def tune_penalty(self, data):
learner = LRRulesLearner(fit_intercept=self.fit_intercept,
intercept_scaling=self.intercept_scaling)
penalties = [0.001, 0.01, 0.1, 0.5, 1.0, 2.0, 5.0, 10., 100.]
scores = []
for pen in penalties:
learner.penalty = pen
res = CrossValidation(data, [learner], k=5, random_state=1111)
ll = LogLoss(res)
scores.append(ll)
return penalties[scores.index(min(scores))]
def test_log_loss_calc(self):
data = Table('titanic')
learner = LogisticRegressionLearner()
results = TestOnTrainingData(data, [learner])
actual = np.copy(results.actual)
actual = actual.reshape(actual.shape[0], 1)
actual = np.hstack((1 - actual, actual))
probab = results.probabilities[0]
ll_calc = self._log_loss(actual, probab)
ll_orange = LogLoss(results)
self.assertAlmostEqual(ll_calc, ll_orange[0])
def test_log_loss(self):
data = Table('iris')
majority = MajorityLearner()
results = TestOnTrainingData(data, [majority])
ll = LogLoss(results)
self.assertAlmostEqual(ll[0], - np.log(1 / 3))
fmodel = open("model.txt".format(cycle), "wt")
fmodel.write(str(classifier))
# test model + other methods
testdata = Orange.data.Table('testdata')
bayes = Orange.classification.NaiveBayesLearner()
logistic = Orange.classification.LogisticRegressionLearner()
random_forest = Orange.classification.RandomForestLearner()
svm = Orange.classification.SVMLearner()
tree = Orange.classification.TreeLearner()
cn2 = Orange.classification.rules.CN2UnorderedLearner()
learners = [learner, logistic, tree, bayes, cn2, random_forest, svm]
res = TestOnTestData(data, testdata, learners)
ca = CA(res)
auc = AUC(res)
ll = LogLoss(res)
names = [
'logrules', 'logistic', 'tree', 'naive-bayes', 'cn2', 'random-forest',
'svm'
]
scores = ""
scores += "CA\tAUC\tLogLoss\tMethod\n"
for ni, n in enumerate(names):
scores += "{}\t{}\t{}\t{}\n".format(ca[ni], auc[ni], ll[ni], n)
print(scores)
fscores = open("scores.txt", "wt")
fscores.write(scores)
# find critical examples
indices, criticality, rules = arg.find_critical(learner, data)
from Orange.classification import LogisticRegressionLearner, NaiveBayesLearner, \
RandomForestLearner
import orangecontrib.evcrules.rules as rules
from orangecontrib.evcrules.logistic import LRRulesLearner
datasets = [
'ionosphere', 'adult_sample', 'iris', 'breast-cancer', 'bupa', 'titanic'
]
for d in datasets:
data = Table(d)
rule_learner = rules.RulesStar(evc=True,
add_sub_rules=True,
parent_alpha=0.5)
rule_learner_m = rules.RulesStar(evc=False,
m=22,
add_sub_rules=True,
parent_alpha=0.5)
# compare lr with rules, lr without rules and sklearn's lr
learners = [
LRRulesLearner(opt_penalty=True, rule_learner=rule_learner),
LRRulesLearner(opt_penalty=True, rule_learner=rule_learner_m),
LRRulesLearner(opt_penalty=True),
LogisticRegressionLearner(C=1),
NaiveBayesLearner(),
RandomForestLearner()
]
res = CrossValidation(data, learners, k=5)
print("Dataset: ", d)
for l, ca, auc, ll in zip(learners, CA(res), AUC(res), LogLoss(res)):
print("learner: {}\nCA: {}\nAUC: {}\n LL: {}".format(l, ca, auc, ll))