def stochasticgbmachine_modular(train=traindat,
train_labels=label_traindat,
ft=feat_types):
try:
from modshogun import RealFeatures, RegressionLabels, CSVFile, CARTree, StochasticGBMachine, SquaredLoss
except ImportError:
print("Could not import Shogun modules")
return
# wrap features and labels into Shogun objects
feats = RealFeatures(CSVFile(train))
labels = RegressionLabels(CSVFile(train_labels))
# divide into training (90%) and test dataset (10%)
p = np.random.permutation(labels.get_num_labels())
num = labels.get_num_labels() * 0.9
cart = CARTree()
cart.set_feature_types(ft)
cart.set_max_depth(1)
loss = SquaredLoss()
s = StochasticGBMachine(cart, loss, 500, 0.01, 0.6)
# train
feats.add_subset(np.int32(p[0:num]))
labels.add_subset(np.int32(p[0:num]))
s.set_labels(labels)
s.train(feats)
feats.remove_subset()
labels.remove_subset()
# apply
feats.add_subset(np.int32(p[num:len(p)]))
labels.add_subset(np.int32(p[num:len(p)]))
output = s.apply_regression(feats)
feats.remove_subset()
labels.remove_subset()
return s, output
def stochasticgbmachine_modular(train=traindat,train_labels=label_traindat,ft=feat_types):
try:
from modshogun import RealFeatures, RegressionLabels, CSVFile, CARTree, StochasticGBMachine, SquaredLoss
except ImportError:
print("Could not import Shogun modules")
return
# wrap features and labels into Shogun objects
feats=RealFeatures(CSVFile(train))
labels=RegressionLabels(CSVFile(train_labels))
# divide into training (90%) and test dataset (10%)
p=np.random.permutation(labels.get_num_labels())
num=labels.get_num_labels()*0.9
cart=CARTree()
cart.set_feature_types(ft)
cart.set_max_depth(1)
loss=SquaredLoss()
s=StochasticGBMachine(cart,loss,500,0.01,0.6)
# train
feats.add_subset(np.int32(p[0:num]))
labels.add_subset(np.int32(p[0:num]))
s.set_labels(labels)
s.train(feats)
feats.remove_subset()
labels.remove_subset()
# apply
feats.add_subset(np.int32(p[num:len(p)]))
labels.add_subset(np.int32(p[num:len(p)]))
output=s.apply_regression(feats)
feats.remove_subset()
labels.remove_subset()
return s,output
