Renamed model.recompute_initial_point to model.compute_initial_point

pymc/backends/base.py

@@ -70,9 +70,9 @@ def __init__(self, name, model=None, vars=None, test_point=None):
         # Get variable shapes. Most backends will need this
         # information.
         if test_point is None:
-            test_point = model.recompute_initial_point()
+            test_point = model.compute_initial_point()
         else:
-            test_point_ = model.recompute_initial_point().copy()
+            test_point_ = model.compute_initial_point().copy()
             test_point_.update(test_point)
             test_point = test_point_
         var_values = list(zip(self.varnames, self.fn(test_point)))

pymc/bart/pgbart.py

@@ -58,7 +58,7 @@ class PGBART(ArrayStepShared):
     def __init__(self, vars=None, num_particles=40, max_stages=100, batch="auto", model=None):
         _log.warning("BART is experimental. Use with caution.")
         model = modelcontext(model)
-        initial_values = model.recompute_initial_point()
+        initial_values = model.compute_initial_point()
         value_bart = inputvars(vars)[0]
         self.bart = model.values_to_rvs[value_bart].owner.op
 

pymc/model.py

@@ -639,7 +639,7 @@ def logp_dlogp_function(self, grad_vars=None, tempered=False, **kwargs):
 
         input_vars = {i for i in graph_inputs(costs) if not isinstance(i, Constant)}
         extra_vars = [self.rvs_to_values.get(var, var) for var in self.free_RVs]
-        ip = self.recompute_initial_point(0)
+        ip = self.compute_initial_point(0)
         extra_vars_and_values = {
             var: ip[var.name] for var in extra_vars if var in input_vars and var not in grad_vars
         }
@@ -987,24 +987,24 @@ def cont_vars(self):
 
     @property
     def test_point(self) -> Dict[str, np.ndarray]:
-        """Deprecated alias for `Model.recompute_initial_point(seed=None)`."""
+        """Deprecated alias for `Model.compute_initial_point(seed=None)`."""
         warnings.warn(
-            "`Model.test_point` has been deprecated. Use `Model.recompute_initial_point(seed=None)`.",
+            "`Model.test_point` has been deprecated. Use `Model.compute_initial_point(seed=None)`.",
             FutureWarning,
         )
-        return self.recompute_initial_point()
+        return self.compute_initial_point()
 
     @property
     def initial_point(self) -> Dict[str, np.ndarray]:
-        """Deprecated alias for `Model.recompute_initial_point(seed=None)`."""
+        """Deprecated alias for `Model.compute_initial_point(seed=None)`."""
         warnings.warn(
-            "`Model.initial_point` has been deprecated. Use `Model.recompute_initial_point(seed=None)`.",
+            "`Model.initial_point` has been deprecated. Use `Model.compute_initial_point(seed=None)`.",
             FutureWarning,
         )
-        return self.recompute_initial_point()
+        return self.compute_initial_point()
 
-    def recompute_initial_point(self, seed=None) -> Dict[str, np.ndarray]:
-        """Recomputes the initial point of the model.
+    def compute_initial_point(self, seed=None) -> Dict[str, np.ndarray]:
+        """Computes the initial point of the model.
 
         Returns
         -------
@@ -1540,7 +1540,7 @@ def profile(self, outs, *, n=1000, point=None, profile=True, **kwargs):
         kwargs.setdefault("on_unused_input", "ignore")
         f = self.compile_fn(outs, inputs=self.value_vars, point_fn=False, profile=profile, **kwargs)
         if point is None:
-            point = self.recompute_initial_point()
+            point = self.compute_initial_point()
 
         for _ in range(n):
             f(**point)
@@ -1699,7 +1699,7 @@ def point_logps(self, point=None, round_vals=2):
         Pandas Series
         """
         if point is None:
-            point = self.recompute_initial_point()
+            point = self.compute_initial_point()
 
         factors = self.basic_RVs + self.potentials
         return Series(

pymc/sampling_jax.py

@@ -170,7 +170,7 @@ def sample_numpyro_nuts(
     print("Compiling...", file=sys.stdout)
 
     rv_names = [rv.name for rv in model.value_vars]
-    initial_point = model.recompute_initial_point()
+    initial_point = model.compute_initial_point()
     init_state = [initial_point[rv_name] for rv_name in rv_names]
     init_state_batched = jax.tree_map(lambda x: np.repeat(x[None, ...], chains, axis=0), init_state)
 

pymc/smc/smc.py

@@ -191,7 +191,7 @@ def _initialize_kernel(self):
 
