497

chainladder/adjustments/parallelogram.py

@@ -36,11 +36,17 @@ class ParallelogramOLF(BaseEstimator, TransformerMixin, EstimatorIO):
     """
 
     def __init__(
-        self, rate_history=None, change_col="", date_col="", vertical_line=False
+        self,
+        rate_history=None,
+        change_col="",
+        date_col="",
+        approximation_grain="M",
+        vertical_line=False,
     ):
         self.rate_history = rate_history
         self.change_col = change_col
         self.date_col = date_col
+        self.approximation_grain = approximation_grain
         self.vertical_line = vertical_line
 
     def fit(self, X, y=None, sample_weight=None):
@@ -77,6 +83,7 @@ def fit(self, X, y=None, sample_weight=None):
             end_date=X.origin[-1].to_timestamp(how="e"),
             grain=X.origin_grain,
             vertical_line=self.vertical_line,
+            approximation_grain=self.approximation_grain,
         )
 
         if len(groups) > 0:
@@ -105,7 +112,7 @@ def fit(self, X, y=None, sample_weight=None):
         return self
 
     def transform(self, X, y=None, sample_weight=None):
-        """ If X and self are of different shapes, align self to X, else
+        """If X and self are of different shapes, align self to X, else
         return self.
 
         Parameters

chainladder/core/base.py

@@ -258,7 +258,7 @@ def _to_datetime(data, fields, period_end=False, format=None):
     def _development_lag(origin, valuation):
         """For tabular format, this will convert the origin/valuation
         difference to a development lag"""
-        return ((valuation - origin) / (365.25/12)).round('1d').dt.days
+        return ((valuation - origin) / (365.25/12)).dt.round('1d').dt.days
 
 
     @staticmethod

chainladder/core/display.py

@@ -13,6 +13,12 @@
 
 class TriangleDisplay:
     def __repr__(self):
+        try:
+            self.values
+        except:
+            print("Triangle is empty")
+            return
+
         if (self.values.shape[0], self.values.shape[1]) == (1, 1):
             data = self._repr_format()
             return data.to_string()
@@ -33,7 +39,7 @@ def _summary_frame(self):
         ).to_frame()
 
     def _repr_html_(self):
-        """ Jupyter/Ipython HTML representation """
+        """Jupyter/Ipython HTML representation"""
         if (self.values.shape[0], self.values.shape[1]) == (1, 1):
             data = self._repr_format()
             fmt_str = self._get_format_str(data)
@@ -66,7 +72,7 @@ def _get_format_str(self, data):
     def _repr_format(self, origin_as_datetime=False):
         out = self.compute().set_backend("numpy").values[0, 0]
         if origin_as_datetime and not self.is_pattern:
-            origin = self.origin.to_timestamp(how='s')
+            origin = self.origin.to_timestamp(how="s")
         else:
             origin = self.origin.copy()
         origin.name = None
@@ -85,7 +91,7 @@ def _repr_format(self, origin_as_datetime=False):
         return pd.DataFrame(out, index=origin, columns=development)
 
     def heatmap(self, cmap="coolwarm", low=0, high=0, axis=0, subset=None):
-        """ Color the background in a gradient according to the data in each
+        """Color the background in a gradient according to the data in each
         column (optionally row). Requires matplotlib
 
         Parameters
@@ -134,7 +140,12 @@ def heatmap(self, cmap="coolwarm", low=0, high=0, axis=0, subset=None):
             else:
                 default_output = (
                     data.style.format(fmt_str)
-                    .background_gradient(cmap=cmap, low=low, high=high, axis=axis,)
+                    .background_gradient(
+                        cmap=cmap,
+                        low=low,
+                        high=high,
+                        axis=axis,
+                    )
                     .render()
                 )
             output_xnan = re.sub("<td.*nan.*td>", "<td></td>", default_output)

chainladder/core/tests/test_display.py

@@ -3,18 +3,18 @@
 
 
 def test_heatmap_render(raa):
-    """ The heatmap method should render correctly given the sample."""
-    return raa.heatmap()
+    """The heatmap method should render correctly given the sample."""
+    assert raa.heatmap()
 
