Rework the MAP metric.

doc/parameter.rst

@@ -408,8 +408,17 @@ Specify the learning task and the corresponding learning objective. The objectiv
 
     - ``ndcg``: `Normalized Discounted Cumulative Gain <http://en.wikipedia.org/wiki/NDCG>`_
     - ``map``: `Mean Average Precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
-    - ``ndcg@n``, ``map@n``: 'n' can be assigned as an integer to cut off the top positions in the lists for evaluation.
-    - ``ndcg-``, ``map-``, ``ndcg@n-``, ``map@n-``: In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding "-" in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions.
+
+      The `average precision` is defined as:
+
+      .. math::
+
+	 AP@l = \frac{1}{min{(l, N)}}\sum^l_{k=1}P@k \cdot I_{(k)}
+
+      where :math:`I_{(k)}` is an indicator function that equals to :math:`1` when the document at :math:`k` is relevant and :math:`0` otherwise. The :math:`P@k` is the precision at :math:`k`, and :math:`N` is the total number of relevant documents. Lastly, the `mean average precision` is defined as the weighted average across all queries.
+
+    - ``ndcg@n``, ``map@n``: :math:`n` can be assigned as an integer to cut off the top positions in the lists for evaluation.
+    - ``ndcg-``, ``map-``, ``ndcg@n-``, ``map@n-``: In XGBoost, the NDCG and MAP evaluate the score of a list without any positive samples as :math:`1`. By appending "-" to the evaluation metric name, we can ask XGBoost to evaluate these scores as :math:`0` to be consistent under some conditions.
     - ``poisson-nloglik``: negative log-likelihood for Poisson regression
     - ``gamma-nloglik``: negative log-likelihood for gamma regression
     - ``cox-nloglik``: negative partial log-likelihood for Cox proportional hazards regression

python-package/xgboost/testing/__init__.py

@@ -14,6 +14,7 @@
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import contextmanager
 from io import StringIO
+from pathlib import Path
 from platform import system
 from typing import (
     Any,
@@ -443,7 +444,7 @@ def get_mq2008(
     from sklearn.datasets import load_svmlight_files
 
     src = "https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip"
-    target = dpath + "/MQ2008.zip"
+    target = os.path.join(os.path.expanduser(dpath), "MQ2008.zip")
     if not os.path.exists(target):
         request.urlretrieve(url=src, filename=target)
 
@@ -462,9 +463,9 @@ def get_mq2008(
         qid_valid,
     ) = load_svmlight_files(
         (
-            dpath + "MQ2008/Fold1/train.txt",
-            dpath + "MQ2008/Fold1/test.txt",
-            dpath + "MQ2008/Fold1/vali.txt",
+            Path(dpath) / "MQ2008" / "Fold1" / "train.txt",
+            Path(dpath) / "MQ2008" / "Fold1" / "test.txt",
+            Path(dpath) / "MQ2008" / "Fold1" / "vali.txt",
         ),
         query_id=True,
         zero_based=False,

python-package/xgboost/testing/ranking.py

@@ -48,7 +48,12 @@ def run_ranking_qid_df(impl: ModuleType, tree_method: str) -> None:
     def neg_mse(*args: Any, **kwargs: Any) -> float:
         return -float(mean_squared_error(*args, **kwargs))
 
-    ranker = xgb.XGBRanker(n_estimators=3, eval_metric=neg_mse, tree_method=tree_method)
+    ranker = xgb.XGBRanker(
+        n_estimators=3,
+        eval_metric=neg_mse,
+        tree_method=tree_method,
+        disable_default_eval_metric=True,
+    )
     ranker.fit(df, y, eval_set=[(valid_df, y)])
     score = ranker.score(valid_df, y)
     assert np.isclose(score, ranker.evals_result()["validation_0"]["neg_mse"][-1])

src/common/error_msg.h

@@ -22,7 +22,7 @@ constexpr StringView LabelScoreSize() {
 }
 
 constexpr StringView InfInData() {
-  return "Input data contains `inf` while `missing` is not set to `inf`";
+  return "Input data contains `inf` or a value too large, while `missing` is not set to `inf`";
 }
 }  // namespace xgboost::error
 #endif  // XGBOOST_COMMON_ERROR_MSG_H_

src/common/numeric.h

@@ -1,22 +1,23 @@
-/*!
- * Copyright 2022, XGBoost contributors.
+/**
+ * Copyright 2022-2023 by XGBoost contributors.
  */
 #ifndef XGBOOST_COMMON_NUMERIC_H_
 #define XGBOOST_COMMON_NUMERIC_H_
 
