Formally addresses implementing the observed hospitalizations module

model/Makefile

@@ -7,9 +7,14 @@ test:
 docs: docs/pyrenew_demo.md docs/getting-started.md
 
 docs/pyrenew_demo.md: docs/pyrenew_demo.qmd
-	quarto render docs/pyrenew_demo.qmd
+	poetry run quarto render docs/pyrenew_demo.qmd
 
 docs/getting-started.md: docs/getting-started.qmd
-	quarto render docs/getting-started.qmd
+	poetry run quarto render docs/getting-started.qmd
 
-.PHONY: install test docs
+clean:
+	rm -rf docs/*_files/
+	rm -f docs/getting-started.ipynb
+	rm -f docs/pyrenew_demo.ipynb
+
+.PHONY: install test docs clean

model/README.md

@@ -1,12 +1,15 @@
-# PyRenew
+# PyRenew: A package for Bayesian renewal modeling with JAX and Numpyro.
 
-A package for Bayesian renewal modeling with JAX and Numpyro.
+`pyrenew` is a flexible tool for simulation and inference of epidemiological models with an emphasis on renewal models. Built on top of `numpyro`, `pyrenew` provides core components for model building as well as pre-defined models for processing various types of observational processes.
 
 ## Installation
+
 Install via pip with
+
 ```bash
 pip install git+https://github.com/cdcent/cfa-pyrenew.git
 ```
 
 ## Demo
-The `docs` folder contains a Jupyter notebook with an interactive demo to get you started. It simulates observed hospitalizations using a simple renewal process model and then fits to it using a No-U-Turn Sampler.
+
+The [`docs`](docs) folder contains quarto documents to get you started. It simulates observed hospitalizations using a simple renewal process model and then fits to it using a No-U-Turn Sampler.

model/docs/.gitignore

@@ -1 +1,2 @@
 !*png
+*_files/libs

model/docs/getting-started.md

@@ -1,127 +1,142 @@
 # Getting started with pyrenew
 
 
-This document illustrates two features of `pyrenew`: (a) the set of
-included `RandomVariable`es, and (b) model composition.
+`pyrenew` is a flexible tool for simulating and making statistical
+inference of epidemiological models, emphasizing renewal models. Built
+on `numpyro`, `pyrenew` provides core components for model building and
+pre-defined models for processing various observational processes. This
+document illustrates how `pyrenew` can be used to build a basic renewal
+model.
+
+## The fundamentals
+
+`pyrenew`’s core components are the metaclasses `RandomVariable` and
+`Model`. From the package’s perspective, a `RandomVariable` is a
+quantity models can sample and estimate, **including deterministic
+quantities**. Mainly, sampling from a `RandomVariable` involves calling
+the `sample()` method. The benefit of this design is the definition of
+the sample function can be arbitrary, allowing the user to either sample
+from a distribution using `numpyro.sample()`, compute fixed quantities
+(like a mechanistic equation,) or return a fixed value (like a
+pre-computed PMF.) For instance, we may be interested in estimating a
+PMF, in which case a `RandomVariable` sampling function may roughly be
+defined as:
 
-## Hospitalizations model
-
-`pyrenew` has five main components:
-
-- Utility and math functions,
-- The `processes` sub-module,
-- The `observations` sub-module,
-- The `latent` sub-module, and
-- The `models` sub-module
-
-All three of `process`, `observation`, and `latent` contain classes that
-inherit from the meta class `RandomVariable`. The classes under `model`
-inherit from the meta class `Model`. The following diagram illustrates
-the composition the model `pyrenew.models.HospitalizationsModel`:
-
-``` mermaid
-flowchart TB
-
-    subgraph randprocmod["Processes module"]
-        direction TB
-        simprw["SimpleRandomWalkProcess"]
-        rtrw["RtRandomWalkProcess"]
-    end
-
-    subgraph latentmod["Latent module"]
-        direction TB
-        hosp_latent["Hospitalizations"]
-        inf_latent["Infections"]
-    end
-
-    subgraph obsmod["Observations module"]
-        direction TB
-        pois["PoissonObservation"]
-        nb["NegativeBinomialObservation"]
-    end
-
-    subgraph models["Models module"]
-        direction TB
-        basic["RtInfectionsRenewalModel"]
-        hosp["HospitalizationsModel"]
-    end
-
-    rp(("RandomVariable")) --> |Inherited by| randprocmod
-    rp -->|Inherited by| latentmod
-    rp -->|Inherited by| obsmod
-
-
-    model(("Model")) -->|Inherited by| models
+``` python
+class MyRandVar(RandomVariable):
+    def sample(...) -> ArrayLike:
+        return numpyro.sample(...)
+```
 
