remove default name arg

docs/source/tutorials/periodic_effects.qmd

@@ -25,6 +25,7 @@ from pyrenew import process, deterministic
 ```{python}
 # The random process for Rt
 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"

model/src/pyrenew/deterministic/deterministic.py

@@ -26,7 +26,7 @@ def __init__(
         ----------
         vars : ArrayLike
             A tuple with arraylike objects.
-        name : str, optional
+        name : str
             A name to assign to the process.
 
         Returns

model/src/pyrenew/deterministic/nullrv.py

@@ -131,7 +131,6 @@ def sample(
         self,
         mu: ArrayLike,
         obs: ArrayLike | None = None,
-        name: str | None = None,
         **kwargs,
     ) -> tuple:
         """
@@ -143,8 +142,6 @@ def sample(
             Unused parameter, represents mean of non-null distributions
         obs : ArrayLike, optional
             Observed data. Defaults to None.
-        name : str, optional
-            Name of the random variable. Defaults to None.
         **kwargs : dict, optional
             Additional keyword arguments passed through to internal sample calls, should there be any.
 

model/src/pyrenew/latent/hospitaladmissions.py

@@ -65,7 +65,6 @@ def __init__(
         self,
         infection_to_admission_interval_rv: RandomVariable,
         infect_hosp_rate_rv: RandomVariable,
-        latent_hospital_admissions_varname: str = "latent_hospital_admissions",
         day_of_week_effect_rv: RandomVariable | None = None,
         hosp_report_prob_rv: RandomVariable | None = None,
     ) -> None:
@@ -79,9 +78,6 @@ def __init__(
             pyrenew.observations.Deterministic).
         infect_hosp_rate_rv : RandomVariable
             Infection to hospitalization rate random variable.
-        latent_hospital_admissions_varname : str
-            Name to assign to the deterministic component in numpyro of
-            observed hospital admissions.
         day_of_week_effect_rv : RandomVariable, optional
             Day of the week effect.
         hosp_report_prob_rv  : RandomVariable, optional
@@ -104,10 +100,6 @@ def __init__(
             hosp_report_prob_rv,
         )
 
-        self.latent_hospital_admissions_varname = (
-            latent_hospital_admissions_varname
-        )
-
         self.infect_hosp_rate_rv = infect_hosp_rate_rv
         self.day_of_week_effect_rv = day_of_week_effect_rv
         self.hosp_report_prob_rv = hosp_report_prob_rv
@@ -200,7 +192,7 @@ def sample(
         )
 
         npro.deterministic(
-            self.latent_hospital_admissions_varname, latent_hospital_admissions
+            "latent_hospital_admissions", latent_hospital_admissions
         )
 
         return HospitalAdmissionsSample(

model/src/pyrenew/observation/poisson.py

@@ -24,7 +24,7 @@ def __init__(
 
         Parameters
         ----------
-        name : str, optional
+        name : str
             Passed to numpyro.sample.
         eps : float, optional
             Small value added to the rate parameter to avoid zero values.

model/src/pyrenew/process/ar.py

@@ -20,6 +20,7 @@ class ARProcess(RandomVariable):
 
     def __init__(
         self,
+        name: str,
         mean: float,
         autoreg: ArrayLike,
         noise_sd: float,
@@ -29,6 +30,8 @@ def __init__(
 
         Parameters
         ----------
+        name : str
+            Name of the parameter passed to numpyro.sample.
         mean: float
             Mean parameter.
         autoreg : ArrayLike
@@ -40,6 +43,7 @@ def __init__(
         -------
         None
         """
+        self.name = name
         self.mean = mean
         self.autoreg = autoreg
         self.noise_sd = noise_sd
@@ -48,7 +52,6 @@ def sample(
         self,
         duration: int,
         inits: ArrayLike = None,
-        name: str = "arprocess",
         **kwargs,
     ) -> tuple:
         """
@@ -61,9 +64,6 @@ def sample(
         inits : ArrayLike, optional
             Initial points, if None, then these are sampled.
             Defaults to None.
-        name : str, optional
-            Name of the parameter passed to numpyro.sample.
-            Defaults to "arprocess".
         **kwargs : dict, optional
             Additional keyword arguments passed through to internal sample()
             calls, should there be any.
@@ -76,7 +76,7 @@ def sample(
         order = self.autoreg.shape[0]
         if inits is None:
             inits = numpyro.sample(
-                name + "_sampled_inits",
+                self.name + "_sampled_inits",
                 dist.Normal(0, self.noise_sd).expand((order,)),
             )
 
@@ -86,7 +86,7 @@ def _ar_scanner(carry, next):  # numpydoc ignore=GL08
             return new_carry, new_term
 
         noise = numpyro.sample(
-            name + "_noise",
+            self.name + "_noise",
             dist.Normal(0, self.noise_sd).expand((duration - inits.size,)),
         )
 

model/src/pyrenew/process/firstdifferencear.py

@@ -18,6 +18,7 @@ class FirstDifferenceARProcess(RandomVariable):
 
     def __init__(
         self,
+        name: str,
         autoreg: ArrayLike,
         noise_sd: float,
     ) -> None:
@@ -26,6 +27,8 @@ def __init__(
 
         Parameters
         ----------
+        name : str
+            Passed to ARProcess()
         autoreg : ArrayLike
             Process parameters pyrenew.processesARprocess.
         noise_sd : float
@@ -35,14 +38,16 @@ def __init__(
         -------
         None
         """
-        self.rate_of_change_proc = ARProcess(0, jnp.array([autoreg]), noise_sd)
+        self.rate_of_change_proc = ARProcess(
+            "arprocess", 0, jnp.array([autoreg]), noise_sd
+        )
+        self.name = name
 
     def sample(
         self,
         duration: int,
         init_val: ArrayLike = None,
         init_rate_of_change: ArrayLike = None,
-        name: str = "trend_rw",
         **kwargs,
     ) -> tuple:
         """
@@ -56,8 +61,6 @@ def sample(
             Starting point of the AR process, by default None.
         init_rate_of_change : ArrayLike, optional
             Passed to ARProcess.sample, by default None.
-        name : str, optional
-            Passed to ARProcess(), by default "trend_rw"
         **kwargs : dict, optional
             Additional keyword arguments passed through to internal sample()
             calls, should there be any.
@@ -70,7 +73,7 @@ def sample(
         rates_of_change, *_ = self.rate_of_change_proc.sample(
             duration=duration,
             inits=jnp.atleast_1d(init_rate_of_change),
-            name=name + "_rate_of_change",
+            name=self.name + "_rate_of_change",
         )
         return (init_val + jnp.cumsum(rates_of_change.flatten()),)
 

model/src/pyrenew/process/rtperiodicdiff.py

@@ -45,18 +45,20 @@ class RtPeriodicDiffProcess(RandomVariable):
 
     def __init__(
         self,
+        name: str,
         offset: int,
         period_size: int,
         log_rt_prior: RandomVariable,
         autoreg: RandomVariable,
         periodic_diff_sd: RandomVariable,
-        site_name: str = "rt_periodic_diff",
     ) -> None:
         """
         Default constructor for RtPeriodicDiffProcess class.
 
         Parameters
         ----------
+        name : str
+            Name of the site.
         offset : int
             Relative point at which data starts, must be between 0 and
             period_size - 1.
@@ -66,14 +68,12 @@ def __init__(
             Autoregressive parameter.
         periodic_diff_sd : RandomVariable
             Standard deviation of the noise.
-        site_name : str, optional
-            Name of the site. Defaults to "rt_periodic_diff".
