def timer(inputfile, trials, datalength):
# load nodedata and graphskeleton
nd = NodeData()
skel = GraphSkeleton()
#print "bp1"
nd.load(inputfile)
#print "bp2"
skel.load(inputfile)
#print "bp3"
# msg = "%d, %d" % (asizeof(nd), asizeof(skel))
# print >>op, msg
# topologically order graphskeleton
skel.toporder()
# load bayesian network
bn = DiscreteBayesianNetwork(skel, nd)
# instantiate pgm learner
l = PGMLearner()
# free unused memory
del nd
#sum1 = summary.summarize(muppy.get_objects())
#summary.print_(sum1)
# TIME
totaltime = 0
for _ in range(trials):
data = bn.randomsample(datalength)
start = time.clock()
ret = l.discrete_mle_estimateparams(skel, data)
elapsed = time.clock() - start
totaltime += elapsed
totaltime /= trials
print json.dumps(ret.Vdata, indent=1)
return totaltime
def timer(inputfile, trials):
# load nodedata and graphskeleton
nd = NodeData()
skel = GraphSkeleton()
nd.load(inputfile)
skel.load(inputfile)
# topologically order graphskeleton
skel.toporder()
# load bayesian network
bn = DiscreteBayesianNetwork(skel, nd)
# TIME
totaltime = 0
for _ in range(trials):
start = time.clock()
ret = bn.randomsample(100)
elapsed = time.clock() - start
totaltime += elapsed
totaltime /= trials
return totaltime
def discrete_mle_estimateparams(self, graphskeleton, data):
'''
Estimate parameters for a discrete Bayesian network with a structure given by *graphskeleton* in order to maximize the probability of data given by *data*. This function takes the following arguments:
1. *graphskeleton* -- An instance of the :doc:`GraphSkeleton <graphskeleton>` class containing vertex and edge data.
2. *data* -- A list of dicts containing samples from the network in {vertex: value} format. Example::
[
{
'Grade': 'B',
'SAT': 'lowscore',
...
},
...
]
This function normalizes the distribution of a node's outcomes for each combination of its parents' outcomes. In doing so it creates an estimated tabular conditional probability distribution for each node. It then instantiates a :doc:`DiscreteBayesianNetwork <discretebayesiannetwork>` instance based on the *graphskeleton*, and modifies that instance's *Vdata* attribute to reflect the estimated CPDs. It then returns the instance.
The Vdata attribute instantiated is in the format seen in :doc:`unittestdict`, as described in :doc:`discretebayesiannetwork`.
Usage example: this would learn parameters from a set of 200 discrete samples::
import json
from libpgm.nodedata import NodeData
from libpgm.graphskeleton import GraphSkeleton
from libpgm.discretebayesiannetwork import DiscreteBayesianNetwork
from libpgm.pgmlearner import PGMLearner
# generate some data to use
nd = NodeData()
nd.load("../tests/unittestdict.txt") # an input file
skel = GraphSkeleton()
skel.load("../tests/unittestdict.txt")
skel.toporder()
bn = DiscreteBayesianNetwork(skel, nd)
data = bn.randomsample(200)
# instantiate my learner
learner = PGMLearner()
# estimate parameters from data and skeleton
result = learner.discrete_mle_estimateparams(skel, data)
# output
print json.dumps(result.Vdata, indent=2)
'''
assert (isinstance(graphskeleton, GraphSkeleton)), "First arg must be a loaded GraphSkeleton class."
assert (isinstance(data, list) and data and isinstance(data[0], dict)), "Second arg must be a list of dicts."
# instantiate Bayesian network, and add parent and children data
bn = DiscreteBayesianNetwork()
graphskeleton.toporder()
bn.V = graphskeleton.V
bn.E = graphskeleton.E
bn.Vdata = dict()
for vertex in bn.V:
bn.Vdata[vertex] = dict()
bn.Vdata[vertex]["children"] = graphskeleton.getchildren(vertex)
bn.Vdata[vertex]["parents"] = graphskeleton.getparents(vertex)
# make placeholders for vals, cprob, and numoutcomes
bn.Vdata[vertex]["vals"] = []
if (bn.Vdata[vertex]["parents"] == []):
bn.Vdata[vertex]["cprob"] = []
else:
bn.Vdata[vertex]["cprob"] = dict()
bn.Vdata[vertex]["numoutcomes"] = 0
# determine which outcomes are possible for each node
for sample in data:
for vertex in bn.V:
if (sample[vertex] not in bn.Vdata[vertex]["vals"]):
bn.Vdata[vertex]["vals"].append(sample[vertex])
bn.Vdata[vertex]["numoutcomes"] += 1
# lay out probability tables, and put a [num, denom] entry in all spots:
# define helper function to recursively set up cprob table
def addlevel(vertex, _dict, key, depth, totaldepth):
if depth == totaldepth:
_dict[str(key)] = []
for _ in range(bn.Vdata[vertex]["numoutcomes"]):
_dict[str(key)].append([0, 0])
return
else:
for val in bn.Vdata[bn.Vdata[vertex]["parents"][depth]]["vals"]:
ckey = key[:]
ckey.append(str(val))
addlevel(vertex, _dict, ckey, depth+1, totaldepth)
# put [0, 0] at each entry of cprob table
for vertex in bn.V:
if (bn.Vdata[vertex]["parents"]):
root = bn.Vdata[vertex]["cprob"]
numparents = len(bn.Vdata[vertex]["parents"])
addlevel(vertex, root, [], 0, numparents)
else:
for _ in range(bn.Vdata[vertex]["numoutcomes"]):
bn.Vdata[vertex]["cprob"].append([0, 0])
# fill out entries with samples:
for sample in data:
for vertex in bn.V:
# compute index of result
rindex = bn.Vdata[vertex]["vals"].index(sample[vertex])
# go to correct place in Vdata
if bn.Vdata[vertex]["parents"]:
pvals = [str(sample[t]) for t in bn.Vdata[vertex]["parents"]]
lev = bn.Vdata[vertex]["cprob"][str(pvals)]
else:
lev = bn.Vdata[vertex]["cprob"]
# increase all denominators for the current condition
for entry in lev:
entry[1] += 1
# increase numerator for current outcome
lev[rindex][0] += 1
# convert arrays to floats
for vertex in bn.V:
if not bn.Vdata[vertex]["parents"]:
bn.Vdata[vertex]["cprob"] = [x[0]/float(x[1]) for x in bn.Vdata[vertex]["cprob"]]
else:
for key in bn.Vdata[vertex]["cprob"].keys():
try:
bn.Vdata[vertex]["cprob"][key] = [x[0]/float(x[1]) for x in bn.Vdata[vertex]["cprob"][key]]
# default to even distribution if no data points
except ZeroDivisionError:
bn.Vdata[vertex]["cprob"][key] = [1/float(bn.Vdata[vertex]["numoutcomes"]) for x in bn.Vdata[vertex]["cprob"][key]]
# return cprob table with estimated probability distributions
return bn
