Enforce correct data shape. (#5191)

* Fix syncing DMatrix columns.
* notes for tree method.
* Enable feature validation for all interfaces except for jvm.
* Better tests for boosting from predictions.
* Disable validation on JVM.

doc/parameter.rst

@@ -112,18 +112,24 @@ Parameters for Tree Booster
 
   - The tree construction algorithm used in XGBoost. See description in the `reference paper <http://arxiv.org/abs/1603.02754>`_.
   - XGBoost supports  ``approx``, ``hist`` and ``gpu_hist`` for distributed training.  Experimental support for external memory is available for ``approx`` and ``gpu_hist``.
-  - Choices: ``auto``, ``exact``, ``approx``, ``hist``, ``gpu_hist``
+
+  - Choices: ``auto``, ``exact``, ``approx``, ``hist``, ``gpu_hist``, this is a
+    combination of commonly used updaters.  For other updaters like ``refresh``, set the
+    parameter ``updater`` directly.
 
     - ``auto``: Use heuristic to choose the fastest method.
 
-      - For small to medium dataset, exact greedy (``exact``) will be used.
-      - For very large dataset, approximate algorithm (``approx``) will be chosen.
-      - Because old behavior is always use exact greedy in single machine,
-        user will get a message when approximate algorithm is chosen to notify this choice.
+      - For small dataset, exact greedy (``exact``) will be used.
+      - For larger dataset, approximate algorithm (``approx``) will be chosen.  It's
+        recommended to try ``hist`` and ``gpu_hist`` for higher performance with large
+        dataset.
+        (``gpu_hist``)has support for ``external memory``.
 
-    - ``exact``: Exact greedy algorithm.
+      - Because old behavior is always use exact greedy in single machine, user will get a
+        message when approximate algorithm is chosen to notify this choice.
+    - ``exact``: Exact greedy algorithm.  Enumerates all split candidates.
     - ``approx``: Approximate greedy algorithm using quantile sketch and gradient histogram.
-    - ``hist``: Fast histogram optimized approximate greedy algorithm. It uses some performance improvements such as bins caching.
+    - ``hist``: Faster histogram optimized approximate greedy algorithm.
     - ``gpu_hist``: GPU implementation of ``hist`` algorithm.
 
 * ``sketch_eps`` [default=0.03]

doc/tutorials/param_tuning.rst

@@ -38,6 +38,11 @@ There are in general two ways that you can control overfitting in XGBoost:
   - This includes ``subsample`` and ``colsample_bytree``.
   - You can also reduce stepsize ``eta``. Remember to increase ``num_round`` when you do so.
 
+***************************
+Faster training performance
+***************************
+There's a parameter called ``tree_method``, set it to ``hist`` or ``gpu_hist`` for faster computation.
+
 *************************
 Handle Imbalanced Dataset
 *************************

include/xgboost/generic_parameters.h

@@ -29,6 +29,7 @@ struct GenericParameter : public XGBoostParameter<GenericParameter> {
   size_t gpu_page_size;
   bool enable_experimental_json_serialization {false};
   bool validate_parameters {false};
+  bool validate_features {true};
 
   void CheckDeprecated() {
     if (this->n_gpus != 0) {
@@ -73,7 +74,10 @@ struct GenericParameter : public XGBoostParameter<GenericParameter> {
                   "rabit checkpoints etc.).");
     DMLC_DECLARE_FIELD(validate_parameters)
         .set_default(false)
-        .describe("Enable to check whether parameters are used or not.");
+        .describe("Enable checking whether parameters are used or not.");
+    DMLC_DECLARE_FIELD(validate_features)
+        .set_default(false)
+        .describe("Enable validating input DMatrix.");
     DMLC_DECLARE_FIELD(n_gpus)
         .set_default(0)
         .set_range(0, 1)

jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Booster.java

@@ -49,7 +49,7 @@ public class Booster implements Serializable, KryoSerializable {
    */
   Booster(Map<String, Object> params, DMatrix[] cacheMats) throws XGBoostError {
     init(cacheMats);
-    setParam("seed", "0");
+    setParam("validate_features", "0");
     setParams(params);
   }
 

src/common/observer.h

@@ -71,6 +71,12 @@ class TrainingObserver {
     auto const& h_vec = vec.HostVector();
     this->Observe(h_vec, name);
   }
+  template <typename T>
+  void Observe(HostDeviceVector<T>* vec, std::string name) const {
+    if (XGBOOST_EXPECT(!observe_, true)) { return; }
+    this->Observe(*vec, name);
+  }
+
   /*\brief Observe objects with `XGBoostParamer' type. */
   template <typename Parameter,
             typename std::enable_if<

src/data/data.cc

@@ -295,32 +295,8 @@ void DMatrix::SaveToLocalFile(const std::string& fname) {
 DMatrix* DMatrix::Create(std::unique_ptr<DataSource<SparsePage>>&& source,
                          const std::string& cache_prefix) {
   if (cache_prefix.length() == 0) {
-    // FIXME(trivialfis): Currently distcol is broken so we here check for number of rows.
-    // If we bring back column split this check will break.
-    bool is_distributed { rabit::IsDistributed() };
-    if (is_distributed) {
-      auto world_size = rabit::GetWorldSize();
-      auto rank = rabit::GetRank();
-      std::vector<uint64_t> ncols(world_size, 0);
-      ncols[rank] = source->info.num_col_;
-      rabit::Allreduce<rabit::op::Sum>(ncols.data(), ncols.size());
-      auto max_cols = std::max_element(ncols.cbegin(), ncols.cend());
-      auto max_ind = std::distance(ncols.cbegin(), max_cols);
-      // FIXME(trivialfis): This is a hack, we should store a reference to global shape if possible.
-      if (source->info.num_col_ == 0 && source->info.num_row_ == 0) {
-        LOG(WARNING) << "DMatrix at rank: " << rank << " worker is empty.";
-        source->info.num_col_ = *max_cols;
-      }
-
-      // validate the number of columns across all workers.
-      for (size_t i = 0; i < ncols.size(); ++i) {
-        auto v = ncols[i];
-        CHECK(v == 0 || v == *max_cols)
-            << "DMatrix at rank: " << i << " worker "
-            << "has different number of columns than rank: " << max_ind << " worker. "
-            << "(" << v << " vs. " << *max_cols << ")";
-      }
-    }
+    // Data split mode is fixed to be row right now.
+    rabit::Allreduce<rabit::op::Max>(&source->info.num_col_, 1);
     return new data::SimpleDMatrix(std::move(source));
   } else {
 #if DMLC_ENABLE_STD_THREAD
@@ -336,6 +312,7 @@ template <typename AdapterT>
 DMatrix* DMatrix::Create(AdapterT* adapter, float missing, int nthread,
                          const std::string& cache_prefix,  size_t page_size ) {
   if (cache_prefix.length() == 0) {
+    // Data split mode is fixed to be row right now.
     return new data::SimpleDMatrix(adapter, missing, nthread);
   } else {
 #if DMLC_ENABLE_STD_THREAD

src/gbm/gbtree.cc

@@ -124,7 +124,6 @@ void GBTree::PerformTreeMethodHeuristic(DMatrix* fmat) {
     return;
   }
 
-  tparam_.updater_seq = "grow_histmaker,prune";
   if (rabit::IsDistributed()) {
     LOG(WARNING) <<
       "Tree method is automatically selected to be 'approx' "

