Add __call__ As Alias For RandomVariable sample Method

docs/source/tutorials/extending_pyrenew.qmd

@@ -197,15 +197,15 @@ class InfFeedback(RandomVariable):
         I0_vec = I0[-gen_int_rev.size :]
 
         # Sampling inf feedback strength and adjusting the shape
-        inf_feedback_strength, *_ = self.infection_feedback_strength.sample(
+        inf_feedback_strength, *_ = self.infection_feedback_strength(
             **kwargs,
         )
         inf_feedback_strength = au.pad_x_to_match_y(
             x=inf_feedback_strength, y=Rt, fill_value=inf_feedback_strength[0]
         )
 
         # Sampling inf feedback and adjusting the shape
-        inf_feedback_pmf, *_ = self.infection_feedback_pmf.sample(**kwargs)
+        inf_feedback_pmf, *_ = self.infection_feedback_pmf(**kwargs)
         inf_fb_pmf_rev = jnp.flip(inf_feedback_pmf)
 
         # Generating the infections with feedback
@@ -230,13 +230,13 @@ class InfFeedback(RandomVariable):
 
 The core of the class is implemented in the `sample()` method. Things to highlight from the above code:
 
-1. **Arguments of `sample`**: The `InfFeedback` class will be used within `RtInfectionsRenewalModel` to generate latent infections. During the sampling process, `InfFeedback.sample()` will receive the reproduction number, the initial number of infections, and the generation interval. `RandomVariable.sample()` calls are expected to include the `**kwargs` argument, even if unused.
+1. **Arguments of `sample`**: The `InfFeedback` class will be used within `RtInfectionsRenewalModel` to generate latent infections. During the sampling process, `InfFeedback()` will receive the reproduction number, the initial number of infections, and the generation interval. `RandomVariable()` calls are expected to include the `**kwargs` argument, even if unused.
 
-2. **Calls to `RandomVariable.sample()`**: All calls to `RandomVariable.sample()` are expected to return a tuple or named tuple. In our implementation, we capture the output of `infection_feedback_strength.sample()` and `infection_feedback_pmf.sample()` in the variables `inf_feedback_strength` and `inf_feedback_pmf`, respectively, disregarding the other outputs (i.e., using `*_`).
+2. **Calls to `RandomVariable()`**: All calls to `RandomVariable()` are expected to return a tuple or named tuple. In our implementation, we capture the output of `infection_feedback_strength()` and `infection_feedback_pmf()` in the variables `inf_feedback_strength` and `inf_feedback_pmf`, respectively, disregarding the other outputs (i.e., using `*_`).
 
 3. **Saving computed quantities**: Since `Rt_adj` is not generated via `numpyro.sample()`, we use `numpyro.deterministic()` to record the quantity to a site; allowing us to access it later.
 
-4. **Return type of `InfFeedback.sample()`**: As said before, the `sample()` method should return a tuple or named tuple. In our case, we return a named tuple `InfFeedbackSample` with two fields: `infections` and `rt`.
+4. **Return type of `InfFeedback()`**: As said before, the `sample()` method should return a tuple or named tuple. In our case, we return a named tuple `InfFeedbackSample` with two fields: `infections` and `rt`.
 
 ```{python}
 # | label: simulation2

docs/source/tutorials/periodic_effects.qmd

@@ -40,7 +40,7 @@ rt_proc = process.RtWeeklyDiffProcess(
 
 ```{python}
 with npro.handlers.seed(rng_seed=20):
-    sim_data = rt_proc.sample(duration=30)
+    sim_data = rt_proc(duration=30)
 
 # Plotting the Rt values
 import matplotlib.pyplot as plt
@@ -84,7 +84,7 @@ Like before, we can use the `sample` method to generate samples from the day of
 
 ```{python}
 with npro.handlers.seed(rng_seed=20):
-    sim_data = dayofweek.sample(duration=30)
+    sim_data = dayofweek(duration=30)
 
 # Plotting the effect values
 import matplotlib.pyplot as plt

docs/source/tutorials/pyrenew_demo.qmd

@@ -37,15 +37,15 @@ import numpyro.distributions as dist
 from pyrenew.process import SimpleRandomWalkProcess
 ```
 