 #include <dmlc/common.h>  // OMPException
 
-#include <algorithm>  // std::max
-#include <iterator>   // std::iterator_traits
+#include <algorithm>  // for std::max
+#include <cstddef>    // for size_t
+#include <cstdint>    // for int32_t
+#include <iterator>   // for iterator_traits
 #include <vector>
 
 #include "common.h"                      // AssertGPUSupport
 #include "threading_utils.h"             // MemStackAllocator, DefaultMaxThreads
 #include "xgboost/context.h"             // Context
 #include "xgboost/host_device_vector.h"  // HostDeviceVector
 
-namespace xgboost {
-namespace common {
+namespace xgboost::common {
 
 /**
  * \brief Run length encode on CPU, input must be sorted.
@@ -111,11 +112,11 @@ inline double Reduce(Context const*, HostDeviceVector<float> const&) {
 namespace cpu_impl {
 template <typename It, typename V = typename It::value_type>
 V Reduce(Context const* ctx, It first, It second, V const& init) {
-  size_t n = std::distance(first, second);
-  common::MemStackAllocator<V, common::DefaultMaxThreads()> result_tloc(ctx->Threads(), init);
-  common::ParallelFor(n, ctx->Threads(),
-                      [&](auto i) { result_tloc[omp_get_thread_num()] += first[i]; });
-  auto result = std::accumulate(result_tloc.cbegin(), result_tloc.cbegin() + ctx->Threads(), init);
+  std::size_t n = std::distance(first, second);
+  auto n_threads = static_cast<std::size_t>(std::min(n, static_cast<std::size_t>(ctx->Threads())));
+  common::MemStackAllocator<V, common::DefaultMaxThreads()> result_tloc(n_threads, init);
+  common::ParallelFor(n, n_threads, [&](auto i) { result_tloc[omp_get_thread_num()] += first[i]; });
+  auto result = std::accumulate(result_tloc.cbegin(), result_tloc.cbegin() + n_threads, init);
   return result;
 }
 }  // namespace cpu_impl
@@ -144,7 +145,6 @@ void Iota(Context const* ctx, It first, It last,
     });
   }
 }
-}  // namespace common
-}  // namespace xgboost
+}  // namespace xgboost::common
 
 #endif  // XGBOOST_COMMON_NUMERIC_H_

src/common/ranking_utils.cc

@@ -114,6 +114,15 @@ void NDCGCache::InitOnCUDA(Context const*, MetaInfo const&) { common::AssertGPUS
 
 DMLC_REGISTER_PARAMETER(LambdaRankParam);
 
+void MAPCache::InitOnCPU(Context const*, MetaInfo const& info) {
+  auto const& h_label = info.labels.HostView().Slice(linalg::All(), 0);
+  CheckMapLabels(h_label, [](auto beg, auto end, auto op) { return std::all_of(beg, end, op); });
+}
+
+#if !defined(XGBOOST_USE_CUDA)
+void MAPCache::InitOnCUDA(Context const*, MetaInfo const&) { common::AssertGPUSupport(); }
+#endif  // !defined(XGBOOST_USE_CUDA)
+
 std::string ParseMetricName(StringView name, StringView param, position_t* topn, bool* minus) {
   std::string out_name;
   if (!param.empty()) {

src/common/ranking_utils.cu

@@ -204,4 +204,9 @@ void NDCGCache::InitOnCUDA(Context const* ctx, MetaInfo const& info) {
   dh::LaunchN(MaxGroupSize(), cuctx->Stream(),
               [=] XGBOOST_DEVICE(std::size_t i) { d_discount[i] = CalcDCGDiscount(i); });
 }
+
+void MAPCache::InitOnCUDA(Context const* ctx, MetaInfo const& info) {
+  auto const d_label = info.labels.View(ctx->gpu_id).Slice(linalg::All(), 0);
+  CheckMapLabels(d_label, CheckMAPOp{ctx->CUDACtx()});
+}
 }  // namespace xgboost::ltr

src/common/ranking_utils.h

@@ -358,6 +358,71 @@ void CheckNDCGLabels(ltr::LambdaRankParam const& p, linalg::VectorView<float con
   }
 }
 
