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[POC] Experimental support for l1 error. (#7812)

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Support adaptive tree, a feature supported by both sklearn and lightgbm.  The tree leaf is recomputed based on residue of labels and predictions after construction.

For l1 error, the optimal value is the median (50 percentile).

This is marked as experimental support for the following reasons:
- The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors.
- Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
This commit is contained in:
Jiaming Yuan
2022-04-26 21:41:55 +08:00
committed by GitHub
parent ad06172c6b
commit fdf533f2b9
64 changed files with 1727 additions and 336 deletions
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8
tests/cpp/gbm/test_gbtree.cc
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@@ -1,5 +1,5 @@
/*!
* Copyright 2019-2021 XGBoost contributors
* Copyright 2019-2022 XGBoost contributors
*/
#include <gtest/gtest.h>
#include <dmlc/filesystem.h>
@@ -69,13 +69,13 @@ TEST(GBTree, PredictionCache) {
auto p_m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();
auto gpair = GenerateRandomGradients(kRows);
PredictionCacheEntry out_predictions;
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions);
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions, nullptr);
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
ASSERT_EQ(1, out_predictions.version);
std::vector<float> first_iter = out_predictions.predictions.HostVector();
// Add 1 more boosted round
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions);
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions, nullptr);
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
ASSERT_EQ(2, out_predictions.version);
// Update the cache for all rounds
@@ -83,7 +83,7 @@ TEST(GBTree, PredictionCache) {
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
ASSERT_EQ(2, out_predictions.version);
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions);
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions, nullptr);
// drop the cache.
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 1, 2);
ASSERT_EQ(0, out_predictions.version);