-To understand the simple random walk process underlying the sampling within the renewal process model, we first examine a single random walk path. Using the `sample` method from an instance of the `SimpleRandomWalkProcess` class, we first create an instance of the `SimpleRandomWalkProcess` class with a normal distribution of mean = 0 and standard deviation = 0.0001 as its input. Next, the `with` statement sets the seed for the random number generator for the n_timepoints of the block that follows. Inside the `with` block, the `q_samp = q.sample(n_timepoints=100)` generates the sample instance over a n_timepoints of 100 time units. Finally, this single random walk process is visualized using `matplot.pyplot` to plot the exponential of the sample instance.
+To understand the simple random walk process underlying the sampling within the renewal process model, we first examine a single random walk path. Using the `sample` method from an instance of the `SimpleRandomWalkProcess` class, we first create an instance of the `SimpleRandomWalkProcess` class with a normal distribution of mean = 0 and standard deviation = 0.0001 as its input. Next, the `with` statement sets the seed for the random number generator for the n_timepoints of the block that follows. Inside the `with` block, the `q_samp = q(n_timepoints=100)` generates the sample instance over a n_timepoints of 100 time units. Finally, this single random walk process is visualized using `matplot.pyplot` to plot the exponential of the sample instance.
 
 ```{python}
 # | label: fig-randwalk
 # | fig-cap: Random walk example
 np.random.seed(3312)
 q = SimpleRandomWalkProcess(dist.Normal(0, 0.001))
 with seed(rng_seed=np.random.randint(0, 1000)):
-    q_samp = q.sample(n_timepoints=100)
+    q_samp = q(n_timepoints=100)
 
 plt.plot(np.exp(q_samp[0]))
 ```

docs/source/tutorials/time.qmd

@@ -25,7 +25,7 @@ The tuple `(t_unit, t_start)` can encode different types of time series data. Fo
 
 ## How it relates to periodicity
 
-The `PeriodicBroadcaster()` class provides a way of tiling and repeating data accounting starting time, but it does not encode the time unit, only the period length and starting point. Furthermore, samples returned from `PeriodicEffect.sample()` and `RtPeriodicDiffProcess.sample()` both currently return daily values shifted so that the first entry of their arrays matches day 0 in the model.
+The `PeriodicBroadcaster()` class provides a way of tiling and repeating data accounting starting time, but it does not encode the time unit, only the period length and starting point. Furthermore, samples returned from `PeriodicEffect()` and `RtPeriodicDiffProcess()` both currently return daily values shifted so that the first entry of their arrays matches day 0 in the model.
 
 ## Unimplemented features
 
@@ -37,7 +37,7 @@ With random variables possibly spanning different time scales, *e.g.*, weekly, d
 
 ### Array alignment
 
-Using `t_unit` and `t_start`, random variables should be able to align input and output data. For example, in the case of the `RtInfectionsRenewalModel.sample()`, the computed values of `Rt` and `infections` are padded left with `nan` values to account for the seeding process. Instead, we expect to either pre-process the padding leveraging the `t_start` information of the involved variables or simplify the process via a function call that aligns the arrays. A possible implementation could be a method `align()` that takes a list of random variables and aligns them based on the `t_unit` and `t_start` information, e.g.:
+Using `t_unit` and `t_start`, random variables should be able to align input and output data. For example, in the case of the `RtInfectionsRenewalModel()`, the computed values of `Rt` and `infections` are padded left with `nan` values to account for the seeding process. Instead, we expect to either pre-process the padding leveraging the `t_start` information of the involved variables or simplify the process via a function call that aligns the arrays. A possible implementation could be a method `align()` that takes a list of random variables and aligns them based on the `t_unit` and `t_start` information, e.g.:
 
 ```python
 Rt_aligned, infections_aligned = align([Rt, infections])

model/src/pyrenew/arrayutils.py

@@ -98,7 +98,7 @@ def pad_x_to_match_y(
 
 class PeriodicProcessSample(NamedTuple):
     """
-    A container for holding the output from `process.PeriodicProcess.sample()`.
+    A container for holding the output from `process.PeriodicProcess()`.
 
     Attributes
     ----------

model/src/pyrenew/latent/hospitaladmissions.py

@@ -14,7 +14,7 @@
 
 class HospitalAdmissionsSample(NamedTuple):
     """
-    A container to hold the output of `latent.HospAdmissions.sample()`.
+    A container to hold the output of `latent.HospAdmissions()`.
 
     Attributes
     ----------
@@ -173,14 +173,14 @@ def sample(
         HospitalAdmissionsSample
         """
 
-        infection_hosp_rate, *_ = self.infect_hosp_rate_rv.sample(**kwargs)
+        infection_hosp_rate, *_ = self.infect_hosp_rate_rv(**kwargs)
 
         infection_hosp_rate_t = infection_hosp_rate * latent_infections
 
         (
             infection_to_admission_interval,
             *_,
-        ) = self.infection_to_admission_interval_rv.sample(**kwargs)
+        ) = self.infection_to_admission_interval_rv(**kwargs)
 
         latent_hospital_admissions = jnp.convolve(
             infection_hosp_rate_t,
@@ -191,13 +191,12 @@ def sample(
         # Applying the day of the week effect
         latent_hospital_admissions = (
             latent_hospital_admissions
-            * self.day_of_week_effect_rv.sample(**kwargs)[0]
+            * self.day_of_week_effect_rv(**kwargs)[0]
         )
 
         # Applying probability of hospitalization effect
         latent_hospital_admissions = (
-            latent_hospital_admissions
-            * self.hosp_report_prob_rv.sample(**kwargs)[0]
+            latent_hospital_admissions * self.hosp_report_prob_rv(**kwargs)[0]
         )
 
         npro.deterministic(

model/src/pyrenew/latent/infection_initialization_method.py

@@ -176,7 +176,7 @@ def seed_infections(self, I_pre_seed: ArrayLike):
             raise ValueError(
                 f"I_pre_seed must be an array of size 1. Got size {I_pre_seed.size}."
             )
-        (rate,) = self.rate.sample()
+        (rate,) = self.rate()
         if rate.size != 1:
             raise ValueError(
                 f"rate must be an array of size 1. Got size {rate.size}."

model/src/pyrenew/latent/infection_initialization_process.py

@@ -95,7 +95,7 @@ def sample(self) -> tuple:
         tuple
             a tuple where the only element is an array with the number of seeded infections at each time point.
         """
-        (I_pre_seed,) = self.I_pre_seed_rv.sample()
+        (I_pre_seed,) = self.I_pre_seed_rv()
         infection_seeding = self.infection_seed_method(I_pre_seed)
         npro.deterministic(self.name, infection_seeding)
 

model/src/pyrenew/latent/infections.py