 
         Returns
         -------
         None
         """
-
+        self.name = name
         self.broadcaster = PeriodicBroadcaster(
             offset=offset,
             period_size=period_size,
@@ -91,7 +91,6 @@ def __init__(
         self.log_rt_prior = log_rt_prior
         self.autoreg = autoreg
         self.periodic_diff_sd = periodic_diff_sd
-        self.site_name = site_name
 
         return None
 
@@ -180,7 +179,7 @@ def sample(
         n_periods = int(jnp.ceil(duration / self.period_size))
 
         # Running the process
-        ar_diff = FirstDifferenceARProcess(autoreg=b, noise_sd=s_r)
+        ar_diff = FirstDifferenceARProcess("trend_rw", autoreg=b, noise_sd=s_r)
         log_rt = ar_diff.sample(
             duration=n_periods,
             init_val=log_rt_prior[1],
@@ -199,17 +198,19 @@ class RtWeeklyDiffProcess(RtPeriodicDiffProcess):
 
     def __init__(
         self,
+        name: str,
         offset: int,
         log_rt_prior: RandomVariable,
         autoreg: RandomVariable,
         periodic_diff_sd: RandomVariable,
-        site_name: str = "rt_weekly_diff",
     ) -> None:
         """
         Default constructor for RtWeeklyDiffProcess class.
 
         Parameters
         ----------
+        name : str
+            Name of the site.
         offset : int
             Relative point at which data starts, must be between 0 and 6.
         log_rt_prior : RandomVariable
@@ -218,21 +219,19 @@ def __init__(
             Autoregressive parameter.
         periodic_diff_sd : RandomVariable
             Standard deviation of the noise.
-        site_name : str, optional
-            Name of the site. Defaults to "rt_weekly_diff".
 
         Returns
         -------
         None
         """
 
         super().__init__(
+            name=name,
             offset=offset,
             period_size=7,
             log_rt_prior=log_rt_prior,
             autoreg=autoreg,
             periodic_diff_sd=periodic_diff_sd,
-            site_name=site_name,
         )
 
         return None

model/src/test/test_ar_process.py

@@ -11,13 +11,15 @@ def test_ar_can_be_sampled():
     Check that an AR process
     can be initialized and sampled from
     """
-    ar1 = ARProcess(5, jnp.array([0.95]), jnp.array([0.5]))
+    ar1 = ARProcess("arprocess", 5, jnp.array([0.95]), jnp.array([0.5]))
     with numpyro.handlers.seed(rng_seed=62):
         # can sample with and without inits
         ar1(duration=3532, inits=jnp.array([50.0]))
         ar1(duration=5023)
 
-    ar3 = ARProcess(5, jnp.array([0.05, 0.025, 0.025]), jnp.array([0.5]))
+    ar3 = ARProcess(
+        "arprocess", 5, jnp.array([0.05, 0.025, 0.025]), jnp.array([0.5])
+    )
     with numpyro.handlers.seed(rng_seed=62):
         # can sample with and without inits
         ar3(duration=1230)
@@ -32,7 +34,7 @@ def test_ar_samples_correctly_distributed():
     ar_mean = 5
     noise_sd = jnp.array([0.5])
     ar_inits = jnp.array([25.0])
-    ar1 = ARProcess(ar_mean, jnp.array([0.75]), noise_sd)
+    ar1 = ARProcess("arprocess", ar_mean, jnp.array([0.75]), noise_sd)
     with numpyro.handlers.seed(rng_seed=62):
         # check it regresses to mean
         # when started away from it

model/src/test/test_first_difference_ar.py

@@ -13,7 +13,7 @@ def test_fd_ar_can_be_sampled():
     can be initialized and sampled
     from
     """
-    ar_fd = FirstDifferenceARProcess(0.5, 0.5)
+    ar_fd = FirstDifferenceARProcess("trend_rw", 0.5, 0.5)
 
     with numpyro.handlers.seed(rng_seed=62):
         # can sample with and without inits

model/src/test/test_model_hosp_admissions.py

@@ -253,7 +253,6 @@ def test_model_hosp_no_obs_model():
 
     latent_admissions = HospitalAdmissions(
         infection_to_admission_interval_rv=inf_hosp,
-        latent_hospital_admissions_varname="latent_hospital_admissions",
         infect_hosp_rate_rv=DistributionalRV(
             dist=dist.LogNormal(jnp.log(0.05), 0.05), name="IHR"
         ),