Change Order Of The name Argument To Match Numpyro

docs/source/tutorials/basic_renewal_model.qmd

@@ -118,12 +118,12 @@ To initialize these five components within the renewal modeling framework, we es
 # | label: creating-elements
 # (1) The generation interval (deterministic)
 pmf_array = jnp.array([0.4, 0.3, 0.2, 0.1])
-gen_int = DeterministicPMF(pmf_array, name="gen_int")
+gen_int = DeterministicPMF(name="gen_int", vars=pmf_array)
 
 # (2) Initial infections (inferred with a prior)
 I0 = InfectionInitializationProcess(
     "I0_initialization",
-    DistributionalRV(dist=dist.LogNormal(2.5, 1), name="I0"),
+    DistributionalRV(name="I0", dist=dist.LogNormal(2.5, 1)),
     InitializeInfectionsZeroPad(pmf_array.size),
     t_unit=1,
 )
@@ -144,12 +144,13 @@ class MyRt(RandomVariable):
             base_rv=SimpleRandomWalkProcess(
                 name="log_rt",
                 step_rv=DistributionalRV(
-                    dist.Normal(0, sd_rt),
-                    "rw_step_rv",
+                    name="rw_step_rv",
+                    dist=dist.Normal(0, sd_rt),
                     reparam=LocScaleReparam(0),
                 ),
                 init_rv=DistributionalRV(
-                    dist.Normal(jnp.log(1), jnp.log(1.2)), "init_log_Rt_rv"
+                    name="init_log_Rt_rv",
+                    dist=dist.Normal(jnp.log(1), jnp.log(1.2)),
                 ),
             ),
             transforms=t.ExpTransform(),

