#525

chainladder/__init__.py

@@ -42,4 +42,4 @@ def auto_sparse(auto_sparse=True):
 from chainladder.methods import *  # noqa (API Import)
 from chainladder.workflow import *  # noqa (API Import)
 
-__version__ = "0.8.22"
+__version__ = "0.8.23"

chainladder/core/tests/test_triangle.py

@@ -245,16 +245,9 @@ def test_df_period_input(raa):
 
 def test_trend_on_vector(raa):
     d = raa.latest_diagonal
-
-    trend_from = datetime.strptime("12-31-1981", "%m-%d-%Y")
-    trend_to = datetime.strptime("12-31-1990", "%m-%d-%Y")
-    days_to_trend = (trend_to - trend_from).days
-    expected_value = np.round(
-        raa.latest_diagonal.to_frame().iloc[0, 0] * (1.05) ** (days_to_trend / 365.25),
-        0,
-    )
-
-    assert np.round(d.trend(0.05, axis=2).to_frame().iloc[0, 0], 0) == expected_value
+    assert (
+        d.trend(0.05, axis=2).to_frame(origin_as_datetime=False).astype(int).iloc[0, 0]
+        == 29217)
 
 
 def test_latest_diagonal_val_to_dev(raa):

chainladder/core/triangle.py

@@ -346,7 +346,7 @@ def origin(self):
     def origin(self, value):
         self._len_check(self.origin, value)
         freq = {
-            "Y": "Y" if float(".".join(pd.__version__.split(".")[:-1])) < 2.2 else "A",
+            "Y": "A" if float(".".join(pd.__version__.split(".")[:-1])) < 2.2 else "Y",
             "S": "2Q",
         }.get(self.origin_grain, self.origin_grain)
         freq = freq if freq == "M" else freq + "-" + self.origin_close
@@ -772,15 +772,15 @@ def trend(
         Parameters
         ----------
         trend : float
-            The annual amount of the trend. Use 1/(1+trend)-1 to de-trend.
+            The annual amount of the trend. Use 1/(1+trend)-1 to detrend.
         axis : str (options: ['origin', 'valuation'])
             The axis on which to apply the trend
         start: date
             The start date from which trend should be calculated. If none is
-            provided, then the earliest date of the triangle is used.
+            provided then the latest date of the triangle is used.
         end: date
             The end date to which the trend should be calculated. If none is
-            provided, then the valuation date of the triangle is used.
+            provided then the earliest period of the triangle is used.
         ultimate_lag : int
             If ultimate valuations are in the triangle, optionally set the overall
             age (in months) of the ultimate to be some lag from the latest non-Ultimate
@@ -795,59 +795,35 @@ def trend(
             raise ValueError(
                 "Only origin and valuation axes are supported for trending"
             )
-
-        # print("====== BEGIN ======")
         xp = self.get_array_module()
-
         start = pd.to_datetime(start) if type(start) is str else start
-        start = self.origin[0].to_timestamp() if start is None else start
-        # print("start", start)
-
+        start = self.valuation_date if start is None else start
         end = pd.to_datetime(end) if type(end) is str else end
-        end = self.valuation_date if end is None else end
-        # print("end", end)
-
+        end = self.origin[0].to_timestamp() if end is None else end
         if axis in ["origin", 2, -2]:
             vector = pd.DatetimeIndex(
                 np.tile(
-                    self.origin.to_timestamp(how="e").date, self.shape[-1]
+                    self.origin.to_timestamp(how="e").values, self.shape[-1]
                 ).flatten()
             )
         else:
             vector = self.valuation
-        # print("vector\n", vector)
-
-        upper, lower = (end, start) if end > start else (start, end)
-        # print("lower", lower)
-        # print("upper", upper)
-
+        lower, upper = (end, start) if end > start else (start, end)
         vector = pd.DatetimeIndex(
             np.maximum(
-                np.minimum(np.datetime64(upper), vector.values), np.datetime64(lower)
+                np.minimum(np.datetime64(lower), vector.values), np.datetime64(upper)
             )
         )
-        # print("vector\n", vector)
-        # print("vector\n", vector)
-        # vector = (
-        #     (end.year - vector.year) * 12 + (end.month - vector.month)
-        # ).values.reshape(self.shape[-2:], order="f")
-        # print("vector\n", vector)
-
-        vector = ((end - vector).days).values.reshape(self.shape[-2:], order="f")
-        # print("days to trend\n", vector)
-
+        vector = (
+            (start.year - vector.year) * 12 + (start.month - vector.month)
+        ).values.reshape(self.shape[-2:], order="f")
         if self.is_ultimate and ultimate_lag is not None and vector.shape[-1] > 1:
             vector[:, -1] = vector[:, -2] + ultimate_lag
-
         trend = (
-            xp.array((1 + trend) ** (vector / 365.25))[None, None, ...]
-            * self.nan_triangle
+            xp.array((1 + trend) ** (vector / 12))[None, None, ...] * self.nan_triangle
         )
-        # print("trend\n", trend)
-
         obj = self.copy()
         obj.values = obj.values * trend
-
         return obj
 
     def broadcast_axis(self, axis, value):

chainladder/development/development.py

@@ -211,7 +211,7 @@ def transform(self, X):
             "w_v2_",
         ]
         for item in triangles:
-            setattr(X_new, item, getattr(self, item))
+            setattr(X_new, item, getattr(self, item), None)
 
