Update pyrenew_hew_model sample signature for PyRenew-HE

demos/hosp_only_ww_model/pyrenew_hew_model.qmd

@@ -50,7 +50,7 @@ We check that we can simulate from the prior predictive
 n_forecast_days = 35
 
 prior_predictive = my_pyrenew_hew_model.prior_predictive(
-    n_datapoints=len(data_observed_disease_ed_visits) + n_forecast_days,
+    n_observed_disease_ed_visits_datapoints=len(data_observed_disease_ed_visits) + n_forecast_days,
     numpyro_predictive_args={"num_samples": 200},
 )
 ```
@@ -75,7 +75,7 @@ Create the posterior predictive and forecast:
 ```{python}
 # | label: posterior predictive
 posterior_predictive = my_pyrenew_hew_model.posterior_predictive(
-    n_datapoints=len(data_observed_disease_ed_visits) + n_forecast_days
+    n_observed_disease_ed_visits_datapoints=len(data_observed_disease_ed_visits) + n_forecast_days
 )
 ```
 

demos/ww_model/ww_model_demo.qmd

@@ -62,7 +62,7 @@ max_ww_sampled_days = max(stan_data["ww_sampled_times"])
 lab_site_to_subpop_map = jnp.array(stan_data["lab_site_to_subpop_map"]) -1   #vector mapping the subpops to lab-site combos
 
 data_observed_log_conc = jnp.array(stan_data["log_conc"])
-data_observed_hospital_admissions = jnp.array(stan_data["hosp"])
+data_observed_disease_hospital_admissions = jnp.array(stan_data["hosp"])
 ```
 
 ```{python}
@@ -327,7 +327,7 @@ Check that we can simulate from the prior predictive
 n_forecast_days = 35
 
 prior_predictive = my_model.prior_predictive(
-    n_datapoints= max(len(data_observed_hospital_admissions), max_ww_sampled_days) + n_forecast_days,
+    n_observed_disease_ed_visits_datapoints= max(len(data_observed_disease_hospital_admissions), max_ww_sampled_days) + n_forecast_days,
     numpyro_predictive_args={"num_samples": 100},
 )
 ```
@@ -339,7 +339,7 @@ my_model.run(
     num_warmup=750,
     num_samples=500,
     rng_key=jax.random.key(223),
-    data_observed_hospital_admissions=data_observed_hospital_admissions,
+    data_observed_disease_hospital_admissions=data_observed_disease_hospital_admissions,
     data_observed_log_conc=data_observed_log_conc,
     mcmc_args=dict(num_chains=4)
 )
@@ -349,7 +349,7 @@ Simulate the posterior predictive distribution
 ```{python}
 # | label: posterior predictive
 posterior_predictive = my_model.posterior_predictive(
-    n_datapoints= max(len(data_observed_hospital_admissions), max_ww_sampled_days) + n_forecast_days, is_predictive=True
+    n_observed_disease_ed_visits_datapoints= max(len(data_observed_disease_hospital_admissions), max_ww_sampled_days) + n_forecast_days, is_predictive=True
 )
 ```
 
@@ -437,14 +437,14 @@ my_model_hosp_only_fit.run(
     num_warmup=750,
     num_samples=500,
     rng_key=jax.random.key(223),
-    data_observed_hospital_admissions=data_observed_hospital_admissions,
+    data_observed_disease_hospital_admissions=data_observed_disease_hospital_admissions,
     mcmc_args=dict(num_chains=4)
 )
 ```
 