+template <typename AllOf>
+bool IsBinaryRel(linalg::VectorView<float const> label, AllOf all_of) {
+  auto s_label = label.Values();
+  return all_of(s_label.data(), s_label.data() + s_label.size(), [] XGBOOST_DEVICE(float y) {
+    return std::abs(y - 1.0f) < kRtEps || std::abs(y - 0.0f) < kRtEps;
+  });
+}
+/**
+ * \brief Validate label for MAP
+ *
+ * \tparam Implementation of std::all_of. Specified as a parameter to reuse the check for
+ *         both CPU and GPU.
+ */
+template <typename AllOf>
+void CheckMapLabels(linalg::VectorView<float const> label, AllOf all_of) {
+  auto s_label = label.Values();
+  auto is_binary = IsBinaryRel(label, all_of);
+  CHECK(is_binary) << "MAP can only be used with binary labels.";
+}
+
+class MAPCache : public RankingCache {
+  // Total number of relevant documents for each group
+  HostDeviceVector<double> n_rel_;
+  // \sum l_k/k
+  HostDeviceVector<double> acc_;
+  HostDeviceVector<double> map_;
+  // Number of samples in this dataset.
+  std::size_t n_samples_{0};
+
+  void InitOnCPU(Context const* ctx, MetaInfo const& info);
+  void InitOnCUDA(Context const* ctx, MetaInfo const& info);
+
+ public:
+  MAPCache(Context const* ctx, MetaInfo const& info, LambdaRankParam const& p)
+      : RankingCache{ctx, info, p}, n_samples_{static_cast<std::size_t>(info.num_row_)} {
+    if (ctx->IsCPU()) {
+      this->InitOnCPU(ctx, info);
+    } else {
+      this->InitOnCUDA(ctx, info);
+    }
+  }
+
+  common::Span<double> NumRelevant(Context const* ctx) {
+    if (n_rel_.Empty()) {
+      n_rel_.SetDevice(ctx->gpu_id);
+      n_rel_.Resize(n_samples_);
+    }
+    return ctx->IsCPU() ? n_rel_.HostSpan() : n_rel_.DeviceSpan();
+  }
+  common::Span<double> Acc(Context const* ctx) {
+    if (acc_.Empty()) {
+      acc_.SetDevice(ctx->gpu_id);
+      acc_.Resize(n_samples_);
+    }
+    return ctx->IsCPU() ? acc_.HostSpan() : acc_.DeviceSpan();
+  }
+  common::Span<double> Map(Context const* ctx) {
+    if (map_.Empty()) {
+      map_.SetDevice(ctx->gpu_id);
+      map_.Resize(this->Groups());
+    }
+    return ctx->IsCPU() ? map_.HostSpan() : map_.DeviceSpan();
+  }
+};
+
 /**
  * \brief Parse name for ranking metric given parameters.
  *

src/common/threading_utils.h

@@ -8,9 +8,11 @@
 #include <dmlc/omp.h>
 
 #include <algorithm>
-#include <cstdint>  // std::int32_t
+#include <cstdint>  // for int32_t
+#include <cstdlib>  // for malloc, free
 #include <limits>
-#include <type_traits>  // std::is_signed
+#include <new>          // for bad_alloc
+#include <type_traits>  // for is_signed
 #include <vector>
 
 #include "xgboost/logging.h"
@@ -266,7 +268,7 @@ class MemStackAllocator {
     if (MaxStackSize >= required_size_) {
       ptr_ = stack_mem_;
     } else {
-      ptr_ = reinterpret_cast<T*>(malloc(required_size_ * sizeof(T)));
+      ptr_ = reinterpret_cast<T*>(std::malloc(required_size_ * sizeof(T)));
     }
     if (!ptr_) {
       throw std::bad_alloc{};
@@ -278,7 +280,7 @@ class MemStackAllocator {
 
   ~MemStackAllocator() {
     if (required_size_ > MaxStackSize) {
-      free(ptr_);
+      std::free(ptr_);
     }
   }
   T& operator[](size_t i) { return ptr_[i]; }

src/metric/rank_metric.cc

@@ -284,37 +284,6 @@ struct EvalPrecision : public EvalRank {
   }
 };
 
