Remove Usage Of npro In Favor Of Just numpyro

docs/source/tutorials/extending_pyrenew.qmd

@@ -23,7 +23,7 @@ Before we start, let's simulate the model with the original `InfectionsWithFeedb
 import jax
 import jax.numpy as jnp
 import numpy as np
-import numpyro as npro
+import numpyro
 import numpyro.distributions as dist
 from pyrenew.deterministic import DeterministicPMF, DeterministicVariable
 from pyrenew.latent import InfectionsWithFeedback
@@ -90,7 +90,7 @@ And simulate from it:
 # | label: simulate1
 # Sampling and fitting model 0 (with no obs for infections)
 np.random.seed(223)
-with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
     model0_samp = model0.sample(n_datapoints=30)
 ```
 
@@ -222,7 +222,7 @@ class InfFeedback(RandomVariable):
         )
 
         # Storing adjusted Rt for future use
-        npro.deterministic("Rt_adjusted", Rt_adj)
+        numpyro.deterministic("Rt_adjusted", Rt_adj)
 
         # Preparing theoutput
 
@@ -259,7 +259,7 @@ model1 = RtInfectionsRenewalModel(
 
 # Sampling and fitting model 0 (with no obs for infections)
 np.random.seed(223)
-with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
     model1_samp = model1.sample(n_datapoints=30)
 ```
 

docs/source/tutorials/periodic_effects.qmd

@@ -18,7 +18,7 @@ The `RtPeriodicDiff` and `RtWeeklyDiff` classes use `PeriodicBroadcaster` to rep
 # | warning: false
 import jax.numpy as jnp
 import numpy as np
-import numpyro as npro
+import numpyro
 from pyrenew import process, deterministic
 ```
 
@@ -40,7 +40,7 @@ rt_proc = process.RtWeeklyDiffProcess(
 ```
 
 ```{python}
-with npro.handlers.seed(rng_seed=20):
+with numpyro.handlers.seed(rng_seed=20):
     sim_data = rt_proc(duration=30)
 
 # Plotting the Rt values
@@ -84,7 +84,7 @@ dayofweek = process.DayOfWeekEffect(
 Like before, we can use the `sample` method to generate samples from the day of week effect:
 
 ```{python}
-with npro.handlers.seed(rng_seed=20):
+with numpyro.handlers.seed(rng_seed=20):
     sim_data = dayofweek(duration=30)
 
 # Plotting the effect values

model/src/pyrenew/deterministic/deterministic.py

@@ -4,7 +4,7 @@
 from __future__ import annotations
 
 import jax.numpy as jnp
-import numpyro as npro
+import numpyro
 from jax.typing import ArrayLike
 from pyrenew.metaclass import RandomVariable
 
@@ -86,5 +86,5 @@ def sample(
             Containing the stored values during construction.
         """
         if record:
-            npro.deterministic(self.name, self.vars)
+            numpyro.deterministic(self.name, self.vars)
         return (self.vars,)

model/src/pyrenew/latent/hospitaladmissions.py

@@ -6,7 +6,7 @@
 from typing import Any, NamedTuple
 
 import jax.numpy as jnp
-import numpyro as npro
+import numpyro
 from jax.typing import ArrayLike
 from pyrenew.deterministic import DeterministicVariable
 from pyrenew.metaclass import RandomVariable
@@ -191,7 +191,7 @@ def sample(
             latent_hospital_admissions * self.hosp_report_prob_rv(**kwargs)[0]
         )
 
-        npro.deterministic(
+        numpyro.deterministic(
             "latent_hospital_admissions", latent_hospital_admissions
         )
 

model/src/pyrenew/latent/infection_initialization_process.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 # numpydoc ignore=GL08
-import numpyro as npro
+import numpyro
 from pyrenew.latent.infection_initialization_method import (
     InfectionInitializationMethod,
 )
@@ -97,6 +97,6 @@ def sample(self) -> tuple:
         """
         (I_pre_init,) = self.I_pre_init_rv()
         infection_initialization = self.infection_init_method(I_pre_init)
-        npro.deterministic(self.name, infection_initialization)
+        numpyro.deterministic(self.name, infection_initialization)
 
         return (infection_initialization,)

model/src/pyrenew/latent/infectionswithfeedback.py

@@ -4,7 +4,7 @@
 from typing import NamedTuple
 
 import jax.numpy as jnp
-import numpyro as npro
+import numpyro
 import pyrenew.arrayutils as au
 import pyrenew.latent.infection_functions as inf
 from numpy.typing import ArrayLike
@@ -192,7 +192,7 @@ def sample(
 
