Use hypothesis (#5759)

* Use hypothesis

* Allow int64 array interface for groups

* Add packages to Windows CI

* Add to travis

* Make sure device index is set correctly

* Fix dask-cudf test

* appveyor

Jenkinsfile-win64

@@ -113,7 +113,7 @@ def TestWin64CPU() {
     """
     echo "Installing Python dependencies..."
     bat """
-     conda activate && conda upgrade scikit-learn pandas numpy
+     conda activate && conda install -y hypothesis && conda upgrade scikit-learn pandas numpy hypothesis
     """
     echo "Running Python tests..."
     bat "conda activate && python -m pytest -v -s --fulltrace tests\\python"
@@ -138,7 +138,7 @@ def TestWin64GPU(args) {
     """
     echo "Installing Python dependencies..."
     bat """
-     conda activate && conda upgrade scikit-learn pandas numpy
+     conda activate && conda install -y hypothesis && conda upgrade scikit-learn pandas numpy hypothesis
     """
     echo "Running Python tests..."
     bat """

appveyor.yml

@@ -44,7 +44,7 @@ install:
     - if /i "%DO_PYTHON%" == "on" (
         conda config --set always_yes true &&
         conda update -q conda &&
-        conda install -y numpy scipy pandas matplotlib pytest scikit-learn graphviz python-graphviz
+        conda install -y numpy scipy pandas matplotlib pytest scikit-learn graphviz python-graphviz hypothesis
       )
     - set PATH=C:\Miniconda3-x64\Library\bin\graphviz;%PATH%
     # R: based on https://github.com/krlmlr/r-appveyor

src/data/data.cu

@@ -34,6 +34,30 @@ void CopyInfoImpl(ArrayInterface column, HostDeviceVector<float>* out) {
   });
 }
 
+void CopyGroupInfoImpl(ArrayInterface column, std::vector<bst_group_t>* out) {
+  CHECK(column.type[1] == 'i' || column.type[1] == 'u')
+      << "Expected integer metainfo";
+  auto SetDeviceToPtr = [](void* ptr) {
+    cudaPointerAttributes attr;
+    dh::safe_cuda(cudaPointerGetAttributes(&attr, ptr));
+    int32_t ptr_device = attr.device;
+    dh::safe_cuda(cudaSetDevice(ptr_device));
+    return ptr_device;
+  };
+  auto ptr_device = SetDeviceToPtr(column.data);
+  dh::TemporaryArray<bst_group_t> temp(column.num_rows);
+  auto d_tmp = temp.data();
+
+  dh::LaunchN(ptr_device, column.num_rows, [=] __device__(size_t idx) {
+    d_tmp[idx] = column.GetElement(idx);
+  });
+  auto length = column.num_rows;
+  out->resize(length + 1);
+  out->at(0) = 0;
+  thrust::copy(temp.data(), temp.data() + length, out->begin() + 1);
+  std::partial_sum(out->begin(), out->end(), out->begin());
+}
+
 void MetaInfo::SetInfo(const char * c_key, std::string const& interface_str) {
   Json j_interface = Json::Load({interface_str.c_str(), interface_str.size()});
   auto const& j_arr = get<Array>(j_interface);
@@ -53,16 +77,7 @@ void MetaInfo::SetInfo(const char * c_key, std::string const& interface_str) {
   } else if (key == "base_margin") {
     CopyInfoImpl(array_interface, &base_margin_);
   } else if (key == "group") {
-    // Ranking is not performed on device.
+    CopyGroupInfoImpl(array_interface, &group_ptr_);
-    thrust::device_ptr<uint32_t> p_src{
-        reinterpret_cast<uint32_t*>(array_interface.data)};
-
-    auto length = array_interface.num_rows;
-    group_ptr_.resize(length + 1);
-    group_ptr_[0] = 0;
-    thrust::copy(p_src, p_src + length, group_ptr_.begin() + 1);
-    std::partial_sum(group_ptr_.begin(), group_ptr_.end(), group_ptr_.begin());
-
     return;
   } else {
     LOG(FATAL) << "Unknown metainfo: " << key;

tests/ci_build/Dockerfile.cpu

@@ -22,7 +22,7 @@ ENV GOSU_VERSION 1.10
 # Install Python packages in default env
 RUN \
     pip install pyyaml cpplint pylint astroid sphinx numpy scipy pandas matplotlib sh \
-    		recommonmark guzzle_sphinx_theme mock breathe graphviz \
+    		recommonmark guzzle_sphinx_theme mock breathe graphviz hypothesis\
 		pytest scikit-learn wheel kubernetes urllib3 jsonschema boto3 && \
     pip install https://h2o-release.s3.amazonaws.com/datatable/stable/datatable-0.7.0/datatable-0.7.0-cp37-cp37m-linux_x86_64.whl && \
     pip install "dask[complete]"

tests/ci_build/Dockerfile.cudf

@@ -19,7 +19,7 @@ ENV PATH=/opt/python/bin:$PATH
 RUN \
     conda create -n cudf_test -c rapidsai -c nvidia -c conda-forge -c defaults \
         python=3.7 cudf cudatoolkit=$CUDA_VERSION dask dask-cuda dask-cudf cupy \
-        numpy pytest scipy scikit-learn pandas matplotlib wheel python-kubernetes urllib3 graphviz
+        numpy pytest scipy scikit-learn pandas matplotlib wheel python-kubernetes urllib3 graphviz hypothesis
 
 ENV GOSU_VERSION 1.10
 

tests/ci_build/Dockerfile.gpu

@@ -18,7 +18,7 @@ ENV PATH=/opt/python/bin:$PATH
 RUN \
     conda create -n gpu_test -c rapidsai -c nvidia -c conda-forge -c defaults \
         python=3.7 dask dask-cuda numpy pytest scipy  scikit-learn pandas \
-        matplotlib wheel python-kubernetes urllib3 graphviz
+        matplotlib wheel python-kubernetes urllib3 graphviz hypothesis
 
