Sparse fixture

chainladder/core/tests/test_grain.py

@@ -6,7 +6,7 @@
 
 
 def test_grain(qtr):
-    #this is a dense only test, as grain() follows auto_sparse
+    #this test is dense only in practice, since grain() applies auto_sparse, which is True by default
     actual = qtr.iloc[0, 0].grain("OYDY")
     nan = np.nan
     expected = np.array(

chainladder/core/tests/test_triangle.py

@@ -440,8 +440,8 @@ def test_auto_sparse_disabled_returns_self(prism_convert: Triangle) -> None:
 
     Parameters
     ----------
-    prism : Triangle
-        The prism sample data set Triangle.
+    prism_convert : Triangle
+        The prism sample data set Triangle, reduced for test speed.
 
     Returns
     -------

chainladder/development/tests/test_development.py

@@ -22,7 +22,7 @@ def __init__(self,dev):
 
     def fit(self, X, y: None = None, sample_weight: None = None):
         if hasattr(X,'age_to_age'):
-            #following precedent _set_fit_groups() from DevelopmentBase
+            #following precedent set by _set_fit_groups() from DevelopmentBase to force triangle to be dense
             backend = "numpy" if X.array_backend in ["sparse", "numpy"] else "cupy"
             obj = X.set_backend(backend)
             super().fit(obj.incr_to_cum().age_to_age)

chainladder/methods/mack.py

@@ -494,6 +494,7 @@ def summary_(self):
         # This might be better as a dataframe
         obj = self.ultimate_.copy()
         backend = obj.array_backend
+        # forcing these four triangles to the same backend
         cols = (
             self.X_.latest_diagonal.set_backend(backend).values,
             self.ibnr_.set_backend(backend).values,

chainladder/utils/tests/test_sparse.py

@@ -4,9 +4,11 @@
     array,
     floor,
     COO,
-    where
+    where,
+    nanquantile
 )
 
+from sparse import all as sparse_all
 
 def test_array_from_list_default_fill_value() -> None:
     """
@@ -111,3 +113,26 @@ def test_floor_returns_copy() -> None:
     assert result is not a
     np.testing.assert_array_equal(result.todense(), [1.0, 2.0, -1.0])
     np.testing.assert_array_equal(a.todense(), [1.2, 2.7, -0.3])
+
+def test_1D_nanquantile() -> None:
+    """
+    Checks that nanquantile performs in 1D special case.
+
+    Returns
+    -------
+    None
+    """
+    a = COO(np.array([1,2,3,4]))
+    assert nanquantile(a,0.5) == 2.5
+    assert sparse_all(nanquantile(a,0.5,keepdims = True) == COO(np.array([2.5])))
+
+def test_keepdims_nanquantile() -> None:
+    """
+    Checks that nanquantile keeps dimension when instructed.
+
+    Returns
+    -------
+    None
+    """
+    a = COO(np.array([[1,2,3,4],[3,4,5,6]]))
+    assert sparse_all(nanquantile(a,0.5,keepdims = True) == COO(np.array([[2,3,4,5]])))