def printGnuplot( key ):
import py_stats
fr = fullResults
#
## Extract keys.
files = map( getLocalName, fList )
#learners = map( lambda x : x[0], localStructureLearner )
# Learners in order required by gnuplot scripts.
learners = ['CPT', 'logit', 'tree', 'dualCTL', 'FBDe', 'FBDe-Tree']
folds = fr[files[0]][learners[0]].keys()
#
## Print Learner Key
print "# ",
for x in learners: print x,
print
#
## Print out actual data.
for f in files:
n = f.split('.')[-1] # Print number of cases
print str(n), "\t",
for x in learners:
data = []
for xx in fr[f][x].values():
if xx.has_key(key): data.append( xx[key] )
if len(data) > 1: print "%4.8f\t" % py_stats.mean(data),
else: print "& XXX & XXX",
for x in learners:
data = []
for xx in fr[f][x].values():
if xx.has_key(key): data.append( xx[key] )
if len(data) > 1: print "%4.8f\t" % py_stats.sterr(data),
else: print "& XXX & XXX",
def printAverage( key, sort = True ):
import py_stats
print "\nTotal - ", key
t = {}
fr = fullResults
netNames = map( getLocalName, fList )
#netNames = fr.keys()
#netNames.sort() # Make a consistent ordering.
for data in netNames:
print "\n",data, key
summary = []
for k in localStructureLearner: #fr['balance-scale.arff'].keys():
if fr[data].keys().count(k[0]) == 0: continue
k = k[0]
t[k] = []
for i in fr[data][k].keys():
## I think there is a bug here in jython serialisation
debug[0] = fr[data][k][i]
try:
if not isNone(debug[0]):# != None:
if fr[data][k][i].has_key(key) and (not isNone(fr[data][k][i][key])):
t[k].append( fr[data][k][i][key] )
elif key.endswith(".2") and fr[data][k][i].has_key(key[:-2]) and (not isNone(fr[data][k][i][key[:-2]])):
t[k].append( fr[data][k][i][key[:-2]] )
except AttributeError:
print "Ignoring exception, probably due to buggy serialisation", debug[0], k, i, str(debug[0])
continue # Do nothing.
if len(t[k]) > 1 :
summary.append( {"mean":py_stats.mean(t[k]), "sterr":py_stats.sterr(t[k]),"len":len(t[k]), "name": k } )
else: summary.append( {"mean":0, "sterr":0,"len":len(t[k]), "name": k } )
if sort: summary.sort(lambda a,b: a["mean"] > b["mean"])
#
for x in summary: print pad(x["name"],11), "\tmean=",x["mean"], "\tsterr=",x["sterr"], "\tlen", x["len"]
def printGnuplot(key):
import py_stats
fr = fullResults
#
## Extract keys.
files = map(getLocalName, fList)
#learners = map( lambda x : x[0], localStructureLearner )
# Learners in order required by gnuplot scripts.
learners = ['CPT', 'logit', 'tree', 'dualCTL', 'FBDe', 'FBDe-Tree']
folds = fr[files[0]][learners[0]].keys()
#
## Print Learner Key
print "# ",
for x in learners:
print x,
print
#
## Print out actual data.
for f in files:
n = f.split('.')[-1] # Print number of cases
print str(n), "\t",
for x in learners:
data = []
for xx in fr[f][x].values():
if xx.has_key(key): data.append(xx[key])
if len(data) > 1: print "%4.8f\t" % py_stats.mean(data),
else: print "& XXX & XXX",
for x in learners:
data = []
for xx in fr[f][x].values():
if xx.has_key(key): data.append(xx[key])
if len(data) > 1: print "%4.8f\t" % py_stats.sterr(data),
else: print "& XXX & XXX",
def printAverage(key, sort=True):
import py_stats
print "\nTotal - ", key
t = {}
fr = fullResults
netNames = map(getLocalName, fList)
#netNames = fr.keys()
#netNames.sort() # Make a consistent ordering.