 ENV GOSU_VERSION 1.10
 

tests/cpp/data/test_metainfo.cu

@@ -21,7 +21,7 @@ std::string PrepareData(std::string typestr, thrust::device_vector<T>* out, cons
 
   std::vector<Json> j_shape {Json(Integer(static_cast<Integer::Int>(kRows)))};
   column["shape"] = Array(j_shape);
-  column["strides"] = Array(std::vector<Json>{Json(Integer(static_cast<Integer::Int>(4)))});
+  column["strides"] = Array(std::vector<Json>{Json(Integer(static_cast<Integer::Int>(sizeof(T))))});
   column["version"] = Integer(static_cast<Integer::Int>(1));
   column["typestr"] = String(typestr);
 
@@ -78,16 +78,32 @@ TEST(MetaInfo, FromInterface) {
 
 TEST(MetaInfo, Group) {
   cudaSetDevice(0);
-  thrust::device_vector<uint32_t> d_data;
-  std::string str = PrepareData<uint32_t>("<u4", &d_data);
 
   MetaInfo info;
 
-  info.SetInfo("group", str.c_str());
+  thrust::device_vector<uint32_t> d_uint;
-  auto const& h_group = info.group_ptr_;
+  std::string uint_str = PrepareData<uint32_t>("<u4", &d_uint);
-  ASSERT_EQ(h_group.size(), d_data.size() + 1);
+  info.SetInfo("group", uint_str.c_str());
+  auto& h_group = info.group_ptr_;
+  ASSERT_EQ(h_group.size(), d_uint.size() + 1);
   for (size_t i = 1; i < h_group.size(); ++i) {
-    ASSERT_EQ(h_group[i], d_data[i-1] + h_group[i-1]) << "i: " << i;
+    ASSERT_EQ(h_group[i], d_uint[i - 1] + h_group[i - 1]) << "i: " << i;
   }
+
+  thrust::device_vector<int64_t> d_int64;
+  std::string int_str = PrepareData<int64_t>("<i8", &d_int64);
+  info = MetaInfo();
+  info.SetInfo("group", int_str.c_str());
+  h_group = info.group_ptr_;
+  ASSERT_EQ(h_group.size(), d_uint.size() + 1);
+  for (size_t i = 1; i < h_group.size(); ++i) {
+    ASSERT_EQ(h_group[i], d_uint[i - 1] + h_group[i - 1]) << "i: " << i;
+  }
+
+  // Incorrect type
+  thrust::device_vector<float> d_float;
+  std::string float_str = PrepareData<float>("<f4", &d_float);
+  info = MetaInfo();
+  EXPECT_ANY_THROW(info.SetInfo("group", float_str.c_str()));
 }
 }  // namespace xgboost

tests/python-gpu/test_gpu_linear.py

@@ -1,30 +1,50 @@
 import sys
-import pytest
+from hypothesis import strategies, given, settings, assume
-import unittest
+import xgboost as xgb
-
+sys.path.append("tests/python")
-sys.path.append('tests/python/')
+import testing as tm
-import test_linear  # noqa: E402
-import testing as tm  # noqa: E402
 
 
-class TestGPULinear(unittest.TestCase):
+parameter_strategy = strategies.fixed_dictionaries({
-    datasets = ["Boston", "Digits", "Cancer", "Sparse regression"]
+    'booster': strategies.just('gblinear'),
-    common_param = {
+    'eta': strategies.floats(0.01, 0.25),
-        'booster': ['gblinear'],
+    'tolerance': strategies.floats(1e-5, 1e-2),
-        'updater': ['gpu_coord_descent'],
+    'nthread': strategies.integers(1, 4),
-        'eta': [0.5],
+    'feature_selector': strategies.sampled_from(['cyclic', 'shuffle',
-        'top_k': [10],
+                                                 'greedy', 'thrifty']),
-        'tolerance': [1e-5],
+    'top_k': strategies.integers(1, 10),
-        'alpha': [.1],
+})
-        'lambda': [0.005],
-        'coordinate_selection': ['cyclic', 'random', 'greedy']}
 
-    @pytest.mark.skipif(**tm.no_sklearn())
+def train_result(param, dmat, num_rounds):
-    def test_gpu_coordinate(self):
+    result = {}
-        parameters = self.common_param.copy()
+    xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
-        parameters['gpu_id'] = [0]
+              evals_result=result)
-        for param in test_linear.parameter_combinations(parameters):
+    return result
-            results = test_linear.run_suite(
+
-                param, 100, self.datasets, scale_features=True)
+
-            test_linear.assert_regression_result(results, 1e-2)
+class TestGPULinear:
-            test_linear.assert_classification_result(results)
+    @given(parameter_strategy, strategies.integers(10, 50),
+           tm.dataset_strategy)
+    @settings(deadline=None)
+    def test_gpu_coordinate(self, param, num_rounds, dataset):
+        assume(len(dataset.y) > 0)
+        param['updater'] = 'gpu_coord_descent'
+        param = dataset.set_params(param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
+        assert tm.non_increasing(result)
+
+    # Loss is not guaranteed to always decrease because of regularisation parameters
+    # We test a weaker condition that the loss has not increased between the first and last
+    # iteration
+    @given(parameter_strategy, strategies.integers(10, 50),
+           tm.dataset_strategy, strategies.floats(1e-5, 2.0),
+           strategies.floats(1e-5, 2.0))
+    @settings(deadline=None)
+    def test_gpu_coordinate_regularised(self, param, num_rounds, dataset, alpha, lambd):
+        assume(len(dataset.y) > 0)
+        param['updater'] = 'gpu_coord_descent'
+        param['alpha'] = alpha
+        param['lambda'] = lambd
+        param = dataset.set_params(param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
+        assert tm.non_increasing([result[0], result[-1]])

tests/python-gpu/test_gpu_pickling.py

@@ -4,9 +4,13 @@ import unittest
 import numpy as np
 import subprocess
 import os
+import sys
 import json
 import pytest
 