-    simprw -->|Composes| rtrw
-    rtrw -->|Composes| basic
-    inf_latent -->|Composes| basic
-    basic -->|Composes| hosp
+Whereas, in some other cases, we may instead use a fixed quantity for
+that variable (like a pre-computed PMF,) where the `RandomVariable`’s
+sample function could be defined like:
 
+``` python
+class MyRandVar(RandomVariable):
+    def sample(...) -> ArrayLike:
+        return jax.numpy.array([0.2, 0.7, 0.1])
+```
 
-    obsmod -->|Composes|models
-    hosp_latent -->|Composes| hosp
+This way, when a `Model` samples from `MyRandVar`, it could be either
+adding random variables to be estimated (first case) or just retrieving
+some quantity needed for other calculations (second case.)
 
-    %% Metaclasses
-    classDef Metaclass color:black,fill:white
-    class rp,model Metaclass
+The `Model` metaclass provides basic functionality for estimating and
+simulation. Like `RandomVariable`, the `Model` metaclass has a
+`sample()` method that defines the model structure. Ultimately, models
+can be nested (or inherited,) providing a straightforward way to add
+layers of complexity.
 
-    %% Random process
-    classDef Randproc fill:purple,color:white
-    class rtrw,simprw Randproc
+## ‘Hello world’ model
 
-    %% Models
-    classDef Models fill:teal,color:white
-    class basic,hosp Models
-```
-
-We start by loading the needed components to build a basic renewal
-model:
+This section will show the steps to build a simple renewal model
+featuring a latent infection process, a random walk Rt process, and an
+observation process for the reported infections. We start by loading the
+needed components to build a basic renewal model:
 
 ``` python
 import jax.numpy as jnp
 import numpy as np
 import numpyro as npro
+import numpyro.distributions as dist
 from pyrenew.process import RtRandomWalkProcess
-from pyrenew.latent import Infections
+from pyrenew.latent import Infections, Infections0
 from pyrenew.observation import PoissonObservation
+from pyrenew.deterministic import DeterministicPMF
 from pyrenew.model import RtInfectionsRenewalModel
 ```
 
-    /home/xrd4/.cache/pypoetry/virtualenvs/pyrenew-B3vwhbMF-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
-      from .autonotebook import tqdm as notebook_tqdm
-    An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.
-
-In the basic renewal model we can define three components: Rt, latent
-infections, and observed infections.
+The basic renewal model defines five components: generation interval,
+initial infections, Rt, latent infections, and observed infections. In
+this example, the generation interval is not estimated but passed as a
+deterministic instance of `RandomVariable`. Here is the code to
+initialize the five components:
 
 ``` python
-latent_infections = Infections(
-    gen_int=jnp.array([0.25, 0.25, 0.25, 0.25]),
-    )
+# (1) The generation interval (deterministic)
+gen_int = DeterministicPMF(
+    (jnp.array([0.25, 0.25, 0.25, 0.25]),),
+)
 
-observed_infections = PoissonObservation(
-    rate_varname='latent',
-    counts_varname='observed_infections',
-    )
+# (2) Initial infections (inferred with a prior)
+I0 = Infections0(I0_dist=dist.LogNormal(0, 1))
 
+# (3) The random process for Rt
 rt_proc = RtRandomWalkProcess()
+
+# (4) Latent infection process (which will use 1 and 2)
+latent_infections = Infections()
+
+# (5) The observed infections process (with mean at the latent infections)
+observed_infections = PoissonObservation(
+    rate_varname   = 'latent',
+    counts_varname = 'observed_infections',
+    )
 ```
 
