[CHORE] Ruff + Test Fixtures for Tail tests

chainladder/tails/__init__.py

@@ -1,11 +1,12 @@
-""" tails should store all tail methodologies
-"""
+"""tails should store all tail methodologies"""
+
 from chainladder.tails.base import TailBase  # noqa (API import)
 from chainladder.tails.constant import TailConstant  # noqa (API import)
 from chainladder.tails.curve import TailCurve  # noqa (API import)
 from chainladder.tails.bondy import TailBondy  # noqa (API import)
 from chainladder.tails.clark import TailClark  # noqa (API import)
 
+
 __all__ = [
     "TailBase",
     "TailConstant",

chainladder/tails/bondy.py

@@ -9,7 +9,8 @@
 
 
 class TailBondy(TailBase):
-    """Estimator for the Generalized Bondy tail factor.
+    """
+    Estimator for the Generalized Bondy tail factor.
 
     .. versionadded:: 0.6.0
 
@@ -121,7 +122,8 @@ def __init__(self, earliest_age=None, attachment_age=None, projection_period=12)
         self.projection_period = projection_period
 
     def fit(self, X, y=None, sample_weight=None):
-        """Fit the model with X.
+        """
+        Fit the model with X.
 
         Parameters
         ----------
@@ -207,7 +209,8 @@ def fit(self, X, y=None, sample_weight=None):
         return self
 
     def transform(self, X):
-        """Transform X.
+        """
+        Transform X.
 
         Parameters
         ----------

chainladder/tails/clark.py

@@ -6,7 +6,8 @@
 
 
 class TailClark(TailBase):
-    """Allows for extraploation of LDFs to form a tail factor.
+    """
+    Allows for extraploation of LDFs to form a tail factor.
 
     .. versionadded:: 0.6.4
 
@@ -112,15 +113,21 @@ class TailClark(TailBase):
 
     """
 
-    def __init__(self, growth="loglogistic", truncation_age=None,
-                 attachment_age=None, projection_period=12):
+    def __init__(
+        self,
+        growth="loglogistic",
+        truncation_age=None,
+        attachment_age=None,
+        projection_period=12,
+    ):
         self.growth = growth
         self.truncation_age = truncation_age
         self.attachment_age = attachment_age
         self.projection_period = projection_period
 
     def fit(self, X, y=None, sample_weight=None):
-        """Fit the model with X.
+        """
+        Fit the model with X.
 
         Parameters
         ----------
@@ -151,14 +158,17 @@ def fit(self, X, y=None, sample_weight=None):
                 fitted.values[..., :-1] / fitted.values[..., 1:],
                 fitted.values[..., -1:],
             ),
-            -1,
+            axis=-1,
         )
         fitted = xp.repeat(fitted, self.ldf_.values.shape[2], 2)
         attachment_age = self.attachment_age if self.attachment_age else X.ddims[-2]
-        self.ldf_.values = xp.concatenate((
-            self.ldf_.values[..., : sum(self.ldf_.ddims < attachment_age)],
-            fitted[..., -sum(self.ldf_.ddims >= attachment_age) :],),
-            axis=-1,)
+        self.ldf_.values = xp.concatenate(
+            (
+                self.ldf_.values[..., : sum(self.ldf_.ddims < attachment_age)],
+                fitted[..., -sum(self.ldf_.ddims >= attachment_age) :],
+            ),
+            axis=-1,
+        )
         self.omega_ = model.omega_
         self.theta_ = model.theta_
         self.G_ = model.G_
@@ -169,14 +179,17 @@ def fit(self, X, y=None, sample_weight=None):
             self.elr_ = model.elr_
         self.norm_resid_ = model.norm_resid_
         if self.truncation_age:
-            self.ldf_.values[..., -1:] = self.ldf_.values[..., -1:] * self.G_(self.truncation_age).values
+            self.ldf_.values[..., -1:] = (
+                self.ldf_.values[..., -1:] * self.G_(self.truncation_age).values
+            )
         # self._get_tail_stats(self)
         if backend == "cupy":
             self = self.set_backend("cupy", inplace=True)
         return self
 
     def transform(self, X):
-        """Transform X.
+        """
+        Transform X.
 
         Parameters
         ----------

chainladder/tails/constant.py

@@ -6,7 +6,8 @@
 
 
 class TailConstant(TailBase):
-    """Allows for the entry of a constant tail factor to LDFs.
+    """
+    Allows for the entry of a constant tail factor to LDFs.
 
     Parameters
     ----------
@@ -129,7 +130,8 @@ def __init__(self, tail=1.0, decay=0.5, attachment_age=None, projection_period=1
         self.projection_period = projection_period
 
     def fit(self, X, y=None, sample_weight=None):
-        """Fit the model with X.
+        """
+        Fit the model with X.
 
         Parameters
         ----------

chainladder/tails/curve.py

@@ -9,7 +9,8 @@
 
 
 class TailCurve(TailBase):
-    """Allows for extraploation of LDFs to form a tail factor.
+    """
+    Allows for extraploation of LDFs to form a tail factor.
 
