def main():
data_sets = ["binary/balance-scale.csv", "binary/tic-tac-toe.csv", "binary/ionosphere.csv",
"multi/splice.csv", "multi/glass.csv", "multi/segment.csv"]
folds = 2
classifier_types = {}
max_depth = [d for d in range(5, 50+1, 5)]
min_samples_leaf = [d for d in range(5, 50+1, 5)]
test_result = numpy.zeros((len(max_depth), len(min_samples_leaf)))
for data in data_sets:
set_data, set_target = functions.get_data_from_csv(data)
kf = cross_validation.KFold(set_data.shape[0], n_folds=folds)
for i in range(len(max_depth)):
for j in range(len(min_samples_leaf)):
classifier_types["dt"] = BinaryTreeClassifier(max_depth=max_depth[i], min_samples_leaf=min_samples_leaf[j])
# classifier_types["dtc"] = BinaryTreeClassifier(max_depth=max_depth[i], min_samples_leaf=min_samples_leaf[j])
# classifier_types["rf"] = RandomForest(max_depth=max_depth[i],min_samples_leaf=min_samples_leaf[j])
# classifier_types["rfc"] = RandomForestClassifier(max_depth=max_depth[i],min_samples_leaf=min_samples_leaf[j])
# classifier_types["knn"] = KNeighborsClassifier(n_neighbors=max_depth[i], leaf_size=min_samples_leaf[j])
for c_type, classifier in classifier_types.iteritems():
result = list()
average_accuracy = 0.0
for train, test in kf:
train_set = set_data[train]
train_class = set_target[train]
test_set = set_data[test]
test_class = set_target[test]
classifier.fit(train_set, train_class.ravel())
pred = classifier.predict(test_set)
result.append(functions.predictions_to_values(test_class, pred))
for res in range(len(result)):
average_accuracy += metrics.accuracy_score(result[res][0], result[res][1])
average_accuracy /= float(len(result))
test_result[i][j] += average_accuracy / len(data_sets) / len(classifier_types)
print "{0:5}".format(""),
for i in min_samples_leaf:
print "{0:<5d}".format(i),
print "\n",
for i in range(len(max_depth)):
print "{0:>5d}".format(max_depth[i]),
for j in range(len(test_result[i])):
print "{0:<5.3f}".format(test_result[i][j]),
print "\n",
def main():
data_sets = ["binary/breast-w.csv", "binary/diabetes.csv", "binary/labor.csv",
"multi/iris.csv", "multi/letter.csv", "multi/vehicle.csv"]
# Data set for experiment 3
# data_sets = ["raop.csv"]
folds = 10
dt = BinaryTreeClassifier(max_depth=30, min_samples_leaf=10)
dtc = DecisionTreeClassifier(max_depth=50, min_samples_leaf=5)
rf = RandomForest(max_depth=40, min_samples_leaf=15)
rfc = RandomForestClassifier(max_depth=50, min_samples_leaf=5)
kNN = KNeighborsClassifier(n_neighbors=10, leaf_size=10)
classifier_types = {"dt": dt, "rf": rf, "dtc": dtc, "rfc": rfc, "kNN": kNN}
for i in data_sets:
set_data, set_target = functions.get_data_from_csv(i)
kf = cross_validation.KFold(set_data.shape[0], n_folds=folds)
print("\n{0}{1}".format("Data set: ", i))
print("{0:5}".format("Accuracy:"))
for c_type, classifier in classifier_types.items():
result = list()
average_acc = 0.0
for train, test in kf:
train_set = set_data[train]
train_class = set_target[train]
test_set = set_data[test]
test_class = set_target[test]
classifier.fit(train_set, train_class.ravel())
pred = classifier.predict(test_set)
result.append(functions.predictions_to_values(test_class, pred))
for r in range(len(result)):
average_acc += metrics.accuracy_score(result[r][0], result[r][1])
average_acc /= float(len(result))
print("{0:5}{1:5.3f}".format(c_type, average_acc))
def main():
folds = 10
dt = BinaryTreeClassifier(max_depth=30, min_samples_leaf=10)
dtc = DecisionTreeClassifier(max_depth=50, min_samples_leaf=5)
rf = RandomForest(max_depth=40, min_samples_leaf=15)
rfc = RandomForestClassifier(max_depth=50, min_samples_leaf=5)
kNN = KNeighborsClassifier(n_neighbors=10, leaf_size=10)
classifier_types = {"dt": dt, "rf": rf, "dtc": dtc, "rfc": rfc, "kNN": kNN}
results = {}
set_data, set_target = functions.get_data_from_csv("raop.csv")
kf = cross_validation.KFold(set_data.shape[0], n_folds=folds)
for c_type, classifier in classifier_types.items():
results[c_type] = 0
for train, test in kf:
train_set = set_data[train]
train_class = set_target[train]
test_set = set_data[test]
test_class = set_target[test]
classifier.fit(train_set, train_class.ravel())
prob = classifier.predict(test_set)
true_set, pred_set = functions.predictions_to_values(test_class, prob)
results[c_type] += true_set[true_set == pred_set].size
processed = []
for c_type, classifier in results.items():
for c_type_2, classifier_2 in results.items():
if c_type != c_type_2:
if c_type_2 not in processed:
print("{0:>7} vs {1:7} {2:<5.6f}".format(c_type, c_type_2, mcnemar_midp(classifier, classifier_2)))
processed.append(c_type)
def main():
# # Tests for multi datasets
set_data, set_target = functions.get_data_from_csv("multi/glass.csv")
