Merge branch 'main' into 332-tests-for-_assert_sample_and_rtype

.github/workflows/deptry.yml

@@ -0,0 +1,32 @@
+name: model
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  dependency-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: cache poetry
+        uses: actions/cache@v4
+        with:
+          path: ~/.local
+          key: ${{ runner.os }}-poetry
+
+      - name: install poetry
+        run: pip install poetry
+
+      - name: install package
+        run: poetry install --with dev
+
+      - name: run deptry
+        run: |
+          poetry run deptry . --per-rule-ignores "DEP001=pyrenew,DEP003=pytest"

docs/source/tutorials/basic_renewal_model.qmd

@@ -152,7 +152,7 @@ class MyRt(RandomVariable):
                     reparam=LocScaleReparam(0),
                 ),
                 init_rv=DistributionalRV(
-                    name="init_log_Rt_rv",
+                    name="init_log_rt",
                     dist=dist.Normal(jnp.log(1), jnp.log(1.2)),
                 ),
             ),

docs/source/tutorials/extending_pyrenew.qmd

@@ -69,9 +69,7 @@ rt = TransformedRandomVariable(
         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)
-        ),
+        init_rv=DistributionalRV(name="init_log_rt", dist=dist.Normal(0, 0.2)),
     ),
     transforms=t.ExpTransform(),
 )

docs/source/tutorials/hospital_admissions_model.qmd

@@ -202,7 +202,7 @@ class MyRt(metaclass.RandomVariable):
                     name="rw_step_rv", dist=dist.Normal(0, sd_rt.value)
                 ),
                 init_rv=metaclass.DistributionalRV(
-                    name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+                    name="init_log_rt", dist=dist.Normal(0, 0.2)
                 ),
             ),
             transforms=transformation.ExpTransform(),

docs/source/tutorials/periodic_effects.qmd

@@ -27,13 +27,13 @@ from pyrenew import process, deterministic
 rt_proc = process.RtWeeklyDiffProcess(
     name="rt_weekly_diff",
     offset=0,
-    log_rt_prior=deterministic.DeterministicVariable(
-        name="log_rt_prior", value=jnp.array([0.1, 0.2])
+    log_rt_rv=deterministic.DeterministicVariable(
+        name="log_rt", value=jnp.array([0.1, 0.2])
     ),
-    autoreg=deterministic.DeterministicVariable(
+    autoreg_rv=deterministic.DeterministicVariable(
         name="autoreg", value=jnp.array([0.7])
     ),
-    periodic_diff_sd=deterministic.DeterministicVariable(
+    periodic_diff_sd_rv=deterministic.DeterministicVariable(
         name="periodic_diff_sd", value=jnp.array([0.1])
     ),
 )

model/src/pyrenew/deterministic/deterministic.py

@@ -40,16 +40,16 @@ def __init__(
         None
         """
         self.name = name
-        self.value = jnp.atleast_1d(value)
         self.validate(value)
+        self.value = jnp.atleast_1d(value)
         self.set_timeseries(t_start, t_unit)
 
         return None
 
     @staticmethod
     def validate(value: ArrayLike) -> None:
         """
-        Validates input to DeterministicPMF
+        Validates input to DeterministicVariable
 
         Parameters
         ----------
@@ -63,10 +63,14 @@ def validate(value: ArrayLike) -> None:
         Raises
         ------
         Exception
-            If the input value object is not a ArrayLike.
+            If the input value object is not an ArrayLike object.
         """
         if not isinstance(value, ArrayLike):
-            raise Exception("value is not a ArrayLike")
+            raise ValueError(
+                f"value {value} passed to a DeterministicVariable "
+                f"is of type {type(value).__name__}, expected "
+                "an ArrayLike object"
+            )
 
         return None
 

model/src/pyrenew/process/rtperiodicdiff.py

@@ -52,9 +52,9 @@ def __init__(
         name: str,
         offset: int,
         period_size: int,
-        log_rt_prior: RandomVariable,
-        autoreg: RandomVariable,
-        periodic_diff_sd: RandomVariable,
+        log_rt_rv: RandomVariable,
+        autoreg_rv: RandomVariable,
+        periodic_diff_sd_rv: RandomVariable,
     ) -> None:
         """
         Default constructor for RtPeriodicDiffProcess class.
@@ -66,11 +66,11 @@ def __init__(
         offset : int
             Relative point at which data starts, must be between 0 and
             period_size - 1.
-        log_rt_prior : RandomVariable
+        log_rt_rv : RandomVariable
             Log Rt prior for the first two observations.
-        autoreg : RandomVariable
+        autoreg_rv : RandomVariable
             Autoregressive parameter.
-        periodic_diff_sd : RandomVariable
+        periodic_diff_sd_rv : RandomVariable
             Standard deviation of the noise.
 
