Fixes valuation grain bug

chainladder/core/tests/test_triangle.py

@@ -1448,6 +1448,75 @@ def test_single_valuation_date_preserves_exact_date():
     assert triangle.valuation_date == pd.Timestamp(val_date_exp)
     assert triangle.development_grain == 'M'
     assert int(triangle.valuation_date.strftime('%Y%m')) == 202510
+
+
+def test_1d_annual_valuation_date() -> None:
+    year_data = [
+        [1998, 2008, 900000, 890000],
+        [1999, 2008, 1200000, 1170000],
+        [2000, 2008, 1300000, 1265000],
+        [2001, 2008, 1800000, 1600000],
+        [2002, 2008, 1450000, 1200000],
+    ]
+    year_df = pd.DataFrame(
+        data=year_data, columns=["origin", "dev", "revenue", "expense"]
+    )
+    tri = cl.Triangle(
+        data=year_df,
+        origin="origin",
+        development="dev",
+        columns="expense",
+        development_format="%Y",
+        cumulative=True,
+    )
+    assert pd.to_datetime(tri.valuation_date).date() == pd.Timestamp("2008-12-31").date()
+    assert tri.development_grain == "Y"
+
+def test_1d_monthly_valuation_date() -> None:
+    year_data = [
+        [1998, "2008-01", 900000, 890000],
+        [1999, "2008-01", 1200000, 1170000],
+        [2000, "2008-01", 1300000, 1265000],
+        [2001, "2008-01", 1800000, 1600000],
+        [2002, "2008-01", 1450000, 1200000],
+    ]
+    year_df = pd.DataFrame(
+        data=year_data, columns=["origin", "dev", "revenue", "expense"]
+    )
+    tri = cl.Triangle(
+        data=year_df,
+        origin="origin",
+        development="dev",
+        columns="expense",
+        development_format="%Y-%m",
+        cumulative=True,
+    )
+    assert pd.to_datetime(tri.valuation_date).date() == pd.Timestamp("2008-01-31").date()
+    assert tri.development_grain == "M"
+
+def test_1d_monthly_valuation_date_expanded_dev_date() -> None:
+    year_df = pd.DataFrame(
+        {
+            "origin": [1998, 1999, 2000, 2001, 2002],
+            "dev": [2008.0, 2008.0, 2008.0, 2008.0, 2008.0],
+            "expense": [890000, 1170000, 1265000, 1600000, 1200000],
+        }
+    )
+    tri = cl.Triangle(
+        data=year_df,
+        origin="origin",
+        development="dev",
+        columns="expense",
+        development_format="%Y",
+        cumulative=True,
+    )
+    assert pd.to_datetime(tri.valuation_date).date() == pd.Timestamp("2008-12-31").date()
+    assert tri.development_grain == "Y"
+
+def test_friedland_gl_self_insurer_grain() -> None:
+    data_valuation_date = cl.load_sample('friedland_gl_self_insurer').valuation_date
+    assert pd.to_datetime(data_valuation_date).date() == pd.Timestamp("2008-12-31").date()
+
 def test_OXDX_triangle():
 
     for x in [12,6,3,1]:

chainladder/core/triangle.py

@@ -482,7 +482,18 @@ def __init__(
         )
 
         if len(development_date.unique()) == 1:
-            if len(data) == 1 and self.origin_grain.split("-")[0] in ["Y", "A"]:
+            # checks if development is not empty, and if ithas any non-yearly values
+            dev_has_no_month = not development or all(
+                pd.to_numeric(data[col], errors="coerce")
+                .astype("Int64")
+                .astype(str)
+                .str.fullmatch(r"\d{4}")
+                .all()
+                for col in development
+            )
+
+            if len(data) == 1 or dev_has_no_month:
+                # if development has no monthly values, match origin
                 self.development_grain = self.origin_grain
             else:
                 dev_date = pd.to_datetime(development_date.iloc[0])
@@ -1466,10 +1477,11 @@ def _val_dev(self, sign, inplace=False):
         ddims = len(ddims.drop_duplicates())
         if ddims == 1 and sign == -1:
             ddims = len(obj.odims)
-        if obj.values.density > 0 and obj.values.coords[-1].min() < 0:
-            obj.values.coords[-1] = obj.values.coords[-1] - min(
-                obj.values.coords[-1].min(), min_slide
-            )
+        if obj.values.density > 0:
+            if obj.values.coords[-1].min() < 0:
+                obj.values.coords[-1] = obj.values.coords[-1] - min(
+                    obj.values.coords[-1].min(), min_slide
+                )
             ddims = np.max([np.max(obj.values.coords[-1]) + 1, ddims])
         obj.values.shape = tuple(list(obj.shape[:-1]) + [ddims])
         if options.AUTO_SPARSE == False or backend == "cupy":

chainladder/utils/data/_manifest.py

@@ -160,7 +160,6 @@
             "Paid Claims",
         ],
         "cumulative": True,
-        "development_format": "%Y-12-31",
     },
     "friedland_med_mal": {
         "origin": "Accident Year",