Remove deprecated stuff

RELEASE-NOTES.md

@@ -21,6 +21,7 @@
 - Deprecated `sd` in version 3.7 has been replaced by `sigma` now raises `DepreciationWarning` on using `sd` in continuous, mixed and timeseries distributions. (see #3837 and #3688).
 - In named models, `pm.Data` objects now get model-relative names (see [#3843](https://github.com/pymc-devs/pymc3/pull/3843)).
 - `pm.sample` now takes 1000 draws and 1000 tuning samples by default, instead of 500 previously (see [#3855](https://github.com/pymc-devs/pymc3/pull/3855)).
+- Dropped some deprecated kwargs and functions (see [#3906](https://github.com/pymc-devs/pymc3/pull/3906))
 - Dropped the outdated 'nuts' initialization method for `pm.sample` (see [#3863](https://github.com/pymc-devs/pymc3/pull/3863)).
 - Moved argument division out of `NegativeBinomial` `random` method. Fixes [#3864](https://github.com/pymc-devs/pymc3/issues/3864) in the style of [#3509](https://github.com/pymc-devs/pymc3/pull/3509).
 - The Dirichlet distribution now raises a ValueError when it's initialized with <= 0 values (see [#3853](https://github.com/pymc-devs/pymc3/pull/3853)).

docs/source/notebooks/api_quickstart.ipynb

@@ -1284,7 +1284,7 @@
     }
    ],
    "source": [
-    "pm.gelman_rubin(trace)"
+    "pm.rhat(trace)"
    ]
   },
   {

docs/source/notebooks/bayes_param_survival_pymc3.ipynb

@@ -594,7 +594,7 @@
     }
    ],
    "source": [
-    "max(np.max(gr_stats) for gr_stats in pm.gelman_rubin(weibull_trace).values())"
+    "max(np.max(gr_stats) for gr_stats in pm.rhat(weibull_trace).values())"
    ]
   },
   {
@@ -904,7 +904,7 @@
     }
    ],
    "source": [
-    "max(np.max(gr_stats) for gr_stats in pm.gelman_rubin(log_logistic_trace).values())"
+    "max(np.max(gr_stats) for gr_stats in pm.rhat(log_logistic_trace).values())"
    ]
   },
   {
@@ -1030,4 +1030,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/source/notebooks/rugby_analytics.ipynb

@@ -801,7 +801,7 @@
    "outputs": [],
    "source": [
     "bfmi = np.max(pm.stats.bfmi(trace))\n",
-    "max_gr = max(np.max(gr_stats) for gr_stats in pm.stats.gelman_rubin(trace).values()).values"
+    "max_gr = max(np.max(gr_stats) for gr_stats in pm.stats.rhat(trace).values()).values"
    ]
   },
   {
@@ -1470,4 +1470,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

pymc3/examples/samplers_mvnormal.py

@@ -52,7 +52,7 @@ def run(steppers, p):
             runtimes[name] = time.time() - t_start
             print('{} samples across {} chains'.format(len(mt) * mt.nchains, mt.nchains))
             traces[name] = mt
-            en = pm.diagnostics.effective_n(mt)
+            en = pm.ess(mt)
             print('effective: {}\r\n'.format(en))
             if USE_XY:
                 effn[name] = np.mean(en['x']) / len(mt) / mt.nchains

pymc3/sampling.py

@@ -368,36 +368,9 @@ def sample(
     """
     model = modelcontext(model)
 
-    nuts_kwargs = kwargs.pop("nuts_kwargs", None)
-    if nuts_kwargs is not None:
-        warnings.warn(
-            "The nuts_kwargs argument has been deprecated. Pass step "
-            "method arguments directly to sample instead",
-            DeprecationWarning,
-        )
-        kwargs.update(nuts_kwargs)
-    step_kwargs = kwargs.pop("step_kwargs", None)
-    if step_kwargs is not None:
-        warnings.warn(
-            "The step_kwargs argument has been deprecated. Pass step "
-            "method arguments directly to sample instead",
-            DeprecationWarning,
-        )
-        kwargs.update(step_kwargs)
-
     if cores is None:
         cores = min(4, _cpu_count())
 
-    if "njobs" in kwargs:
-        cores = kwargs["njobs"]
-        warnings.warn(
-            "The njobs argument has been deprecated. Use cores instead.", DeprecationWarning
-        )
-    if "nchains" in kwargs:
-        chains = kwargs["nchains"]
-        warnings.warn(
-            "The nchains argument has been deprecated. Use chains instead.", DeprecationWarning
-        )
     if chains is None:
         chains = max(2, cores)
     if isinstance(start, dict):
@@ -412,11 +385,6 @@ def sample(
         random_seed = [np.random.randint(2 ** 30) for _ in range(chains)]
     if not isinstance(random_seed, Iterable):
         raise TypeError("Invalid value for `random_seed`. Must be tuple, list or int")
-    if "chain" in kwargs:
-        chain_idx = kwargs["chain"]
-        warnings.warn(
-            "The chain argument has been deprecated. Use chain_idx instead.", DeprecationWarning
-        )
 
     if start is not None:
         for start_vals in start:

pymc3/stats/__init__.py

@@ -53,19 +53,6 @@ def wrapped(*args, **kwargs):
 waic = map_args(az.waic)
 
 
-def gelman_rubin(*args, **kwargs):
-    warnings.warn("gelman_rubin has been deprecated. In the future, use rhat instead.")
-    return rhat(*args, **kwargs)
-
-gelman_rubin.__doc__ = rhat.__doc__
-
-
-def effective_n(*args, **kwargs):
-    warnings.warn("effective_n has been deprecated. In the future, use ess instead.")
-    return ess(*args, **kwargs)
-
-effective_n.__doc__ = ess.__doc__
-
 __all__ = [
     "bfmi",
     "compare",
@@ -78,6 +65,4 @@ def effective_n(*args, **kwargs):
     "rhat",
     "summary",
     "waic",
-    "gelman_rubin",  # deprecated, remove after 3.8
-    "effective_n",  # deprecated, remove after 3.8
 ]

pymc3/tests/sampler_fixtures.py

@@ -149,12 +149,12 @@ def setup_class(cls):
 
     def test_neff(self):
         if hasattr(self, 'min_n_eff'):
-            n_eff = pm.effective_n(self.trace[self.burn:])
+            n_eff = pm.ess(self.trace[self.burn:])
             for var in n_eff:
                 npt.assert_array_less(self.min_n_eff, n_eff[var])
 
     def test_Rhat(self):
-        rhat = pm.gelman_rubin(self.trace[self.burn:])
+        rhat = pm.rhat(self.trace[self.burn:])
         for var in rhat:
             npt.assert_allclose(rhat[var], 1, rtol=0.01)
 

pymc3/tests/test_sampling.py

@@ -103,7 +103,7 @@ def test_sample_args(self):
             assert "'foo'" in str(excinfo.value)
 
             with pytest.raises(ValueError) as excinfo:
-                pm.sample(50, tune=0, init=None, step_kwargs={"foo": {}})
+                pm.sample(50, tune=0, init=None, foo={})
             assert "foo" in str(excinfo.value)
 
             with pytest.raises(ValueError) as excinfo: