Merge pull request #121 from xuyuon/98-moving-naming-tracking-into-ji…

…m-class-from-prior-class

Fixing PowerLaw

src/jimgw/prior.py

@@ -12,8 +12,8 @@
     ScaleTransform,
     OffsetTransform,
     ArcSineTransform,
-    # PowerLawTransform,
-    # ParetoTransform,
+    PowerLawTransform,
+    ParetoTransform,
 )
 
 

src/jimgw/transforms.py

@@ -368,55 +368,78 @@ def __init__(
         }
 
 
+class PowerLawTransform(BijectiveTransform):
+    """
+    PowerLaw transformation
+    Parameters
+    ----------
+    name_mapping : tuple[list[str], list[str]]
+            The name mapping between the input and output dictionary.
+    """
+
+    xmin: Float
+    xmax: Float
+    alpha: Float
+
+    def __init__(
+        self,
+        name_mapping: tuple[list[str], list[str]],
+        xmin: Float,
+        xmax: Float,
+        alpha: Float,
+    ):
+        super().__init__(name_mapping)
+        self.xmin = xmin
+        self.xmax = xmax
+        self.alpha = alpha
+        self.transform_func = lambda x: {
+            name_mapping[1][i]: (
+                self.xmin ** (1.0 + self.alpha)
+                + x[name_mapping[0][i]]
+                * (self.xmax ** (1.0 + self.alpha) - self.xmin ** (1.0 + self.alpha))
+            )
+            ** (1.0 / (1.0 + self.alpha))
+            for i in range(len(name_mapping[0]))
+        }
+        self.inverse_transform_func = lambda x: {
+            name_mapping[0][i]: (
+                (
+                    x[name_mapping[1][i]] ** (1.0 + self.alpha)
+                    - self.xmin ** (1.0 + self.alpha)
+                )
+                / (self.xmax ** (1.0 + self.alpha) - self.xmin ** (1.0 + self.alpha))
+            )
+            for i in range(len(name_mapping[1]))
+        }
 
-# class PowerLawTransform(UnivariateTransform):
-#     """
-#     PowerLaw transformation
-#     Parameters
-#     ----------
-#     name_mapping : tuple[list[str], list[str]]
-#             The name mapping between the input and output dictionary.
-#     """
-
-#     xmin: Float
-#     xmax: Float
-#     alpha: Float
-
-#     def __init__(
-#         self,
-#         name_mapping: tuple[list[str], list[str]],
-#         xmin: Float,
-#         xmax: Float,
-#         alpha: Float,
-#     ):
-#         super().__init__(name_mapping)
-#         self.xmin = xmin
-#         self.xmax = xmax
-#         self.alpha = alpha
-#         self.transform_func = lambda x: (
-#             self.xmin ** (1.0 + self.alpha)
-#             + x * (self.xmax ** (1.0 + self.alpha) - self.xmin ** (1.0 + self.alpha))
-#         ) ** (1.0 / (1.0 + self.alpha))
-
-
-# class ParetoTransform(UnivariateTransform):
-#     """
-#     Pareto transformation: Power law when alpha = -1
-#     Parameters
-#     ----------
-#     name_mapping : tuple[list[str], list[str]]
-#             The name mapping between the input and output dictionary.
-#     """
-
-#     def __init__(
-#         self,
-#         name_mapping: tuple[list[str], list[str]],
-#         xmin: Float,
-#         xmax: Float,
-#     ):
-#         super().__init__(name_mapping)
-#         self.xmin = xmin
-#         self.xmax = xmax
-#         self.transform_func = lambda x: self.xmin * jnp.exp(
-#             x * jnp.log(self.xmax / self.xmin)
-#         )
+
+class ParetoTransform(BijectiveTransform):
+    """
+    Pareto transformation: Power law when alpha = -1
+    Parameters
+    ----------
+    name_mapping : tuple[list[str], list[str]]
+            The name mapping between the input and output dictionary.
+    """
+
+    def __init__(
+        self,
+        name_mapping: tuple[list[str], list[str]],
+        xmin: Float,
+        xmax: Float,
+    ):
+        super().__init__(name_mapping)
+        self.xmin = xmin
+        self.xmax = xmax
+        self.transform_func = lambda x: {
+            name_mapping[1][i]: self.xmin
+            * jnp.exp(x[name_mapping[0][i]] * jnp.log(self.xmax / self.xmin))
+            for i in range(len(name_mapping[0]))
+        }
+        self.inverse_transform_func = lambda x: {
+            name_mapping[0][i]: (
+                jnp.log(x[name_mapping[1][i]] / self.xmin)
+                / jnp.log(self.xmax / self.xmin)
+            )
+            for i in range(len(name_mapping[1]))
+        }

test/unit/test_prior.py

@@ -30,7 +30,6 @@ def test_uniform(self):
         samples = p.sample(jax.random.PRNGKey(0), 10000)
         assert jnp.all(jnp.isfinite(samples['x']))
         # Check that the log_prob is correct in the support
-        samples = trace_prior_parent(p, [])[0].sample(jax.random.PRNGKey(0), 10000)
         log_prob = jax.vmap(p.log_prob)(samples)
         assert jnp.allclose(log_prob, jnp.log(1.0 / (xmax - xmin)))
 
