Remove TweedieLink.__new__

src/glum/_link.py

@@ -322,19 +322,6 @@ def _to_return(*args, **kwargs):
 class TweedieLink(Link):
     """The Tweedie link function ``x^(1-p)`` if ``p≠1`` and ``log(x)`` if ``p=1``."""
 
-    def __new__(cls, p: float):
-        """Create a new ``TweedieLink`` object.
-
-        Parameters
-        ----------
-        p: scalar
-        """
-        if p == 0:
-            return IdentityLink()
-        if p == 1:
-            return LogLink()
-        return super().__new__(cls)
-
     def __init__(self, p):
         self.p = p
 
@@ -350,6 +337,10 @@ def link(self, mu):
         ----------
         mu: array-like
         """
+        if self.p == 0:
+            return _asanyarray(mu)
+        if self.p == 1:
+            return np.log(_asanyarray(mu))
         return _asanyarray(mu) ** (1 - self.p)
 
     def derivative(self, mu):
@@ -361,6 +352,10 @@ def derivative(self, mu):
         ----------
         mu: array-like
         """
+        if self.p == 0:
+            return 1.0 if np.isscalar(mu) else np.ones_like(mu)
+        if self.p == 1:
+            return 1 / _asanyarray(mu)
         return (1 - self.p) * _asanyarray(mu) ** (-self.p)
 
     @catch_p
@@ -373,6 +368,10 @@ def inverse(self, lin_pred):
         ----------
         mu: array-like
         """
+        if self.p == 0:
+            return _asanyarray(lin_pred)
+        if self.p == 1:
+            return np.exp(_asanyarray(lin_pred))
         return _asanyarray(lin_pred) ** (1 / (1 - self.p))
 
     @catch_p
@@ -384,6 +383,10 @@ def inverse_derivative(self, lin_pred):
         lin_pred : array-like, shape (n_samples,)
             Usually the (fitted) linear predictor.
         """
+        if self.p == 0:
+            return 1.0 if np.isscalar(lin_pred) else np.ones_like(lin_pred)
+        if self.p == 1:
+            return np.exp(_asanyarray(lin_pred))
         return (1 / (1 - self.p)) * _asanyarray(lin_pred) ** (self.p / (1 - self.p))
 
     @catch_p
@@ -396,8 +399,14 @@ def inverse_derivative2(self, lin_pred):
         lin_pred : array, shape (n_samples,)
             Usually the (fitted) linear predictor.
         """
+        if self.p == 0:
+            return 0.0 if np.isscalar(lin_pred) else np.zeros_like(lin_pred)
+        if self.p == 1:
+            return np.exp(_asanyarray(lin_pred))
+
         result = _asanyarray(lin_pred) ** ((2 * self.p - 1) / (1 - self.p))
         result *= self.p / (1 - self.p) ** 2
+
         return result