Addressed #420 and #491

chainladder/core/display.py

@@ -13,15 +13,15 @@
 
 class TriangleDisplay:
     def __repr__(self):
-        # try:
-        #     self.values
-        # except:
-        #     print("Triangle is empty")
-        #     return
+        try:
+            self.values
+        except:
+            return "Empty Triangle."
 
         if (self.values.shape[0], self.values.shape[1]) == (1, 1):
             data = self._repr_format()
             return data.to_string()
+
         else:
             return self._summary_frame().__repr__()
 
@@ -40,6 +40,11 @@
 
     def _repr_html_(self):
         """Jupyter/Ipython HTML representation"""
+        try:
+            self.values
+        except:
+            return "Triangle is empty."
+
         if (self.values.shape[0], self.values.shape[1]) == (1, 1):
             data = self._repr_format()
             fmt_str = self._get_format_str(data)
@@ -150,7 +155,7 @@
                 )
             output_xnan = re.sub("<td.*nan.*td>", "<td></td>", default_output)
         else:
-            raise ValueError("heatmap only works with single triangles")
+            raise ValueError("heatmap() only works with a single triangle")
         if HTML:
             return HTML(output_xnan)
         elif HTML is None:

chainladder/core/tests/test_display.py

@@ -26,3 +26,16 @@ def test_to_frame(raa):
 
     except:
         assert False
+
+
+def test_labels(xyz):
+    assert (
+        xyz.valuation_date.strftime("%Y-%m-%d %H:%M:%S.%f")
+        == "2008-12-31 23:59:59.999999"
+    )
+    assert xyz.origin_grain == "Y"
+    assert xyz.development_grain == "Y"
+    assert xyz.shape == (1, 5, 11, 11)
+    assert xyz.index_label == ["Total"]
+    assert xyz.columns_label == ["Incurred", "Paid", "Reported", "Closed", "Premium"]
+    assert xyz.origin_label == ["AccidentYear"]

chainladder/core/tests/test_triangle.py

@@ -665,7 +665,7 @@ def test_halfyear_grain():
         {"AccMo": [201409, 201503, 201603], "ValMo": [202203] * 3, "value": [100] * 3}
     )
     assert cl.Triangle(
-        data=data, origin="AccMo", development="ValMo", columns="value"
+        data=data, origin="AccMo", development="ValMo", columns="value", cumulative=True
     ).shape == (1, 1, 16, 1)
 
 
@@ -775,21 +775,18 @@ def test_halfyear_development():
         )
     ) == cl.Triangle
 
-    assert (
-            cl.Triangle(
-                data=pd.DataFrame(data, columns=["origin", "val_date", "idx", "value"]),
-                index="idx",
-                columns="value",
-                origin="origin",
-                development="val_date",
-                cumulative=True,
-            ) ==
-            cl.Triangle(
-                data=df_polars,
-                index="idx",
-                columns="value",
-                origin="origin",
-                development="val_date",
-                cumulative=True,
-            )
-    )
+    assert cl.Triangle(
+        data=pd.DataFrame(data, columns=["origin", "val_date", "idx", "value"]),
+        index="idx",
+        columns="value",
+        origin="origin",
+        development="val_date",
+        cumulative=True,
+    ) == cl.Triangle(
+        data=df_polars,
+        index="idx",
+        columns="value",
+        origin="origin",
+        development="val_date",
+        cumulative=True,
+    )

chainladder/utils/utility_functions.py

@@ -185,14 +185,11 @@ def parallelogram_olf(
     )
 
     rate_changes = pd.Series(np.array(values), np.array(date))
-    # print("rate_changes:\n", rate_changes)
     rate_changes = rate_changes.reindex(date_idx, fill_value=0)
-    # print("rate_changes:\n", rate_changes)
+
     cum_rate_changes = np.cumprod(1 + rate_changes.values)
     cum_rate_changes = pd.Series(cum_rate_changes, rate_changes.index)
-    # print("cum_rate_changes:\n", cum_rate_changes)
     crl = cum_rate_changes[-1]
-    # print("crl:", crl)
 
     cum_avg_rate_non_leaps = cum_rate_changes
     cum_avg_rate_leaps = cum_rate_changes
@@ -216,8 +213,6 @@ def parallelogram_olf(
         cum_avg_rate_leaps = (
             cum_avg_rate_leaps + cum_avg_rate_leaps.shift(1).values
         ) / 2
-    # print("cum_avg_rate_non_leaps\n", cum_avg_rate_non_leaps)
-    # print("cum_avg_rate_leaps\n", cum_avg_rate_leaps)
 