# set_data, set_target = functions.get_data_from_csv("multi/iris.csv")
# set_data, set_target = functions.get_data_from_csv("multi/letter.csv")
# set_data, set_target = functions.get_data_from_csv("multi/segment.csv")
# set_data, set_target = functions.get_data_from_csv("multi/splice.csv")
# set_data, set_target = functions.get_data_from_csv("multi/waveform-5000.csv")
# set_data, set_target = functions.get_data_from_csv("multi/vehicle.csv")
#
# # Tests for binary datasets
# set_data, set_target = functions.get_data_from_csv("binary/balance-scale.csv")
# set_data, set_target = functions.get_data_from_csv("binary/breast-cancer.csv")
# set_data, set_target = functions.get_data_from_csv("binary/breast-w.csv")
# set_data, set_target = functions.get_data_from_csv("binary/credit-a.csv")
# set_data, set_target = functions.get_data_from_csv("binary/credit-g.csv")
# set_data, set_target = functions.get_data_from_csv("binary/diabetes.csv")
# set_data, set_target = functions.get_data_from_csv("binary/haberman.csv")
# set_data, set_target = functions.get_data_from_csv("binary/heart-c.csv")
# set_data, set_target = functions.get_data_from_csv("binary/heart-h.csv")
# set_data, set_target = functions.get_data_from_csv("binary/heart-s.csv")
# set_data, set_target = functions.get_data_from_csv("binary/hepatitis.csv")
# set_data, set_target = functions.get_data_from_csv("binary/ionosphere.csv")
# set_data, set_target = functions.get_data_from_csv("binary/kr-vs-kp.csv")
# set_data, set_target = functions.get_data_from_csv("binary/labor.csv")
# set_data, set_target = functions.get_data_from_csv("binary/liver-disorders.csv")
# set_data, set_target = functions.get_data_from_csv("binary/mushroom.csv")
# set_data, set_target = functions.get_data_from_csv("binary/sick.csv")
# set_data, set_target = functions.get_data_from_csv("binary/sonar.csv")
# set_data, set_target = functions.get_data_from_csv("binary/spambase.csv")
# set_data, set_target = functions.get_data_from_csv("binary/tic-tac-toe.csv")
# dt, rf are our implementations
dt = BinaryTreeClassifier()
dtc = DecisionTreeClassifier()
rf = RandomForest()
rfc = RandomForestClassifier()
classifier_types = {"dt": dt, "rf": rf, "dtc": dtc, "rfc": rfc}
printed = False
folds = 10
kf = cross_validation.KFold(set_data.shape[0], n_folds=folds)
accuracy_result = {}
for c_type, classifier in classifier_types.iteritems():
result = list()
prob = list()
average_accuracy = 0.0
average_precision = 0.0
average_recall = 0.0
average_auc = 0.0
average_train_time = 0.0
average_test_time = 0.0
print(c_type)
for train, test in kf:
train_set = set_data[train]
train_class = set_target[train]
test_set = set_data[test]
test_class = set_target[test]
timer = time()
classifier.fit(train_set, train_class.ravel())
if c_type == 'dt' and not printed:
classifier.print_tree_wrapper()
printed = True
average_train_time += time() - timer
timer = time()
pred = classifier.predict(test_set)
prob.append(classifier.predict_proba(test_set))
average_test_time += time() - timer
result.append(functions.predictions_to_values(test_class, pred))
accuracy_result[c_type] = list()
for res in range(len(result)):
average_accuracy += metrics.accuracy_score(result[res][0], result[res][1])
accuracy_result[c_type].append(metrics.accuracy_score(result[res][0], result[res][1]))
average_precision += metrics.precision_score(result[res][0], result[res][1])
average_recall += metrics.recall_score(result[res][0], result[res][1])
unique = numpy.unique(result[res][0])
proba = numpy.array(prob[res])
max_proba = numpy.array([max(d) for d in proba])
if len(unique) > 2:
for i in unique:
fpr, tpr, thresholds = metrics.roc_curve(result[res][0], max_proba, pos_label=int(i))
average_auc += metrics.auc(fpr, tpr)
average_auc /= unique.size / 2.0
else:
fpr, tpr, thresholds = metrics.roc_curve(result[res][0], max_proba)
average_auc += metrics.auc(fpr, tpr)
print ("Accuracy: "),
print('{0:.3f}'.format(average_accuracy / float(len(result))))
print ("Precision: "),
print('{0:.3f}'.format(average_precision / float(len(result))))
print ("Recall: "),
print('{0:.3f}'.format(average_recall / float(len(result))))
print ("Auc: "),
print('{0:.3f}'.format(average_auc / float(len(result))))
print ("")
print('Training time: '),
print(average_train_time / float(len(result)))
print('Test time: '),
print(average_test_time / float(len(result)))
print ("Wilcoxon Decision Tree Classifiers: "),
print (wilcoxon(accuracy_result['dt'], accuracy_result['dtc']))
print ("Wilcoxon Random Forest Classifiers: "),
print (wilcoxon(accuracy_result['rf'], accuracy_result['rfc']))