Add back support for scipy.sparse.coo_matrix (#6162)

python-package/xgboost/data.py

@@ -82,6 +82,15 @@ def _from_scipy_csc(data, missing, feature_names, feature_types):
     return handle, feature_names, feature_types
 
 
+def _is_scipy_coo(data):
+    try:
+        import scipy
+    except ImportError:
+        scipy = None
+        return False
+    return isinstance(data, scipy.sparse.coo_matrix)
+
+
 def _is_numpy_array(data):
     return isinstance(data, (np.ndarray, np.matrix))
 
@@ -504,6 +513,8 @@ def dispatch_data_backend(data, missing, threads,
         return _from_scipy_csr(data, missing, feature_names, feature_types)
     if _is_scipy_csc(data):
         return _from_scipy_csc(data, missing, feature_names, feature_types)
+    if _is_scipy_coo(data):
+        return _from_scipy_csr(data.tocsr(), missing, feature_names, feature_types)
     if _is_numpy_array(data):
         return _from_numpy_array(data, missing, threads, feature_names,
                                  feature_types)

tests/python/test_dmatrix.py

@@ -76,6 +76,15 @@ def test_csc(self):
         assert dtrain.num_row() == 3
         assert dtrain.num_col() == 3
 
+    def test_coo(self):
+        row = np.array([0, 2, 2, 0, 1, 2])
+        col = np.array([0, 0, 1, 2, 2, 2])
+        data = np.array([1, 2, 3, 4, 5, 6])
+        X = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
+        dtrain = xgb.DMatrix(X)
+        assert dtrain.num_row() == 3
+        assert dtrain.num_col() == 3
+
     def test_np_view(self):
         # Sliced Float32 array
         y = np.array([12, 34, 56], np.float32)[::2]