Fix approximated predict contribution. (#6811)

include/xgboost/c_api.h

@@ -740,15 +740,17 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
  *
  * \param handle Booster handle
  * \param dmat   DMatrix handle
- * \param c_json_config String encoded predict configuration in JSON format.
+ * \param c_json_config String encoded predict configuration in JSON format, with
+ *                      following available fields in the JSON object:
  *
- *    "type": [0, 5]
+ *    "type": [0, 6]
  *      0: normal prediction
  *      1: output margin
  *      2: predict contribution
- *      3: predict approxmated contribution
+ *      3: predict approximated contribution
  *      4: predict feature interaction
- *      5: predict leaf
+ *      5: predict approximated feature interaction
+ *      6: predict leaf
  *    "training": bool
  *      Whether the prediction function is used as part of a training loop.  **Not used
  *      for inplace prediction**.
@@ -764,7 +766,7 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
  *    "iteration_begin": int
  *      Beginning iteration of prediction.
  *    "iteration_end": int
- *      End iteration of prediction.  Set to 0 this will become the size of tree model.
+ *      End iteration of prediction.  Set to 0 this will become the size of tree model (all the trees).
  *    "strict_shape": bool
  *      Whether should we reshape the output with stricter rules.  If set to true,
  *      normal/margin/contrib/interaction predict will output consistent shape

include/xgboost/learner.h

@@ -36,7 +36,8 @@ enum class PredictionType : std::uint8_t {  // NOLINT
   kContribution = 2,
   kApproxContribution = 3,
   kInteraction = 4,
-  kLeaf = 5
+  kApproxInteraction = 5,
+  kLeaf = 6
 };
 
 /*! \brief entry to to easily hold returning information */

python-package/xgboost/core.py

@@ -1647,7 +1647,9 @@ class Booster(object):
             prediction. Note the final column is the bias term.
 
         approx_contribs :
-            Approximate the contributions of each feature
+            Approximate the contributions of each feature.  Used when ``pred_contribs`` or
+            ``pred_interactions`` is set to True.  Changing the default of this parameter
+            (False) is not recommended.
 
         pred_interactions :
             When this is True the output will be a matrix of size (nsample,
@@ -1716,9 +1718,9 @@ class Booster(object):
         if pred_contribs:
             assign_type(2 if not approx_contribs else 3)
         if pred_interactions:
-            assign_type(4)
+            assign_type(4 if not approx_contribs else 5)
         if pred_leaf:
-            assign_type(5)
+            assign_type(6)
         preds = ctypes.POINTER(ctypes.c_float)()
         shape = ctypes.POINTER(c_bst_ulong)()
         dims = c_bst_ulong()

src/c_api/c_api.cc

@@ -651,13 +651,17 @@ XGB_DLL int XGBoosterPredictFromDMatrix(BoosterHandle handle,
   auto type = PredictionType(get<Integer const>(config["type"]));
   auto iteration_begin = get<Integer const>(config["iteration_begin"]);
   auto iteration_end = get<Integer const>(config["iteration_end"]);
-  learner->Predict(
+  bool approximate = type == PredictionType::kApproxContribution ||
-      *static_cast<std::shared_ptr<DMatrix> *>(dmat),
+                     type == PredictionType::kApproxInteraction;
-      type == PredictionType::kMargin, &entry.predictions, iteration_begin,
+  bool contribs = type == PredictionType::kContribution ||
-      iteration_end, get<Boolean const>(config["training"]),
+                  type == PredictionType::kApproxContribution;
-      type == PredictionType::kLeaf, type == PredictionType::kContribution,
+  bool interactions = type == PredictionType::kInteraction ||
-      type == PredictionType::kApproxContribution,
+                      type == PredictionType::kApproxInteraction;
-      type == PredictionType::kInteraction);
+  bool training = get<Boolean const>(config["training"]);
+  learner->Predict(p_m, type == PredictionType::kMargin, &entry.predictions,
+                   iteration_begin, iteration_end, training,
+                   type == PredictionType::kLeaf, contribs, approximate,
+                   interactions);
   *out_result = dmlc::BeginPtr(entry.predictions.ConstHostVector());
   auto &shape = learner->GetThreadLocal().prediction_shape;
   auto chunksize = p_m->Info().num_row_ == 0 ? 0 : entry.predictions.Size() / p_m->Info().num_row_;

src/c_api/c_api_utils.h

@@ -56,7 +56,6 @@ inline void CalcPredictShape(bool strict_shape, PredictionType type, size_t rows
   }
   case PredictionType::kApproxContribution:
   case PredictionType::kContribution: {
-    auto groups = chunksize / (cols + 1);
     if (groups == 1 && !strict_shape) {
       *out_dim = 2;
       shape.resize(*out_dim);
@@ -71,6 +70,7 @@ inline void CalcPredictShape(bool strict_shape, PredictionType type, size_t rows
     }
     break;
   }
+  case PredictionType::kApproxInteraction:
   case PredictionType::kInteraction: {
     if (groups == 1 && !strict_shape) {
       *out_dim = 3;

tests/python/test_predict.py

@@ -98,6 +98,27 @@ def test_predict_shape():
     assert len(contrib.shape) == 3
     assert contrib.shape[1] == 1
 
+    contrib = reg.get_booster().predict(
+        xgb.DMatrix(X), pred_contribs=True, approx_contribs=True
+    )
+    assert len(contrib.shape) == 2
+    assert contrib.shape[1] == X.shape[1] + 1
+
+    interaction = reg.get_booster().predict(
+        xgb.DMatrix(X), pred_interactions=True, approx_contribs=True
+    )
+    assert len(interaction.shape) == 3
+    assert interaction.shape[1] == X.shape[1] + 1
+    assert interaction.shape[2] == X.shape[1] + 1
+
+    interaction = reg.get_booster().predict(
+        xgb.DMatrix(X), pred_interactions=True, approx_contribs=True, strict_shape=True
+    )
+    assert len(interaction.shape) == 4
+    assert interaction.shape[1] == 1
+    assert interaction.shape[2] == X.shape[1] + 1
+    assert interaction.shape[3] == X.shape[1] + 1
+
 
 class TestInplacePredict:
     '''Tests for running inplace prediction'''