src/learner.cc

@@ -925,6 +925,25 @@ class LearnerImpl : public Learner {
           << "num rows: "     << p_fmat->Info().num_row_   << "\n"
           << "Number of weights should be equal to number of groups in ranking task.";
     }
+
+    auto const row_based_split = [this]() {
+      return tparam_.dsplit == DataSplitMode::kRow ||
+             tparam_.dsplit == DataSplitMode::kAuto;
+    };
+    bool const valid_features =
+        !row_based_split() ||
+        (learner_model_param_.num_feature == p_fmat->Info().num_col_);
+    std::string const msg {
+      "Number of columns does not match number of features in booster."
+    };
+    if (generic_parameters_.validate_features) {
+      CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_) << msg;
+    } else if (!valid_features) {
+      // Remove this and make the equality check fatal once spark can fix all failing tests.
+      LOG(WARNING) << msg << " "
+                   << "Columns: " << p_fmat->Info().num_col_ << " "
+                   << "Features: " << learner_model_param_.num_feature;
+    }
   }
 
   // model parameter

src/tree/updater_colmaker.cc

@@ -80,6 +80,10 @@ class ColMaker: public TreeUpdater {
   void Update(HostDeviceVector<GradientPair> *gpair,
               DMatrix* dmat,
               const std::vector<RegTree*> &trees) override {
+    if (rabit::IsDistributed()) {
+      LOG(FATAL) << "Updater `grow_colmaker` or `exact` tree method doesn't "
+                    "support distributed training.";
+    }
     // rescale learning rate according to size of trees
     float lr = param_.learning_rate;
     param_.learning_rate = lr / trees.size();

tests/cpp/test_learner.cc

@@ -91,7 +91,6 @@ TEST(Learner, CheckGroup) {
 }
 
 TEST(Learner, SLOW_CheckMultiBatch) {
-  using Arg = std::pair<std::string, std::string>;
   // Create sufficiently large data to make two row pages
   dmlc::TemporaryDirectory tempdir;
   const std::string tmp_file = tempdir.path + "/big.libsvm";
@@ -107,7 +106,7 @@ TEST(Learner, SLOW_CheckMultiBatch) {
   dmat->Info().SetInfo("label", labels.data(), DataType::kFloat32, num_row);
   std::vector<std::shared_ptr<DMatrix>> mat{dmat};
   auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
-  learner->SetParams({Arg{"objective", "binary:logistic"}, Arg{"verbosity", "3"}});
+  learner->SetParams(Args{{"objective", "binary:logistic"}});
   learner->UpdateOneIter(0, dmat.get());
 }
 

tests/python-gpu/test_gpu_pickling.py

@@ -6,7 +6,6 @@ import subprocess
 import os
 import json
 import pytest
-import copy
 
 import xgboost as xgb
 from xgboost import XGBClassifier

tests/python-gpu/test_gpu_prediction.py

@@ -13,6 +13,10 @@ class TestGPUPredict(unittest.TestCase):
         np.random.seed(1)
         test_num_rows = [10, 1000, 5000]
         test_num_cols = [10, 50, 500]
+        # This test passes for tree_method=gpu_hist and tree_method=exact. but
+        # for `hist` and `approx` the floating point error accumulates faster
+        # and fails even tol is set to 1e-4.  For `hist`, the mismatching rate
+        # with 5000 rows is 0.04.
         for num_rows in test_num_rows:
             for num_cols in test_num_cols:
                 dtrain = xgb.DMatrix(np.random.randn(num_rows, num_cols),
@@ -27,7 +31,7 @@ class TestGPUPredict(unittest.TestCase):
                     "objective": "binary:logistic",
                     "predictor": "gpu_predictor",
                     'eval_metric': 'auc',
-                    'verbosity': '3'
+                    'tree_method': 'gpu_hist'
                 }
                 bst = xgb.train(param, dtrain, iterations, evals=watchlist,
                                 evals_result=res)
@@ -43,11 +47,11 @@ class TestGPUPredict(unittest.TestCase):
                 cpu_pred_val = bst_cpu.predict(dval, output_margin=True)
 
                 np.testing.assert_allclose(cpu_pred_train, gpu_pred_train,
-                                           rtol=1e-3)
+                                           rtol=1e-6)
                 np.testing.assert_allclose(cpu_pred_val, gpu_pred_val,
-                                           rtol=1e-3)
+                                           rtol=1e-6)
                 np.testing.assert_allclose(cpu_pred_test, gpu_pred_test,
-                                           rtol=1e-3)
+                                           rtol=1e-6)
 
     def non_decreasing(self, L):
         return all((x - y) < 0.001 for x, y in zip(L, L[1:]))

tests/python-gpu/test_gpu_with_sklearn.py

@@ -2,9 +2,11 @@ import xgboost as xgb
 import pytest
 import sys
 import numpy as np
+import unittest
 
 sys.path.append("tests/python")
-import testing as tm
+import testing as tm               # noqa
+import test_with_sklearn as twskl  # noqa
 
 pytestmark = pytest.mark.skipif(**tm.no_sklearn())
 
@@ -29,3 +31,10 @@ def test_gpu_binary_classification():
             err = sum(1 for i in range(len(preds))
                       if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
             assert err < 0.1
+
+
+class TestGPUBoostFromPrediction(unittest.TestCase):
+    cpu_test = twskl.TestBoostFromPrediction()
+
+    def test_boost_from_prediction_gpu_hist(self):
+        self.cpu_test.run_boost_from_prediction('gpu_hist')

tests/python/test_with_sklearn.py

@@ -5,6 +5,7 @@ import tempfile
 import os
 import shutil
 import pytest
+import unittest
 
 rng = np.random.RandomState(1994)
 
@@ -697,21 +698,37 @@ def test_XGBClassifier_resume():
         assert log_loss1 > log_loss2
 
 
-def test_boost_from_prediction():
-    from sklearn.datasets import load_breast_cancer
-    X, y = load_breast_cancer(return_X_y=True)
-    model_0 = xgb.XGBClassifier(
-        learning_rate=0.3, random_state=0, n_estimators=4)
-    model_0.fit(X=X, y=y)
-    margin = model_0.predict(X, output_margin=True)
+class TestBoostFromPrediction(unittest.TestCase):
+    def run_boost_from_prediction(self, tree_method):
+        from sklearn.datasets import load_breast_cancer
+        X, y = load_breast_cancer(return_X_y=True)
+        model_0 = xgb.XGBClassifier(
+            learning_rate=0.3, random_state=0, n_estimators=4,
+            tree_method=tree_method)
+        model_0.fit(X=X, y=y)
+        margin = model_0.predict(X, output_margin=True)
 
-    model_1 = xgb.XGBClassifier(
-        learning_rate=0.3, random_state=0, n_estimators=4)
-    model_1.fit(X=X, y=y, base_margin=margin)
-    predictions_1 = model_1.predict(X, base_margin=margin)
+        model_1 = xgb.XGBClassifier(
+            learning_rate=0.3, random_state=0, n_estimators=4,
+            tree_method=tree_method)
+        model_1.fit(X=X, y=y, base_margin=margin)
+        predictions_1 = model_1.predict(X, base_margin=margin)
 
-    cls_2 = xgb.XGBClassifier(
-        learning_rate=0.3, random_state=0, n_estimators=8)
-    cls_2.fit(X=X, y=y)
-    predictions_2 = cls_2.predict(X, base_margin=margin)
-    assert np.all(predictions_1 == predictions_2)
+        cls_2 = xgb.XGBClassifier(
+            learning_rate=0.3, random_state=0, n_estimators=8,
+            tree_method=tree_method)
+        cls_2.fit(X=X, y=y)
+        predictions_2 = cls_2.predict(X)
+        assert np.all(predictions_1 == predictions_2)
+
+    @pytest.mark.skipif(**tm.no_sklearn())
+    def test_boost_from_prediction_hist(self):
+        self.run_boost_from_prediction('hist')
+
+    @pytest.mark.skipif(**tm.no_sklearn())
+    def test_boost_from_prediction_approx(self):
+        self.run_boost_from_prediction('approx')
+
+    @pytest.mark.skipif(**tm.no_sklearn())
+    def test_boost_from_prediction_exact(self):
+        self.run_boost_from_prediction('exact')