def print_results(learners, results):
# output the results
print "Learner CA IS Brier AUC"
for i in range(len(learners)):
print "%-8s %5.4f %5.3f %5.3f %5.3f" % (
learners[i].name, orngStat.CA(results)[i], orngStat.IS(results)[i],
orngStat.BrierScore(results)[i], orngStat.AUC(results)[i])
def cforange_information_score(input_dict):
import orngStat
results = input_dict['results']
if input_dict['reportSE']=='true':
reportSE = True
else:
reportSE = False
ISs = orngStat.IS(results,apriori=None,reportSE=reportSE)
if len(ISs)==1:
ISs = ISs[0]
output_dict = {}
output_dict['is']=ISs
return output_dict
# Description: Demostration of use of cross-validation as provided in orngEval module
# Category: evaluation
# Uses: voting.tab
# Classes: orngTest.crossValidation
# Referenced: c_performance.htm
import orange, orngTest, orngStat, orngTree
# set up the learners
bayes = orange.BayesLearner()
tree = orngTree.TreeLearner(mForPruning=2)
bayes.name = "bayes"
tree.name = "tree"
learners = [bayes, tree]
# compute accuracies on data
data = orange.ExampleTable("voting")
results = orngTest.crossValidation(learners, data, folds=10)
# output the results
print "Learner CA IS Brier AUC"
for i in range(len(learners)):
print "%-8s %5.3f %5.3f %5.3f %5.3f" % (learners[i].name, \
orngStat.CA(results)[i], orngStat.IS(results)[i],
orngStat.BrierScore(results)[i], orngStat.AUC(results)[i])
learners = [orange.BayesLearner(name = "bayes"),
orngTree.TreeLearner(name="tree"),
orange.MajorityLearner(name="majrty")]
voting = orange.ExampleTable("voting")
res = orngTest.crossValidation(learners, voting)
vehicle = orange.ExampleTable("vehicle")
resVeh = orngTest.crossValidation(learners, vehicle)
import orngStat
CAs = orngStat.CA(res)
APs = orngStat.AP(res)
Briers = orngStat.BrierScore(res)
ISs = orngStat.IS(res)
print
print "method\tCA\tAP\tBrier\tIS"
for l in range(len(learners)):
print "%s\t%5.3f\t%5.3f\t%5.3f\t%6.3f" % (learners[l].name, CAs[l], APs[l], Briers[l], ISs[l])
CAs = orngStat.CA(res, reportSE=True)
APs = orngStat.AP(res, reportSE=True)
Briers = orngStat.BrierScore(res, reportSE=True)
ISs = orngStat.IS(res, reportSE=True)
print
print "method\tCA\tAP\tBrier\tIS"
for l in range(len(learners)):
data_scored.append(0)
import matplotlib.pyplot as plt
import matplotlib
X1w = []
X2w = []
for i in range(len(X)):
if data_scored[i] == 0:
X1w.append(X[i][0])
X2w.append(X[i][1])
########################
# Plot the misclassified data
########################
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.scatter(X11, X12, s=10, c='b', marker="+")
ax1.scatter(X21, X22, s=10, c='c', marker="o")
ax1.scatter(X31, X32, s=10, c='y', marker="x")
ax1.scatter(X1w, X2w, s=10, c='m', marker="^")
plt.title('Plot of Three Classes of Data, Showing the Misclassified Elements')
plt.show()
# output the results
print "Learner CA IS Brier AUC"
for i in range(len(learners)):
print "%-8s %5.3f %5.3f %5.3f %5.3f" % (learners[i].name, \
orngStat.CA(results)[i], orngStat.IS(results)[i], orngStat.BrierScore(results)[i], orngStat.AUC(results)[i])
bayes = orange.BayesLearner()
tree = orngTree.TreeLearner(mForPruning=2)
bayes.name = "bayes"
tree.name = "tree"
learners = [bayes, tree]
# compute accuracies on data
data = orange.ExampleTable("voting")
res = orngTest.crossValidation(learners, data, folds=10)
cm = orngStat.computeConfusionMatrices(
res, classIndex=data.domain.classVar.values.index('democrat'))
stat = (
('CA', lambda res, cm: orngStat.CA(res)),
('Sens', lambda res, cm: orngStat.sens(cm)),
('Spec', lambda res, cm: orngStat.spec(cm)),
('AUC', lambda res, cm: orngStat.AUC(res)),
('IS', lambda res, cm: orngStat.IS(res)),
('Brier', lambda res, cm: orngStat.BrierScore(res)),
('F1', lambda res, cm: orngStat.F1(cm)),
('F2', lambda res, cm: orngStat.Falpha(cm, alpha=2.0)),
('MCC', lambda res, cm: orngStat.MCC(cm)),
('sPi', lambda res, cm: orngStat.scottsPi(cm)),
)
scores = [s[1](res, cm) for s in stat]
print
print "Learner " + "".join(["%-7s" % s[0] for s in stat])
for (i, l) in enumerate(learners):
print "%-8s " % l.name + "".join(["%5.3f " % s[i] for s in scores])