+sys.path.append("tests/python")
+import testing as tm
+
 import xgboost as xgb
 from xgboost import XGBClassifier
 
@@ -90,7 +94,6 @@ class TestPickling(unittest.TestCase):
         )
         status = subprocess.call(args, env=env)
         assert status == 0
-
         os.remove(model_path)
 
     def test_pickled_predictor(self):

tests/python-gpu/test_gpu_prediction.py

@@ -158,10 +158,10 @@ class TestGPUPredict(unittest.TestCase):
         rows = 1000
         cols = 10
         rng = np.random.RandomState(1994)
+        cp.cuda.runtime.setDevice(0)
         X = rng.randn(rows, cols)
         X = pd.DataFrame(X)
         y = rng.randn(rows)
-
         X = cudf.from_pandas(X)
 
         dtrain = xgb.DMatrix(X, y)

tests/python-gpu/test_gpu_updaters.py

@@ -1,74 +1,71 @@
 import numpy as np
 import sys
-import unittest
 import pytest
 import xgboost as xgb
+from hypothesis import given, strategies, assume, settings, note
 
 sys.path.append("tests/python")
 import testing as tm
-from regression_test_utilities import run_suite, parameter_combinations, \
+
-    assert_results_non_increasing
+parameter_strategy = strategies.fixed_dictionaries({
+    'max_depth': strategies.integers(0, 11),
+    'max_leaves': strategies.integers(0, 256),
+    'max_bin': strategies.integers(2, 1024),
+    'grow_policy': strategies.sampled_from(['lossguide', 'depthwise']),
+    'single_precision_histogram': strategies.booleans(),
+    'min_child_weight': strategies.floats(0.5, 2.0),
+    'seed': strategies.integers(0, 10),
+    # We cannot enable subsampling as the training loss can increase
+    # 'subsample': strategies.floats(0.5, 1.0),
+    'colsample_bytree': strategies.floats(0.5, 1.0),
+    'colsample_bylevel': strategies.floats(0.5, 1.0),
+}).filter(lambda x: (x['max_depth'] > 0 or x['max_leaves'] > 0) and (
+    x['max_depth'] > 0 or x['grow_policy'] == 'lossguide'))
 
 
-def assert_gpu_results(cpu_results, gpu_results):
+def train_result(param, dmat, num_rounds):
-    for cpu_res, gpu_res in zip(cpu_results, gpu_results):
+    result = {}
-        # Check final eval result roughly equivalent
+    xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
-        assert np.allclose(cpu_res["eval"][-1],
+              evals_result=result)
-                           gpu_res["eval"][-1], 1e-1, 1e-1)
+    return result
 
 
-datasets = ["Boston", "Cancer", "Digits", "Sparse regression",
+class TestGPUUpdaters:
-            "Sparse regression with weights", "Small weights regression"]
+    @given(parameter_strategy, strategies.integers(1, 20),
-
+           tm.dataset_strategy)
-test_param = parameter_combinations({
+    @settings(deadline=None)
-    'gpu_id': [0],
+    def test_gpu_hist(self, param, num_rounds, dataset):
-    'max_depth': [2, 8],
+        param['tree_method'] = 'gpu_hist'
-    'max_leaves': [255, 4],
+        param = dataset.set_params(param)
-    'max_bin': [4, 256],
+        result = train_result(param, dataset.get_dmat(), num_rounds)
-    'grow_policy': ['lossguide'],
+        note(result)
-    'single_precision_histogram': [True],
+        assert tm.non_increasing(result['train'][dataset.metric])
-    'min_child_weight': [0],
-    'lambda': [0]})
-
-
-class TestGPU(unittest.TestCase):
-    def test_gpu_hist(self):
-        for param in test_param:
-            param['tree_method'] = 'gpu_hist'
-            gpu_results = run_suite(param, select_datasets=datasets)
-            assert_results_non_increasing(gpu_results, 1e-2)
-            param['tree_method'] = 'hist'
-            cpu_results = run_suite(param, select_datasets=datasets)
-            assert_gpu_results(cpu_results, gpu_results)
 
     @pytest.mark.skipif(**tm.no_cupy())
-    def test_gpu_hist_device_dmatrix(self):
+    @given(parameter_strategy, strategies.integers(1, 20),
-        # DeviceDMatrix does not currently accept sparse formats
+           tm.dataset_strategy)
-        device_dmatrix_datasets = ["Boston", "Cancer", "Digits"]
+    @settings(deadline=None)
-        for param in test_param:
+    def test_gpu_hist_device_dmatrix(self, param, num_rounds, dataset):
-            param['tree_method'] = 'gpu_hist'
+        # We cannot handle empty dataset yet
+        assume(len(dataset.y) > 0)
+        param['tree_method'] = 'gpu_hist'
+        param = dataset.set_params(param)
+        result = train_result(param, dataset.get_device_dmat(), num_rounds)
+        note(result)
+        assert tm.non_increasing(result['train'][dataset.metric])
 
-            gpu_results_device_dmatrix = run_suite(param, select_datasets=device_dmatrix_datasets,
+    @given(parameter_strategy, strategies.integers(1, 20),
-                                                   DMatrixT=xgb.DeviceQuantileDMatrix,
+           tm.dataset_strategy)
-                                                   dmatrix_params={'max_bin': param['max_bin']})
+    @settings(deadline=None)
-            assert_results_non_increasing(gpu_results_device_dmatrix, 1e-2)
+    def test_external_memory(self, param, num_rounds, dataset):
-            gpu_results = run_suite(param, select_datasets=device_dmatrix_datasets)
+        # We cannot handle empty dataset yet
-            assert_gpu_results(gpu_results, gpu_results_device_dmatrix)
+        assume(len(dataset.y) > 0)
+        param['tree_method'] = 'gpu_hist'
+        param = dataset.set_params(param)
+        external_result = train_result(param, dataset.get_external_dmat(), num_rounds)
+        assert tm.non_increasing(external_result['train'][dataset.metric])
 
