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decomposable_score.py
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decomposable_score.py
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# Copyright 2021 Juan L Gamella
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Module containing the DecomposableScore class, inherited by all
classes which implement a locally decomposable score for directed
acyclic graphs. By default, the class also caches the results of
computing local scores.
"""
import copy
# --------------------------------------------------------------------
# l0-penalized Gaussian log-likelihood score for a sample from a single
# (observational) environment
class DecomposableScore():
def __init__(self, data, cache=True, debug=0):
self._data = copy.deepcopy(data)
self._cache = {} if cache else None
self._debug = debug
self.p = None
def local_score(self, x, pa):
"""
Return the local score of a given node and a set of
parents. If self._cache is defined, will use previously computed
score if possible.
Parameters
----------
x : int
a node
pa : set of ints
the node's parents
Returns
-------
score : float
the corresponding score
"""
if self._cache is None:
return self._compute_local_score(x, pa)
else:
key = (x, tuple(sorted(pa)))
try:
score = self._cache[key]
print("score%s: using cached value %0.2f" %
(key, score)) if self._debug >= 2 else None
except KeyError:
score = self._compute_local_score(x, pa)
self._cache[key] = score
print("score%s = %0.2f" % (key, score)) if self._debug >= 2 else None
return score
def _compute_local_score(self, x, pa):
"""
Compute the local score of a given node and a set of
parents.
Parameters
----------
x : int
a node
pa : set of ints
the node's parents
Returns
-------
score : float
the corresponding score
"""
return 0
def prune_cache(self, V):
"""Remove all entries for local scores of variables in V from the
cache"""
new_cache = dict((((j,pa),s) for ((j,pa),s) in self._cache.items() if j not in V))
self._cache = new_cache