         X_new._set_slicers()
 

chainladder/development/tests/test_development.py

@@ -197,21 +197,21 @@ def test_hilo_multiple_indices(clrd):
 def test_new_drop_1(clrd):
     clrd = clrd.groupby("LOB")[["IncurLoss", "CumPaidLoss"]].sum()
     # n_periods
-    return compare_new_drop(cl.Development(n_periods=4).fit(clrd), clrd)
+    compare_new_drop(cl.Development(n_periods=4).fit(clrd), clrd)
 
 
 def test_new_drop_2(clrd):
     clrd = clrd.groupby("LOB")[["IncurLoss", "CumPaidLoss"]].sum()
     # single drop and drop_valuation
-    return compare_new_drop(
+    compare_new_drop(
         cl.Development(drop=("1992", 12), drop_valuation=1993).fit(clrd), clrd
     )
 
 
 def test_new_drop_3(clrd):
     clrd = clrd.groupby("LOB")[["IncurLoss", "CumPaidLoss"]].sum()
     # multiple drop and drop_valuation
-    return compare_new_drop(
+    compare_new_drop(
         cl.Development(
             drop=[("1992", 12), ("1996", 24)], drop_valuation=[1993, 1995]
         ).fit(clrd),
@@ -222,13 +222,13 @@ def test_new_drop_3(clrd):
 def test_new_drop_4(clrd):
     clrd = clrd.groupby("LOB")[["IncurLoss", "CumPaidLoss"]].sum()
     # drop_hi/low without preserve
-    return compare_new_drop(cl.Development(drop_high=1, drop_low=1).fit(clrd), clrd)
+    compare_new_drop(cl.Development(drop_high=1, drop_low=1).fit(clrd), clrd)
 
 
 def test_new_drop_5(clrd):
     clrd = clrd.groupby("LOB")[["IncurLoss", "CumPaidLoss"]].sum()
     # drop_hi/low without preserve
-    return compare_new_drop(
+    compare_new_drop(
         cl.Development(drop_high=1, drop_low=1, preserve=3).fit(clrd), clrd
     )
 
@@ -254,15 +254,15 @@ def test_new_drop_5a(clrd):
 def test_new_drop_6(clrd):
     clrd = clrd.groupby("LOB")[["IncurLoss", "CumPaidLoss"]].sum()
     # drop_above/below without preserve
-    return compare_new_drop(
+    compare_new_drop(
         cl.Development(drop_above=1.01, drop_below=0.95).fit(clrd), clrd
     )
 
 
 def test_new_drop_7(clrd):
     clrd = clrd.groupby("LOB")[["IncurLoss", "CumPaidLoss"]].sum()
     # drop_above/below with preserve
-    return compare_new_drop(
+    compare_new_drop(
         cl.Development(drop_above=1.01, drop_below=0.95, preserve=3).fit(clrd), clrd
     )
 

chainladder/utils/utility_functions.py

@@ -12,6 +12,7 @@
 from patsy import dmatrix
 from sklearn.base import BaseEstimator, TransformerMixin
 from typing import Iterable, Union
+from io import StringIO
 
 
 def load_sample(key: str, *args, **kwargs):
@@ -110,7 +111,7 @@ def read_json(json_str, array_backend=None):
         )
     elif "metadata" in json_dict.keys():
         j = json.loads(json_str)
-        y = pd.read_json(j["data"], orient="split", date_unit="ns")
+        y = pd.read_json(StringIO(j["data"]), orient="split", date_unit="ns")
         y["origin"] = pd.to_datetime(y["origin"])
         y.columns = [c if c != "valuation" else "development" for c in y.columns]
         y["development"] = pd.to_datetime(y["development"])
@@ -185,7 +186,7 @@ def parallelogram_olf(
 
     cum_rate_changes = np.cumprod(1 + rate_changes.values)
     cum_rate_changes = pd.Series(cum_rate_changes, rate_changes.index)
-    crl = cum_rate_changes[-1]
+    crl = cum_rate_changes.iloc[-1]
 
     cum_avg_rate_non_leaps = cum_rate_changes
     cum_avg_rate_leaps = cum_rate_changes

setup.py

@@ -14,7 +14,7 @@
 descr = "Chainladder Package - P&C Loss Reserving package "
 name = 'chainladder'
 url = 'https://github.com/casact/chainladder-python'
-version='0.8.22' # Put this in __init__.py
+version='0.8.23' # Put this in __init__.py
 
 data_path = ''
 setup(