-    # NOTE(rongou): Because the `Boston` dataset is too small, this only tests external memory mode
+    def test_empty_dmatrix_prediction(self):
-    # with a single page. To test multiple pages, set DMatrix::kPageSize to, say, 1024.
-    def test_external_memory(self):
-        for param in reversed(test_param):
-            param['tree_method'] = 'gpu_hist'
-            param['gpu_page_size'] = 1024
-            gpu_results = run_suite(param, select_datasets=["Boston"])
-            assert_results_non_increasing(gpu_results, 1e-2)
-            ext_mem_results = run_suite(param, select_datasets=["Boston External Memory"])
-            assert_results_non_increasing(ext_mem_results, 1e-2)
-            assert_gpu_results(gpu_results, ext_mem_results)
-            break
-
-    def test_with_empty_dmatrix(self):
         # FIXME(trivialfis): This should be done with all updaters
         kRows = 0
         kCols = 100
@@ -94,13 +91,10 @@ class TestGPU(unittest.TestCase):
         np.testing.assert_allclose(predictions, 0.5, 1e-6)
 
     @pytest.mark.mgpu
-    def test_specified_gpu_id_gpu_update(self):
+    @given(tm.dataset_strategy, strategies.integers(0, 10))
-        variable_param = {'gpu_id': [1],
+    @settings(deadline=None, max_examples=10)
-                          'max_depth': [8],
+    def test_specified_gpu_id_gpu_update(self, dataset, gpu_id):
-                          'max_leaves': [255, 4],
+        param = {'tree_method': 'gpu_hist', 'gpu_id': gpu_id}
-                          'max_bin': [2, 64],
+        param = dataset.set_params(param)
-                          'grow_policy': ['lossguide'],
+        result = train_result(param, dataset.get_dmat(), 10)
-                          'tree_method': ['gpu_hist']}
+        assert tm.non_increasing(result['train'][dataset.metric])
-        for param in parameter_combinations(variable_param):
-            gpu_results = run_suite(param, select_datasets=datasets)
-            assert_results_non_increasing(gpu_results, 1e-2)

tests/python-gpu/test_gpu_with_dask.py

@@ -31,7 +31,8 @@ class TestDistributedGPU(unittest.TestCase):
     def test_dask_dataframe(self):
         with LocalCUDACluster() as cluster:
             with Client(cluster) as client:
-                import cupy
+                import cupy as cp
+                cp.cuda.runtime.setDevice(0)
                 X, y = generate_array()
 
                 X = dd.from_dask_array(X)
@@ -59,8 +60,8 @@ class TestDistributedGPU(unittest.TestCase):
                 single_node = out['booster'].predict(
                     xgboost.DMatrix(X.compute()))
 
-                cupy.testing.assert_allclose(single_node, predictions)
+                cp.testing.assert_allclose(single_node, predictions)
-                cupy.testing.assert_allclose(single_node, series_predictions)
+                np.testing.assert_allclose(single_node, series_predictions.to_array())
 
                 predt = dxgb.predict(client, out, X)
                 assert isinstance(predt, dd.Series)
@@ -73,7 +74,7 @@ class TestDistributedGPU(unittest.TestCase):
                     is_df,
                     meta=dd.utils.make_meta({'prediction': 'f4'}))
 
-                cupy.testing.assert_allclose(
+                cp.testing.assert_allclose(
                     predt.values.compute(), single_node)
 
     @pytest.mark.skipif(**tm.no_cupy())
@@ -81,11 +82,12 @@ class TestDistributedGPU(unittest.TestCase):
     def test_dask_array(self):
         with LocalCUDACluster() as cluster:
             with Client(cluster) as client:
-                import cupy
+                import cupy as cp
+                cp.cuda.runtime.setDevice(0)
                 X, y = generate_array()
 
-                X = X.map_blocks(cupy.asarray)
+                X = X.map_blocks(cp.asarray)
-                y = y.map_blocks(cupy.asarray)
+                y = y.map_blocks(cp.asarray)
                 dtrain = dxgb.DaskDMatrix(client, X, y)
                 out = dxgb.train(client, {'tree_method': 'gpu_hist'},
                                  dtrain=dtrain,
@@ -97,11 +99,11 @@ class TestDistributedGPU(unittest.TestCase):
                 single_node = out['booster'].predict(
                     xgboost.DMatrix(X.compute()))
                 np.testing.assert_allclose(single_node, from_dmatrix)
-                device = cupy.cuda.runtime.getDevice()
+                device = cp.cuda.runtime.getDevice()
                 assert device == inplace_predictions.device.id
-                single_node = cupy.array(single_node)
+                single_node = cp.array(single_node)
                 assert device == single_node.device.id
-                cupy.testing.assert_allclose(
+                cp.testing.assert_allclose(
                     single_node,
                     inplace_predictions)
 

tests/python/regression_test_utilities.py

@@ -1,198 +0,0 @@
-import glob
-import itertools as it
-import numpy as np
-import os
-import sys
-import xgboost as xgb
-from joblib import Memory
-memory = Memory('./cachedir', verbose=0)
-
-try:
-    from sklearn import datasets
-    from sklearn.preprocessing import scale
-except ImportError:
-    None
-
-
-class Dataset:
-    def __init__(self, name, get_dataset, objective, metric,
-                 has_weights=False, use_external_memory=False):
-        self.name = name
-        self.objective = objective
-        self.metric = metric
-        if has_weights:
-            self.X, self.y, self.w = get_dataset()
-        else:
-            self.X, self.y = get_dataset()
-            self.w = None
-        self.use_external_memory = use_external_memory
-
-    def __str__(self):
-        a = 'name: {name}\nobjective:{objective}, metric:{metric}, '.format(
-            name=self.name,
-            objective=self.objective,
-            metric=self.metric)
-        b = 'external memory:{use_external_memory}\n'.format(
-            use_external_memory=self.use_external_memory
-        )
-        return a + b
-
-    def __repr__(self):
-        return self.__str__()
-
-
-@memory.cache
-def get_boston():
-    data = datasets.load_boston()
-    return data.data, data.target
-
-
-@memory.cache
-def get_digits():
-    data = datasets.load_digits()
-    return data.data, data.target
-
-
-@memory.cache
-def get_cancer():
-    data = datasets.load_breast_cancer()
-    return data.data, data.target
-
-
-@memory.cache
-def get_sparse():
-    rng = np.random.RandomState(199)
-    n = 2000
-    sparsity = 0.75
-    X, y = datasets.make_regression(n, random_state=rng)
-    flag = rng.binomial(1, sparsity, X.shape)
-    for i in range(X.shape[0]):
-        for j in range(X.shape[1]):
-            if flag[i, j]:
-                X[i, j] = 0.0
-    from scipy import sparse
-    X = sparse.csr_matrix(X)
-    return X, y
-
-
-def get_sparse_weights():
-    return get_weights_regression(1, 10)
-
-
-def get_small_weights():
-    return get_weights_regression(1e-6, 1e-5)
-
-
-@memory.cache
-def get_weights_regression(min_weight, max_weight):
-    rng = np.random.RandomState(199)
-    n = 2000
-    sparsity = 0.25
-    X, y = datasets.make_regression(n, random_state=rng)
-    flag = rng.binomial(1, sparsity, X.shape)
-    for i in range(X.shape[0]):
-        for j in range(X.shape[1]):
-            if flag[i, j]:
-                X[i, j] = np.nan
-    w = rng.uniform(min_weight, max_weight, n)
-    return X, y, w
-
-
-def train_dataset(dataset, param_in, num_rounds=10, scale_features=False, DMatrixT=xgb.DMatrix,
-                  dmatrix_params={}):
-    param = param_in.copy()
-    param["objective"] = dataset.objective
-    if dataset.objective == "multi:softmax":
-        param["num_class"] = int(np.max(dataset.y) + 1)
-    param["eval_metric"] = dataset.metric
-
-    if scale_features:
-        X = scale(dataset.X, with_mean=isinstance(dataset.X, np.ndarray))
-    else:
-        X = dataset.X
-
-    if dataset.use_external_memory:
-        np.savetxt('tmptmp_1234.csv', np.hstack((dataset.y.reshape(len(dataset.y), 1), X)),
-                   delimiter=',')
-        dtrain = DMatrixT('tmptmp_1234.csv?format=csv&label_column=0#tmptmp_',
-                          weight=dataset.w)
-    elif DMatrixT is xgb.DeviceQuantileDMatrix:
-        import cupy as cp
-        dtrain = DMatrixT(cp.array(X), cp.array(dataset.y),
-                          weight=None if dataset.w is None else cp.array(dataset.w),
-                          **dmatrix_params)
-    else:
-        dtrain = DMatrixT(X, dataset.y, weight=dataset.w, **dmatrix_params)
-
-    print("Training on dataset: " + dataset.name, file=sys.stderr)
-    print("Using parameters: " + str(param), file=sys.stderr)
-    res = {}
-    bst = xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')],
-                    evals_result=res, verbose_eval=False)
-
-    # Free the booster and dmatrix so we can delete temporary files
-    bst_copy = bst.copy()
-    del bst
-    del dtrain
-
-    # Cleanup temporary files
-    if dataset.use_external_memory:
-        for f in glob.glob("tmptmp_*"):
-            os.remove(f)
-
-    return {"dataset": dataset, "bst": bst_copy, "param": param.copy(),
-            "eval": res['train'][dataset.metric]}
-
-
-def parameter_combinations(variable_param):
-    """
-    Enumerate all possible combinations of parameters
-    """
-    result = []
-    names = sorted(variable_param)
-    combinations = it.product(*(variable_param[Name] for Name in names))
-    for set in combinations:
-        param = {}
-        for i, name in enumerate(names):
-            param[name] = set[i]
-        result.append(param)
-    return result
-
-
-def run_suite(param, num_rounds=10, select_datasets=None, scale_features=False,
-              DMatrixT=xgb.DMatrix, dmatrix_params={}):
-    """
-    Run the given parameters on a range of datasets. Objective and eval metric will be
-    automatically set
-    """
-    datasets = [
-        Dataset("Boston", get_boston, "reg:squarederror", "rmse"),
-        Dataset("Digits", get_digits, "multi:softmax", "mlogloss"),
-        Dataset("Cancer", get_cancer, "binary:logistic", "logloss"),
-        Dataset("Sparse regression", get_sparse, "reg:squarederror", "rmse"),
-        Dataset("Sparse regression with weights", get_sparse_weights,
-                "reg:squarederror", "rmse", has_weights=True),
-        Dataset("Small weights regression", get_small_weights,
-                "reg:squarederror", "rmse", has_weights=True),
-        Dataset("Boston External Memory", get_boston,
-                "reg:squarederror", "rmse",
-                use_external_memory=True)
-    ]
-
-    results = [
-    ]
-    for d in datasets:
-        if select_datasets is None or d.name in select_datasets:
-            results.append(
-                train_dataset(d, param, num_rounds=num_rounds, scale_features=scale_features,
-                              DMatrixT=DMatrixT, dmatrix_params=dmatrix_params))
-    return results
-
-
-def non_increasing(L, tolerance):
-    return all((y - x) < tolerance for x, y in zip(L, L[1:]))
-
-
-def assert_results_non_increasing(results, tolerance=1e-5):
-    for r in results:
-        assert non_increasing(r['eval'], tolerance), r

tests/python/test_linear.py

@@ -1,87 +1,72 @@
-import numpy as np
 import testing as tm
-import unittest
+from hypothesis import strategies, given, settings, note
-import pytest
-
 import xgboost as xgb
 
