prep for 0.8.16

chainladder/core/base.py

@@ -256,7 +256,7 @@ 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) / (365/12)).round('1d').dt.days
+        return ((valuation - origin) / (365.25/12)).round('1d').dt.days
 
 
     @staticmethod

chainladder/core/pandas.py

@@ -389,7 +389,6 @@ def add_groupby_agg_func(cls, k, v):
 
     def agg_func(self, *args, **kwargs):
         from chainladder.utils import concat
-
         xp = self.obj.get_array_module()
         obj = self.obj.copy()
         auto_sparse = kwargs.pop("auto_sparse", True)

chainladder/core/slice.py

@@ -14,36 +14,22 @@ def __init__(self, obj):
 
     def get_idx(self, idx):
         """ Returns a slice of the original Triangle """
-        from chainladder.core.triangle import Triangle
-        obj = Triangle()
+        obj = self.obj.copy()
         i_idx = _LocBase._contig_slice(idx[0])
         c_idx = _LocBase._contig_slice(idx[1])
         o_idx = _LocBase._contig_slice(idx[2])
         d_idx = _LocBase._contig_slice(idx[3])
         if type(o_idx) != slice or type(d_idx) != slice:
             raise ValueError("Fancy indexing on origin/development is not supported.")
         if type(i_idx) is slice or type(c_idx) is slice:
-            obj.values = self.obj.values[i_idx, c_idx, o_idx, d_idx]
+            obj.values = obj.values[i_idx, c_idx, o_idx, d_idx]
         else:
-            obj.values = self.obj.values[i_idx, :, o_idx, d_idx][:, c_idx, ...]
-        obj.kdims = self.obj.kdims[i_idx]
-        obj.vdims = self.obj.vdims[c_idx]
-        obj.odims = self.obj.odims[o_idx]
-        obj.ddims = self.obj.ddims[d_idx]
-        obj.iloc = Ilocation(obj) 
-        obj.loc = Location(obj)
-        obj.origin_grain = self.obj.origin_grain
-        obj.development_grain = self.obj.development_grain
-        obj.key_labels = self.obj.key_labels
-        obj.origin_close = self.obj.origin_close
-        obj.is_pattern = self.obj.is_pattern
-        obj.is_cumulative = self.obj.is_cumulative
-        obj.virtual_columns = self.obj.virtual_columns
-        obj.array_backend = self.obj.array_backend
-        obj.valuation_date = self.obj.valuation_date
-        obj.valuation_date = np.minimum(obj.valuation.max(), self.obj.valuation_date)
-        for t in self.obj.subtriangles + ['w_'] if hasattr(self.obj, 'w_') else []:
-            setattr(obj, t, getattr(self.obj, t))
+            obj.values = obj.values[i_idx, :, o_idx, d_idx][:, c_idx, ...]
+        obj.kdims = obj.kdims[i_idx]
+        obj.vdims = obj.vdims[c_idx]
+        obj.odims, obj.ddims = obj.odims[o_idx], obj.ddims[d_idx]
+        obj.iloc, obj.loc = Ilocation(obj), Location(obj)
+        obj.valuation_date = np.minimum(obj.valuation.max(), obj.valuation_date)
         return obj
 
     @staticmethod

chainladder/development/development.py

@@ -43,6 +43,12 @@ class Development(DevelopmentBase):
         The minimum number of link ratio(s) required for LDF calculation
     drop_valuation: str or list of str (default = None)
         Drops specific valuation periods. str must be date convertible.
+    fillna: float, (default = None)
+        Used to fill in zero or nan values of an triangle with some non-zero
+        amount.  When an link-ratio has zero as its denominator, it is automatically
+        excluded from the ``ldf_`` calculation.  For the specific case of 'volume'
+        averaging in a deterministic method, this may be reasonable.  For all other
+        averages and stochastic methods, this assumption should be avoided.
     groupby:
         An option to group levels of the triangle index together for the purposes
         of estimating patterns.  If omitted, each level of the triangle
@@ -115,7 +121,10 @@ def fit(self, X, y=None, sample_weight=None):
         # Triangle must be cumulative and in "development" mode
         obj = self._set_fit_groups(X).incr_to_cum().val_to_dev().copy()
         xp = obj.get_array_module()
-        tri_array = num_to_nan(obj.values.copy())
+        if self.fillna:
+            tri_array = num_to_nan((obj + self.fillna).values)
+        else:
+            tri_array = num_to_nan(obj.values.copy())
         average_ = self._validate_assumption(X, self.average, axis=3)[... , :X.shape[3]-1]
         self.average_ = average_.flatten()
         n_periods_ = self._validate_assumption(X, self.n_periods, axis=3)[... , :X.shape[3]-1]

docs/library/releases.md

@@ -1,6 +1,22 @@
 # {octicon}`megaphone` Releases & Changelog
 
 ## Version 0.8
+### Version 0.8.16
+
+Release Date: Jun 17, 2023
+
+**What's Changed**
+* fix for [\#409](https://github.com/casact/chainladder-python/issues/409)  
+* fix for [\#411](https://github.com/casact/chainladder-python/issues/411) 
+* fix for [\#438](https://github.com/casact/chainladder-python/issues/438) 
+
+## New Contributors
+* [@MatthewCaseres](https://github.com/MatthewCaseres) made their first contribution.
+* [@andrejakobsen](https://github.com/andrejakobsen) made their first contribution.
+
+
+
+
 
 ### Version 0.8.15