         """
         # Create dictionary that stores original variables shape and size
-        initial_point = self.model.recompute_initial_point(seed=self.rng.integers(2 ** 30))
+        initial_point = self.model.compute_initial_point(seed=self.rng.integers(2 ** 30))
         for v in self.variables:
             self.var_info[v.name] = (initial_point[v.name].shape, initial_point[v.name].size)
         # Create particles bijection map

pymc/step_methods/hmc/base_hmc.py

@@ -102,7 +102,7 @@ def __init__(
         # size.
         # XXX: If the dimensions of these terms change, the step size
         # dimension-scaling should change as well, no?
-        test_point = self._model.recompute_initial_point()
+        test_point = self._model.compute_initial_point()
 
         nuts_vars = [test_point[v.name] for v in vars]
         size = sum(v.size for v in nuts_vars)

pymc/step_methods/metropolis.py

@@ -161,7 +161,7 @@ def __init__(
         """
 
         model = pm.modelcontext(model)
-        initial_values = model.recompute_initial_point()
+        initial_values = model.compute_initial_point()
 
         if vars is None:
             vars = model.value_vars
@@ -425,7 +425,7 @@ def __init__(self, vars, order="random", transit_p=0.8, model=None):
         # transition probabilities
         self.transit_p = transit_p
 
-        initial_point = model.recompute_initial_point()
+        initial_point = model.compute_initial_point()
         vars = [model.rvs_to_values.get(var, var) for var in vars]
         self.dim = sum(initial_point[v.name].size for v in vars)
 
@@ -510,7 +510,7 @@ def __init__(self, vars, proposal="uniform", order="random", model=None):
         vars = [model.rvs_to_values.get(var, var) for var in vars]
         vars = pm.inputvars(vars)
 
-        initial_point = model.recompute_initial_point()
+        initial_point = model.compute_initial_point()
 
         dimcats = []
         # The above variable is a list of pairs (aggregate dimension, number
@@ -712,7 +712,7 @@ def __init__(
     ):
 
         model = pm.modelcontext(model)
-        initial_values = model.recompute_initial_point()
+        initial_values = model.compute_initial_point()
         initial_values_size = sum(initial_values[n.name].size for n in model.value_vars)
 
         if vars is None:
@@ -863,7 +863,7 @@ def __init__(
         **kwargs
     ):
         model = pm.modelcontext(model)
-        initial_values = model.recompute_initial_point()
+        initial_values = model.compute_initial_point()
         initial_values_size = sum(initial_values[n.name].size for n in model.value_vars)
 
         if vars is None:

pymc/step_methods/mlda.py

@@ -52,7 +52,7 @@ def __init__(self, *args, **kwargs):
         and some extra code specific for MLDA.
         """
         model = pm.modelcontext(kwargs.get("model", None))
-        initial_values = model.recompute_initial_point()
+        initial_values = model.compute_initial_point()
 
         # flag to that variance reduction is activated - forces MetropolisMLDA
         # to store quantities of interest in a register if True
@@ -114,7 +114,7 @@ def __init__(self, *args, **kwargs):
         self.tuning_end_trigger = False
 
         model = pm.modelcontext(kwargs.get("model", None))
-        initial_values = model.recompute_initial_point()
+        initial_values = model.compute_initial_point()
 
         # flag to that variance reduction is activated - forces DEMetropolisZMLDA
         # to store quantities of interest in a register if True
@@ -381,7 +381,7 @@ def __init__(
 