         # Appending initial infections to the infections
 
-        npro.deterministic("Rt_adjusted", Rt_adj)
+        numpyro.deterministic("Rt_adjusted", Rt_adj)
 
         return InfectionsRtFeedbackSample(
             post_initialization_infections=post_initialization_infections,

model/src/pyrenew/metaclass.py

@@ -517,7 +517,7 @@ def posterior_predictive(
             Random key for the Predictive function call. Defaults to None.
         numpyro_predictive_args : dict, optional
             Dictionary of arguments to be passed to the
-            :class:`numpyro.inference.Predictive` constructor.
+            :class:`numpyro.infer.Predictive` constructor.
         **kwargs
             Additional named arguments passed to the
             `__call__()` method of :class:`numpyro.infer.Predictive`
@@ -559,9 +559,9 @@ def prior_predictive(
         rng_key : ArrayLike, optional
             Random key for the Predictive function call. Defaults to None.
         numpyro_predictive_args : dict, optional
-            Dictionary of arguments to be passed to the numpyro.inference.Predictive constructor.
+            Dictionary of arguments to be passed to the numpyro.infer.Predictive constructor.
         **kwargs
-            Additional named arguments passed to the `__call__()` method of numpyro.inference.Predictive
+            Additional named arguments passed to the `__call__()` method of numpyro.infer.Predictive
 
         Returns
         -------

model/src/test/test_forecast.py

@@ -3,7 +3,7 @@
 import jax.numpy as jnp
 import jax.random as jr
 import numpy as np
-import numpyro as npro
+import numpyro
 import numpyro.distributions as dist
 import pyrenew.transformation as t
 from numpy.testing import assert_array_equal
@@ -51,7 +51,7 @@ def test_forecast():
 
     n_datapoints = 30
     n_forecast_points = 10
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         model_sample = model.sample(n_datapoints=n_datapoints)
 
     model.run(

model/src/test/test_infection_seeding_process.py

@@ -1,6 +1,6 @@
 # numpydoc ignore=GL08
 import jax.numpy as jnp
-import numpyro as npro
+import numpyro
 import numpyro.distributions as dist
 import pytest
 from pyrenew.deterministic import DeterministicVariable
@@ -41,7 +41,7 @@ def test_infection_initialization_process():
     )
 
     for model in [zero_pad_model, exp_model, vec_model]:
-        with npro.handlers.seed(rng_seed=1):
+        with numpyro.handlers.seed(rng_seed=1):
             model()
 
     # Check that the InfectionInitializationProcess class raises an error when the wrong type of I0 is passed

model/src/test/test_infectionsrtfeedback.py

@@ -4,7 +4,7 @@
 
 import jax.numpy as jnp
 import numpy as np
-import numpyro as npro
+import numpyro
 import pyrenew.latent as latent
 from jax.typing import ArrayLike
 from numpy.testing import assert_array_almost_equal, assert_array_equal
@@ -81,7 +81,7 @@ def test_infectionsrtfeedback():
 
     infections = latent.Infections()
 
-    with npro.handlers.seed(rng_seed=0):
+    with numpyro.handlers.seed(rng_seed=0):
         samp1 = InfectionsWithFeedback(
             gen_int=gen_int,
             Rt=Rt,
@@ -125,7 +125,7 @@ def test_infectionsrtfeedback_feedback():
 
     infections = latent.Infections()
 
-    with npro.handlers.seed(rng_seed=0):
+    with numpyro.handlers.seed(rng_seed=0):
         samp1 = InfectionsWithFeedback(
             gen_int=gen_int,
             Rt=Rt,

model/src/test/test_latent_admissions.py

@@ -4,7 +4,7 @@
 import jax.numpy as jnp
 import numpy as np
 import numpy.testing as testing
-import numpyro as npro
+import numpyro
 import numpyro.distributions as dist
 from pyrenew import transformation as t
 from pyrenew.deterministic import DeterministicPMF
@@ -32,15 +32,15 @@ def test_admissions_sample():
         transforms=t.ExpTransform(),
     )
 
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         sim_rt, *_ = rt(n_steps=30)
 
     gen_int = jnp.array([0.5, 0.1, 0.1, 0.2, 0.1])
     i0 = 10 * jnp.ones_like(gen_int)
 
     inf1 = Infections()
 
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         inf_sampled1 = inf1(Rt=sim_rt, gen_int=gen_int, I0=i0)
 
     # Testing the hospital admissions
@@ -77,7 +77,7 @@ def test_admissions_sample():
         ),
     )
 
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         sim_hosp_1 = hosp1(latent_infections=inf_sampled1[0])
 
     testing.assert_array_less(

model/src/test/test_latent_infections.py

@@ -4,7 +4,7 @@
 import jax.numpy as jnp
 import numpy as np
 import numpy.testing as testing
-import numpyro as npro
+import numpyro
 import numpyro.distributions as dist
 import pyrenew.transformation as t
 import pytest
@@ -30,7 +30,7 @@ def test_infections_as_deterministic():
         transforms=t.ExpTransform(),
     )
 
