Add high level tests for categorical data. (#6179)

* Fix unique.

python-package/xgboost/data.py

@@ -160,6 +160,7 @@ def _is_pandas_df(data):
         return False
     return isinstance(data, pd.DataFrame)
 
+
 def _is_modin_df(data):
     try:
         import modin.pandas as pd
@@ -188,11 +189,11 @@ def _transform_pandas_df(data, enable_categorical,
                          feature_names=None, feature_types=None,
                          meta=None, meta_type=None):
     from pandas import MultiIndex, Int64Index
-    from pandas.api.types import is_sparse, is_categorical
+    from pandas.api.types import is_sparse, is_categorical_dtype
 
     data_dtypes = data.dtypes
     if not all(dtype.name in _pandas_dtype_mapper or is_sparse(dtype) or
-               (is_categorical(dtype) and enable_categorical)
+               (is_categorical_dtype(dtype) and enable_categorical)
                for dtype in data_dtypes):
         bad_fields = [
             str(data.columns[i]) for i, dtype in enumerate(data_dtypes)
@@ -220,7 +221,7 @@ def _transform_pandas_df(data, enable_categorical,
             if is_sparse(dtype):
                 feature_types.append(_pandas_dtype_mapper[
                     dtype.subtype.name])
-            elif is_categorical(dtype) and enable_categorical:
+            elif is_categorical_dtype(dtype) and enable_categorical:
                 feature_types.append('categorical')
             else:
                 feature_types.append(_pandas_dtype_mapper[dtype.name])

src/common/hist_util.cu

@@ -131,42 +131,50 @@ struct IsCatOp {
 void RemoveDuplicatedCategories(
     int32_t device, MetaInfo const &info, Span<bst_row_t> d_cuts_ptr,
     dh::device_vector<Entry> *p_sorted_entries,
-    dh::caching_device_vector<size_t> const &column_sizes_scan) {
+    dh::caching_device_vector<size_t>* p_column_sizes_scan) {
   auto d_feature_types = info.feature_types.ConstDeviceSpan();
+  auto& column_sizes_scan = *p_column_sizes_scan;
   if (!info.feature_types.Empty() &&
       thrust::any_of(dh::tbegin(d_feature_types), dh::tend(d_feature_types),
                      IsCatOp{})) {
     auto& sorted_entries = *p_sorted_entries;
     // Removing duplicated entries in categorical features.
     dh::caching_device_vector<size_t> new_column_scan(column_sizes_scan.size());
-    dh::SegmentedUnique(column_sizes_scan.data().get(),
+    dh::SegmentedUnique(
-                        column_sizes_scan.data().get() +
+        column_sizes_scan.data().get(),
-                            column_sizes_scan.size(),
+        column_sizes_scan.data().get() + column_sizes_scan.size(),
-                        sorted_entries.begin(), sorted_entries.end(),
+        sorted_entries.begin(), sorted_entries.end(),
-                        new_column_scan.data().get(), sorted_entries.begin(),
+        new_column_scan.data().get(), sorted_entries.begin(),
-                        [=] __device__(Entry const &l, Entry const &r) {
+        [=] __device__(Entry const &l, Entry const &r) {
-                          if (l.index == r.index) {
+          if (l.index == r.index) {
-                            if (IsCat(d_feature_types, l.index)) {
+            if (IsCat(d_feature_types, l.index)) {
-                              return l.fvalue == r.fvalue;
+              return l.fvalue == r.fvalue;
-                            }
+            }
-                          }
+          }
-                          return false;
+          return false;
-                        });
+        });
 
