sklearn and shogun's multiclass SVC produce different results (predictions and confidence values)

shogun-multiclass-svc.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

# import some data to play with
iris = datasets.load_iris()

num_samples = len(iris.data)

np.random.seed(seed=42)

idx = np.arange(num_samples)
np.random.shuffle(idx)

spl = int(num_samples * 0.70)

X_train = iris.data[idx][:spl, :2] 
y_train = iris.target[idx][:spl]
X_test = iris.data[idx][spl:, :2]
y_test = iris.target[idx][spl:]

C = 1.0  # SVM regularization parameter

lsvc = svm.LinearSVC(C=C).fit(X_train, y_train)
sklearn_pred = lsvc.predict(X_test)
print("sklearn predictions:", sklearn_pred)

# Shogun

classifier = LibLinear()
strategy = MulticlassOneVsOneStrategy()
strategy = MulticlassOneVsRestStrategy()

mc_classifier = LinearMulticlassMachine(strategy, RealFeatures(X_train.T), classifier, MulticlassLabels(y_train.astype('d')))
mc_classifier.train()

y_pred = mc_classifier.apply_multiclass(RealFeatures(X_test.T))

print("Shogun predictions:", y_pred.get_labels())

print("sklearn confidence values:", lsvc.decision_function())

print("Shogun confidence values:", y_pred.get_values())