RandomForest doesn't give the same outputs when comparing shogun & sklearn

rf-output-sklearn.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.ensemble import RandomForestClassifier
from modshogun import *

#np.random.seed(42)

X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
y = np.array([0, 0, 1, 1]).astype(np.float)

features = RealFeatures(X.T)
labels = MulticlassLabels(y)

rand_forest = RandomForest(features, labels, num_rand_bags = 10)
rand_forest.set_combination_rule(MajorityVote())

ft = np.ones(2).astype('bool')
rand_forest.set_feature_types(ft)

rand_forest.train()
y_pred = rand_forest.apply_binary(features)

print("Shogun results")
print("predicted labels:", y_pred.get_labels())
print("probabilities:", y_pred.get_values())

# sklearn

rf = RandomForestClassifier(n_estimators=10).fit(X, y)

y_pred = rf.predict(X)
y_prob = rf.predict_proba(X)

print("sklearn results")
print("predicted labels:", y_pred)
print("probabilities:",y_prob)