     dropdates_num = {
         "M": 12,

conftest.py

@@ -1,76 +1,86 @@
 import pytest
 import chainladder as cl
 
+
 def pytest_generate_tests(metafunc):
     if "raa" in metafunc.fixturenames:
-        metafunc.parametrize(
-            "raa", ["normal_run", "sparse_only_run"], indirect=True)
+        metafunc.parametrize("raa", ["normal_run", "sparse_only_run"], indirect=True)
     if "qtr" in metafunc.fixturenames:
-        metafunc.parametrize(
-                "qtr", ["normal_run", "sparse_only_run"], indirect=True)
+        metafunc.parametrize("qtr", ["normal_run", "sparse_only_run"], indirect=True)
     if "clrd" in metafunc.fixturenames:
-        metafunc.parametrize(
-                "clrd", ["normal_run", "sparse_only_run"], indirect=True)
+        metafunc.parametrize("clrd", ["normal_run", "sparse_only_run"], indirect=True)
     if "genins" in metafunc.fixturenames:
-        metafunc.parametrize(
-                "genins", ["normal_run", "sparse_only_run"], indirect=True)
+        metafunc.parametrize("genins", ["normal_run", "sparse_only_run"], indirect=True)
     if "prism_dense" in metafunc.fixturenames:
         metafunc.parametrize(
-                "prism_dense", ["normal_run", "sparse_only_run"], indirect=True)
+            "prism_dense", ["normal_run", "sparse_only_run"], indirect=True
+        )
     if "prism" in metafunc.fixturenames:
         metafunc.parametrize("prism", ["normal_run"], indirect=True)
+    if "xyz" in metafunc.fixturenames:
+        metafunc.parametrize("xyz", ["normal_run", "sparse_only_run"], indirect=True)
 
 
 @pytest.fixture
 def raa(request):
     if request.param == "sparse_only_run":
-        cl.options.set_option('ARRAY_BACKEND', 'sparse')
+        cl.options.set_option("ARRAY_BACKEND", "sparse")
     else:
-        cl.options.set_option('ARRAY_BACKEND', 'numpy')
-    return cl.load_sample('raa')
+        cl.options.set_option("ARRAY_BACKEND", "numpy")
+    return cl.load_sample("raa")
 
 
 @pytest.fixture
 def qtr(request):
     if request.param == "sparse_only_run":
-        cl.options.set_option('ARRAY_BACKEND', 'sparse')
+        cl.options.set_option("ARRAY_BACKEND", "sparse")
     else:
-        cl.options.set_option('ARRAY_BACKEND', 'numpy')
-    return cl.load_sample('quarterly')
+        cl.options.set_option("ARRAY_BACKEND", "numpy")
+    return cl.load_sample("quarterly")
 
 
 @pytest.fixture
 def clrd(request):
     if request.param == "sparse_only_run":
-        cl.options.set_option('ARRAY_BACKEND', 'sparse')
+        cl.options.set_option("ARRAY_BACKEND", "sparse")
     else:
-        cl.options.set_option('ARRAY_BACKEND', 'numpy')
-    return cl.load_sample('clrd')
+        cl.options.set_option("ARRAY_BACKEND", "numpy")
+    return cl.load_sample("clrd")
 
 
 @pytest.fixture
 def genins(request):
     if request.param == "sparse_only_run":
-        cl.options.set_option('ARRAY_BACKEND', 'sparse')
+        cl.options.set_option("ARRAY_BACKEND", "sparse")
     else:
-        cl.options.set_option('ARRAY_BACKEND', 'numpy')
-    return cl.load_sample('genins')
+        cl.options.set_option("ARRAY_BACKEND", "numpy")
+    return cl.load_sample("genins")
 
 
 @pytest.fixture
 def prism(request):
-    cl.options.set_option('ARRAY_BACKEND', 'numpy')
-    return cl.load_sample('prism')
+    cl.options.set_option("ARRAY_BACKEND", "numpy")
+    return cl.load_sample("prism")
 
 
 @pytest.fixture
 def prism_dense(request):
     if request.param == "sparse_only_run":
-        cl.options.set_option('ARRAY_BACKEND', 'sparse')
+        cl.options.set_option("ARRAY_BACKEND", "sparse")
+    else:
+        cl.options.set_option("ARRAY_BACKEND", "numpy")
+    return cl.load_sample("prism").sum()
+
+
+@pytest.fixture
+def xyz(request):
+    if request.param == "sparse_only_run":
+        cl.options.set_option("ARRAY_BACKEND", "sparse")
     else:
-        cl.options.set_option('ARRAY_BACKEND', 'numpy')
-    return cl.load_sample('prism').sum()
+        cl.options.set_option("ARRAY_BACKEND", "numpy")
+    return cl.load_sample("xyz")
 
 
 @pytest.fixture
-def atol(): return 1e-4
+def atol():
+    return 1e-4

environment-dev.yaml

@@ -15,6 +15,7 @@ dependencies:
 
   - pandas
   - polars
+  - polars-lts-cpu
   - scikit-learn
   - sparse
   - numba