         # assign internal state
         model = pm.modelcontext(model)
-        initial_values = model.recompute_initial_point()
+        initial_values = model.compute_initial_point()
         self.model = model
         self.coarse_models = coarse_models
         self.model_below = self.coarse_models[-1]

pymc/tests/models.py

@@ -32,7 +32,7 @@ def simple_model():
     with Model() as model:
         Normal("x", mu, tau=tau, size=2, initval=floatX_array([0.1, 0.1]))
 
-    return model.recompute_initial_point(), model, (mu, tau ** -0.5)
+    return model.compute_initial_point(), model, (mu, tau ** -0.5)
 
 
 def simple_categorical():
@@ -43,7 +43,7 @@ def simple_categorical():
 
     mu = np.dot(p, v)
     var = np.dot(p, (v - mu) ** 2)
-    return model.recompute_initial_point(), model, (mu, var)
+    return model.compute_initial_point(), model, (mu, var)
 
 
 def multidimensional_model():
@@ -52,7 +52,7 @@ def multidimensional_model():
     with Model() as model:
         Normal("x", mu, tau=tau, size=(3, 2), initval=0.1 * np.ones((3, 2)))
 
-    return model.recompute_initial_point(), model, (mu, tau ** -0.5)
+    return model.compute_initial_point(), model, (mu, tau ** -0.5)
 
 
 def simple_arbitrary_det():
@@ -67,7 +67,7 @@ def arbitrary_det(value):
         b = arbitrary_det(a)
         Normal("obs", mu=b.astype("float64"), observed=floatX_array([1, 3, 5]))
 
-    return model.recompute_initial_point(), model
+    return model.compute_initial_point(), model
 
 
 def simple_init():
@@ -84,7 +84,7 @@ def simple_2model():
         x = pm.Normal("x", mu, tau=tau, initval=0.1)
         pm.Deterministic("logx", at.log(x))
         pm.Bernoulli("y", p)
-    return model.recompute_initial_point(), model
+    return model.compute_initial_point(), model
 
 
 def simple_2model_continuous():
@@ -94,7 +94,7 @@ def simple_2model_continuous():
         x = pm.Normal("x", mu, tau=tau, initval=0.1)
         pm.Deterministic("logx", at.log(x))
         pm.Beta("y", alpha=1, beta=1, size=2)
-    return model.recompute_initial_point(), model
+    return model.compute_initial_point(), model
 
 
 def mv_simple():
@@ -110,7 +110,7 @@ def mv_simple():
         )
     H = tau
     C = np.linalg.inv(H)
-    return model.recompute_initial_point(), model, (mu, C)
+    return model.compute_initial_point(), model, (mu, C)
 
 
 def mv_simple_coarse():
@@ -126,7 +126,7 @@ def mv_simple_coarse():
         )
     H = tau
     C = np.linalg.inv(H)
-    return model.recompute_initial_point(), model, (mu, C)
+    return model.compute_initial_point(), model, (mu, C)
 
 
 def mv_simple_very_coarse():
@@ -142,7 +142,7 @@ def mv_simple_very_coarse():
         )
     H = tau
     C = np.linalg.inv(H)
-    return model.recompute_initial_point(), model, (mu, C)
+    return model.compute_initial_point(), model, (mu, C)
 
 
 def mv_simple_discrete():
@@ -160,7 +160,7 @@ def mv_simple_discrete():
             else:
                 C[i, j] = -n * p[i] * p[j]
 
-    return model.recompute_initial_point(), model, (mu, C)
+    return model.compute_initial_point(), model, (mu, C)
 
 
 def mv_prior_simple():
@@ -186,27 +186,27 @@ def mv_prior_simple():
         x = pm.Flat("x", size=n)
         x_obs = pm.MvNormal("x_obs", observed=obs, mu=x, cov=noise * np.eye(n))
 
