Fix prediction heuristic

demo/regression/machine.conf

@@ -7,24 +7,22 @@ objective = reg:squarederror
 
 # Tree Booster Parameters
 # step size shrinkage
-eta = 1.0 
+eta = 1.0
 # minimum loss reduction required to make a further partition
-gamma = 1.0 
+gamma = 1.0
 # minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1 
+min_child_weight = 1
 # maximum depth of a tree
-max_depth = 3 
+max_depth = 3
 
 # Task parameters
 # the number of round to do boosting
 num_round = 2
 # 0 means do not save any model except the final round model
-save_period = 0 
+save_period = 0
 # The path of training data
-data = "machine.txt.train" 
+data = "machine.txt.train"
 # The path of validation data, used to monitor training process, here [test] sets name of the validation set
-eval[test] = "machine.txt.test" 
-# The path of test data 
-test:data = "machine.txt.test"      
-
-
+eval[test] = "machine.txt.test"
+# The path of test data
+test:data = "machine.txt.test"

jvm-packages/.gitignore

@@ -1,3 +1,2 @@
 tracker.py
 build.sh
-

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/FeatureSizeValidatingSuite.scala

@@ -21,19 +21,23 @@ import org.apache.spark.Partitioner
 import org.apache.spark.ml.feature.VectorAssembler
 import org.apache.spark.sql.SparkSession
 import org.scalatest.FunSuite
+import org.apache.spark.sql.functions._
 
 import scala.util.Random
 
 class FeatureSizeValidatingSuite extends FunSuite with PerTest {
 
-  test("transform throwing exception if feature size of dataset is different with model's") {
+  test("transform throwing exception if feature size of dataset is greater than model's") {
     val modelPath = getClass.getResource("/model/0.82/model").getPath
     val model = XGBoostClassificationModel.read.load(modelPath)
     val r = new Random(0)
     // 0.82/model was trained with 251 features. and transform will throw exception
     // if feature size of data is not equal to 251
-    val df = ss.createDataFrame(Seq.fill(100)(r.nextInt(2)).map(i => (i, i))).
+    var df = ss.createDataFrame(Seq.fill(100)(r.nextInt(2)).map(i => (i, i))).
       toDF("feature", "label")
+    for (x <- 1 to 252) {
+      df = df.withColumn(s"feature_${x}", lit(1))
+    }
     val assembler = new VectorAssembler()
       .setInputCols(df.columns.filter(!_.contains("label")))
       .setOutputCol("features")
@@ -67,5 +71,4 @@ class FeatureSizeValidatingSuite extends FunSuite with PerTest {
       xgb.fit(repartitioned)
     }
   }
-
 }

src/learner.cc

@@ -946,7 +946,7 @@ class LearnerImpl : public LearnerIO {
       common::GlobalRandom().seed(generic_parameters_.seed * kRandSeedMagic + iter);
     }
     this->CheckDataSplitMode();
-    this->ValidateDMatrix(train.get());
+    this->ValidateDMatrix(train.get(), true);
 
     auto& predt = this->cache_.Cache(train, generic_parameters_.gpu_id);
 
@@ -972,7 +972,7 @@ class LearnerImpl : public LearnerIO {
       common::GlobalRandom().seed(generic_parameters_.seed * kRandSeedMagic + iter);
     }
     this->CheckDataSplitMode();
-    this->ValidateDMatrix(train.get());
+    this->ValidateDMatrix(train.get(), true);
     this->cache_.Cache(train, generic_parameters_.gpu_id);
 
     gbm_->DoBoost(train.get(), in_gpair, &cache_.Entry(train.get()));
@@ -994,7 +994,7 @@ class LearnerImpl : public LearnerIO {
     for (size_t i = 0; i < data_sets.size(); ++i) {
       std::shared_ptr<DMatrix> m = data_sets[i];
       auto &predt = this->cache_.Cache(m, generic_parameters_.gpu_id);
-      this->ValidateDMatrix(m.get());
+      this->ValidateDMatrix(m.get(), false);
       this->PredictRaw(m.get(), &predt, false);
 
       auto &out = output_predictions_.Cache(m, generic_parameters_.gpu_id).predictions;
@@ -1079,11 +1079,11 @@ class LearnerImpl : public LearnerIO {
                   bool training,
                   unsigned ntree_limit = 0) const {
     CHECK(gbm_ != nullptr) << "Predict must happen after Load or configuration";
-    this->ValidateDMatrix(data);
+    this->ValidateDMatrix(data, false);
     gbm_->PredictBatch(data, out_preds, training, ntree_limit);
   }
 
-  void ValidateDMatrix(DMatrix* p_fmat) const {
+  void ValidateDMatrix(DMatrix* p_fmat, bool is_training) const {
     MetaInfo const& info = p_fmat->Info();
     info.Validate(generic_parameters_.gpu_id);
 
@@ -1092,8 +1092,15 @@ class LearnerImpl : public LearnerIO {
              tparam_.dsplit == DataSplitMode::kAuto;
     };
     if (row_based_split()) {
-      CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_)
-          << "Number of columns does not match number of features in booster.";
+      if (is_training) {
+        CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_)
+            << "Number of columns does not match number of features in "
+               "booster.";
+      } else {
+        CHECK_GE(learner_model_param_.num_feature, p_fmat->Info().num_col_)
+            << "Number of columns does not match number of features in "
+               "booster.";
+      }
     }
   }
 

tests/cpp/predictor/test_cpu_predictor.cc

@@ -165,4 +165,8 @@ TEST(CpuPredictor, InplacePredict) {
     TestInplacePrediction(x, "cpu_predictor", kRows, kCols, -1);
   }
 }
+
+TEST(CpuPredictor, LesserFeatures) {
+  TestPredictionWithLesserFeatures("cpu_predictor");
+}
 }  // namespace xgboost

tests/cpp/predictor/test_gpu_predictor.cu

@@ -160,5 +160,8 @@ TEST(GPUPredictor, MGPU_InplacePredict) {  // NOLINT
                dmlc::Error);
 }
 
