From 97dc4bd233a49aca8e7f7a84fab8eb87d1feb8d6 Mon Sep 17 00:00:00 2001
From: ricardoV94 <ricardo.vieira1994@gmail.com>
Date: Tue, 21 Sep 2021 22:57:20 +0200
Subject: [PATCH] Remove deprecated ElemwiseCategorical

---
 pymc3/step_methods/__init__.py |   1 -
 pymc3/step_methods/gibbs.py    | 105 ---------------------------------
 2 files changed, 106 deletions(-)
 delete mode 100644 pymc3/step_methods/gibbs.py

diff --git a/pymc3/step_methods/__init__.py b/pymc3/step_methods/__init__.py
index 1cd281752e..e37a24fe68 100644
--- a/pymc3/step_methods/__init__.py
+++ b/pymc3/step_methods/__init__.py
@@ -14,7 +14,6 @@
 
 from pymc3.step_methods.compound import CompoundStep
 from pymc3.step_methods.elliptical_slice import EllipticalSlice
-from pymc3.step_methods.gibbs import ElemwiseCategorical
 from pymc3.step_methods.hmc import NUTS, HamiltonianMC
 from pymc3.step_methods.metropolis import (
     BinaryGibbsMetropolis,
diff --git a/pymc3/step_methods/gibbs.py b/pymc3/step_methods/gibbs.py
deleted file mode 100644
index 14fb6eaa18..0000000000
--- a/pymc3/step_methods/gibbs.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#   Copyright 2020 The PyMC Developers
-#
-#   Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-#
-#       http://www.apache.org/licenses/LICENSE-2.0
-#
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
-"""
-Created on May 12, 2012
-
-@author: john
-"""
-from warnings import warn
-
-import aesara.tensor as at
-
-from aesara.graph.basic import graph_inputs
-from numpy import arange, array, cumsum, empty, exp, max, nested_iters, searchsorted
-from numpy.random import uniform
-
-from pymc3.distributions import logpt
-from pymc3.distributions.discrete import Categorical
-from pymc3.model import modelcontext
-from pymc3.step_methods.arraystep import ArrayStep, Competence
-
-__all__ = ["ElemwiseCategorical"]
-
-
-class ElemwiseCategorical(ArrayStep):
-    """
-    Gibbs sampling for categorical variables that only have ElemwiseCategoricalise effects
-    the variable can't be indexed into or transposed or anything otherwise that will mess things up
-
-    """
-
-    # TODO: It would be great to come up with a way to make
-    # ElemwiseCategorical  more general (handling more complex elementwise
-    # variables)
-
-    def __init__(self, vars, values=None, model=None):
-        warn(
-            "ElemwiseCategorical is deprecated, switch to CategoricalGibbsMetropolis.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        model = modelcontext(model)
-        self.var = vars[0]
-        # XXX: This needs to be refactored
-        self.sh = None  # ones(self.var.dshape, self.var.dtype)
-        if values is None:
-            self.values = arange(self.var.distribution.k)
-        else:
-            self.values = values
-
-        super().__init__(vars, [elemwise_logp(model, self.var)])
-
-    def astep(self, q, logp):
-        p = array([logp(v * self.sh) for v in self.values])
-        # XXX: This needs to be refactored
-        shape = None  # self.var.dshape
-        return categorical(p, shape)
-
-    @staticmethod
-    def competence(var, has_grad):
-        dist = getattr(var.owner, "op", None)
-        if isinstance(dist, Categorical):
-            return Competence.COMPATIBLE
-        return Competence.INCOMPATIBLE
-
-
-def elemwise_logp(model, var):
-    terms = []
-    for v in model.basic_RVs:
-        v_logp = logpt(v)
-        if var in graph_inputs([v_logp]):
-            terms.append(v_logp)
-    return model.fn(at.add(*terms))
-
-
-def categorical(prob, shape):
-    out = empty([1] + list(shape))
-
-    n = len(shape)
-    it0, it1 = nested_iters(
-        [prob, out],
-        [list(range(1, n + 1)), [0]],
-        op_flags=[["readonly"], ["readwrite"]],
-        flags=["reduce_ok"],
-    )
-
-    for _ in it0:
-        p, o = it1.itviews
-        p = cumsum(exp(p - max(p, axis=0)))
-        r = uniform() * p[-1]
-
-        o[0] = searchsorted(p, r)
-
-    return out[0, ...]