-try:
+parameter_strategy = strategies.fixed_dictionaries({
-    from sklearn.linear_model import ElasticNet
+    'booster': strategies.just('gblinear'),
-    from sklearn.preprocessing import scale
+    'eta': strategies.floats(0.01, 0.25),
-    from regression_test_utilities import run_suite, parameter_combinations
+    'tolerance': strategies.floats(1e-5, 1e-2),
-except ImportError:
+    'nthread': strategies.integers(1, 4),
-    None
+})
+
+coord_strategy = strategies.fixed_dictionaries({
+    'feature_selector': strategies.sampled_from(['cyclic', 'shuffle',
+                                                 'greedy', 'thrifty']),
+    'top_k': strategies.integers(1, 10),
+})
 
 
-def is_float(s):
+def train_result(param, dmat, num_rounds):
-    try:
+    result = {}
-        float(s)
+    xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
-        return 1
+              evals_result=result)
-    except ValueError:
+    return result
-        return 0
 
 
-def xgb_get_weights(bst):
+class TestLinear:
-    return np.array([float(s) for s in bst.get_dump()[0].split() if
+    @given(parameter_strategy, strategies.integers(10, 50),
-                     is_float(s)])
+           tm.dataset_strategy, coord_strategy)
+    @settings(deadline=None)
+    def test_coordinate(self, param, num_rounds, dataset, coord_param):
+        param['updater'] = 'coord_descent'
+        param.update(coord_param)
+        param = dataset.set_params(param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
+        assert tm.non_increasing(result)
 
+    # Loss is not guaranteed to always decrease because of regularisation parameters
+    # We test a weaker condition that the loss has not increased between the first and last
+    # iteration
+    @given(parameter_strategy, strategies.integers(10, 50),
+           tm.dataset_strategy, coord_strategy, strategies.floats(1e-5, 2.0),
+           strategies.floats(1e-5, 2.0))
+    @settings(deadline=None)
+    def test_coordinate_regularised(self, param, num_rounds, dataset, coord_param, alpha, lambd):
+        param['updater'] = 'coord_descent'
+        param['alpha'] = alpha
+        param['lambda'] = lambd
+        param.update(coord_param)
+        param = dataset.set_params(param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
+        assert tm.non_increasing([result[0], result[-1]])
 
-def assert_regression_result(results, tol):
+    @given(parameter_strategy, strategies.integers(10, 50),
-    regression_results = [r for r in results if
+           tm.dataset_strategy)
-                          r["param"]["objective"] == "reg:squarederror"]
+    @settings(deadline=None)
-    for res in regression_results:
+    def test_shotgun(self, param, num_rounds, dataset):
-        X = scale(res["dataset"].X,
+        param['updater'] = 'shotgun'
-                  with_mean=isinstance(res["dataset"].X, np.ndarray))
+        param = dataset.set_params(param)
-        y = res["dataset"].y
+        result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
-        reg_alpha = res["param"]["alpha"]
+        assert tm.non_increasing(result)
-        reg_lambda = res["param"]["lambda"]
-        pred = res["bst"].predict(xgb.DMatrix(X))
-        weights = xgb_get_weights(res["bst"])[1:]
-        enet = ElasticNet(alpha=reg_alpha + reg_lambda,
-                          l1_ratio=reg_alpha / (reg_alpha + reg_lambda))
-        enet.fit(X, y)
-        enet_pred = enet.predict(X)
-        assert np.isclose(weights, enet.coef_, rtol=tol,
-                          atol=tol).all(), (weights, enet.coef_)
-        assert np.isclose(enet_pred, pred, rtol=tol, atol=tol).all(), (
-            res["dataset"].name, enet_pred[:5], pred[:5])
 
-
+    @given(parameter_strategy, strategies.integers(10, 50),
-# TODO: More robust classification tests
+           tm.dataset_strategy, strategies.floats(1e-5, 2.0),
-def assert_classification_result(results):
+           strategies.floats(1e-5, 2.0))
-    classification_results = [r for r in results if
+    @settings(deadline=None)
-                              r["param"]["objective"] != "reg:squarederror"]
+    def test_shotgun_regularised(self, param, num_rounds, dataset, alpha, lambd):
-    for res in classification_results:
+        param['updater'] = 'shotgun'
-        # Check accuracy  is reasonable
+        param['alpha'] = alpha
-        assert res["eval"][-1] < 2.0, (res["dataset"].name, res["eval"][-1])
+        param['lambda'] = lambd
-
+        param = dataset.set_params(param)
-
+        result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
-class TestLinear(unittest.TestCase):
+        assert tm.non_increasing([result[0], result[-1]])
-
-    datasets = ["Boston", "Digits", "Cancer", "Sparse regression",
-                "Boston External Memory"]
-
-    @pytest.mark.skipif(**tm.no_sklearn())
-    def test_coordinate(self):
-        variable_param = {'booster': ['gblinear'], 'updater':
-                          ['coord_descent'], 'eta': [0.5], 'top_k':
-                          [10], 'tolerance': [1e-5], 'nthread': [2],
-                          'alpha': [.005, .1], 'lambda': [.005],
-                          'feature_selector': ['cyclic', 'shuffle',
-                                               'greedy', 'thrifty']}
-        for param in parameter_combinations(variable_param):
-            results = run_suite(param, 150, self.datasets, scale_features=True)
-            assert_regression_result(results, 1e-2)
-            assert_classification_result(results)
-
-    @pytest.mark.skipif(**tm.no_sklearn())
-    def test_shotgun(self):
-        variable_param = {'booster': ['gblinear'], 'updater':
-                          ['shotgun'], 'eta': [0.5], 'top_k': [10],
-                          'tolerance': [1e-5], 'nthread': [2],
-                          'alpha': [.005, .1], 'lambda': [.005],
-                          'feature_selector': ['cyclic', 'shuffle']}
-        for param in parameter_combinations(variable_param):
-            results = run_suite(param, 200, self.datasets, True)
-            assert_regression_result(results, 1e-2)
-            assert_classification_result(results)

tests/python/test_updaters.py

@@ -3,28 +3,57 @@ import unittest
 import pytest
 import xgboost as xgb
 import numpy as np
+from hypothesis import given, strategies, settings, note
 
