import collections
import re
from copy import copy
from itertools import product
from string import Template
import numpy as np
import pyparsing as pp
from joblib import Parallel, delayed
try:
from pyparsing import (
CharsNotIn,
Group,
OneOrMore,
Optional,
Suppress,
Word,
ZeroOrMore,
alphanums,
cppStyleComment,
nums,
printables,
)
except ImportError:
raise ImportError(
e.message()
+ ". pyparsing is required for using read/write methods. Please install using: pip install pyparsing."
)
from pgmpy.factors.discrete import TabularCPD
from pgmpy.models import BayesianNetwork
from pgmpy.utils import compat_fns
[docs]
class BIFReader(object):
"""
Initializes a BIFReader object.
Parameters
----------
path : file or str
File of bif data
string : str
String of bif data
include_properties: boolean
If True, gets the properties tag from the file and stores in graph properties.
n_jobs: int (default: -1)
Number of jobs to run in parallel. `-1` means use all processors.
Examples
--------
# dog-problem.bif file is present at
# http://www.cs.cmu.edu/~javabayes/Examples/DogProblem/dog-problem.bif
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
<pgmpy.readwrite.BIF.BIFReader object at 0x7f2375621cf8>
>>> model = reader.get_model()
Reference
---------
[1] Geoff Hulten and Pedro Domingos. The interchange format for bayesian networks.
http://www.cs.washington.edu/dm/vfml/appendixes/bif.htm, 2003.
"""
def __init__(self, path=None, string=None, include_properties=False, n_jobs=-1):
if path:
with open(path, "r") as network:
self.network = network.read()
elif string:
self.network = string
else:
raise ValueError("Must specify either path or string")
self.n_jobs = n_jobs
self.include_properties = include_properties
if '"' in self.network:
# Replacing quotes by spaces to remove case sensitivity like:
# "Dog-Problem" and Dog-problem
# or "true""false" and "true" "false" and true false
self.network = self.network.replace('"', " ")
if "/*" in self.network or "//" in self.network:
self.network = cppStyleComment.suppress().transformString(
self.network
) # removing comments from the file
(
self.name_expr,
self.state_expr,
self.property_expr,
) = self.get_variable_grammar()
self.probability_expr, self.cpd_expr = self.get_probability_grammar()
self.network_name = self.get_network_name()
self.variable_names = self.get_variables()
self.variable_states = self.get_states()
if self.include_properties:
self.variable_properties = self.get_property()
self.variable_parents = self.get_parents()
self.variable_cpds = self.get_values()
self.variable_edges = self.get_edges()
[docs]
def get_variable_grammar(self):
"""
A method that returns variable grammar
"""
# Defining an expression for valid word
word_expr = Word(pp.unicode.alphanums + "_" + "-" + ".")
word_expr2 = Word(initChars=printables, excludeChars=["{", "}", ",", " "])
name_expr = Suppress("variable") + word_expr + Suppress("{")
state_expr = ZeroOrMore(word_expr2 + Optional(Suppress(",")))
# Defining a variable state expression
variable_state_expr = (
Suppress("type")
+ Suppress(word_expr)
+ Suppress("[")
+ Suppress(Word(nums))
+ Suppress("]")
+ Suppress("{")
+ Group(state_expr)
+ Suppress("}")
+ Suppress(";")
)
# variable states is of the form type description [args] { val1, val2 }; (comma may or may not be present)
property_expr = (
Suppress("property") + CharsNotIn(";") + Suppress(";")
) # Creating an expr to find property
return name_expr, variable_state_expr, property_expr
[docs]
def get_probability_grammar(self):
"""
A method that returns probability grammar
"""
# Creating valid word expression for probability, it is of the format
# wor1 | var2 , var3 or var1 var2 var3 or simply var
word_expr = (
Word(pp.unicode.alphanums + "-" + "_")
+ Suppress(Optional("|"))
+ Suppress(Optional(","))
)
word_expr2 = Word(
initChars=printables, excludeChars=[",", ")", " ", "("]
) + Suppress(Optional(","))
# creating an expression for valid numbers, of the format
# 1.00 or 1 or 1.00. 0.00 or 9.8e-5 etc
num_expr = Word(nums + "-" + "+" + "e" + "E" + ".") + Suppress(Optional(","))
probability_expr = (
Suppress("probability")
+ Suppress("(")
+ OneOrMore(word_expr)
+ Suppress(")")
)
optional_expr = Suppress("(") + OneOrMore(word_expr2) + Suppress(")")
probab_attributes = optional_expr | Suppress("table") | Suppress("default")
cpd_expr = probab_attributes + OneOrMore(num_expr)
return probability_expr, cpd_expr
def variable_block(self):
start = re.finditer("variable", self.network)
for index in start:
end = self.network.find("}\n", index.start())
yield self.network[index.start() : end]
def probability_block(self):
start = re.finditer("probability", self.network)
for index in start:
end = self.network.find("}\n", index.start())
yield self.network[index.start() : end]
[docs]
def get_network_name(self):
"""
Returns the name of the network
Example
---------------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIF.BifReader("bif_test.bif")
>>> reader.network_name()
'Dog-Problem'
"""
start = self.network.find("network")
end = self.network.find("}\n", start)
# Creating a network attribute
network_attribute = (
Suppress("network") + Word(pp.unicode.alphanums + "_" + "-") + "{"
)
network_name = network_attribute.searchString(self.network[start:end])[0][0]
return network_name
[docs]
def get_variables(self):
"""
Returns list of variables of the network
Example
-------------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_variables()
['light-on','bowel_problem','dog-out','hear-bark','family-out']
"""
variable_names = []
for block in self.variable_block():
name = self.name_expr.searchString(block)[0][0]
variable_names.append(name)
return variable_names
[docs]
def get_states(self):
"""
Returns the states of variables present in the network
Example
-----------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_states()
{'bowel-problem': ['true','false'],
'dog-out': ['true','false'],
'family-out': ['true','false'],
'hear-bark': ['true','false'],
'light-on': ['true','false']}
"""
variable_states = {}
for block in self.variable_block():
name = self.name_expr.searchString(block)[0][0]
variable_states[name] = list(self.state_expr.searchString(block)[0][0])
return variable_states
[docs]
def get_property(self):
"""
Returns the property of the variable
Example
-------------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_property()
{'bowel-problem': ['position = (335, 99)'],
'dog-out': ['position = (300, 195)'],
'family-out': ['position = (257, 99)'],
'hear-bark': ['position = (296, 268)'],
'light-on': ['position = (218, 195)']}
"""
variable_properties = {}
for block in self.variable_block():
name = self.name_expr.searchString(block)[0][0]
properties = self.property_expr.searchString(block)
variable_properties[name] = [y.strip() for x in properties for y in x]
return variable_properties
[docs]
def get_parents(self):
"""
Returns the parents of the variables present in the network
Example
--------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_parents()
{'bowel-problem': [],
'dog-out': ['family-out', 'bowel-problem'],
'family-out': [],
'hear-bark': ['dog-out'],
'light-on': ['family-out']}
"""
variable_parents = {}
for block in self.probability_block():
names = self.probability_expr.searchString(block.split("\n")[0])[0]
variable_parents[names[0]] = names[1:]
return variable_parents
def _get_values_from_block(self, block):
names = self.probability_expr.searchString(block)
var_name, parents = names[0][0], names[0][1:]
cpds = self.cpd_expr.searchString(block)
# Check if the block is a table.
if bool(re.search(".*\n[ ]*(table|default) .*\n.*", block)):
arr = np.array([float(j) for i in cpds for j in i])
arr = arr.reshape(
(
len(self.variable_states[var_name]),
arr.size // len(self.variable_states[var_name]),
)
)
else:
arr_length = np.prod([len(self.variable_states[var]) for var in parents])
arr = np.zeros((len(self.variable_states[var_name]), arr_length))
values_dict = {}
for prob_line in cpds:
states = prob_line[: len(parents)]
vals = [float(i) for i in prob_line[len(parents) :]]
values_dict[tuple(states)] = vals
for index, combination in enumerate(
product(*[self.variable_states[var] for var in parents])
):
arr[:, index] = values_dict[combination]
return var_name, arr
[docs]
def get_values(self):
"""
Returns the CPD of the variables present in the network
Example
--------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_values()
{'bowel-problem': np.array([[0.01],
[0.99]]),
'dog-out': np.array([[0.99, 0.97, 0.9, 0.3],
[0.01, 0.03, 0.1, 0.7]]),
'family-out': np.array([[0.15],
[0.85]]),
'hear-bark': np.array([[0.7, 0.01],
[0.3, 0.99]]),
'light-on': np.array([[0.6, 0.05],
[0.4, 0.95]])}
"""
cpd_values = Parallel(n_jobs=self.n_jobs)(
delayed(self._get_values_from_block)(block)
for block in self.probability_block()
)
variable_cpds = {}
for var_name, arr in cpd_values:
variable_cpds[var_name] = arr
return variable_cpds
[docs]
def get_edges(self):
"""
Returns the edges of the network
Example
--------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_edges()
[['family-out', 'light-on'],
['family-out', 'dog-out'],
['bowel-problem', 'dog-out'],
['dog-out', 'hear-bark']]
"""
edges = [
[value, key]
for key in self.variable_parents.keys()
for value in self.variable_parents[key]
]
return edges
[docs]
def get_model(self, state_name_type=str):
"""
Returns the Bayesian Model read from the file/str.