     // Renew the column scan and cut scan based on categorical data.
+    auto d_old_column_sizes_scan = dh::ToSpan(column_sizes_scan);
     dh::caching_device_vector<SketchContainer::OffsetT> new_cuts_size(
         info.num_col_ + 1);
     auto d_new_cuts_size = dh::ToSpan(new_cuts_size);
     auto d_new_columns_ptr = dh::ToSpan(new_column_scan);
     CHECK_EQ(new_column_scan.size(), new_cuts_size.size());
-    dh::LaunchN(device, new_column_scan.size() - 1, [=] __device__(size_t idx) {
+    dh::LaunchN(device, new_column_scan.size(), [=] __device__(size_t idx) {
+      d_old_column_sizes_scan[idx] = d_new_columns_ptr[idx];
+      if (idx == d_new_columns_ptr.size() - 1) {
+        return;
+      }
       if (IsCat(d_feature_types, idx)) {
+        // Cut size is the same as number of categories in input.
         d_new_cuts_size[idx] =
             d_new_columns_ptr[idx + 1] - d_new_columns_ptr[idx];
       } else {
         d_new_cuts_size[idx] = d_cuts_ptr[idx] - d_cuts_ptr[idx];
       }
     });
+    // Turn size into ptr.
     thrust::exclusive_scan(thrust::device, new_cuts_size.cbegin(),
                            new_cuts_size.cend(), d_cuts_ptr.data());
   }
@@ -197,7 +205,8 @@ void ProcessBatch(int device, MetaInfo const &info, const SparsePage &page,
                              &cuts_ptr, &column_sizes_scan);
   auto d_cuts_ptr = cuts_ptr.DeviceSpan();
   detail::RemoveDuplicatedCategories(device, info, d_cuts_ptr, &sorted_entries,
-                                     column_sizes_scan);
+                                     &column_sizes_scan);
+
   auto const& h_cuts_ptr = cuts_ptr.ConstHostVector();
   CHECK_EQ(d_cuts_ptr.size(), column_sizes_scan.size());
 

src/tree/tree_model.cc

@@ -801,7 +801,8 @@ void RegTree::LoadCategoricalSplit(Json const& in) {
       size_t size = max_cat == std::numeric_limits<bst_cat_t>::min()
                         ? 0
                         : common::KCatBitField::ComputeStorageSize(max_cat);
-      std::vector<uint32_t> cat_bits_storage(size);
+      size = size == 0 ? 1 : size;
+      std::vector<uint32_t> cat_bits_storage(size, 0);
       common::CatBitField cat_bits{common::Span<uint32_t>(cat_bits_storage)};
       for (auto j = j_begin; j < j_end; ++j) {
         cat_bits.Set(common::AsCat(get<Integer const>(categories[j])));
@@ -818,7 +819,7 @@ void RegTree::LoadCategoricalSplit(Json const& in) {
       if (cnt == categories_nodes.size()) {
         last_cat_node = -1;
       } else {
-        last_cat_node = get<Integer const>(categories_nodes[++cnt]);
+        last_cat_node = get<Integer const>(categories_nodes[cnt]);
       }
     } else {
       split_categories_segments_[nidx].beg = categories.size();

tests/python-gpu/test_gpu_updaters.py

@@ -41,6 +41,52 @@ class TestGPUUpdaters:
         note(result)
         assert tm.non_increasing(result['train'][dataset.metric])
 
+    def run_categorical_basic(self, cat, onehot, label, rounds):
+        by_etl_results = {}
+        by_builtin_results = {}
+
+        parameters = {'tree_method': 'gpu_hist',
+                      'predictor': 'gpu_predictor',
+                      'enable_experimental_json_serialization': True}
+
+        m = xgb.DMatrix(onehot, label, enable_categorical=True)
+        xgb.train(parameters, m,
+                  num_boost_round=rounds,
+                  evals=[(m, 'Train')], evals_result=by_etl_results)
+
+        m = xgb.DMatrix(cat, label, enable_categorical=True)
+        xgb.train(parameters, m,
+                  num_boost_round=rounds,
+                  evals=[(m, 'Train')], evals_result=by_builtin_results)
+        np.testing.assert_allclose(
+            np.array(by_etl_results['Train']['rmse']),
+            np.array(by_builtin_results['Train']['rmse']),
+            rtol=1e-3)
+        assert tm.non_increasing(by_builtin_results['Train']['rmse'])
+
+    @given(strategies.integers(10, 400), strategies.integers(5, 10),
+           strategies.integers(1, 5), strategies.integers(4, 8))
+    @settings(deadline=None)
+    @pytest.mark.skipif(**tm.no_pandas())
+    def test_categorical(self, rows, cols, rounds, cats):
+        import pandas as pd
+        rng = np.random.RandomState(1994)
+
+        pd_dict = {}
+        for i in range(cols):
+            c = rng.randint(low=0, high=cats+1, size=rows)
+            pd_dict[str(i)] = pd.Series(c, dtype=np.int64)
+
+        df = pd.DataFrame(pd_dict)
+        label = df.iloc[:, 0]
+        for i in range(0, cols-1):
+            label += df.iloc[:, i]
+        label += 1
+        df = df.astype('category')
+        x = pd.get_dummies(df)
+
+        self.run_categorical_basic(df, x, label, rounds)
+
     @pytest.mark.skipif(**tm.no_cupy())
     @given(parameter_strategy, strategies.integers(1, 20),
            tm.dataset_strategy)