-try:
+exact_parameter_strategy = strategies.fixed_dictionaries({
-    from regression_test_utilities import run_suite, parameter_combinations, \
+    'nthread': strategies.integers(1, 4),
-        assert_results_non_increasing
+    'max_depth': strategies.integers(1, 11),
-except ImportError:
+    'min_child_weight': strategies.floats(0.5, 2.0),
-    None
+    'alpha': strategies.floats(0.0, 2.0),
+    'lambda': strategies.floats(1e-5, 2.0),
+    'eta': strategies.floats(0.01, 0.5),
+    'gamma': strategies.floats(0.0, 2.0),
+    'seed': strategies.integers(0, 10),
+    # We cannot enable subsampling as the training loss can increase
+    # 'subsample': strategies.floats(0.5, 1.0),
+    'colsample_bytree': strategies.floats(0.5, 1.0),
+    'colsample_bylevel': strategies.floats(0.5, 1.0),
+})
+
+hist_parameter_strategy = strategies.fixed_dictionaries({
+    'max_depth': strategies.integers(1, 11),
+    'max_leaves': strategies.integers(0, 1024),
+    'max_bin': strategies.integers(2, 512),
+    'grow_policy': strategies.sampled_from(['lossguide', 'depthwise']),
+}).filter(lambda x: (x['max_depth'] > 0 or x['max_leaves'] > 0) and (
+    x['max_depth'] > 0 or x['grow_policy'] == 'lossguide'))
 
 
-class TestUpdaters(unittest.TestCase):
+def train_result(param, dmat, num_rounds):
-    @pytest.mark.skipif(**tm.no_sklearn())
+    result = {}
-    def test_histmaker(self):
+    xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
-        variable_param = {'updater': ['grow_histmaker'], 'max_depth': [2, 8]}
+              evals_result=result)
-        for param in parameter_combinations(variable_param):
+    return result
-            result = run_suite(param)
-            assert_results_non_increasing(result, 1e-2)
 
-    @pytest.mark.skipif(**tm.no_sklearn())
+
-    def test_colmaker(self):
+class TestTreeMethod(unittest.TestCase):
-        variable_param = {'updater': ['grow_colmaker'], 'max_depth': [2, 8]}
+    @given(exact_parameter_strategy, strategies.integers(1, 20),
-        for param in parameter_combinations(variable_param):
+           tm.dataset_strategy)
-            result = run_suite(param)
+    @settings(deadline=None)
-            assert_results_non_increasing(result, 1e-2)
+    def test_exact(self, param, num_rounds, dataset):
+        param['tree_method'] = 'exact'
+        param = dataset.set_params(param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)
+        assert tm.non_increasing(result['train'][dataset.metric])
+
+    @given(exact_parameter_strategy, strategies.integers(1, 20),
+           tm.dataset_strategy)
+    @settings(deadline=None)
+    def test_approx(self, param, num_rounds, dataset):
+        param['tree_method'] = 'approx'
+        param = dataset.set_params(param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)
+        assert tm.non_increasing(result['train'][dataset.metric], 1e-3)
 
     @pytest.mark.skipif(**tm.no_sklearn())
     def test_pruner(self):
@@ -50,19 +79,18 @@ class TestUpdaters(unittest.TestCase):
         # Second prune should not change the tree
         assert after_prune == second_prune
 
-    @pytest.mark.skipif(**tm.no_sklearn())
+    @given(exact_parameter_strategy, hist_parameter_strategy, strategies.integers(1, 20),
-    def test_fast_histmaker(self):
+           tm.dataset_strategy)
-        variable_param = {'tree_method': ['hist'],
+    @settings(deadline=None)
-                          'max_depth': [2, 8],
+    def test_hist(self, param, hist_param, num_rounds, dataset):
-                          'max_bin': [2, 256],
+        param['tree_method'] = 'hist'
-                          'grow_policy': ['depthwise', 'lossguide'],
+        param = dataset.set_params(param)
-                          'max_leaves': [64, 0],
+        param.update(hist_param)
-                          'verbosity': [0],
+        result = train_result(param, dataset.get_dmat(), num_rounds)
-                          'single_precision_histogram': [True, False]}
+        note(result)
-        for param in parameter_combinations(variable_param):
+        assert tm.non_increasing(result['train'][dataset.metric])
-            result = run_suite(param)
-            assert_results_non_increasing(result, 1e-2)
 
+    def test_hist_categorical(self):
         # hist must be same as exact on all-categorial data
         dpath = 'demo/data/'
         ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
@@ -87,7 +115,7 @@ class TestUpdaters(unittest.TestCase):
         assert hist_res['test']['auc'] == exact_res['test']['auc']
 
