Revert "Add start_sigma to ADVI (#6096)" (#6130)

This reverts commit ec27b5c.

Author: ricardoV94

pymc/tests/test_variational_inference.py

@@ -571,28 +571,6 @@ def test_fit_oo(inference, fit_kwargs, simple_model_data):
     np.testing.assert_allclose(np.std(trace.posterior["mu"]), np.sqrt(1.0 / d), rtol=0.2)
 
 
-def test_fit_start(inference_spec, simple_model):
-    mu_init = 17
-    mu_sigma_init = 13
-
-    with simple_model:
-        if type(inference_spec()) == ADVI:
-            has_start_sigma = True
-        else:
-            has_start_sigma = False
-
-    kw = {"start": {"mu": mu_init}}
-    if has_start_sigma:
-        kw.update({"start_sigma": {"mu": mu_sigma_init}})
-
-    with simple_model:
-        inference = inference_spec(**kw)
-    trace = inference.fit(n=0).sample(10000)
-    np.testing.assert_allclose(np.mean(trace.posterior["mu"]), mu_init, rtol=0.05)
-    if has_start_sigma:
-        np.testing.assert_allclose(np.std(trace.posterior["mu"]), mu_sigma_init, rtol=0.05)
-
-
 def test_profile(inference):
     inference.run_profiling(n=100).summary()
 

pymc/variational/approximations.py

@@ -67,27 +67,12 @@ def std(self):
     def __init_group__(self, group):
         super().__init_group__(group)
         if not self._check_user_params():
-            self.shared_params = self.create_shared_params(
-                self._kwargs.get("start", None), self._kwargs.get("start_sigma", None)
-            )
+            self.shared_params = self.create_shared_params(self._kwargs.get("start", None))
         self._finalize_init()
 
-    def create_shared_params(self, start=None, start_sigma=None):
-        # NOTE: `Group._prepare_start` uses `self.model.free_RVs` to identify free variables and
-        # `DictToArrayBijection` to turn them into a flat array, while `Approximation.rslice` assumes that the free
-        # variables are given by `self.group` and that the mapping between original variables and flat array is given
-        # by `self.ordering`. In the cases I looked into these turn out to be the same, but there may be edge cases or
-        # future code changes that break this assumption.
+    def create_shared_params(self, start=None):
         start = self._prepare_start(start)
-        rho1 = np.zeros((self.ddim,))
-
-        if start_sigma is not None:
-            for name, slice_, *_ in self.ordering.values():
-                sigma = start_sigma.get(name)
-                if sigma is not None:
-                    rho1[slice_] = np.log(np.expm1(np.abs(sigma)))
-        rho = rho1
-
+        rho = np.zeros((self.ddim,))
         return {
             "mu": aesara.shared(pm.floatX(start), "mu"),
             "rho": aesara.shared(pm.floatX(rho), "rho"),

pymc/variational/inference.py

@@ -257,9 +257,7 @@ def _infmean(input_array):
                     )
                 )
         else:
-            if n == 0:
-                logger.info(f"Initialization only")
-            elif n < 10:
+            if n < 10:
                 logger.info(f"Finished [100%]: Loss = {scores[-1]:,.5g}")
             else:
                 avg_loss = _infmean(scores[max(0, i - 1000) : i + 1])
@@ -435,10 +433,8 @@ class ADVI(KLqp):
     random_seed: None or int
         leave None to use package global RandomStream or other
         valid value to create instance specific one
-    start: `dict[str, np.ndarray]` or `StartDict`
+    start: `Point`
         starting point for inference
-    start_sigma: `dict[str, np.ndarray]`
-        starting standard deviation for inference, only available for method 'advi'
 
     References
     ----------
@@ -468,7 +464,7 @@ class FullRankADVI(KLqp):
     random_seed: None or int
         leave None to use package global RandomStream or other
         valid value to create instance specific one
-    start: `dict[str, np.ndarray]` or `StartDict`
+    start: `Point`
         starting point for inference
 
     References
@@ -536,11 +532,13 @@ class SVGD(ImplicitGradient):
         kernel function for KSD :math:`f(histogram) -> (k(x,.), \nabla_x k(x,.))`
     temperature: float
         parameter responsible for exploration, higher temperature gives more broad posterior estimate
-    start: `dict[str, np.ndarray]` or `StartDict`
+    start: `dict`
         initial point for inference
     random_seed: None or int
         leave None to use package global RandomStream or other
         valid value to create instance specific one
+    start: `Point`
+        starting point for inference
     kwargs: other keyword arguments passed to estimator
 
     References
@@ -631,11 +629,7 @@ def __init__(self, approx=None, estimator=KSD, kernel=test_functions.rbf, **kwar
             "is often **underestimated** when using temperature = 1."
         )
         if approx is None:
-            approx = FullRank(
-                model=kwargs.pop("model", None),
-                random_seed=kwargs.pop("random_seed", None),
-                start=kwargs.pop("start", None),
-            )
+            approx = FullRank(model=kwargs.pop("model", None))
         super().__init__(estimator=estimator, approx=approx, kernel=kernel, **kwargs)
 
     def fit(
@@ -666,7 +660,6 @@ def fit(
     model=None,
     random_seed=None,
     start=None,
-    start_sigma=None,
     inf_kwargs=None,
     **kwargs,
 ):
@@ -691,10 +684,8 @@ def fit(
         valid value to create instance specific one
     inf_kwargs: dict
         additional kwargs passed to :class:`Inference`
-    start: `dict[str, np.ndarray]` or `StartDict`
+    start: `Point`
         starting point for inference
-    start_sigma: `dict[str, np.ndarray]`
-        starting standard deviation for inference, only available for method 'advi'
 
     Other Parameters
     ----------------
@@ -737,10 +728,6 @@ def fit(
         inf_kwargs["random_seed"] = random_seed
     if start is not None:
         inf_kwargs["start"] = start
-    if start_sigma is not None:
-        if method != "advi":
-            raise NotImplementedError("start_sigma is only available for method advi")
-        inf_kwargs["start_sigma"] = start_sigma
     if model is None:
         model = pm.modelcontext(model)
     _select = dict(advi=ADVI, fullrank_advi=FullRankADVI, svgd=SVGD, asvgd=ASVGD)