docs/source/tutorials/extending_pyrenew.qmd

@@ -42,15 +42,15 @@ The following code-chunk defines the model components. Notice that for both the
 ```{python}
 # | label: model-components
 gen_int_array = jnp.array([0.25, 0.5, 0.15, 0.1])
-gen_int = DeterministicPMF(gen_int_array, name="gen_int")
-feedback_strength = DeterministicVariable(0.05, name="feedback_strength")
+gen_int = DeterministicPMF(name="gen_int", vars=gen_int_array)
+feedback_strength = DeterministicVariable(name="feedback_strength", vars=0.05)
 
 I0 = InfectionInitializationProcess(
     "I0_initialization",
-    DistributionalRV(dist=dist.LogNormal(0, 1), name="I0"),
+    DistributionalRV(name="I0", dist=dist.LogNormal(0, 1)),
     InitializeInfectionsExponentialGrowth(
         gen_int_array.size,
-        DeterministicVariable(0.5, name="rate"),
+        DeterministicVariable(name="rate", vars=0.5),
     ),
     t_unit=1,
 )
@@ -64,8 +64,12 @@ rt = TransformedRandomVariable(
     "Rt_rv",
     base_rv=SimpleRandomWalkProcess(
         name="log_rt",
-        step_rv=DistributionalRV(dist.Normal(0, 0.025), "rw_step_rv"),
-        init_rv=DistributionalRV(dist.Normal(0, 0.2), "init_log_Rt_rv"),
+        step_rv=DistributionalRV(
+            name="rw_step_rv", dist=dist.Normal(0, 0.025)
+        ),
+        init_rv=DistributionalRV(
+            name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+        ),
     ),
     transforms=t.ExpTransform(),
 )

docs/source/tutorials/hospital_admissions_model.qmd

@@ -142,12 +142,11 @@ import jax.numpy as jnp
 import numpyro.distributions as dist
 
 inf_hosp_int = deterministic.DeterministicPMF(
-    inf_hosp_int, name="inf_hosp_int"
+    name="inf_hosp_int", vars=inf_hosp_int
 )
 
 hosp_rate = metaclass.DistributionalRV(
-    dist=dist.LogNormal(jnp.log(0.05), jnp.log(1.1)),
-    name="IHR",
+    name="IHR", dist=dist.LogNormal(jnp.log(0.05), jnp.log(1.1))
 )
 
 latent_hosp = latent.HospitalAdmissions(
@@ -172,17 +171,17 @@ latent_inf = latent.Infections()
 I0 = InfectionInitializationProcess(
     "I0_initialization",
     metaclass.DistributionalRV(
-        dist=dist.LogNormal(loc=jnp.log(100), scale=jnp.log(1.75)), name="I0"
+        name="I0", dist=dist.LogNormal(loc=jnp.log(100), scale=jnp.log(1.75))
     ),
     InitializeInfectionsExponentialGrowth(
         gen_int_array.size,
-        deterministic.DeterministicVariable(0.05, name="rate"),
+        deterministic.DeterministicVariable(name="rate", vars=0.05),
     ),
     t_unit=1,
 )
 
 # Generation interval and Rt
-gen_int = deterministic.DeterministicPMF(gen_int, name="gen_int")
+gen_int = deterministic.DeterministicPMF(name="gen_int", vars=gen_int)
 
 
 class MyRt(metaclass.RandomVariable):
@@ -200,10 +199,10 @@ class MyRt(metaclass.RandomVariable):
             base_rv=process.SimpleRandomWalkProcess(
                 name="log_rt",
                 step_rv=metaclass.DistributionalRV(
-                    dist.Normal(0, sd_rt), "rw_step_rv"
+                    name="rw_step_rv", dist=dist.Normal(0, sd_rt)
                 ),
                 init_rv=metaclass.DistributionalRV(
-                    dist.Normal(0, 0.2), "init_log_Rt_rv"
+                    name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
                 ),
             ),
             transforms=transformation.ExpTransform(),
@@ -213,7 +212,9 @@ class MyRt(metaclass.RandomVariable):
 
 
 rtproc = MyRt(
-    metaclass.DistributionalRV(dist.HalfNormal(0.025), "Rt_random_walk_sd")
+    metaclass.DistributionalRV(
+        name="Rt_random_walk_sd", dist=dist.HalfNormal(0.025)
+    )
 )
 
 # The observation model
@@ -223,7 +224,8 @@ rtproc = MyRt(
 nb_conc_rv = metaclass.TransformedRandomVariable(
     "concentration",
     metaclass.DistributionalRV(
-        dist.TruncatedNormal(loc=0, scale=1, low=0.01), "concentration_raw"
+        name="concentration_raw",
+        dist=dist.TruncatedNormal(loc=0, scale=1, low=0.01),
     ),
     transformation.PowerTransform(-2),
 )

docs/source/tutorials/periodic_effects.qmd

@@ -28,13 +28,13 @@ rt_proc = process.RtWeeklyDiffProcess(
     name="rt_weekly_diff",
     offset=0,
     log_rt_prior=deterministic.DeterministicVariable(
-        jnp.array([0.1, 0.2]), name="log_rt_prior"
+        name="log_rt_prior", vars=jnp.array([0.1, 0.2])
     ),
     autoreg=deterministic.DeterministicVariable(
-        jnp.array([0.7]), name="autoreg"
+        name="autoreg", vars=jnp.array([0.7])
     ),
     periodic_diff_sd=deterministic.DeterministicVariable(
-        jnp.array([0.1]), name="periodic_diff_sd"
+        name="periodic_diff_sd", vars=jnp.array([0.1])
     ),
 )
 ```
@@ -76,7 +76,9 @@ mysimplex = dist.TransformedDistribution(
 # Constructing the day of week effect
 dayofweek = process.DayOfWeekEffect(
     offset=0,
-    quantity_to_broadcast=metaclass.DistributionalRV(mysimplex, "simp"),
+    quantity_to_broadcast=metaclass.DistributionalRV(
+        name="simp", dist=mysimplex
+    ),
     t_start=0,
 )
 ```

model/src/pyrenew/deterministic/deterministic.py

@@ -17,26 +17,26 @@ class DeterministicVariable(RandomVariable):
 
     def __init__(
         self,
-        vars: ArrayLike,
         name: str,
+        vars: ArrayLike,
     ) -> None:
         """Default constructor
 
         Parameters
         ----------
-        vars : ArrayLike
-            A tuple with arraylike objects.
         name : str
             A name to assign to the process.
+        vars : ArrayLike
+            An ArrayLike object.
 
         Returns
         -------
         None
         """
 
-        self.validate(vars)
-        self.vars = jnp.atleast_1d(vars)
         self.name = name
+        self.vars = jnp.atleast_1d(vars)
+        self.validate(vars)
 
         return None
 

model/src/pyrenew/deterministic/deterministicpmf.py

@@ -15,8 +15,8 @@ class DeterministicPMF(RandomVariable):
 
     def __init__(
         self,
-        vars: ArrayLike,
         name: str,
+        vars: ArrayLike,
         tol: float = 1e-5,
     ) -> None:
         """
@@ -29,10 +29,10 @@ def __init__(
 