 
-def test_to_frame(raa):
-    try:
-        cl.Chainladder().fit(raa).cdf_.to_frame()
-        cl.Chainladder().fit(raa).cdf_.to_frame(origin_as_datetime=False)
-        cl.Chainladder().fit(raa).cdf_.to_frame(origin_as_datetime=True)
-        cl.Chainladder().fit(raa).ultimate_.to_frame()
-        cl.Chainladder().fit(raa).ultimate_.to_frame(origin_as_datetime=False)
-        cl.Chainladder().fit(raa).ultimate_.to_frame(origin_as_datetime=True)
+def test_empty_triangle():
+    assert cl.Triangle()
+
 
-    except:
-        assert False
+def test_to_frame(raa):
+    assert cl.Chainladder().fit(raa).cdf_.to_frame()
+    assert cl.Chainladder().fit(raa).cdf_.to_frame(origin_as_datetime=False)
+    assert cl.Chainladder().fit(raa).cdf_.to_frame(origin_as_datetime=True)
+    assert cl.Chainladder().fit(raa).ultimate_.to_frame()
+    assert cl.Chainladder().fit(raa).ultimate_.to_frame(origin_as_datetime=False)
+    assert cl.Chainladder().fit(raa).ultimate_.to_frame(origin_as_datetime=True)

chainladder/core/triangle.py

@@ -130,6 +130,9 @@ def __init__(
             data, index, columns, origin, development
         )
 
+        self.columns_label = columns
+        self.origin_label = origin
+
         # Handle any ultimate vectors in triangles separately
         data, ult = self._split_ult(data, index, columns, origin, development)
         # Conform origins and developments to datetimes and determine lowest grains
@@ -172,6 +175,7 @@ def __init__(
         # Deal with labels
         if not index:
             index = ["Total"]
+            self.index_label = index
             data_agg[index[0]] = "Total"
 
         self.kdims, key_idx = self._set_kdims(data_agg, index)
@@ -672,8 +676,8 @@ def grain(self, grain="", trailing=False, inplace=False):
         obj = self.dev_to_val()
         if ograin_new != ograin_old:
             freq = {"Y": "A", "S": "2Q"}.get(ograin_new, ograin_new)
-            if trailing or (obj.origin.freqstr[-3:] != "DEC" and ograin_old != 'M'):
-                origin_period_end = self.origin[-1].strftime("%b").upper()  
+            if trailing or (obj.origin.freqstr[-3:] != "DEC" and ograin_old != "M"):
+                origin_period_end = self.origin[-1].strftime("%b").upper()
             else:
                 origin_period_end = "DEC"
             indices = (
@@ -687,12 +691,16 @@ def grain(self, grain="", trailing=False, inplace=False):
             obj = obj.groupby(groups, axis=2).sum()
             obj.origin_close = origin_period_end
             d_start = pd.Period(
-                obj.valuation[0], 
-                freq=dgrain_old if dgrain_old == 'M' else dgrain_old + obj.origin.freqstr[-4:]
-            ).to_timestamp(how='s')
-            if (len(obj.ddims) > 1 and obj.origin.to_timestamp(how='s')[0] != d_start):
+                obj.valuation[0],
+                freq=dgrain_old
+                if dgrain_old == "M"
+                else dgrain_old + obj.origin.freqstr[-4:],
+            ).to_timestamp(how="s")
+            if len(obj.ddims) > 1 and obj.origin.to_timestamp(how="s")[0] != d_start:
                 addl_ts = (
-                    pd.period_range(obj.odims[0], obj.valuation[0], freq=dgrain_old)[:-1]
+                    pd.period_range(obj.odims[0], obj.valuation[0], freq=dgrain_old)[
+                        :-1
+                    ]
                     .to_timestamp()
                     .values
                 )