     Parameters
     ----------
@@ -158,7 +159,8 @@ def __init__(
         self.projection_period = projection_period
 
     def fit(self, X, y=None, sample_weight=None):
-        """Fit the model with X.
+        """
+        Fit the model with X.
 
         Parameters
         ----------

chainladder/tails/tests/test_bondy.py

@@ -1,7 +1,17 @@
+from __future__ import annotations
+
 import chainladder as cl
 
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from chainladder import Triangle
+
 
-def test_bondy1():
-    tri = cl.load_sample("tail_sample")["paid"]
+def test_bondy1(tail_sample: Triangle) -> None:
+    tri = tail_sample["paid"]
     dev = cl.Development(average="simple").fit_transform(tri)
-    assert round(float(cl.TailBondy(earliest_age=12).fit(dev).cdf_.values[0, 0, 0, -2]), 3) == 1.028
+    assert (
+        round(float(cl.TailBondy(earliest_age=12).fit(dev).cdf_.values[0, 0, 0, -2]), 3)
+        == 1.028
+    )

chainladder/tails/tests/test_clark.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+import numpy as np
+import chainladder as cl
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from chainladder import Triangle
+
+
+def test_truncation_age(genins: Triangle, atol: float) -> None:
+    """
+    Validate that sufficiently distant truncation age is equivalent to
+    not truncating
+    """
+    long_truncation = (
+        cl.TailClark(truncation_age=99999).fit(cl.ClarkLDF().fit_transform(genins)).cdf_
+    )
+    no_truncation = cl.TailClark().fit(cl.ClarkLDF().fit_transform(genins)).cdf_
+    assert np.allclose(
+        long_truncation.values[..., -1], no_truncation.values[..., -1], atol=atol
+    )

chainladder/tails/tests/test_constant.py

@@ -1,12 +1,14 @@
 import chainladder as cl
 
+
 def test_constant_balances(qtr):
     xp = qtr.get_array_module()
     assert (
         round(
             float(
                 xp.prod(
-                    cl.TailConstant(1.05, decay=0.8)
+                    cl
+                    .TailConstant(1.05, decay=0.8)
                     .fit(qtr)
                     .ldf_.iloc[0, 1]
                     .values[0, 0, 0, -5:]

chainladder/tails/tests/test_exponential.py

@@ -1,13 +1,20 @@
+from __future__ import annotations
+
 import chainladder as cl
 import pytest
 
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from chainladder import Triangle
+
 
-def test_fit_period():
-    tri = cl.load_sample("tail_sample")
-    dev = cl.Development(average="simple").fit_transform(tri)
+def test_fit_period(tail_sample: Triangle) -> None:
+    dev = cl.Development(average="simple").fit_transform(tail_sample)
     assert (
         round(
-            cl.TailCurve(fit_period=(tri.ddims[-7], None), extrap_periods=10)
+            cl
+            .TailCurve(fit_period=(tail_sample.ddims[-7], None), extrap_periods=10)
             .fit(dev)
             .cdf_["paid"]
             .set_backend("numpy", inplace=True)
@@ -18,24 +25,18 @@ def test_fit_period():
     )
 
 
-def test_curve_validation():
+def test_curve_validation(tail_sample: Triangle) -> None:
     """
     Test validation of the curve parameter. Should raise a value error if an incorrect argument is supplied.
     """
 
     with pytest.raises(ValueError):
-        tri = cl.load_sample('tail_sample')
-        cl.TailCurve(
-            curve='Exponential'
-        ).fit_transform(tri)
+        cl.TailCurve(curve="Exponential").fit_transform(tail_sample)
 
 
-def test_errors_validation():
+def test_errors_validation(tail_sample: Triangle) -> None:
     """
     Test validation of the errors parameter. Should raise a value error if an incorrect argument is supplied.
     """
     with pytest.raises(ValueError):
-        tri = cl.load_sample('tail_sample')
-        cl.TailCurve(
-            errors='Ignore'
-        ).fit_transform(tri)
+        cl.TailCurve(errors="Ignore").fit_transform(tail_sample)

conftest.py

@@ -30,6 +30,10 @@ def pytest_generate_tests(metafunc):
         )
     if "prism" in metafunc.fixturenames:
         metafunc.parametrize("prism", ["sparse_only_run"], indirect=True)
+    if "tail_sample" in metafunc.fixturenames:
+        metafunc.parametrize(
+            "tail_sample", ["normal_run", "sparse_only_run"], indirect=True
+        )
     if "xyz" in metafunc.fixturenames:
         metafunc.parametrize("xyz", ["normal_run", "sparse_only_run"], indirect=True)
 
@@ -116,6 +120,11 @@ def monthly(request):
     yield from _sample_fixture(request, "prism", transform=lambda t: t.sum())
 
 
+@pytest.fixture
+def tail_sample(request):
+    yield from _sample_fixture(request, "tail_sample")
+
+
 @pytest.fixture
 def xyz(request):
     yield from _sample_fixture(request, "xyz")