classifiers['d2'] = classifier2
classifiers['d3'] = classifier3
classifiers['d4'] = classifier4
classifiers['d5'] = classifier5
classifiers['d6'] = classifier6
classifiers['d7'] = classifier7
classifiers['d8'] = classifier8
classifiers['d9'] = classifier9
classifiers['d10'] = classifier10
for c in classifiers.itervalues():
cm.train(c, training)
perf = {}
for n, c in classifiers.iteritems():
perf[n] = cm.perf([c], testing)
print "Performances"
print perf
best_classifier_name = max(perf.iterkeys(), key=(lambda k: perf[k]))
print "Best classifier: ", best_classifier_name, " -> ", perf[best_classifier_name]
print "Saving the model"
with open(args.dest_file, 'w') as f:
cm.save(classifiers[best_classifier_name], f)
except psycopg2.DatabaseError, e:
print 'Error %s' % e
sys.exit(1)
mySet = s.load(fe, args.number_pos, args.number_pos, args.number_neut)
testingSet, _ = s.splitTrainingAndTestingSet(mySet, 1)
# print testingSet.X, testingSet.Y
except psycopg2.DatabaseError, e:
print 'Error %s' % e
sys.exit(1)
finally:
if con:
con.close()
# Testing the performance
# Loading the classifiers
path = args.origin_path
list_classifier_filename = os.listdir(path)
classifiers = []
# Retrieve the classifiers
for classifier_filename in list_classifier_filename:
with open(path + '/' + classifier_filename, 'r') as f:
cm = ClassifierManager()
classifier = cm.load(f)
classifiers.append(classifier)
# Evaluate the perfs
print "Performance: ", cm.perf(classifiers, testingSet)
