def regression_chaidtree_modular(num_train=500,num_test=50,x_range=15,noise_var=0.2,ft=feattypes):
	try:
		from modshogun import RealFeatures, RegressionLabels, CSVFile, CHAIDTree, 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])

	# CHAID Tree formation
	c=CHAIDTree(2,feattypes,50)
	c.set_labels(train_labels)
	c.train(feats_train)

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

	return c,output
def regression_chaidtree_modular(num_train=500,
                                 num_test=50,
                                 x_range=15,
                                 noise_var=0.2,
                                 ft=feattypes):
    try:
        from modshogun import RealFeatures, RegressionLabels, CSVFile, CHAIDTree, 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])

    # CHAID Tree formation
    c = CHAIDTree(2, feattypes, 50)
    c.set_labels(train_labels)
    c.train(feats_train)

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

    return c, output
def multiclass_chaidtree_modular(train=traindat,test=testdat,labels=label_traindat,ft=feattypes):
	try:
		from modshogun import RealFeatures, MulticlassLabels, CSVFile, CHAIDTree
	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))

	# CHAID Tree formation with nominal dependent variable
	c=CHAIDTree(0,feattypes,10)
	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_chaidtree_modular(train=traindat,
                                 test=testdat,
                                 labels=label_traindat,
                                 ft=feattypes):
    try:
        from modshogun import RealFeatures, MulticlassLabels, CSVFile, CHAIDTree
    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))

    # CHAID Tree formation with nominal dependent variable
    c = CHAIDTree(0, feattypes, 10)
    c.set_labels(train_labels)
    c.train(feats_train)

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

    return c, output