/usr/share/pyshared/pebl/result.py is in python-pebl 1.0.2-2+b1.
This file is owned by root:root, with mode 0o644.
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from __future__ import with_statement
import time
import socket
from bisect import insort, bisect
from copy import deepcopy, copy
import cPickle
import os.path
import shutil
import tempfile
from numpy import exp
try:
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
import simplejson
from pkg_resources import resource_filename
_can_create_html = True
except:
_can_create_html = False
from pebl import posterior, config
from pebl.util import flatten, rescale_logvalues
from pebl.network import Network
class _ScoredNetwork(Network):
"""A class for representing scored networks.
Supports comparision of networks based on score and equality based on first
checking score equality (MUCH faster than checking network edges), then edges.
Note: This is a private class used by LearnerResult. It's interface is
not guaranteed to ramain stable.
"""
def __init__(self, edgelist, score):
self.edges = edgelist
self.score = score
def __cmp__(self, other):
return cmp(self.score, other.score)
def __eq__(self, other):
return self.score == other.score and self.edges == other.edges
def __hash__(self):
return hash(self.edges)
class LearnerRunStats:
def __init__(self, start):
self.start = start
self.end = None
self.host = socket.gethostname()
class LearnerResult:
"""Class for storing any and all output of a learner.
This is a mutable container for networks and scores. In the future, it will
also be the place to collect statistics related to the learning task.
"""
#
# Parameters
#
_params = (
config.StringParameter(
'result.filename',
'The name of the result output file',
default='result.pebl'
),
config.StringParameter(
'result.format',
'The format for the pebl result file (pickle or html)',
config.oneof('pickle', 'html'),
default='pickle'
),
config.StringParameter(
'result.outdir',
'Directory for html report.',
default='result'
),
config.IntParameter(
'result.size',
"""Number of top-scoring networks to save. Specify 0 to indicate that
all scored networks should be saved.""",
default=1000
)
)
def __init__(self, learner_=None, size=None):
self.data = learner_.data if learner_ else None
self.nodes = self.data.variables if self.data else None
self.size = size or config.get('result.size')
self.networks = []
self.nethashes = {}
self.runs = []
def start_run(self):
"""Indicates that the learner is starting a new run."""
self.runs.append(LearnerRunStats(time.time()))
def stop_run(self):
"""Indicates that the learner is stopping a run."""
self.runs[-1].end = time.time()
def add_network(self, net, score):
"""Add a network and score to the results."""
nets = self.networks
nethashes = self.nethashes
nethash = hash(net.edges)
if self.size == 0 or len(nets) < self.size:
if nethash not in nethashes:
snet = _ScoredNetwork(copy(net.edges), score)
insort(nets, snet)
nethashes[nethash] = 1
elif score > nets[0].score and nethash not in nethashes:
nethashes.pop(hash(nets[0].edges))
nets.remove(nets[0])
snet = _ScoredNetwork(copy(net.edges), score)
insort(nets, snet)
nethashes[nethash] = 1
def tofile(self, filename=None):
"""Save the result to a python pickle file.
The result can be later read using the result.fromfile function.
"""
filename = filename or config.get('result.filename')
with open(filename, 'w') as fp:
cPickle.dump(self, fp)
def tohtml(self, outdir=None):
"""Create a html report of the result.
outdir is a directory to create html files inside.
"""
if _can_create_html:
HtmlFormatter().htmlreport(
self,
outdir or config.get('result.outdir')
)
else:
print "Cannot create html reports because some dependencies are missing."
@property
def posterior(self):
"""Returns a posterior object for this result."""
return posterior.from_sorted_scored_networks(
self.nodes,
list(reversed(self.networks))
)
class HtmlFormatter:
def htmlreport(self, result_, outdir, numnetworks=10):
"""Create a html report for the given result."""