-With observation process for the latent infections, we can build the
-basic renewal model, and generate a sample calling the `sample()`
-method:
+With these five pieces, we can build the basic renewal model:
 
 ``` python
 model1 = RtInfectionsRenewalModel(
-    Rt_process=rt_proc,
-    latent_infections=latent_infections,
-    observed_infections=observed_infections,
+    gen_int             = gen_int,
+    I0                  = I0,
+    Rt_process          = rt_proc,
+    latent_infections   = latent_infections,
+    observed_infections = observed_infections,
     )
+```
+
+The following diagram summarizes how the modules interact via
+composition; notably, `gen_int`, `I0`, `rt_proc`, `latent_infections`,
+and `observed_infections` are instances of `RandomVariable`, which means
+these can be easily replaced to generate a different version of
+`RtInfectionsRenewalModel`:
+
+``` mermaid
+flowchart TB
+    genint["(1) gen_int\n(DetermnisticPMF)"]
+    i0["(2) I0\n(Infections0)"]
+    rt["(3) rt_proc\n(RtRandomWalkProcess)"]
+    inf["(4) latent_infections\n(Infections)"]
+    obs["(5) observed_infections\n(PoissonObservation)"]
+
+    model1["model1\n(RtInfectionsRenewalModel)"]
+
+    i0-->|Composes|model1
+    genint-->|Composes|model1
+    rt-->|Composes|model1
+    obs-->|Composes|model1
+    inf-->|Composes|model1
+```
 
+Using `numpyro`, we can simulate data using the `sample()` member
+function of `RtInfectionsRenewalModel`:
+
+``` python
 np.random.seed(223)
-with npro.handlers.seed(rng_seed=np.random.randint(1, 60)):
-    sim_data = model1.sample(constants=dict(n_timepoints=30))
+with npro.handlers.seed(rng_seed = np.random.randint(1, 60)):
+    sim_data = model1.sample(constants = dict(n_timepoints=30))
 
 sim_data
 ```
@@ -131,14 +146,14 @@ sim_data
            1.271196 , 1.3189521, 1.3054799, 1.3165426, 1.291952 , 1.3026639,
            1.2619467, 1.2852622, 1.3121517, 1.2888998, 1.2641873, 1.2580931,
            1.2545817, 1.3092988, 1.2488269, 1.2397509, 1.2071848, 1.2334517,
-           1.21868  ], dtype=float32), latent=Array([ 3.7023427,  4.850682 ,  6.4314823,  8.26245  ,  6.9874763,
-            7.940377 ,  9.171101 , 10.051114 , 10.633459 , 11.729475 ,
-           12.559867 , 13.422887 , 15.364211 , 17.50132  , 19.206314 ,
-           21.556652 , 23.78112  , 26.719398 , 28.792412 , 32.40454  ,
-           36.641006 , 40.135487 , 43.60607  , 48.055103 , 52.829704 ,
-           60.43277  , 63.97854  , 69.82776  , 74.564415 , 82.88904  ,
-           88.73811  ], dtype=float32), observed=Array([ 4,  3,  6,  5,  7,  7, 10, 11,  6,  9,  7, 13, 16, 19, 20, 27, 23,
-           31, 28, 30, 43, 42, 55, 57, 44, 52, 64, 52, 77, 85, 94],      dtype=int32))
+           1.21868  ], dtype=float32), latent=Array([ 2.3215084,  3.0415602,  4.0327816,  5.180868 ,  4.381411 ,
+            4.978916 ,  5.750626 ,  6.3024273,  6.66758  ,  7.354823 ,
+            7.8755097,  8.416656 ,  9.63394  , 10.973988 , 12.043082 ,
+           13.516833 , 14.911659 , 16.75407  , 18.053928 , 20.318869 ,
+           22.975292 , 25.166464 , 27.34265  , 30.13236  , 33.126217 ,
+           37.89362  , 40.11695  , 43.784634 , 46.754696 , 51.974545 ,
+           55.642136 ], dtype=float32), observed=Array([ 1,  2,  3,  5,  4,  4,  7,  4,  8,  4,  7,  3,  8, 12, 13, 18, 14,
+           20, 17, 18, 28, 27, 36, 37, 26, 31, 40, 27, 48, 54, 60],      dtype=int32))
 
