Addressing discussion on PR

chainladder/development/development.py

@@ -119,13 +119,14 @@ def fit(self, X, y=None, sample_weight=None):
         from chainladder.utils.utility_functions import num_to_nan
 
         # 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()
+
         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
         ]
@@ -143,17 +144,20 @@ def fit(self, X, y=None, sample_weight=None):
         if hasattr(X, "w_v2_"):
             self.w_v2_ = self._set_weight_func(
                 factor=obj.age_to_age * X.w_v2_,
+                # secondary_rank=obj.iloc[..., :-1, :-1]
             )
         else:
             self.w_v2_ = self._set_weight_func(
                 factor=obj.age_to_age,
+                # secondary_rank=obj.iloc[..., :-1, :-1]
             )
 
         self.w_ = self._assign_n_periods_weight(
             obj, n_periods_
         ) * self._drop_adjustment(obj, link_ratio)
         w = num_to_nan(self.w_ / (x ** (exponent)))
         params = WeightedRegression(axis=2, thru_orig=True, xp=xp).fit(x, y, w)
+
         if self.n_periods != 1:
             params = params.sigma_fill(self.sigma_interpolation)
         else:
@@ -162,11 +166,13 @@ def fit(self, X, y=None, sample_weight=None):
                 "of freedom to support calculation of all regression"
                 " statistics.  Only LDFs have been calculated."
             )
+
         params.std_err_ = xp.nan_to_num(params.std_err_) + xp.nan_to_num(
             (1 - xp.nan_to_num(params.std_err_ * 0 + 1))
             * params.sigma_
             / xp.swapaxes(xp.sqrt(x ** (2 - exponent))[..., 0:1, :], -1, -2)
         )
+
         params = xp.concatenate((params.slope_, params.sigma_, params.std_err_), 3)
         params = xp.swapaxes(params, 2, 3)
         self.ldf_ = self._param_property(obj, params, 0)
@@ -176,6 +182,7 @@ def fit(self, X, y=None, sample_weight=None):
         std = xp.sqrt((1 / num_to_nan(w)) * (self.sigma_**2).values)
         resid = resid / num_to_nan(std)
         self.std_residuals_ = resid[resid.valuation < obj.valuation_date]
+
         return self
 
     def transform(self, X):
@@ -205,7 +212,9 @@ def transform(self, X):
         ]
         for item in triangles:
             setattr(X_new, item, getattr(self, item))
+
         X_new._set_slicers()
+
         return X_new
 
     def _param_property(self, X, params, idx):
@@ -220,4 +229,5 @@ def _param_property(self, X, params, idx):
         obj.is_cumulative = False
         obj.virtual_columns.columns = {}
         obj._set_slicers()
+
         return obj

chainladder/development/tests/test_development.py

@@ -266,6 +266,24 @@ def test_new_drop_7(clrd):
     )
 
 
+def test_new_drop_8():
+    tri = cl.load_sample("prism")["Paid"].sum().grain("OYDQ")
+
+    try:
+        cl.Development(drop_high=False).fit_transform(tri)
+    except:
+        assert False
+
+    assert (
+        cl.Development(drop_high=True).fit(tri).cdf_.to_frame().fillna(0).values
+        == cl.Development(drop_high=1).fit(tri).cdf_.to_frame().fillna(0).values
+    ).all()
+    assert (
+        cl.Development(drop_high=True).fit(tri).cdf_.to_frame().fillna(0).values
+        >= cl.Development(drop_high=2).fit(tri).cdf_.to_frame().fillna(0).values
+    ).all()
+
+
 def compare_new_drop(dev, tri):
     assert np.array_equal(
         dev._set_weight_func(tri.age_to_age, tri.age_to_age).values,