from __future__ import absolute_import, print_function, unicode_literals
from builtins import dict, str
import numpy
import networkx
default_probs = {
'rand': {
'biopax': 0.2,
'bel': 0.1,
'trips': 0.3,
'reach': 0.3,
'biogrid': 0.01,
'sparser': 0.3,
'r3': 0.1,
'phosphosite': 0.01,
'ndex': 0.049,
'assertion': 0.0,
},
'syst': {
'biopax': 0.01,
'bel': 0.01,
'trips': 0.05,
'reach': 0.05,
'biogrid': 0.01,
'sparser': 0.05,
'r3': 0.05,
'phosphosite': 0.01,
'ndex': 0.01,
'assertion': 0.0,
}
}
[docs]class BeliefEngine(object):
"""Assigns beliefs to INDRA Statements based on supporting evidence.
Parameters
----------
prior_probs : Optional[dict[dict]]
A dictionary of prior probabilities used to override/extend the
default ones. There are two types of prior probabilities: rand and syst
corresponding to random error and systematic error rate for each
knowledge source. The prior_probs dictionary has the general structure
{'rand': {'s1': pr1, ..., 'sn': prn},
'syst': {'s1': ps1, ..., 'sn': psn}}
where 's1' ... 'sn' are names of input sources and pr1 ... prn and
ps1 ... psn are error probabilities.
Examples: {'rand': {'some_source': 0.1}} sets the random error rate
for some_source to 0.1; {'rand': {''}}
Attributes
----------
prior_probs : dict[dict]
A dictionary of prior systematic and random error probabilities for
each knowledge source.
"""
def __init__(self, prior_probs=None):
self.prior_probs = default_probs
if prior_probs:
self.prior_probs.update(prior_probs)
[docs] def set_prior_probs(self, statements):
"""Sets the prior belief probabilities for a list of INDRA Statements.
The Statements are assumed to be de-duplicated. In other words,
each Statement in the list passed to this function is assumed to have
a list of Evidence objects that support it. The prior probability of
each Statement is calculated based on the number of Evidences it has
and their sources.
Parameters
----------
statements : list[indra.statements.Statement]
A list of INDRA Statements whose belief scores are to
be calculated. Each Statement object's belief attribute is updated
by this function.
"""
self._check_prior_probs(statements)
for st in statements:
sources = [ev.source_api for ev in st.evidence]
uniq_sources = numpy.unique(sources)
syst_factors = {s: self.prior_probs['syst'][s]
for s in uniq_sources}
rand_factors = {k: [] for k in uniq_sources}
for s in sources:
rand_factors[s].append(self.prior_probs['rand'][s])
neg_prob_prior = 1
for s in uniq_sources:
neg_prob_prior *= (syst_factors[s] +
numpy.prod(rand_factors[s]))
prob_prior = 1 - neg_prob_prior
st.belief = prob_prior
[docs] def set_hierarchy_probs(self, statements):
"""Sets hierarchical belief probabilities for a list of INDRA Statements.
The Statements are assumed to be in a hierarchical relation graph with
the supports and supported_by attribute of each Statement object having
been set.
The hierarchical belief probability of each Statement is calculated
based on its prior probability and the probabilities propagated from
Statements supporting it in the hierarchy graph.
Parameters
----------
statements : list[indra.statements.Statement]
A list of INDRA Statements whose belief scores are to
be calculated. Each Statement object's belief attribute is updated
by this function.
"""
def build_hierarchy_graph(stmts):
g = networkx.DiGraph()
for st1 in stmts:
g.add_node(st1.matches_key(), {'stmt': st1})
for st2 in st1.supported_by:
g.add_node(st2.matches_key(), {'stmt': st2})
g.add_edge(st2.matches_key(), st1.matches_key())
return g
def get_ranked_stmts(g):
node_ranks = networkx.topological_sort(g, reverse=True)
stmts = [g.node[n]['stmt'] for n in node_ranks]
return stmts
g = build_hierarchy_graph(statements)
ranked_stmts = get_ranked_stmts(g)
new_beliefs = []
for st in ranked_stmts:
bps = _get_belief_package(st)
beliefs = [bp[0] for bp in bps]
belief = 1 - numpy.prod([(1-b) for b in beliefs])
new_beliefs.append(belief)
for st, bel in zip(ranked_stmts, new_beliefs):
st.belief = bel
[docs] def set_linked_probs(self, linked_statements):
"""Sets the belief probabilities for a list of linked INDRA Statements.
The list of LinkedStatement objects is assumed to come from the
MechanismLinker. The belief probability of the inferred Statement is
assigned the joint probability of its source Statements.
Parameters
----------
linked_statements : list[indra.mechlinker.LinkedStatement]
A list of INDRA LinkedStatements whose belief scores are to
be calculated. The belief attribute of the inferred Statement in
the LinkedStatement object is updated by this function.
"""
for st in linked_statements:
source_probs = [s.belief for s in st.source_stmts]
st.inferred_stmt.belief = numpy.prod(source_probs)
def _check_prior_probs(self, statements):
"""Check that we have probabilities for sources in statements."""
sources = set()
for stmt in statements:
sources |= set([ev.source_api for ev in stmt.evidence])
for err_type in ('rand', 'syst'):
for source in sources:
if source not in self.prior_probs[err_type]:
msg = 'BeliefEngine missing probability parameter' + \
'for source: %s' % source
raise Exception(msg)
def _get_belief_package(stmt, n=1):
def belief_stmts(belief_pkgs):
return [pkg[1] for pkg in belief_pkgs]
belief_packages = []
for st in stmt.supports:
parent_packages = _get_belief_package(st, n+1)
belief_st = belief_stmts(belief_packages)
for package in parent_packages:
if not package[1] in belief_st:
belief_packages.append(package)
belief_package = (stmt.belief, stmt.matches_key())
belief_packages.append(belief_package)
return belief_packages