 The `sample()` method of the `RtInfectionsRenewalModel` returns a list
 composed of the `Rt` and `infections` sequences.
@@ -162,11 +177,12 @@ plt.tight_layout()
 plt.show()
 ```
 
-<img
-src="getting-started_files/figure-commonmark/basic-fig-output-1.png"
-id="basic-fig" />
+![Rt and
+Infections](getting-started_files/figure-commonmark/basic-fig-output-1.png)
 
-Let’s see how the estimation would go
+To fit the model, we can use the `run()` method of the model
+`RtInfectionsRenewalModel`; an inherited method from the metaclass
+`Model`:
 
 ``` python
 import jax
@@ -183,7 +199,8 @@ model1.run(
     )
 ```
 
-Now, let’s investigate the output
+Now, let’s investigate the output, particularly the posterior
+distribution of the Rt estimates:
 
 ``` python
 import polars as pl
@@ -202,6 +219,64 @@ ax.set_yticks([0.5, 1, 2])
 ax.set_yscale("log")
 ```
 
-<img
-src="getting-started_files/figure-commonmark/output-rt-output-1.png"
-id="output-rt" />
+![Rt posterior
+distribution](getting-started_files/figure-commonmark/output-rt-output-1.png)
+
+## Architecture of pyrenew
+
+`pyrenew` leverages `numpyro`’s flexibility to build models via
+composition. As a principle, most objects in `pyrenew` can be treated as
+random variables we can sample. At the top-level `pyrenew` has two
+metaclass from which most objects inherit: `RandomVariable` and `Model`.
+From them, the following four sub-modules arise:
+
+- The `process` sub-module,
+- The `deterministic` sub-module,
+- The `observation` sub-module,
+- The `latent` sub-module, and
+- The `models` sub-module
+
+The first four are collections of instances of `RandomVariable`, and the
+last is a collection of instances of `Model`. The following diagram
+shows a detailed view of how meta classes, modules, and classes interact
+to create the `RtInfectionsRenewalModel` instantiated in the previous
+section:
+
+``` mermaid
+flowchart LR
+    rand((RandomVariable\nmetaclass))
+    models((Model\nmetaclass))
+
+    subgraph observations[Observations module]
+        obs["observed_infections\n(PoissonObservation)"]
+    end
+
+    subgraph latent[Latent module]
+        inf["latent_infections\n(Infections)"]
+        i0["I0\n(Infections0)"]
+    end
+
+    subgraph process[Process module]
+        rt["rt_proc\n(RtRandomWalkProcess)"]
+    end
+
+    subgraph deterministic[Deterministic module]
+        detpmf["gen_int\n(DeterministicPMF)"]
+    end
+
+    subgraph model[Model module]
+        model1["model1\n(RtInfectionsRenewalModel)"]
+    end
+
+    rand-->|Inherited by|observations
+    rand-->|Inherited by|process
+    rand-->|Inherited by|latent
+    rand-->|Inherited by|deterministic
+    models-->|Inherited by|model
+
+    detpmf-->|Composes|model1
+    i0-->|Composes|model1
+    rt-->|Composes|model1
+    obs-->|Composes|model1
+    inf-->|Composes|model1
+```