@@ -40,30 +39,28 @@ def test_sine(self):
         samples = p.sample(jax.random.PRNGKey(0), 10000)
         assert jnp.all(jnp.isfinite(samples['x']))
         # Check that the log_prob is finite
-        samples = trace_prior_parent(p, [])[0].sample(jax.random.PRNGKey(0), 10000)
         log_prob = jax.vmap(p.log_prob)(samples)
         assert jnp.all(jnp.isfinite(log_prob))
         # Check that the log_prob is correct in the support
         x = trace_prior_parent(p, [])[0].add_name(jnp.linspace(-10.0, 10.0, 1000)[None])
         y = jax.vmap(p.base_prior.base_prior.transform)(x)
         y = jax.vmap(p.base_prior.transform)(y)
         y = jax.vmap(p.transform)(y)
-        assert jnp.allclose(jax.vmap(p.log_prob)(x), jnp.log(jnp.sin(y['x'])/2.0))
+        assert jnp.allclose(jax.vmap(p.log_prob)(y), jnp.log(jnp.sin(y['x'])/2.0))
 
     def test_cosine(self):
         p = CosinePrior(["x"])
         # Check that all the samples are finite
         samples = p.sample(jax.random.PRNGKey(0), 10000)
         assert jnp.all(jnp.isfinite(samples['x']))
         # Check that the log_prob is finite
-        samples = trace_prior_parent(p, [])[0].sample(jax.random.PRNGKey(0), 10000)
         log_prob = jax.vmap(p.log_prob)(samples)
         assert jnp.all(jnp.isfinite(log_prob))
         # Check that the log_prob is correct in the support
         x = trace_prior_parent(p, [])[0].add_name(jnp.linspace(-10.0, 10.0, 1000)[None])
         y = jax.vmap(p.base_prior.transform)(x)
         y = jax.vmap(p.transform)(y)
-        assert jnp.allclose(jax.vmap(p.log_prob)(x), jnp.log(jnp.cos(y['x'])/2.0))
+        assert jnp.allclose(jax.vmap(p.log_prob)(y), jnp.log(jnp.cos(y['x'])/2.0))
 
     def test_uniform_sphere(self):
         p = UniformSpherePrior(["x"])
@@ -73,12 +70,10 @@ def test_uniform_sphere(self):
         assert jnp.all(jnp.isfinite(samples['x_theta']))
         assert jnp.all(jnp.isfinite(samples['x_phi']))
         # Check that the log_prob is finite
-        samples = {}
-        for i in range(3):
-            samples.update(trace_prior_parent(p, [])[i].sample(jax.random.PRNGKey(0), 10000))
         log_prob = jax.vmap(p.log_prob)(samples)
         assert jnp.all(jnp.isfinite(log_prob))
 
+
     def test_power_law(self):
         def powerlaw_log_pdf(x, alpha, xmin, xmax):
             if alpha == -1.0:
@@ -96,20 +91,14 @@ def func(alpha):
             assert jnp.all(jnp.isfinite(powerlaw_samples['x']))
 
             # Check that all the log_probs are finite
-            samples = (trace_prior_parent(p, [])[0].sample(jax.random.PRNGKey(0), 10000))['x_base']
-            base_log_p = jax.vmap(p.log_prob, [0])({'x_base':samples})
-            assert jnp.all(jnp.isfinite(base_log_p))
+            log_p = jax.vmap(p.log_prob, [0])(powerlaw_samples)
+            assert jnp.all(jnp.isfinite(log_p))
 
             # Check that the log_prob is correct in the support
-            samples = jnp.linspace(-10.0, 10.0, 1000)
-            transformed_samples = jax.vmap(p.transform)({'x_base': samples})['x']
-            # cut off the samples that are outside the support
-            samples = samples[transformed_samples >= xmin]
-            transformed_samples = transformed_samples[transformed_samples >= xmin]
-            samples = samples[transformed_samples <= xmax]
-            transformed_samples = transformed_samples[transformed_samples <= xmax]
+            log_prob = jax.vmap(p.log_prob)(powerlaw_samples)
+            standard_log_prob = powerlaw_log_pdf(powerlaw_samples['x'], alpha, xmin, xmax)
             # log pdf of powerlaw
-            assert jnp.allclose(jax.vmap(p.log_prob)({'x_base':samples}), powerlaw_log_pdf(transformed_samples, alpha, xmin, xmax), atol=1e-4)
+            assert jnp.allclose(log_prob, standard_log_prob, atol=1e-4)
 
         # Test Pareto Transform
         func(-1.0)
@@ -120,4 +109,4 @@ def func(alpha):
             func(alpha_val)
         negative_alpha = [-0.5, -1.5, -2.0, -2.5, -3.0, -3.5, -4.0, -4.5, -5.0]
         for alpha_val in negative_alpha:
-            func(alpha_val)
+            func(alpha_val)