for data in netNames:
print "\n", data, key
summary = []
for k in localStructureLearner: #fr['balance-scale.arff'].keys():
if fr[data].keys().count(k[0]) == 0: continue
k = k[0]
t[k] = []
for i in fr[data][k].keys():
## I think there is a bug here in jython serialisation
debug[0] = fr[data][k][i]
try:
if not isNone(debug[0]): # != None:
if fr[data][k][i].has_key(key) and (not isNone(
fr[data][k][i][key])):
t[k].append(fr[data][k][i][key])
elif key.endswith(".2") and fr[data][k][i].has_key(
key[:-2]) and (not isNone(
fr[data][k][i][key[:-2]])):
t[k].append(fr[data][k][i][key[:-2]])
except AttributeError:
print "Ignoring exception, probably due to buggy serialisation", debug[
0], k, i, str(debug[0])
continue # Do nothing.
if len(t[k]) > 1:
summary.append({
"mean": py_stats.mean(t[k]),
"sterr": py_stats.sterr(t[k]),
"len": len(t[k]),
"name": k
})
else:
summary.append({
"mean": 0,
"sterr": 0,
"len": len(t[k]),
"name": k
})
if sort: summary.sort(lambda a, b: a["mean"] > b["mean"])
#
for x in summary:
print pad(
x["name"], 11
), "\tmean=", x["mean"], "\tsterr=", x["sterr"], "\tlen", x["len"]
def printLatex(key):
import py_stats
fr = fullResults
#
## Extract keys.
files = fr.keys()
learners = fr[files[0]].keys()
folds = fr[files[0]][learners[0]].keys()
#
## Begin printing latex.
## -- Header.
print "\\begin{table}[htbp]"
print "\t\\centering"
print "\t\\scriptsize"
print "\t\\begin{tabular}{|c|",
for _ in files:
print "rr|",
print "} \\hline"
#
## File names.
print "\tDataset ",
for f in files:
print "& \\multicolumn{2}{|c|}{", f[:-4], "} ",
print "\\tabularnewline \\hline \\hline"
print "\tMetric ",
for _ in files:
print "& Mean & SE ",
print "\\tabularnewline \\hline"
#
## Print out actual data.
for x in learners:
print "\t" + str(x), "\t",
for f in files:
data = []
for xx in fr[f][x].values():
if xx.has_key(key): data.append(xx[key])
#print "&", py_stats.mean(data), "& ", py_stats.sterr(data),
if len(data) > 1:
print "& %4.3f & %4.3f" % (py_stats.mean(data),
py_stats.sterr(data)),
else:
print "& XXX & XXX",
print " \\tabularnewline"
#
## Print footer.
print "\t\\hline"
print "\t\\end{tabular}"
print "\t\\caption{", key, "}"
print "\t\\label{}"
print "\\end{table}"
def printLatex( key ):
import py_stats
fr = fullResults
#
## Extract keys.
files = fr.keys()
learners = fr[files[0]].keys()
folds = fr[files[0]][learners[0]].keys()
#
## Begin printing latex.
## -- Header.
print "\\begin{table}[htbp]"
print "\t\\centering"
print "\t\\scriptsize"
print "\t\\begin{tabular}{|c|",
for _ in files: print "rr|",
print "} \\hline"
#
## File names.
print "\tDataset ",
for f in files: print "& \\multicolumn{2}{|c|}{",f[:-4],"} ",
print "\\tabularnewline \\hline \\hline"
print "\tMetric ",
for _ in files: print "& Mean & SE ",
print "\\tabularnewline \\hline"
#
## Print out actual data.
for x in learners:
print "\t"+str(x), "\t",
for f in files:
data = []
for xx in fr[f][x].values():
if xx.has_key(key): data.append( xx[key] )
#print "&", py_stats.mean(data), "& ", py_stats.sterr(data),
if len(data) > 1: print "& %4.3f & %4.3f" % (py_stats.mean(data), py_stats.sterr(data)),
else: print "& XXX & XXX",
print " \\tabularnewline"