@@ -13,7 +13,7 @@
 
 class InfectionsSample(NamedTuple):
     """
-    A container for holding the output from `latent.Infections.sample()`.
+    A container for holding the output from `latent.Infections()`.
 
     Attributes
     ----------

model/src/pyrenew/latent/infectionswithfeedback.py

@@ -156,7 +156,7 @@ def sample(
         I0 = I0[-gen_int_rev.size :]
 
         # Sampling inf feedback strength
-        inf_feedback_strength, *_ = self.infection_feedback_strength.sample(
+        inf_feedback_strength, *_ = self.infection_feedback_strength(
             **kwargs,
         )
 
@@ -175,7 +175,7 @@ def sample(
             )
 
         # Sampling inf feedback pmf
-        inf_feedback_pmf, *_ = self.infection_feedback_pmf.sample(**kwargs)
+        inf_feedback_pmf, *_ = self.infection_feedback_pmf(**kwargs)
 
         inf_fb_pmf_rev = jnp.flip(inf_feedback_pmf)
 

model/src/pyrenew/metaclass.py

@@ -25,7 +25,7 @@ def _assert_sample_and_rtype(
     """
     Return type-checking for RandomVariable's sample function
 
-    Objects passed as `RandomVariable` should (a) have a sample() method that
+    Objects passed as `RandomVariable` should (a) have a `sample()` method that
     (b) returns either a tuple or a named tuple.
 
     Parameters
@@ -103,7 +103,7 @@ class RandomVariable(metaclass=ABCMeta):
     are expected to be used internally mostly for tasks including padding,
     alignment of time series, and other time-aware operations.
 
-    Both attributes give information about the output of the sample() method,
+    Both attributes give information about the output of the `sample()` method,
     in other words, the relative time units of the returning value.
 
     Attributes
@@ -138,7 +138,7 @@ def set_timeseries(
         t_start : int
             The start of the time series relative to the
             model time. It could be negative, indicating
-            that the sample() method returns timepoints
+            that the `sample()` method returns timepoints
             that occur prior to the model t = 0.
 
         t_unit : int
@@ -202,6 +202,12 @@ def validate(**kwargs) -> None:
         """
         pass
 
+    def __call__(self, **kwargs):
+        """
+        Alias for `sample()`.
+        """
+        return self.sample(**kwargs)
+
 
 class DistributionalRVSample(NamedTuple):
     """

model/src/pyrenew/model/admissionsmodel.py

@@ -197,15 +197,15 @@ def sample(
             infection_hosp_rate,
             latent_hosp_admissions,
             *_,
-        ) = self.latent_hosp_admissions_rv.sample(
+        ) = self.latent_hosp_admissions_rv(
             latent_infections=basic_model.latent_infections,
             **kwargs,
         )
 
         (
             observed_hosp_admissions,
             *_,
-        ) = self.hosp_admission_obs_process_rv.sample(
+        ) = self.hosp_admission_obs_process_rv(
             mu=latent_hosp_admissions[-n_datapoints:],
             obs=data_observed_hosp_admissions,
             **kwargs,

model/src/pyrenew/model/rtinfectionsrenewalmodel.py

@@ -192,28 +192,28 @@ def sample(
             n_timepoints = n_timepoints_to_simulate + padding
         # Sampling from Rt (possibly with a given Rt, depending on
         # the Rt_process (RandomVariable) object.)
-        Rt, *_ = self.Rt_process_rv.sample(
+        Rt, *_ = self.Rt_process_rv(
             n_timepoints=n_timepoints,
             **kwargs,
         )
 
         # Getting the generation interval
-        gen_int, *_ = self.gen_int_rv.sample(**kwargs)
+        gen_int, *_ = self.gen_int_rv(**kwargs)
 
         # Sampling initial infections
-        I0, *_ = self.I0_rv.sample(**kwargs)
+        I0, *_ = self.I0_rv(**kwargs)
         # Sampling from the latent process
         (
             post_initialization_latent_infections,
             *_,
-        ) = self.latent_infections_rv.sample(
+        ) = self.latent_infections_rv(
             Rt=Rt,
             gen_int=gen_int,
             I0=I0,
             **kwargs,
         )
 
-        observed_infections, *_ = self.infection_obs_process_rv.sample(
+        observed_infections, *_ = self.infection_obs_process_rv(