def jsonize_run(r):
return {
'start': time.asctime(time.localtime(r.start)),
'end': time.asctime(time.localtime(r.end)),
'runtime': round((r.end - r.start)/60, 3),
'host': r.host
}
pjoin = os.path.join
# make outdir if it does not exist
if not os.path.exists(outdir):
os.makedirs(outdir)
# copy static files to outdir
staticdir = resource_filename('pebl', 'resources/htmlresult')
shutil.copy2(pjoin(staticdir, 'index.html'), outdir)
shutil.copytree(pjoin(staticdir, 'lib'), pjoin(outdir, 'lib'))
# change outdir to outdir/data
outdir = pjoin(outdir, 'data')
os.mkdir(outdir)
# get networks and scores
post = result_.posterior
numnetworks = numnetworks if len(post) >= numnetworks else len(post)
topscores = post.scores[:numnetworks]
norm_topscores = exp(rescale_logvalues(topscores))
# create json-able datastructure
resultsdata = {
'topnets_normscores': [round(s,3) for s in norm_topscores],
'topnets_scores': [round(s,3) for s in topscores],
'runs': [jsonize_run(r) for r in result_.runs],
}
# write out results related data (in json format)
open(pjoin(outdir, 'result.data.js'), 'w').write(
"resultdata=" + simplejson.dumps(resultsdata)
)
# create network images
top = post[0]
top.layout()
for i,net in enumerate(post[:numnetworks]):
self.network_image(
net,
pjoin(outdir, "%s.png" % i),
pjoin(outdir, "%s-common.png" % i),
top.node_positions
)
# create consensus network images
cm = post.consensus_matrix
for threshold in xrange(10):
self.consensus_network_image(
post.consensus_network(threshold/10.0),
pjoin(outdir, "consensus.%s.png" % threshold),
cm, top.node_positions
)
# create score plot
self.plot(post.scores, pjoin(outdir, "scores.png"))
def plot(self, values, outfile):
fig = Figure(figsize=(5,5))
ax = fig.add_axes([0.18, 0.15, 0.75, 0.75])
ax.scatter(range(len(values)), values, edgecolors='None',s=10)
ax.set_title("Scores (in sorted order)")
ax.set_xlabel("Networks")
ax.set_ylabel("Log score")
ax.set_xbound(-20, len(values)+20)
canvas = FigureCanvasAgg(fig)
canvas.print_figure(outfile, dpi=80)
def network_image(self, net, outfile1, outfile2, node_positions,
dot="dot", neato="neato"):
# with network's optimal layout
fd,fname = tempfile.mkstemp()
net.as_dotfile(fname)
os.system("%s -Tpng -o%s %s" % (dot, outfile1, fname))
os.remove(fname)
# with given layout
net.node_positions = node_positions
fd,fname = tempfile.mkstemp()
net.as_dotfile(fname)
os.system("%s -n1 -Tpng -o%s %s" % (neato, outfile2, fname))
os.remove(fname)
def consensus_network_image(self, net, outfile, cm, node_positions):
def colorize_edge(weight):
colors = "9876543210"
breakpoints = [.1, .2, .3, .4, .5, .6, .7, .8, .9]
return "#" + str(colors[bisect(breakpoints, weight)])*6
def node(n, position):
s = "\t\"%s\"" % n.name
if position:
x,y = position
s += " [pos=\"%d,%d\"]" % (x,y)
return s + ";"
nodes = net.nodes
positions = node_positions
dotstr = "\n".join(
["digraph G {"] +
[node(n, pos) for n,pos in zip(nodes, positions)] +
["\t\"%s\" -> \"%s\" [color=\"%s\"];" % \
(nodes[src].name, nodes[dest].name, colorize_edge(cm[src][dest])) \
for src,dest in net.edges
] +
["}"]
)
fd,fname = tempfile.mkstemp()
open(fname, 'w').write(dotstr)
os.system("neato -n1 -Tpng -o%s %s" % (outfile, fname))
os.remove(fname)
#
# Factory and other functions
#
def merge(*args):
"""Returns a merged result object.
Example::
merge(result1, result2, result3)
results = [result1, result2, result3]
merge(results)
merge(*results)
"""
results = flatten(args)
if len(results) is 1:
return results[0]
# create new result object
newresults = LearnerResult()
newresults.data = results[0].data
newresults.nodes = results[0].nodes
# merge all networks, remove duplicates, then sort
allnets = list(set([net for net in flatten(r.networks for r in results)]))
allnets.sort()
newresults.networks = allnets
newresults.nethashes = dict([(net, 1) for net in allnets])
# merge run statistics
if hasattr(results[0], 'runs'):
newresults.runs = flatten([r.runs for r in results])
else:
newresults.runs = []
return newresults
def fromfile(filename):
"""Loads a learner result from file."""
return cPickle.load(open(filename))
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