 ```{python}
 posterior_predictive_hosp_only = my_model_hosp_only_fit.posterior_predictive(
-    n_datapoints= len(data_observed_hospital_admissions) + n_forecast_days,is_predictive=True
+    n_observed_disease_ed_visits_datapoints= len(data_observed_disease_hospital_admissions) + n_forecast_days,is_predictive=True
 )
 ```
 

pipelines/build_pyrenew_model.py

@@ -34,6 +34,9 @@
     data_observed_disease_ed_visits = jnp.array(
         model_data["data_observed_disease_ed_visits"]
     )
+    data_observed_disease_hospital_admissions = jnp.array(
+        model_data["data_observed_disease_hospital_admissions"]
+    )
     state_pop = jnp.array(model_data["state_pop"])
 
     right_truncation_pmf_rv = DeterministicVariable(
@@ -75,5 +78,6 @@
     return (
         my_model,
         data_observed_disease_ed_visits,
+        data_observed_disease_hospital_admissions,
         right_truncation_offset,
     )

pipelines/fit_pyrenew_model.py

@@ -29,6 +29,7 @@ def fit_and_save_model(
     (
         my_model,
         data_observed_disease_ed_visits,
+        data_observed_disease_hospital_admissions,
         right_truncation_offset,
     ) = build_model_from_dir(model_run_dir)
     my_model.run(

pipelines/generate_predictive.py

@@ -18,6 +18,7 @@ def generate_and_save_predictions(
     (
         my_model,
         data_observed_disease_ed_visits,
+        data_observed_disease_hospital_admissions,
         right_truncation_offset,
     ) = build_model_from_dir(model_run_dir)
 
@@ -33,7 +34,14 @@ def generate_and_save_predictions(
     my_model.mcmc.sampler = fresh_sampler
 
     posterior_predictive = my_model.posterior_predictive(
-        n_datapoints=len(data_observed_disease_ed_visits) + n_forecast_points
+        n_observed_disease_ed_visits_datapoints=len(
+            data_observed_disease_ed_visits
+        )
+        + n_forecast_points,
+        n_observed_hospital_admissions_datapoints=len(
+            data_observed_disease_hospital_admissions
+        )
+        + n_forecast_points // 7,
     )
 
     idata = az.from_numpyro(

pyrenew_hew/pyrenew_hew_model.py

@@ -14,7 +14,7 @@
     InitializeInfectionsExponentialGrowth,
 )
 from pyrenew.metaclass import Model
-from pyrenew.observation import NegativeBinomialObservation
+from pyrenew.observation import NegativeBinomialObservation, PoissonObservation
 from pyrenew.process import ARProcess, DifferencedProcess
 from pyrenew.randomvariable import DistributionalVariable, TransformedVariable
 
@@ -84,28 +84,41 @@
 
     def sample(
         self,
-        n_datapoints=None,
+        n_observed_disease_ed_visits_datapoints=None,
+        n_observed_hospital_admissions_datapoints=None,
         data_observed_disease_ed_visits=None,
+        data_observed_disease_hospital_admissions=None,
         right_truncation_offset=None,
     ):  # numpydoc ignore=GL08
-        if n_datapoints is None and data_observed_disease_ed_visits is None:
+        if (
+            n_observed_disease_ed_visits_datapoints is None
+            and data_observed_disease_ed_visits is None
+        ):
             raise ValueError(
-                "Either n_datapoints or data_observed_disease_ed_visits "
+                "Either n_observed_disease_ed_visits_datapoints or data_observed_disease_ed_visits "
                 "must be passed."
             )
         elif (
-            n_datapoints is not None
+            n_observed_disease_ed_visits_datapoints is not None
             and data_observed_disease_ed_visits is not None
         ):
             raise ValueError(
-                "Cannot pass both n_datapoints and data_observed_disease_ed_visits."
+                "Cannot pass both n_observed_disease_ed_visits_datapoints and data_observed_disease_ed_visits."
+            )
+        elif n_observed_disease_ed_visits_datapoints is None:
+            n_observed_disease_ed_visits_datapoints = len(
+                data_observed_disease_ed_visits
+            )
+
+        if (
+            n_observed_hospital_admissions_datapoints is None
+            and data_observed_disease_hospital_admissions is not None
+        ):
+            n_observed_hospital_admissions_datapoints = len(
+                data_observed_disease_hospital_admissions
             )
-        elif n_datapoints is None:
-            n_datapoints = len(data_observed_disease_ed_visits)
-        else:
-            n_datapoints = n_datapoints
 
-        n_weeks_post_init = n_datapoints // 7 + 1
+        n_weeks_post_init = n_observed_disease_ed_visits_datapoints // 7 + 1
         i0 = self.infection_initialization_process()
 
         eta_sd = self.eta_sd_rv()
@@ -127,7 +140,7 @@
 
         rtu = repeat_until_n(
             data=jnp.exp(log_rtu_weekly),
-            n_timepoints=n_datapoints,
+            n_timepoints=n_observed_disease_ed_visits_datapoints,
             offset=0,
             period_size=7,
         )
@@ -174,22 +187,24 @@
         iedr = jnp.repeat(
             transformation.SigmoidTransform()(p_ed_ar + p_ed_mean),
             repeats=7,
-        )[:n_datapoints]
+        )[:n_observed_disease_ed_visits_datapoints]
         # this is only applied after the ed visits are generated, not to all the latent infections. This is why we cannot apply the iedr in compute_delay_ascertained_incidence
         # see https://github.com/CDCgov/ww-inference-model/issues/43
 
         numpyro.deterministic("iedr", iedr)
 
         ed_wday_effect_raw = self.ed_wday_effect_rv()
-        ed_wday_effect = tile_until_n(ed_wday_effect_raw, n_datapoints)
+        ed_wday_effect = tile_until_n(
+            ed_wday_effect_raw, n_observed_disease_ed_visits_datapoints
+        )
 
         inf_to_ed = self.inf_to_ed_rv()
 
         potential_latent_ed_visits = compute_delay_ascertained_incidence(
             p_observed_given_incident=1,
             latent_incidence=latent_infections,
             delay_incidence_to_observation_pmf=inf_to_ed,
-        )[-n_datapoints:]
+        )[-n_observed_disease_ed_visits_datapoints:]
 
         latent_ed_visits_final = (
             potential_latent_ed_visits * iedr * ed_wday_effect * self.state_pop
@@ -200,7 +215,8 @@
                 self.right_truncation_cdf_rv()[right_truncation_offset:]
             )
             n_points_to_prepend = (
-                n_datapoints - prop_already_reported_tail.shape[0]
+                n_observed_disease_ed_visits_datapoints
+                - prop_already_reported_tail.shape[0]
             )
             prop_already_reported = jnp.pad(
                 prop_already_reported_tail,
@@ -223,6 +239,16 @@
             obs=data_observed_disease_ed_visits,
         )
 
+        if n_observed_hospital_admissions_datapoints is not None:
+            hospital_admissions_obs_rv = PoissonObservation(
+                "observed_hospital_admissions"
+            )
+            data_observed_disease_hospital_admissions = (
+                hospital_admissions_obs_rv(
+                    mu=jnp.ones(n_observed_hospital_admissions_datapoints) + 50
+                )
+            )
+
         return observed_ed_visits