def Boost_J48(data, rnm):
data.class_is_last()
fc1 = FilteredClassifier()
fc1.classifier = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.25", "-M", "2"])
fc1.filter = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "first"])
fc2 = SingleClassifierEnhancer(classname="weka.classifiers.meta.AdaBoostM1", options=["-P", "100", "-S", "1", "-I", "10"])
fc2.classifier = fc1
pred_output = PredictionOutput(classname="weka.classifiers.evaluation.output.prediction.CSV", options=["-p", "1"])
folds = 10
fc2.build_classifier(data)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(fc2, data, folds, Random(1), pred_output)
f0 = open(rnm + '_Boost_J48_Tree.txt', 'w')
print >> f0, "Filename: ", rnm
print >> f0, '\n\n'
print >> f0, str(fc2)
f0.close()
f1 = open(rnm + '_Boost_J48_Prediction.txt', 'w')
print >> f1, 'Filename:', rnm
print >> f1, 'Prediction Summary:', (pred_output.buffer_content())
f1.close()
f2 = open(rnm + '_Boost_j48_Evaluation.txt', 'w')
print >> f2, 'Filename:', rnm
print >> f2, 'Evaluation Summary:', (evaluation.summary())
print >> f2, '\n\n\n'
print >> f2, (evaluation.class_details())
f2.close()
plot_roc(evaluation, class_index=[0,1], title=rnm, key_loc='best', outfile=rnm + '_Boost_J48_ROC.png', wait=False)
value_Boost_J48 = str(evaluation.percent_correct)
return value_Boost_J48
def classification(data, train, test, num_clases):
baseClassifiers_list = [
"weka.classifiers.bayes.NaiveBayes",
"weka.classifiers.functions.MultilayerPerceptron",
"weka.classifiers.functions.SMO", "weka.classifiers.lazy.IBk",
"weka.classifiers.lazy.KStar", "weka.classifiers.meta.AdaBoostM1",
"weka.classifiers.meta.Bagging", "weka.classifiers.meta.LogitBoost",
"weka.classifiers.trees.J48", "weka.classifiers.trees.DecisionStump",
"weka.classifiers.trees.LMT", "weka.classifiers.trees.RandomForest",
"weka.classifiers.trees.REPTree", "weka.classifiers.rules.PART",
"weka.classifiers.rules.JRip", "weka.classifiers.functions.Logistic",
"weka.classifiers.meta.ClassificationViaRegression",
"weka.classifiers.bayes.BayesNet"
]
results_train = pd.DataFrame()
results_test = pd.DataFrame()
cost_matrix_list = [
"[]", "[0]", "[0.0 1.0; 1.0 0.0]",
"[0.0 1.0 2.0; 1.0 0.0 1.0; 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0; 1.0 0.0 1.0 2.0; 2.0 1.0 0.0 1.0; 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0; 1.0 0.0 1.0 2.0 3.0; 2.0 1.0 0.0 1.0 2.0; 3.0 2.0 1.0 0.0 1.0; 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0; 1.0 0.0 1.0 2.0 3.0 4.0; 2.0 1.0 0.0 1.0 2.0 3.0; 3.0 2.0 1.0 0.0 1.0 2.0; 4.0 3.0 2.0 1.0 0.0 1.0; 5.0 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0 6.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]"
]
real_train = [] # the real label of the dataset
for i in range(train.num_instances):
real_train.append(
train.get_instance(i).values[(train.num_attributes - 1)])
results_train['real'] = real_train
real_test = [] # the real label of the dataset
for i in range(test.num_instances):
real_test.append(
test.get_instance(i).values[(test.num_attributes - 1)])
results_test['real'] = real_test
num = 0
for clas in baseClassifiers_list:
column = "p" + np.str(num)
#classifier
classifier = SingleClassifierEnhancer(
classname="weka.classifiers.meta.CostSensitiveClassifier",
options=[
"-cost-matrix", cost_matrix_list[num_clases], "-M", "-S", "1"
])
base = Classifier(classname=clas)
classifier.classifier = base
predicted_data_train = None
predicted_data_test = None
evaluation = Evaluation(data)
classifier.build_classifier(train)
#evaluation.test_model(classifier, train)
# add predictions
addcls = Filter(
classname="weka.filters.supervised.attribute.AddClassification",
options=["-classification"])
addcls.set_property("classifier", Classifier.make_copy(classifier))
addcls.inputformat(train)
#addcls.filter(train) # trains the classifier
pred_train = addcls.filter(train)
pred_test = addcls.filter(test)
if predicted_data_train is None:
predicted_data_train = Instances.template_instances(pred_train, 0)
for n in range(pred_train.num_instances):
predicted_data_train.add_instance(pred_train.get_instance(n))
if predicted_data_test is None:
predicted_data_test = Instances.template_instances(pred_test, 0)
for n in range(pred_test.num_instances):
predicted_data_test.add_instance(pred_test.get_instance(n))
preds_train = [
] #labels predicted for the classifer trained in the iteration
preds_test = []
for i in range(predicted_data_train.num_instances):
preds_train.append(
predicted_data_train.get_instance(i).values[(
predicted_data_train.num_attributes - 1)])
for i in range(predicted_data_test.num_instances):
preds_test.append(
predicted_data_test.get_instance(i).values[(
predicted_data_test.num_attributes - 1)])
results_train[column] = preds_train
results_test[column] = preds_test
num = num + 1
return results_train, results_test
