
 } // namespace mlpack