Parameters
----------
state_name_type: int, str or bool (default: str)
The data type to which to convert the state names of the variables.
Example
----------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_model()
<pgmpy.models.BayesianNetwork.BayesianNetwork object at 0x7f20af154320>
"""
try:
model = BayesianNetwork()
model.add_nodes_from(self.variable_names)
model.add_edges_from(self.variable_edges)
model.name = self.network_name
tabular_cpds = []
for var in sorted(self.variable_cpds.keys()):
values = self.variable_cpds[var]
sn = {
p_var: list(map(state_name_type, self.variable_states[p_var]))
for p_var in self.variable_parents[var]
}
sn[var] = list(map(state_name_type, self.variable_states[var]))
cpd = TabularCPD(
var,
len(self.variable_states[var]),
values,
evidence=self.variable_parents[var],
evidence_card=[
len(self.variable_states[evidence_var])
for evidence_var in self.variable_parents[var]
],
state_names=sn,
)
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
if self.include_properties:
for node, properties in self.variable_properties.items():
for prop in properties:
prop_name, prop_value = map(
lambda t: t.strip(), prop.split("=")
)
model.nodes[node][prop_name] = prop_value
return model
except AttributeError:
raise AttributeError(
"First get states of variables, edges, parents and network name"
)
[docs]
class BIFWriter(object):
"""
Initialise a BIFWriter Object
Parameters
----------
model: BayesianNetwork Instance
round_values: int (default: None)
Round the probability values to `round_values` decimals. If None, keeps all decimal points.
Examples
---------
>>> from pgmpy.readwrite import BIFWriter
>>> from pgmpy.utils import get_example_model
>>> asia = get_example_model('asia')
>>> writer = BIFWriter(asia)
>>> writer
<writer_BIF.BIFWriter at 0x7f05e5ea27b8>
>>> writer.write_bif('asia.bif')
"""
def __init__(self, model, round_values=None):
if not isinstance(model, BayesianNetwork):
raise TypeError("model must be an instance of BayesianNetwork")
self.model = model
self.round_values = round_values
if not self.model.name:
self.network_name = "unknown"
else:
self.network_name = self.model.name
self.variable_states = self.get_states()
self.property_tag = self.get_properties()
self.variable_parents = self.get_parents()
self.tables = self.get_cpds()
[docs]
def BIF_templates(self):
"""
Create template for writing in BIF format
"""
network_template = Template("network $name {\n}\n")
# property tag may or may not be present in model,and since no of properties
# can be more than one, will replace them according to format otherwise null
variable_template = Template(
"""variable $name {
type discrete [ $no_of_states ] { $states };
$properties}\n"""
)
property_template = Template(" property $prop ;\n")
# $variable_ here is name of variable, used underscore for clarity
probability_template = Template(
"""probability ( $variable_$separator_$parents ) {
table $values ;
}\n"""
)
conditional_probability_template_total = Template(
"""probability ( $variable_$separator_$parents ) {
$values
}\n"""
)
conditional_probability_template = Template(""" ( $state ) $values;\n""")
return (
network_template,
variable_template,
property_template,
probability_template,
conditional_probability_template_total,
conditional_probability_template,
)
def __str__(self):
"""
Returns the BIF format as string
"""
(
network_template,
variable_template,
property_template,
probability_template,
conditional_probability_template_total,
conditional_probability_template,
) = self.BIF_templates()
network = ""
network += network_template.substitute(name=self.network_name)
variables = self.model.nodes()
for var in sorted(variables):
no_of_states = str(len(self.variable_states[var]))
states = ", ".join(self.variable_states[var])
if not self.property_tag[var]:
properties = ""
else:
properties = ""
for prop_val in self.property_tag[var]:
properties += property_template.substitute(prop=prop_val)
network += variable_template.substitute(
name=var,
no_of_states=no_of_states,
states=states,
properties=properties,
)
for var in sorted(variables):
if not self.variable_parents[var]:
parents = ""
separator = ""
cpd = ", ".join(map(str, self.tables[var]))
network += probability_template.substitute(
variable_=var, separator_=separator, parents=parents, values=cpd
)
else:
parents_str = ", ".join(self.variable_parents[var])