         Returns
@@ -85,45 +85,45 @@ def __init__(
         )
 
         self.validate(
-            log_rt_prior=log_rt_prior,
-            autoreg=autoreg,
-            periodic_diff_sd=periodic_diff_sd,
+            log_rt_rv=log_rt_rv,
+            autoreg_rv=autoreg_rv,
+            periodic_diff_sd_rv=periodic_diff_sd_rv,
         )
 
         self.period_size = period_size
         self.offset = offset
-        self.log_rt_prior = log_rt_prior
-        self.autoreg = autoreg
-        self.periodic_diff_sd = periodic_diff_sd
+        self.log_rt_rv = log_rt_rv
+        self.autoreg_rv = autoreg_rv
+        self.periodic_diff_sd_rv = periodic_diff_sd_rv
 
         return None
 
     @staticmethod
     def validate(
-        log_rt_prior: any,
-        autoreg: any,
-        periodic_diff_sd: any,
+        log_rt_rv: any,
+        autoreg_rv: any,
+        periodic_diff_sd_rv: any,
     ) -> None:
         """
         Validate the input parameters.
 
         Parameters
         ----------
-        log_rt_prior : any
+        log_rt_rv : any
             Log Rt prior for the first two observations.
-        autoreg : any
+        autoreg_rv : any
             Autoregressive parameter.
-        periodic_diff_sd : any
+        periodic_diff_sd_rv : any
             Standard deviation of the noise.
 
         Returns
         -------
         None
         """
 
-        _assert_sample_and_rtype(log_rt_prior)
-        _assert_sample_and_rtype(autoreg)
-        _assert_sample_and_rtype(periodic_diff_sd)
+        _assert_sample_and_rtype(log_rt_rv)
+        _assert_sample_and_rtype(autoreg_rv)
+        _assert_sample_and_rtype(periodic_diff_sd_rv)
 
         return None
 
@@ -175,19 +175,19 @@ def sample(
         """
 
         # Initial sample
-        log_rt_prior = self.log_rt_prior.sample(**kwargs)[0].value
-        b = self.autoreg.sample(**kwargs)[0].value
-        s_r = self.periodic_diff_sd.sample(**kwargs)[0].value
+        log_rt_rv = self.log_rt_rv.sample(**kwargs)[0].value
+        b = self.autoreg_rv.sample(**kwargs)[0].value
+        s_r = self.periodic_diff_sd_rv.sample(**kwargs)[0].value
 
         # How many periods to sample?
         n_periods = int(jnp.ceil(duration / self.period_size))
 
         # Running the process
-        ar_diff = FirstDifferenceARProcess("trend_rw", autoreg=b, noise_sd=s_r)
+        ar_diff = FirstDifferenceARProcess(self.name, autoreg=b, noise_sd=s_r)
         log_rt = ar_diff.sample(
             duration=n_periods,
-            init_val=log_rt_prior[1],
-            init_rate_of_change=log_rt_prior[1] - log_rt_prior[0],
+            init_val=log_rt_rv[1],
+            init_rate_of_change=log_rt_rv[1] - log_rt_rv[0],
         )[0]
 
         return RtPeriodicDiffProcessSample(
@@ -208,9 +208,9 @@ def __init__(
         self,
         name: str,
         offset: int,
-        log_rt_prior: RandomVariable,
-        autoreg: RandomVariable,
-        periodic_diff_sd: RandomVariable,
+        log_rt_rv: RandomVariable,
+        autoreg_rv: RandomVariable,
+        periodic_diff_sd_rv: RandomVariable,
     ) -> None:
         """
         Default constructor for RtWeeklyDiffProcess class.
@@ -221,11 +221,11 @@ def __init__(
             Name of the site.
         offset : int
             Relative point at which data starts, must be between 0 and 6.
-        log_rt_prior : RandomVariable
+        log_rt_rv : RandomVariable
             Log Rt prior for the first two observations.
-        autoreg : RandomVariable
+        autoreg_rv : RandomVariable
             Autoregressive parameter.
-        periodic_diff_sd : RandomVariable
+        periodic_diff_sd_rv : RandomVariable
             Standard deviation of the noise.
 