-    return model.recompute_initial_point(), model, (K, L, mu_post, std_post, noise)
+    return model.compute_initial_point(), model, (K, L, mu_post, std_post, noise)
 
 
 def non_normal(n=2):
     with pm.Model() as model:
         pm.Beta("x", 3, 3, size=n, transform=None)
-    return model.recompute_initial_point(), model, (np.tile([0.5], n), None)
+    return model.compute_initial_point(), model, (np.tile([0.5], n), None)
 
 
 def exponential_beta(n=2):
     with pm.Model() as model:
         pm.Beta("x", 3, 1, size=n, transform=None)
         pm.Exponential("y", 1, size=n, transform=None)
-    return model.recompute_initial_point(), model, None
+    return model.compute_initial_point(), model, None
 
 
 def beta_bernoulli(n=2):
     with pm.Model() as model:
         pm.Beta("x", 3, 1, size=n, transform=None)
         pm.Bernoulli("y", 0.5)
-    return model.recompute_initial_point(), model, None
+    return model.compute_initial_point(), model, None
 
 
 def simple_normal(bounded_prior=False):
@@ -222,4 +222,4 @@ def simple_normal(bounded_prior=False):
             mu_i = pm.Flat("mu_i")
         pm.Normal("X_obs", mu=mu_i, sigma=sd, observed=x0)
 
-    return model.recompute_initial_point(), model, None
+    return model.compute_initial_point(), model, None

pymc/tests/test_aesaraf.py

@@ -108,7 +108,7 @@ def test_make_shared_replacements(self):
 
         # Replace test1 with a shared variable, keep test 2 the same
         replacement = pm.make_shared_replacements(
-            test_model.recompute_initial_point(), [test_model.test2], test_model
+            test_model.compute_initial_point(), [test_model.test2], test_model
         )
         assert (
             test_model.test1.broadcastable

pymc/tests/test_data_container.py

@@ -34,7 +34,7 @@ def test_deterministic(self):
         with pm.Model() as model:
             X = pm.MutableData("X", data_values)
             pm.Normal("y", 0, 1, observed=X)
-            model.compile_logp()(model.recompute_initial_point())
+            model.compile_logp()(model.compute_initial_point())
 
     def test_sample(self):
         x = np.random.normal(size=100)

pymc/tests/test_distributions.py

@@ -2670,7 +2670,7 @@ def test_bound_shapes(self):
             bound_shaped = Bound("boundedshaped", dist, lower=1, upper=10, shape=(3, 5))
             bound_dims = Bound("boundeddims", dist, lower=1, upper=10, dims="sample")
 
-        initial_point = m.recompute_initial_point()
+        initial_point = m.compute_initial_point()
         dist_size = initial_point["boundedsized_interval__"].shape
         dist_shape = initial_point["boundedshaped_interval__"].shape
         dist_dims = initial_point["boundeddims_interval__"].shape

pymc/tests/test_distributions_random.py

@@ -1810,7 +1810,7 @@ def test_mixture_random_shape():
     assert rand3.shape == (100, 20)
 
     with m:
-        ppc = pm.sample_posterior_predictive([m.recompute_initial_point()], samples=200)
+        ppc = pm.sample_posterior_predictive([m.compute_initial_point()], samples=200)
     assert ppc["like0"].shape == (200, 20)
     assert ppc["like1"].shape == (200, 20)
     assert ppc["like2"].shape == (200, 20)

pymc/tests/test_distributions_timeseries.py

@@ -42,7 +42,7 @@ def test_AR():
         rho = Normal("rho", 0.0, 1.0)
         y1 = AR1("y1", rho, 1.0, observed=data)
         y2 = AR("y2", rho, 1.0, init=Normal.dist(0, 1), observed=data)
-    initial_point = t.recompute_initial_point()
+    initial_point = t.compute_initial_point()
     np.testing.assert_allclose(y1.logp(initial_point), y2.logp(initial_point))
 
     # AR1 + constant
@@ -78,7 +78,7 @@ def test_AR_nd():
             AR("y_%d" % i, beta[:, i], sigma=1.0, shape=T, initval=y_tp[:, i])
 
     np.testing.assert_allclose(
-        t0.logp(t0.recompute_initial_point()), t1.logp(t1.recompute_initial_point())
+        t0.logp(t0.compute_initial_point()), t1.logp(t1.compute_initial_point())
     )