             mu=post_initialization_latent_infections[padding:],
             obs=data_observed_infections,
             **kwargs,

model/src/pyrenew/process/firstdifferencear.py

@@ -51,13 +51,13 @@ def sample(
         Parameters
         ----------
         duration : int
-            Passed to ARProcess.sample().s
+            Passed to ARProcess()
         init_val : ArrayLike, optional
             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.sample(), by default "trend_rw"
+            Passed to ARProcess(), by default "trend_rw"
         **kwargs : dict, optional
             Additional keyword arguments passed through to internal sample()
             calls, should there be any.

model/src/pyrenew/process/periodiceffect.py

@@ -10,7 +10,7 @@
 class PeriodicEffectSample(NamedTuple):
     """
     A container for holding the output from
-    `process.PeriodicEffect.sample()`.
+    `process.PeriodicEffect()`.
 
     Attributes
     ----------

model/src/pyrenew/process/rtperiodicdiff.py

@@ -10,7 +10,7 @@
 
 class RtPeriodicDiffProcessSample(NamedTuple):
     """
-    A container for holding the output from `process.RtPeriodicDiffProcess.sample()`.
+    A container for holding the output from `process.RtPeriodicDiffProcess()`.
 
     Attributes
     ----------

model/src/pyrenew/process/rtrandomwalk.py

@@ -114,7 +114,7 @@ def sample(
 
         Rt0_trans = self.Rt_transform(Rt0)
         Rt_trans_proc = SimpleRandomWalkProcess(self.Rt_rw_dist)
-        Rt_trans_ts, *_ = Rt_trans_proc.sample(
+        Rt_trans_ts, *_ = Rt_trans_proc(
             n_timepoints=n_timepoints,
             name="Rt_transformed_rw",
             init=Rt0_trans,

model/src/pyrenew/regression.py

@@ -148,5 +148,11 @@ def sample(self) -> dict:
             coefficients=coefficients,
         )
 
+    def __call__(self, **kwargs):
+        """
+        Alias for `sample()`.
+        """
+        return self.sample(**kwargs)
+
     def __repr__(self):
         return "GLMPrediction " + str(self.name)

model/src/test/test_ar_process.py

@@ -14,14 +14,14 @@ def test_ar_can_be_sampled():
     ar1 = ARProcess(5, jnp.array([0.95]), jnp.array([0.5]))
     with numpyro.handlers.seed(rng_seed=62):
         # can sample with and without inits
-        ar1.sample(3532, inits=jnp.array([50.0]))
-        ar1.sample(5023)
+        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]))
     with numpyro.handlers.seed(rng_seed=62):
         # can sample with and without inits
-        ar3.sample(1230)
-        ar3.sample(52, inits=jnp.array([50.0, 49.9, 48.2]))
+        ar3(duration=1230)
+        ar3(duration=52, inits=jnp.array([50.0, 49.9, 48.2]))
 
 
 def test_ar_samples_correctly_distributed():
@@ -36,6 +36,6 @@ def test_ar_samples_correctly_distributed():
     with numpyro.handlers.seed(rng_seed=62):
         # check it regresses to mean
         # when started away from it
-        long_ts, *_ = ar1.sample(10000, inits=ar_inits)
+        long_ts, *_ = ar1(duration=10000, inits=ar_inits)
         assert_almost_equal(long_ts[0], ar_inits)
         assert jnp.abs(long_ts[-1] - ar_mean) < 4 * noise_sd

model/src/test/test_deterministic.py

@@ -31,24 +31,24 @@ def test_deterministic():
     var5 = NullProcess()
 
     testing.assert_array_equal(
-        var1.sample()[0],
+        var1()[0],
         jnp.array(
             [
                 1,
             ]
         ),
     )
     testing.assert_array_equal(
-        var2.sample()[0],
+        var2()[0],
         jnp.array([0.25, 0.25, 0.2, 0.3]),
     )
     testing.assert_array_equal(
-        var3.sample(duration=5)[0],
+        var3(duration=5)[0],
         jnp.array([1, 2, 3, 4, 4]),
     )
 
     testing.assert_array_equal(
-        var3.sample(duration=3)[0],
+        var3(duration=3)[0],
         jnp.array(
             [
                 1,
@@ -58,5 +58,5 @@ def test_deterministic():
         ),
     )
 
-    testing.assert_equal(var4.sample()[0], None)
-    testing.assert_equal(var5.sample(duration=1)[0], None)
+    testing.assert_equal(var4()[0], None)
+    testing.assert_equal(var5(duration=1)[0], None)