         Returns
@@ -237,9 +237,9 @@ def __init__(
             name=name,
             offset=offset,
             period_size=7,
-            log_rt_prior=log_rt_prior,
-            autoreg=autoreg,
-            periodic_diff_sd=periodic_diff_sd,
+            log_rt_rv=log_rt_rv,
+            autoreg_rv=autoreg_rv,
+            periodic_diff_sd_rv=periodic_diff_sd_rv,
         )
 
         return None

model/src/test/test_deterministic.py

@@ -1,7 +1,11 @@
 # numpydoc ignore=GL08
 
+import re
+
 import jax.numpy as jnp
+import numpy as np
 import numpy.testing as testing
+import pytest
 from pyrenew.deterministic import (
     DeterministicPMF,
     DeterministicProcess,
@@ -62,3 +66,49 @@ def test_deterministic():
 
     testing.assert_equal(var4()[0].value, None)
     testing.assert_equal(var5(duration=1)[0].value, None)
+
+
+def test_deterministic_validation():
+    """
+    Check that validation methods for DeterministicVariable
+    work as expected.
+    """
+    # validation should fail on construction
+    some_non_array_likes = [
+        {"a": jnp.array([1, 2.5, 3])},
+        # a valid pytree, but not an arraylike
+        "a string",
+    ]
+    some_array_likes = [
+        5,
+        -3.023523,
+        np.array([1, 3.32, 5]),
+        jnp.array([-32, 23]),
+        jnp.array(-32),
+        np.array(5),
+    ]
+
+    for non_arraylike_val in some_non_array_likes:
+        matchval = re.escape(
+            f"value {non_arraylike_val} passed to a "
+            "DeterministicVariable is of type "
+            f"{type(non_arraylike_val).__name__}, expected "
+            "an ArrayLike object"
+        )
+
+        with pytest.raises(ValueError, match=matchval):
+            # the class's validation function itself
+            # should raise an error when passed a
+            # non arraylike value
+            DeterministicVariable.validate(non_arraylike_val)
+
+        with pytest.raises(ValueError, match=matchval):
+            # validation should fail on constructor call
+            DeterministicVariable(
+                value=non_arraylike_val, name="invalid_variable"
+            )
+
+    # validation should succeed with ArrayLike
+    for arraylike_val in some_array_likes:
+        DeterministicVariable.validate(arraylike_val)
+        DeterministicVariable(value=arraylike_val, name="valid_variable")

model/src/test/test_forecast.py

@@ -38,7 +38,7 @@ def test_forecast():
                 name="rw_step_rv", dist=dist.Normal(0, 0.025)
             ),
             init_rv=DistributionalRV(
-                name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+                name="init_log_rt", dist=dist.Normal(0, 0.2)
             ),
         ),
         transforms=t.ExpTransform(),

model/src/test/test_latent_admissions.py

@@ -28,7 +28,7 @@ def test_admissions_sample():
                 name="rw_step_rv", dist=dist.Normal(0, 0.025)
             ),
             init_rv=DistributionalRV(
-                name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+                name="init_log_rt", dist=dist.Normal(0, 0.2)
             ),
         ),
         transforms=t.ExpTransform(),

model/src/test/test_latent_infections.py

@@ -26,7 +26,7 @@ def test_infections_as_deterministic():
                 name="rw_step_rv", dist=dist.Normal(0, 0.025)
             ),
             init_rv=DistributionalRV(
-                name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+                name="init_log_rt", dist=dist.Normal(0, 0.2)
             ),
         ),
         transforms=t.ExpTransform(),

model/src/test/test_model_basic_renewal.py

@@ -41,7 +41,7 @@ def get_default_rt():
                 name="rw_step_rv", dist=dist.Normal(0, 0.025)
             ),
             init_rv=DistributionalRV(
-                name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+                name="init_log_rt", dist=dist.Normal(0, 0.2)
             ),
         ),
         transforms=t.ExpTransform(),

model/src/test/test_model_hosp_admissions.py

@@ -51,7 +51,7 @@ def get_default_rt():
                 name="rw_step_rv", dist=dist.Normal(0, 0.025)
             ),
             init_rv=DistributionalRV(
-                name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+                name="init_log_rt", dist=dist.Normal(0, 0.2)
             ),
         ),
         transforms=t.ExpTransform(),

model/src/test/test_predictive.py

@@ -36,9 +36,7 @@
         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)
-        ),
+        init_rv=DistributionalRV(name="init_log_rt", dist=dist.Normal(0, 0.2)),
     ),
     transforms=t.ExpTransform(),
 )

model/src/test/test_random_key.py

@@ -42,7 +42,7 @@ def create_test_model():  # numpydoc ignore=GL08
                 name="rw_step_rv", dist=dist.Normal(0, 0.025)
             ),
             init_rv=DistributionalRV(
-                name="init_log_Rt_rv", dist=dist.Normal(0, 0.2)
+                name="init_log_rt", dist=dist.Normal(0, 0.2)
             ),
         ),
         transforms=t.ExpTransform(),