     @pytest.mark.skipif(**tm.no_sklearn())
-    def test_fast_histmaker_degenerate_case(self):
+    def test_hist_degenerate_case(self):
         # Test a degenerate case where the quantile sketcher won't return any
         # quantile points for a particular feature (the second feature in
         # this example). Source: https://github.com/dmlc/xgboost/issues/2943

tests/python/testing.py

@@ -1,6 +1,19 @@
 # coding: utf-8
 from xgboost.compat import SKLEARN_INSTALLED, PANDAS_INSTALLED
 from xgboost.compat import DASK_INSTALLED
+from hypothesis import strategies
+from hypothesis.extra.numpy import arrays
+from joblib import Memory
+from sklearn import datasets
+import xgboost as xgb
+import numpy as np
+
+try:
+    import cupy as cp
+except ImportError:
+    cp = None
+
+memory = Memory('./cachedir', verbose=0)
 
 
 def no_sklearn():
@@ -39,7 +52,7 @@ def no_matplotlib():
 def no_dask_cuda():
     reason = 'dask_cuda is not installed.'
     try:
-        import dask_cuda as _   # noqa
+        import dask_cuda as _  # noqa
         return {'condition': False, 'reason': reason}
     except ImportError:
         return {'condition': True, 'reason': reason}
@@ -47,7 +60,7 @@ def no_dask_cuda():
 
 def no_cudf():
     try:
-        import cudf             # noqa
+        import cudf  # noqa
         CUDF_INSTALLED = True
     except ImportError:
         CUDF_INSTALLED = False
@@ -59,7 +72,7 @@ def no_cudf():
 def no_cupy():
     reason = 'cupy is not installed.'
     try:
-        import cupy as _   # noqa
+        import cupy as _  # noqa
         return {'condition': False, 'reason': reason}
     except ImportError:
         return {'condition': True, 'reason': reason}
@@ -68,7 +81,7 @@ def no_cupy():
 def no_dask_cudf():
     reason = 'dask_cudf is not installed.'
     try:
-        import dask_cudf as _   # noqa
+        import dask_cudf as _  # noqa
         return {'condition': False, 'reason': reason}
     except ImportError:
         return {'condition': True, 'reason': reason}
@@ -77,7 +90,101 @@ def no_dask_cudf():
 def no_json_schema():
     reason = 'jsonschema is not installed'
     try:
-        import jsonschema       # noqa
+        import jsonschema  # noqa
         return {'condition': False, 'reason': reason}
     except ImportError:
         return {'condition': True, 'reason': reason}
+
+
+# Contains a dataset in numpy format as well as the relevant objective and metric
+class TestDataset:
+    def __init__(self, name, get_dataset, objective, metric
+                 ):
+        self.name = name
+        self.objective = objective
+        self.metric = metric
+        self.X, self.y = get_dataset()
+        self.w = None
+
+    def set_params(self, params_in):
+        params_in['objective'] = self.objective
+        params_in['eval_metric'] = self.metric
+        if self.objective == "multi:softmax":
+            params_in["num_class"] = int(np.max(self.y) + 1)
+        return params_in
+
+    def get_dmat(self):
+        return xgb.DMatrix(self.X, self.y, self.w)
+
+    def get_device_dmat(self):
+        w = None if self.w is None else cp.array(self.w)
+        X = cp.array(self.X, dtype=np.float32)
+        y = cp.array(self.y, dtype=np.float32)
+        return xgb.DeviceQuantileDMatrix(X, y, w)
+
+    def get_external_dmat(self):
+        np.savetxt('tmptmp_1234.csv', np.hstack((self.y.reshape(len(self.y), 1), self.X)),
+                   delimiter=',')
+        return xgb.DMatrix('tmptmp_1234.csv?format=csv&label_column=0#tmptmp_',
+                           weight=self.w)
+
+    def __repr__(self):
+        return self.name
+
+
+@memory.cache
+def get_boston():
+    data = datasets.load_boston()
+    return data.data, data.target
+
+
+@memory.cache
+def get_digits():
+    data = datasets.load_digits()
+    return data.data, data.target
+
+
+@memory.cache
+def get_cancer():
+    data = datasets.load_breast_cancer()
+    return data.data, data.target
+
+
+@memory.cache
+def get_sparse():
+    rng = np.random.RandomState(199)
+    n = 2000
+    sparsity = 0.75
+    X, y = datasets.make_regression(n, random_state=rng)
+    flag = rng.binomial(1, sparsity, X.shape)
+    for i in range(X.shape[0]):
+        for j in range(X.shape[1]):
+            if flag[i, j]:
+                X[i, j] = np.nan
+    return X, y
+
+
+_unweighted_datasets_strategy = strategies.sampled_from(
+    [TestDataset('boston', get_boston, 'reg:squarederror', 'rmse'),
+     TestDataset('digits', get_digits, 'multi:softmax', 'mlogloss'),
+     TestDataset("cancer", get_cancer, "binary:logistic", "logloss"),
+     TestDataset
+     ("sparse", get_sparse, "reg:squarederror", "rmse"),
+     TestDataset("empty", lambda: (np.empty((0, 100)), np.empty(0)), "reg:squarederror",
+                 "rmse")])
+
+
+@strategies.composite
+def _dataset_and_weight(draw):
+    data = draw(_unweighted_datasets_strategy)
+    if draw(strategies.booleans()):
+        data.w = draw(arrays(np.float64, (len(data.y)), elements=strategies.floats(0.1, 2.0)))
+    return data
+
+# A strategy for drawing from a set of example datasets
+# May add random weights to the dataset
+dataset_strategy = _dataset_and_weight()
+
+
+def non_increasing(L, tolerance=1e-4):
+    return all((y - x) < tolerance for x, y in zip(L, L[1:]))

tests/travis/run_test.sh

@@ -39,7 +39,7 @@ if [ ${TASK} == "python_test" ]; then
     # Run unit tests
     cd ..
     python -m pip install graphviz pytest pytest-cov codecov
-    python -m pip install datatable
+    python -m pip install datatable hypothesis
     python -m pip install numpy scipy pandas matplotlib scikit-learn dask[complete]
     python -m pytest -v --fulltrace -s tests/python --cov=python-package/xgboost || exit -1
     codecov