         Parameters
         ----------
-        vars : tuple
-            A tuple with arraylike objects.
         name : str
-            A name to assign to the process.
+            A name to assign to the variable.
+        vars : tuple
+            An ArrayLike object.
         tol : float, optional
             Passed to pyrenew.distutil.validate_discrete_dist_vector. Defaults
             to 1e-5.
@@ -46,7 +46,7 @@ def __init__(
             tol=tol,
         )
 
-        self.basevar = DeterministicVariable(vars, name)
+        self.basevar = DeterministicVariable(name=name, vars=vars)
 
         return None
 

model/src/pyrenew/latent/hospitaladmissions.py

@@ -90,9 +90,13 @@ def __init__(
         """
 
         if day_of_week_effect_rv is None:
-            day_of_week_effect_rv = DeterministicVariable(1, "weekday_effect")
+            day_of_week_effect_rv = DeterministicVariable(
+                name="weekday_effect", vars=1
+            )
         if hosp_report_prob_rv is None:
-            hosp_report_prob_rv = DeterministicVariable(1, "hosp_report_prob")
+            hosp_report_prob_rv = DeterministicVariable(
+                name="hosp_report_prob", vars=1
+            )
 
         HospitalAdmissions.validate(
             infect_hosp_rate_rv,

model/src/pyrenew/metaclass.py

@@ -142,7 +142,6 @@ def set_timeseries(
             model time. It could be negative, indicating
             that the `sample()` method returns timepoints
             that occur prior to the model t = 0.
-
         t_unit : int
             The unit of the time series relative
             to the model's fundamental (smallest)
@@ -219,20 +218,19 @@ class DistributionalRV(RandomVariable):
 
     def __init__(
         self,
-        dist: numpyro.distributions.Distribution,
         name: str,
+        dist: numpyro.distributions.Distribution,
         reparam: Reparam = None,
     ) -> None:
         """
         Default constructor for DistributionalRV.
 
         Parameters
         ----------
-        dist : numpyro.distributions.Distribution
-            Distribution of the random variable.
         name : str
             Name of the random variable.
-
+        dist : numpyro.distributions.Distribution
+            Distribution of the random variable.
         reparam : numpyro.infer.reparam.Reparam
             If not None, reparameterize sampling
             from the distribution according to the
@@ -243,10 +241,9 @@ def __init__(
         None
         """
 
+        self.name = name
         self.validate(dist)
-
         self.dist = dist
-        self.name = name
         if reparam is not None:
             self.reparam_dict = {self.name: reparam}
         else:
@@ -603,19 +600,16 @@ def __init__(
 
         Parameters
         ----------
-
         name : str
-            A name for the random variable instance
-
+            A name for the random variable instance.
         base_rv : RandomVariable
-            The underlying (untransformed) RandomVariable
-
+            The underlying (untransformed) RandomVariable.
         transforms : Transform
             Transformation or tuple of transformations
             to apply to the output of
             `base_rv.sample()`; single values will be coerced to
             a length-one tuple. If a tuple, should be the same
-            length as the tuple returned by `base_rv.sample()`
+            length as the tuple returned by `base_rv.sample()`.
 
         Returns
         -------

model/src/pyrenew/process/firstdifferencear.py

@@ -71,9 +71,9 @@ def sample(
             With a single array of shape (duration,).
         """
         rates_of_change, *_ = self.rate_of_change_proc.sample(
+            name=self.name + "_rate_of_change",
             duration=duration,
             inits=jnp.atleast_1d(init_rate_of_change),
-            name=self.name + "_rate_of_change",
         )
         return (init_val + jnp.cumsum(rates_of_change.flatten()),)
 

model/src/test/test_deterministic.py

@@ -18,15 +18,17 @@ def test_deterministic():
     """
 
     var1 = DeterministicVariable(
-        jnp.array(
+        name="var1",
+        vars=jnp.array(
             [
                 1,
             ]
         ),
-        name="var1",
     )
-    var2 = DeterministicPMF(jnp.array([0.25, 0.25, 0.2, 0.3]), name="var2")
-    var3 = DeterministicProcess(jnp.array([1, 2, 3, 4]), name="var3")
+    var2 = DeterministicPMF(
+        name="var2", vars=jnp.array([0.25, 0.25, 0.2, 0.3])
+    )
+    var3 = DeterministicProcess(name="var3", vars=jnp.array([1, 2, 3, 4]))
     var4 = NullVariable()
     var5 = NullProcess()
 

model/src/test/test_forecast.py

@@ -21,21 +21,25 @@
 def test_forecast():
     """Check that forecasts are the right length and match the posterior up until forecast begins."""
     pmf_array = jnp.array([0.25, 0.25, 0.25, 0.25])
-    gen_int = DeterministicPMF(pmf_array, name="gen_int")
+    gen_int = DeterministicPMF(name="gen_int", vars=pmf_array)
     I0 = InfectionInitializationProcess(
         "I0_initialization",
-        DistributionalRV(dist=dist.LogNormal(0, 1), name="I0"),
+        DistributionalRV(name="I0", dist=dist.LogNormal(0, 1)),
         InitializeInfectionsZeroPad(n_timepoints=gen_int.size()),
         t_unit=1,
     )
     latent_infections = Infections()
-    observed_infections = PoissonObservation("poisson_rv")
+    observed_infections = PoissonObservation(name="poisson_rv")
     rt = TransformedRandomVariable(
-        "Rt_rv",
+        name="Rt_rv",
         base_rv=SimpleRandomWalkProcess(
             name="log_rt",
-            step_rv=DistributionalRV(dist.Normal(0, 0.025), "rw_step_rv"),
-            init_rv=DistributionalRV(dist.Normal(0, 0.2), "init_log_Rt_rv"),
+            step_rv=DistributionalRV(
+                name="rw_step_rv", dist=dist.Normal(0, 0.025)
+            ),
+            init_rv=DistributionalRV(
+                name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+            ),
         ),
         transforms=t.ExpTransform(),
     )