Example #1
0
    def BuildModel(self, data, labels, options):
        cart = CARTree()
        cart.set_feature_types(np.array([False] * data.get_num_features()))
        cart.set_labels(labels)
        cart.train(data)

        return cart
Example #2
0
def regression_cartree_modular(num_train=500,num_test=50,x_range=15,noise_var=0.2,ft=feattypes):
	try:
		from modshogun import RealFeatures, RegressionLabels, CSVFile, CARTree, PT_REGRESSION
		from numpy import random
	except ImportError:
		print("Could not import Shogun and/or numpy modules")
		return

	random.seed(1)

	# form training dataset : y=x with noise
	X_train=random.rand(1,num_train)*x_range;
	Y_train=X_train+random.randn(num_train)*noise_var

	# form test dataset
	X_test=array([[float(i)/num_test*x_range for i in range(num_test)]])

	# wrap features and labels into Shogun objects
	feats_train=RealFeatures(X_train)
	feats_test=RealFeatures(X_test)
	train_labels=RegressionLabels(Y_train[0])

	# CART Tree formation
	c=CARTree(ft,PT_REGRESSION,5,True)
	c.set_labels(train_labels)
	c.train(feats_train)

	# Classify test data
	output=c.apply_regression(feats_test).get_labels()

	return c,output
Example #3
0
  def BuildModel(self, data, labels, options):
    cart = CARTree()
    cart.set_feature_types(np.array([False] * data.get_num_features()))
    cart.set_labels(labels)
    cart.train(data)

    return cart
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
Example #5
0
def multiclass_cartree_modular(train=traindat,
                               test=testdat,
                               labels=label_traindat,
                               ft=feattypes):
    try:
        from modshogun import RealFeatures, MulticlassLabels, CSVFile, CARTree, PT_MULTICLASS
    except ImportError:
        print("Could not import Shogun modules")
        return

    # wrap features and labels into Shogun objects
    feats_train = RealFeatures(CSVFile(train))
    feats_test = RealFeatures(CSVFile(test))
    train_labels = MulticlassLabels(CSVFile(labels))

    # CART Tree formation with 5 fold cross-validation pruning
    c = CARTree(ft, PT_MULTICLASS, 5, True)
    c.set_labels(train_labels)
    c.train(feats_train)

    # Classify test data
    output = c.apply_multiclass(feats_test).get_labels()

    return c, output
def multiclass_cartree_modular(train=traindat,test=testdat,labels=label_traindat,ft=feattypes):	
	try:
		from modshogun import RealFeatures, MulticlassLabels, CSVFile, CARTree, PT_MULTICLASS
	except ImportError:
		print("Could not import Shogun modules")
		return

	# wrap features and labels into Shogun objects
	feats_train=RealFeatures(CSVFile(train))
	feats_test=RealFeatures(CSVFile(test))
	train_labels=MulticlassLabels(CSVFile(labels))

	# CART Tree formation with 5 fold cross-validation pruning
	c=CARTree(ft,PT_MULTICLASS,5,True)
	c.set_labels(train_labels)
	c.train(feats_train)

	# Classify test data
	output=c.apply_multiclass(feats_test).get_labels()

	return c,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