Bump version for release and address #117 mutation

casact · Feb 16, 2021 · 68a1163 · 68a1163
1 parent 87bbb20
commit 68a1163
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Showing 5 changed files with 30 additions and 25 deletions.
diff --git a/README.rst b/README.rst
@@ -54,6 +54,8 @@ Available Estimators
 +------------------------------+------------------+-------------------------+-----------------------+-----------------------+
 | `IncrementalAdditive`_       |                  | `CapeCod`_              |                       |                       |
 +------------------------------+------------------+-------------------------+-----------------------+-----------------------+
+| `CaseOutstanding`_           |                  |                         |                       |                       |
++------------------------------+------------------+-------------------------+-----------------------+-----------------------+
 
 Documentation
 -------------
@@ -79,9 +81,10 @@ code documentation.
 .. _GridSearch: https://chainladder-python.readthedocs.io/en/latest/modules/workflow.html#gridsearch
 .. _IncrementalAdditive: https://chainladder-python.readthedocs.io/en/latest/modules/development.html#incremental-additive
 .. _CapeCod: https://chainladder-python.readthedocs.io/en/latest/modules/methods.html#cape-cod
-.. _ParallelogramOLF: https://chainladder-python.readthedocs.io/en/latest/modules/generated/chainladder.ParallelogramOLF.html#chainladder.ParallelogramOLF
-.. _VotingChainladder: https://chainladder-python.readthedocs.io/en/latest/modules/generated/chainladder.VotingChainladder.html#chainladder.VotingChainladder
-.. _Trend: https://chainladder-python.readthedocs.io/en/latest/modules/generated/chainladder.Trend.html#chainladder.Trend
+.. _ParallelogramOLF: https://chainladder-python.readthedocs.io/en/latest/modules/adjustments.html#parallelogramolf
+.. _VotingChainladder: https://chainladder-python.readthedocs.io/en/latest/modules/workflow.html#votingchainladder
+.. _Trend: https://chainladder-python.readthedocs.io/en/latest/modules/adjustments.html#trend
+.. _CaseOutstanding: https://chainladder-python.readthedocs.io/en/latest/modules/development.html#caseoutstanding
 .. _Documentation: https://chainladder-python.readthedocs.io/en/latest/
 
 Getting Started Tutorials

diff --git a/chainladder/__init__.py b/chainladder/__init__.py
@@ -22,4 +22,4 @@ def auto_sparse(auto_sparse=True):
 from chainladder.methods import *  # noqa (API Import)
 from chainladder.workflow import *  # noqa (API Import)
 
-__version__ = "0.7.13"
+__version__ = "0.8.0"
diff --git a/chainladder/development/outstanding.py b/chainladder/development/outstanding.py
@@ -17,6 +17,8 @@ class CaseOutstanding(DevelopmentBase):
     into comparable multiplicative patterns for usage with the various IBNR
     methods.
 
+    .. versionadded:: 0.8.0
+
     Parameters
     ----------
     paid_to_incurred : tuple or list of tuples

diff --git a/chainladder/workflow/voting.py b/chainladder/workflow/voting.py
@@ -48,10 +48,10 @@ class _BaseChainladderVoting(_BaseVoting, _BaseTriangleEnsemble):
     """
 
     def _broadcast_weights(self, X):
-        if self.weights.ndim == 3:
-            weights = np.repeat(self.weights[np.newaxis, ...], X.shape[1], axis=0)
-        if self.weights.ndim == 4:
-            weights = np.repeat(self.weights[np.newaxis, ...], X.shape[0], axis=0)
+        if self.weights_.ndim == 3:
+            weights = np.repeat(self.weights_[np.newaxis, ...], X.shape[1], axis=0)
+        if self.weights_.ndim == 4:
+            weights = np.repeat(self.weights_[np.newaxis, ...], X.shape[0], axis=0)
         return weights
 
     def _predict(self, X, sample_weight=None):
@@ -62,22 +62,22 @@ def _predict(self, X, sample_weight=None):
     def fit(self, X, y, sample_weight=None):
         """Get common fit operations."""
         names, clfs = self._validate_estimators()
-
-        if self.weights is not None:
-            self.weights = self.weights[..., np.newaxis]
-            if self.weights.shape[-3] != X.shape[2]:
+        self.weights_ = self.weights
+        if self.weights_ is not None:
+            self.weights_ = self.weights_[..., np.newaxis]
+            if self.weights_.shape[-3] != X.shape[2]:
                 raise ValueError('Length of weight arrays do not equal'
                                  f' number of accident periods; found'
-                                 f' {self.weights.shape[-3]} weights'
+                                 f' {self.weights_.shape[-3]} weights'
                                  f' and {X.shape[2]} accident periods.')
-            if self.weights.shape[-2] != len(self.estimators):
+            if self.weights_.shape[-2] != len(self.estimators):
                 raise ValueError('Number of weight arrays does not equal'
                                  f' number of estimators array; '
-                                 f' found {self.weights.shape[-2]} weights'
+                                 f' found {self.weights_.shape[-2]} weights'
                                  f' arrays and {len(self.estimators)}'
                                  ' estimators.')
         else:
-            self.weights = np.ones(X.shape[:3] + (len(self.estimators), 1))
+            self.weights_ = np.ones(X.shape[:3] + (len(self.estimators), 1))
 
         self.estimators_ = Parallel(n_jobs=self.n_jobs)(
                 delayed(_fit_single_estimator)(
@@ -111,7 +111,7 @@ class VotingChainladder(_BaseChainladderVoting, MethodBase):
 
     Read more in the :ref:`User Guide <voting>`.
 
-    .. versionadded:: 0.7.12
+    .. versionadded:: 0.8.0
 
     Parameters
     ----------
@@ -275,20 +275,20 @@ def fit_transform(self, X, y=None, sample_weight=None):
         return self.fit(X, y, sample_weight).transform(X, sample_weight=sample_weight)
 
     def _get_ultimate(self, X, sample_weight=None):
-        if self.weights.ndim < 5:
+        if self.weights_.ndim < 5:
             weights = self._broadcast_weights(X)
-        elif self.weights.ndim == 5:
-            if self.weights.shape[0] != X.shape[0]:
+        elif self.weights_.ndim == 5:
+            if self.weights_.shape[0] != X.shape[0]:
                 raise ValueError('Index length (axis 0) of weights does'
                                  ' not equal index length of X; found'
-                                 f' {self.weights.shape[0]} for weights'
+                                 f' {self.weights_.shape[0]} for weights'
                                  f' and {X.shape[0]} for X.')
-            if self.weights.shape[1] != X.shape[1]:
+            if self.weights_.shape[1] != X.shape[1]:
                 raise ValueError('Column length (axis 1) of weights does'
                                  f' not equal column length of X; found'
-                                 f' {self.weights.shape[1]} for weights'
+                                 f' {self.weights_.shape[1]} for weights'
                                  f' and {X.shape[1]} for X.')
-            weights = self.weights
+            weights = self.weights_
 
         ultimate = sum([
                 est.predict(X, sample_weight).ultimate_ * weights[..., i, :]

diff --git a/setup.py b/setup.py
@@ -14,7 +14,7 @@
 descr = "Chainladder Package - P&C Loss Reserving package "
 name = 'chainladder'
 url = 'https://github.com/casact/chainladder-python'
-version='0.7.13' # Put this in __init__.py
+version='0.8.0' # Put this in __init__.py
 
 data_path = ''
 setup(