helpers_iforest.cpp in isotree-0.1.5

- old
+ new

@@ -20,11 +20,11 @@
 *     [7] Quinlan, J. Ross. C4. 5: programs for machine learning. Elsevier, 2014.
 *     [8] Cortes, David. "Distance approximation using Isolation Forests." arXiv preprint arXiv:1910.12362 (2019).
 *     [9] Cortes, David. "Imputing missing values with unsupervised random trees." arXiv preprint arXiv:1911.06646 (2019).
 * 
 *     BSD 2-Clause License
-*     Copyright (c) 2019, David Cortes
+*     Copyright (c) 2020, David Cortes
 *     All rights reserved.
 *     Redistribution and use in source and binary forms, with or without
 *     modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright notice, this
 *       list of conditions and the following disclaimer.
@@ -89,11 +89,11 @@
 /* for use in regular model */
 void get_split_range(WorkerMemory &workspace, InputData &input_data, ModelParams &model_params, IsoTree &tree)
 {
     if (tree.col_type == Numeric)
     {
-        if (input_data.Xc == NULL)
+        if (input_data.Xc_indptr == NULL)
             get_range(workspace.ix_arr.data(), input_data.numeric_data + input_data.nrows * tree.col_num,
                       workspace.st, workspace.end, model_params.missing_action,
                       workspace.xmin, workspace.xmax, workspace.unsplittable);
         else
             get_range(workspace.ix_arr.data(), workspace.st, workspace.end, tree.col_num,
@@ -112,11 +112,11 @@
 /* for use in extended model */
 void get_split_range(WorkerMemory &workspace, InputData &input_data, ModelParams &model_params)
 {
     if (workspace.col_type == Numeric)
     {
-        if (input_data.Xc == NULL)
+        if (input_data.Xc_indptr == NULL)
             get_range(workspace.ix_arr.data(), input_data.numeric_data + input_data.nrows * workspace.col_chosen,
                       workspace.st, workspace.end, model_params.missing_action,
                       workspace.xmin, workspace.xmax, workspace.unsplittable);
         else
             get_range(workspace.ix_arr.data(), workspace.st, workspace.end, workspace.col_chosen,
@@ -279,14 +279,23 @@
     recursion_state.col_sampler   = workspace.col_sampler;
 
     /* for the extended model, it's not necessary to copy everything */
     if (!workspace.comb_val.size())
     {
-        /* TODO: here only need to copy the left half, as the right one is untouched */
-        recursion_state.ix_arr        = workspace.ix_arr;
-        recursion_state.weights_map   = workspace.weights_map;
-        recursion_state.weights_arr   = workspace.weights_arr;
+        recursion_state.ix_arr = std::vector<size_t>(workspace.ix_arr.begin() + workspace.st_NA,
+                                                     workspace.ix_arr.begin() + workspace.end + 1);
+        size_t tot = workspace.end - workspace.st_NA + 1;
+        if (workspace.weights_arr.size() || workspace.weights_map.size())
+            recursion_state.weights_arr = std::unique_ptr<double[]>(new double[tot]);
+        if (workspace.weights_arr.size())
+            for (size_t ix = 0; ix < tot; ix++)
+                recursion_state.weights_arr[ix] = workspace.weights_arr[workspace.ix_arr[ix + workspace.st_NA]];
+        else if (workspace.weights_map.size())
+            for (size_t ix = 0; ix < tot; ix++)
+                recursion_state.weights_arr[ix] = workspace.weights_map[workspace.ix_arr[ix + workspace.st_NA]];
+        
+
     }
 }
 
 
 void restore_recursion_state(WorkerMemory &workspace, RecursionState &recursion_state)
@@ -299,11 +308,17 @@
     workspace.cols_possible = std::move(recursion_state.cols_possible);
     workspace.col_sampler   = std::move(recursion_state.col_sampler);
 
     if (!workspace.comb_val.size())
     {
-        /* TODO: here only need to copy the left half, as the right one is untouched */
-        workspace.ix_arr        = std::move(recursion_state.ix_arr);
-        workspace.weights_map   = std::move(recursion_state.weights_map);
-        workspace.weights_arr   = std::move(recursion_state.weights_arr);
+        std::copy(recursion_state.ix_arr.begin(),
+                  recursion_state.ix_arr.end(),
+                  workspace.ix_arr.begin() + recursion_state.st_NA);
+        size_t tot = workspace.end - workspace.st_NA + 1;
+        if (workspace.weights_arr.size())
+            for (size_t ix = 0; ix < tot; ix++)
+                workspace.weights_arr[workspace.ix_arr[ix + workspace.st_NA]] = recursion_state.weights_arr[ix];
+        else if (workspace.weights_map.size())
+            for (size_t ix = 0; ix < tot; ix++)
+                workspace.weights_map[workspace.ix_arr[ix + workspace.st_NA]] = recursion_state.weights_arr[ix];
     }
 }