-/*! \brief Mean Average Precision at N, for both classification and rank */
-struct EvalMAP : public EvalRank {
- public:
-  explicit EvalMAP(const char* name, const char* param) : EvalRank(name, param) {}
-
-  double EvalGroup(PredIndPairContainer *recptr) const override {
-    PredIndPairContainer &rec(*recptr);
-    std::stable_sort(rec.begin(), rec.end(), common::CmpFirst);
-    unsigned nhits = 0;
-    double sumap = 0.0;
-    for (size_t i = 0; i < rec.size(); ++i) {
-      if (rec[i].second != 0) {
-        nhits += 1;
-        if (i < this->topn) {
-          sumap += static_cast<double>(nhits) / (i + 1);
-        }
-      }
-    }
-    if (nhits != 0) {
-      sumap /= nhits;
-      return sumap;
-    } else {
-      if (this->minus) {
-        return 0.0;
-      } else {
-        return 1.0;
-      }
-    }
-  }
-};
-
 /*! \brief Cox: Partial likelihood of the Cox proportional hazards model */
 struct EvalCox : public MetricNoCache {
  public:
@@ -370,10 +339,6 @@ XGBOOST_REGISTER_METRIC(Precision, "pre")
 .describe("precision@k for rank.")
 .set_body([](const char* param) { return new EvalPrecision("pre", param); });
 
-XGBOOST_REGISTER_METRIC(MAP, "map")
-.describe("map@k for rank.")
-.set_body([](const char* param) { return new EvalMAP("map", param); });
-
 XGBOOST_REGISTER_METRIC(Cox, "cox-nloglik")
 .describe("Negative log partial likelihood of Cox proportional hazards model.")
 .set_body([](const char*) { return new EvalCox(); });
@@ -516,6 +481,68 @@ class EvalNDCG : public EvalRankWithCache<ltr::NDCGCache> {
   }
 };
 
+class EvalMAPScore : public EvalRankWithCache<ltr::MAPCache> {
+ public:
+  using EvalRankWithCache::EvalRankWithCache;
+  const char* Name() const override { return name_.c_str(); }
+
+  double Eval(HostDeviceVector<float> const& predt, MetaInfo const& info,
+              std::shared_ptr<ltr::MAPCache> p_cache) override {
+    if (ctx_->IsCUDA()) {
+      auto map = cuda_impl::MAPScore(ctx_, info, predt, minus_, p_cache);
+      return Finalize(map.Residue(), map.Weights());
+    }
+
+    auto gptr = p_cache->DataGroupPtr(ctx_);
+    auto h_label = info.labels.HostView().Slice(linalg::All(), 0);
+    auto h_predt = linalg::MakeTensorView(ctx_, &predt, predt.Size());
+
+    auto map_gloc = p_cache->Map(ctx_);
+    std::fill_n(map_gloc.data(), map_gloc.size(), 0.0);
+    auto rank_idx = p_cache->SortedIdx(ctx_, predt.ConstHostSpan());
+
+    common::ParallelFor(p_cache->Groups(), ctx_->Threads(), [&](auto g) {
+      auto g_predt = h_predt.Slice(linalg::Range(gptr[g], gptr[g + 1]));
+      auto g_label = h_label.Slice(linalg::Range(gptr[g], gptr[g + 1]));
+      auto g_rank = rank_idx.subspan(gptr[g]);
+
+      auto n = std::min(static_cast<std::size_t>(param_.TopK()), g_label.Size());
+      double n_hits{0.0};
+      for (std::size_t i = 0; i < n; ++i) {
+        auto p = g_label(g_rank[i]);
+        n_hits += p;
+        map_gloc[g] += n_hits / static_cast<double>((i + 1)) * p;
+      }
+      for (std::size_t i = n; i < g_label.Size(); ++i) {
+        n_hits += g_label(g_rank[i]);
+      }
+      if (n_hits > 0.0) {
+        map_gloc[g] /= std::min(n_hits, static_cast<double>(param_.TopK()));
+      } else {
+        map_gloc[g] = minus_ ? 0.0 : 1.0;
+      }
+    });
+
+    auto sw = 0.0;
+    auto weight = common::MakeOptionalWeights(ctx_, info.weights_);
+    if (!weight.Empty()) {
+      CHECK_EQ(weight.weights.size(), p_cache->Groups());
+    }
+    for (std::size_t i = 0; i < map_gloc.size(); ++i) {
+      map_gloc[i] = map_gloc[i] * weight[i];
+      sw += weight[i];
+    }
+    auto sum = std::accumulate(map_gloc.cbegin(), map_gloc.cend(), 0.0);
+    return Finalize(sum, sw);
+  }
+};
+
+XGBOOST_REGISTER_METRIC(EvalMAP, "map")
+    .describe("map@k for ranking.")
+    .set_body([](char const* param) {
+      return new EvalMAPScore{"map", param};
+    });
+
 XGBOOST_REGISTER_METRIC(EvalNDCG, "ndcg")
     .describe("ndcg@k for ranking.")
     .set_body([](char const* param) {