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         sim_rt, *_ = rt(n_steps=30)
 
     gen_int = jnp.array([0.25, 0.25, 0.25, 0.25])
@@ -42,7 +42,7 @@ def test_infections_as_deterministic():
         I0=jnp.zeros(gen_int.size),
         gen_int=gen_int,
     )
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         inf_sampled1 = inf1(**obs)
         inf_sampled2 = inf1(**obs)
 
@@ -52,7 +52,7 @@ def test_infections_as_deterministic():
     )
 
     # Check that Initial infections vector must be at least as long as the generation interval.
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         with pytest.raises(ValueError):
             obs["I0"] = jnp.array([1])
             inf1(**obs)

model/src/test/test_model_basic_renewal.py

@@ -5,7 +5,7 @@
 import jax.numpy as jnp
 import jax.random as jr
 import numpy as np
-import numpyro as npro
+import numpyro
 import numpyro.distributions as dist
 import polars as pl
 import pyrenew.transformation as t
@@ -72,7 +72,7 @@ def test_model_basicrenewal_no_timepoints_or_observations():
     )
 
     np.random.seed(2203)
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         with pytest.raises(ValueError, match="Either"):
             model1.sample(n_datapoints=None, data_observed_infections=None)
 
@@ -103,7 +103,7 @@ def test_model_basicrenewal_both_timepoints_and_observations():
     )
 
     np.random.seed(2203)
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         with pytest.raises(ValueError, match="Cannot pass both"):
             model1.sample(
                 n_datapoints=30,
@@ -146,7 +146,7 @@ def test_model_basicrenewal_no_obs_model():
 
     # Sampling and fitting model 0 (with no obs for infections)
     np.random.seed(223)
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         model0_samp = model0.sample(n_datapoints=30)
     model0_samp.Rt
     model0_samp.latent_infections
@@ -155,7 +155,7 @@ def test_model_basicrenewal_no_obs_model():
     # Generating
     model0.infection_obs_process_rv = NullObservation()
     np.random.seed(223)
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         model1_samp = model0.sample(n_datapoints=30)
 
     np.testing.assert_array_equal(model0_samp.Rt, model1_samp.Rt)
@@ -219,7 +219,7 @@ def test_model_basicrenewal_with_obs_model():
 
     # Sampling and fitting model 1 (with obs infections)
     np.random.seed(2203)
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         model1_samp = model1.sample(n_datapoints=30)
 
     model1.run(
@@ -270,7 +270,7 @@ def test_model_basicrenewal_padding() -> None:  # numpydoc ignore=GL08
     # Sampling and fitting model 1 (with obs infections)
     np.random.seed(2203)
     pad_size = 5
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         model1_samp = model1.sample(n_datapoints=30, padding=pad_size)
 
     model1.run(

model/src/test/test_observation_negativebinom.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 import numpy.testing as testing
-import numpyro as npro
+import numpyro
 from jax.typing import ArrayLike
 from pyrenew.deterministic import DeterministicVariable
 from pyrenew.observation import NegativeBinomialObservation
@@ -21,7 +21,7 @@ def test_negativebinom_deterministic_obs():
 
     np.random.seed(223)
     rates = np.random.randint(1, 5, size=10)
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         sim_nb1 = negb(mu=rates, obs=rates)
         sim_nb2 = negb(mu=rates, obs=rates)
 
@@ -48,7 +48,7 @@ def test_negativebinom_random_obs():
 
     np.random.seed(223)
     rates = np.repeat(5, 20000)
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         sim_nb1 = negb(mu=rates)
         sim_nb2 = negb(mu=rates)
     assert isinstance(sim_nb1, tuple)

model/src/test/test_observation_poisson.py

@@ -4,7 +4,7 @@
 import jax.numpy as jnp
 import numpy as np
 import numpy.testing as testing
-import numpyro as npro
+import numpyro
 from pyrenew.observation import PoissonObservation
 
 
@@ -17,7 +17,7 @@ def test_poisson_obs():
 
     np.random.seed(223)
     rates = np.random.randint(1, 5, size=10)
-    with npro.handlers.seed(rng_seed=np.random.randint(1, 600)):
+    with numpyro.handlers.seed(rng_seed=np.random.randint(1, 600)):
         sim_pois, *_ = pois(mu=rates)
 
     testing.assert_array_equal(sim_pois, jnp.ceil(sim_pois))