Disposal

chainladder/adjustments/disposal.py

@@ -1,13 +1,19 @@
 # This Source Code Form is subject to the terms of the Mozilla Public
 # License, v. 2.0. If a copy of the MPL was not distributed with this
 # file, You can obtain one at https://mozilla.org/MPL/2.0/.
+from __future__ import annotations
 
-from chainladder.methods import Chainladder
+from chainladder.methods import Chainladder, MethodBase
 from chainladder.development import DevelopmentBase
 import numpy as np
 import copy
 from chainladder.utils import TriangleWeight, concat
-from chainladder import Triangle
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from chainladder.core import Triangle
+
 
 class DisposalRate(DevelopmentBase):
     """
@@ -192,18 +198,22 @@ def fit(
         """
         if sample_weight is None:
             raise ValueError("sample_weight is required.")
-        #convert to numpy
+        #validate dimensions of sample weight
+        MethodBase().validate_weight(X, sample_weight)
+        #set backeneds to numpy
         if X.array_backend == "sparse":
-            X = X.set_backend("numpy").incr_to_cum()
+            X = X.set_backend("numpy")
         else:
-            X = X.copy().incr_to_cum()
+            X = X.copy()
         if sample_weight.array_backend == "sparse":
             ult = sample_weight.set_backend("numpy")
         else:
             ult = sample_weight.copy()
-        #get backend
+        #calculate disposal rate triangle
         self.xp = X.get_array_module()
+        self.X_ = X.sort_index()
         self.disposal_rate_tri = X / ult.values
+        #get weights for estimation
         tw = TriangleWeight(
             n_periods = self.n_periods,
             drop_high = self.drop_high,
@@ -214,16 +224,17 @@ def fit(
             preserve = self.preserve,
             drop = self.drop
         )
-        if hasattr(X, "w_"):
-            self.w_ = tw.fit(X=self.disposal_rate_tri * X.w_).w_.values
+        if hasattr(self.X_, "w_"):
+            self.w_ = tw.fit(X=self.disposal_rate_tri * self.X_.w_).w_.values
         else:
             self.w_ = tw.fit(X=self.disposal_rate_tri).w_.values
         #calculate factors
-        super().fit(ult.values,X.values,self.w_)
+        super().fit(ult.values,self.X_.values,self.w_)
         #keep attributes
         self.disposal_ = self._param_property(self.disposal_rate_tri,self.params_.slope_[...,0][..., None, :])
-        self.disposal_ = concat((self.disposal_,(X.latest_diagonal*0 + 1).iloc[:,:,0,:].rename("development", [9999])),axis=3)
+        self.disposal_ = concat((self.disposal_,(self.X_.latest_diagonal*0 + 1).iloc[:,:,0,:].rename("development", [9999])),axis=3)
         self.disposal_.is_cumulative = True
+        #pattern multiples from tail and additive adds from head
         self.disposal_.is_pattern = False
         self.incr_disposal_ = self.disposal_.cum_to_incr()
         self.incr_disposal_.is_pattern = True
@@ -253,10 +264,13 @@ def transform(
         if sample_weight is None:
             raise ValueError("sample_weight is required.")
         X_new = copy.deepcopy(X)
+        #validate dimensions of sample weight
+        MethodBase().validate_weight(X, sample_weight)
+        #align backeneds
+        X_new.ultimate_ = sample_weight.set_backend(self.X_.array_backend).latest_diagonal
         X_new.disposal_rate_tri = self.disposal_rate_tri
         X_new.disposal_ = self.disposal_
         X_new.incr_disposal_ = self.incr_disposal_
-        X_new.ultimate_ = sample_weight.latest_diagonal
         ibnr_pct = 1 - X_new.disposal_.align_pattern(X_new.disposal_rate_tri)
         run_off = X_new.incr_disposal_ / ibnr_pct * X_new.ibnr_
         run_off = run_off[run_off.valuation > X_new.valuation_date]

chainladder/methods/base.py

@@ -1,6 +1,8 @@
 # This Source Code Form is subject to the terms of the Mozilla Public
 # License, v. 2.0. If a copy of the MPL was not distributed with this
 # file, You can obtain one at https://mozilla.org/MPL/2.0/.
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import warnings
@@ -10,6 +12,10 @@
 from chainladder.core.io import EstimatorIO
 from chainladder.core.common import Common
 
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from chainladder.core import Triangle
 
 class MethodBase(BaseEstimator, EstimatorIO, Common):
 
@@ -138,7 +144,14 @@ def _include_process_variance(self):
             process_var = None
         return process_var
 
-    def validate_weight(self, X, sample_weight):
+    @staticmethod
+    def validate_weight(
+            X: Triangle,
+            sample_weight: Triangle
+    ) -> None:
+        '''
+        Checks that the a aprior has valid dimensions
+        '''
         if (
             sample_weight
             and X.shape[:-1] != sample_weight.shape[:-1]