separator = " | "
cpd = self.model.get_cpds(var)
cpd_values_transpose = cpd.get_values().T
parent_states = product(
*[cpd.state_names[var] for var in cpd.variables[1:]]
)
all_cpd = ""
for index, state in enumerate(parent_states):
all_cpd += conditional_probability_template.substitute(
state=", ".join(map(str, state)),
values=", ".join(
map(
str,
compat_fns.to_numpy(
cpd_values_transpose[index, :],
decimals=self.round_values,
),
)
),
)
network += conditional_probability_template_total.substitute(
variable_=var,
separator_=separator,
parents=parents_str,
values=all_cpd,
)
return network
[docs]
def get_variables(self):
"""
Add variables to BIF
Returns
-------
list: a list containing names of variable
Example
-------
>>> from pgmpy.readwrite import BIFReader, BIFWriter
>>> model = BIFReader('dog-problem.bif').get_model()
>>> writer = BIFWriter(model)
>>> writer.get_variables()
['bowel-problem', 'family-out', 'hear-bark', 'light-on', 'dog-out']
"""
variables = self.model.nodes()
return variables
[docs]
def get_states(self):
"""
Add states to variable of BIF
Returns
-------
dict: dict of type {variable: a list of states}
Example
-------
>>> from pgmpy.readwrite import BIFReader, BIFWriter
>>> model = BIFReader('dog-problem.bif').get_model()
>>> writer = BIFWriter(model)
>>> writer.get_states()
{'bowel-problem': ['bowel-problem_0', 'bowel-problem_1'],
'dog-out': ['dog-out_0', 'dog-out_1'],
'family-out': ['family-out_0', 'family-out_1'],
'hear-bark': ['hear-bark_0', 'hear-bark_1'],
'light-on': ['light-on_0', 'light-on_1']}
"""
variable_states = {}
cpds = self.model.get_cpds()
for cpd in cpds:
variable = cpd.variable
variable_states[variable] = []
for state in cpd.state_names[variable]:
variable_states[variable].append(str(state))
return variable_states
[docs]
def get_properties(self):
"""
Add property to variables in BIF
Returns
-------
dict: dict of type {variable: list of properties }
Example
-------
>>> from pgmpy.readwrite import BIFReader, BIFWriter
>>> model = BIFReader('dog-problem.bif').get_model()
>>> writer = BIFWriter(model)
>>> writer.get_properties()
{'bowel-problem': ['position = (335, 99)'],
'dog-out': ['position = (300, 195)'],
'family-out': ['position = (257, 99)'],
'hear-bark': ['position = (296, 268)'],
'light-on': ['position = (218, 195)']}
"""
variables = self.model.nodes()
property_tag = {}
for variable in sorted(variables):
properties = self.model.nodes[variable]
properties = collections.OrderedDict(sorted(properties.items()))
property_tag[variable] = []
for prop, val in properties.items():
property_tag[variable].append(str(prop) + " = " + str(val))
return property_tag
[docs]
def get_parents(self):
"""
Add the parents to BIF
Returns
-------
dict: dict of type {variable: a list of parents}
Example
-------
>>> from pgmpy.readwrite import BIFReader, BIFWriter
>>> model = BIFReader('dog-problem.bif').get_model()
>>> writer = BIFWriter(model)
>>> writer.get_parents()
{'bowel-problem': [],
'dog-out': ['bowel-problem', 'family-out'],
'family-out': [],
'hear-bark': ['dog-out'],
'light-on': ['family-out']}
"""
cpds = self.model.get_cpds()
variable_parents = {}
for cpd in cpds:
variable_parents[cpd.variable] = cpd.variables[1:]
return variable_parents
[docs]
def get_cpds(self):
"""
Adds tables to BIF
Returns
-------
dict: dict of type {variable: array}
Example
-------
>>> from pgmpy.readwrite import BIFReader, BIFWriter
>>> model = BIFReader('dog-problem.bif').get_model()
>>> writer = BIFWriter(model)
>>> writer.get_cpds()
{'bowel-problem': array([ 0.01, 0.99]),
'dog-out': array([ 0.99, 0.97, 0.9 , 0.3 , 0.01, 0.03, 0.1 , 0.7 ]),
'family-out': array([ 0.15, 0.85]),
'hear-bark': array([ 0.7 , 0.01, 0.3 , 0.99]),
'light-on': array([ 0.6 , 0.05, 0.4 , 0.95])}
"""
cpds = self.model.get_cpds()
tables = {}
for cpd in cpds:
tables[cpd.variable] = compat_fns.to_numpy(
cpd.values.ravel(), decimals=self.round_values
)
return tables
[docs]
def write_bif(self, filename):
"""
Writes the BIF data into a file
Parameters
----------
filename : Name of the file
Example
-------
>>> from pgmpy.utils import get_example_model
>>> from pgmpy.readwrite import BIFReader, BIFWriter
>>> asia = get_example_model('asia')
>>> writer = BIFWriter(asia)
>>> writer.write_bif(filename='asia.bif')
"""
writer = self.__str__()
with open(filename, "w") as fout:
fout.write(writer)