+TEST(GpuPredictor, LesserFeatures) {
+  TestPredictionWithLesserFeatures("gpu_predictor");
+}
 }  // namespace predictor
 }  // namespace xgboost

tests/cpp/predictor/test_predictor.cc

@@ -134,4 +134,44 @@ void TestInplacePrediction(dmlc::any x, std::string predictor,
   learner->SetParam("gpu_id", "-1");
   learner->Configure();
 }
+
+void TestPredictionWithLesserFeatures(std::string predictor_name) {
+  size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
+  auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
+  auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
+  std::unique_ptr<Learner> learner{Learner::Create({m_train})};
+
+  for (size_t i = 0; i < kIters; ++i) {
+    learner->UpdateOneIter(i, m_train);
+  }
+
+  HostDeviceVector<float> prediction;
+  learner->SetParam("predictor", predictor_name);
+  learner->Configure();
+  Json config{Object()};
+  learner->SaveConfig(&config);
+  ASSERT_EQ(get<String>(config["learner"]["gradient_booster"]["gbtree_train_param"]["predictor"]), predictor_name);
+
+  learner->Predict(m_test, false, &prediction);
+  ASSERT_EQ(prediction.Size(), kRows);
+
+  auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
+  ASSERT_THROW({learner->Predict(m_invalid, false, &prediction);}, dmlc::Error);
+
+#if defined(XGBOOST_USE_CUDA)
+  HostDeviceVector<float> from_cpu;
+  learner->SetParam("predictor", "cpu_predictor");
+  learner->Predict(m_test, false, &from_cpu);
+
+  HostDeviceVector<float> from_cuda;
+  learner->SetParam("predictor", "gpu_predictor");
+  learner->Predict(m_test, false, &from_cuda);
+
+  auto const& h_cpu = from_cpu.ConstHostVector();
+  auto const& h_gpu = from_cuda.ConstHostVector();
+  for (size_t i = 0; i < h_cpu.size(); ++i) {
+    ASSERT_NEAR(h_cpu[i], h_gpu[i], kRtEps);
+  }
+#endif  // defined(XGBOOST_USE_CUDA)
+}
 }  // namespace xgboost

tests/cpp/predictor/test_predictor.h

@@ -59,6 +59,8 @@ void TestTrainingPrediction(size_t rows, size_t bins, std::string tree_method,
 void TestInplacePrediction(dmlc::any x, std::string predictor,
                            bst_row_t rows, bst_feature_t cols,
                            int32_t device = -1);
+
+void TestPredictionWithLesserFeatures(std::string preditor_name);
 }  // namespace xgboost
 
 #endif  // XGBOOST_TEST_PREDICTOR_H_

tests/python/test_cli.py

@@ -6,6 +6,7 @@
 import subprocess
 import numpy
 import json
+import testing as tm
 
 
 class TestCLI(unittest.TestCase):
@@ -28,22 +29,20 @@ class TestCLI(unittest.TestCase):
 eval[test] = {data_path}
 '''
 
-    curdir = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
-    project_root = os.path.normpath(
-        os.path.join(curdir, os.path.pardir, os.path.pardir))
+    PROJECT_ROOT = tm.PROJECT_ROOT
 
     def get_exe(self):
         if platform.system() == 'Windows':
             exe = 'xgboost.exe'
         else:
             exe = 'xgboost'
-        exe = os.path.join(self.project_root, exe)
+        exe = os.path.join(self.PROJECT_ROOT, exe)
         assert os.path.exists(exe)
         return exe
 
     def test_cli_model(self):
         data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
-            root=self.project_root)
+            root=self.PROJECT_ROOT)
         exe = self.get_exe()
         seed = 1994
 
@@ -128,7 +127,7 @@ def test_cli_help(self):
     def test_cli_model_json(self):
         exe = self.get_exe()
         data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
-            root=self.project_root)
+            root=self.PROJECT_ROOT)
         seed = 1994
 
         with tempfile.TemporaryDirectory() as tmpdir:

tests/python/test_demos.py

@@ -117,3 +117,18 @@ def test_aft_demo():
 # gamma regression is not tested as it requires running a R script first.
 # aft viz is not tested due to ploting is not controled
 # aft tunning is not tested due to extra dependency.
+
+
+def test_cli_regression_demo():
+    reg_dir = os.path.join(DEMO_DIR, 'regression')
+    script = os.path.join(reg_dir, 'mapfeat.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd, cwd=reg_dir)
+
+    script = os.path.join(reg_dir, 'mknfold.py')
+    cmd = ['python', script, 'machine.txt', '1']
+    subprocess.check_call(cmd, cwd=reg_dir)
+
+    exe = os.path.join(tm.PROJECT_ROOT, 'xgboost')
+    conf = os.path.join(reg_dir, 'machine.conf')
+    subprocess.check_call([exe, conf], cwd=reg_dir)

tests/python/testing.py

@@ -216,3 +216,8 @@ def _dataset_and_weight(draw):
 
 def non_increasing(L, tolerance=1e-4):
     return all((y - x) < tolerance for x, y in zip(L, L[1:]))
+
+
+CURDIR = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
+PROJECT_ROOT = os.path.normpath(
+    os.path.join(CURDIR, os.path.pardir, os.path.pardir))