def regression_randomforest_modular(num_train=500,num_test=50,x_range=15,noise_var=0.2,ft=feattypes):
	try:
		from modshogun import RealFeatures, RegressionLabels, CSVFile, RandomForest, MeanRule, PT_REGRESSION
	except ImportError:
		print("Could not import Shogun 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])

	# Random Forest formation
	rand_forest=RandomForest(feats_train,train_labels,20,1)
	rand_forest.set_feature_types(ft)
	rand_forest.set_machine_problem_type(PT_REGRESSION)
	rand_forest.set_combination_rule(MeanRule())
	rand_forest.train()

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

	return rand_forest,output
def multiclass_randomforest_modular(train=traindat,
                                    test=testdat,
                                    labels=label_traindat,
                                    ft=feattypes):
    try:
        from modshogun import RealFeatures, MulticlassLabels, CSVFile, RandomForest, MajorityVote
    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))

    # Random Forest formation
    rand_forest = RandomForest(feats_train, train_labels, 20, 1)
    rand_forest.set_feature_types(ft)
    rand_forest.set_combination_rule(MajorityVote())
    rand_forest.train()

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

    return rand_forest, output
Example #3
0
    def BuildModel(self, data, labels, options):
        mVote = MajorityVote()
        randomForest = RandomForest(self.form, self.numTrees)
        randomForest.set_combination_rule(mVote)
        randomForest.set_labels(labels)
        randomForest.train(data)

        return randomForest
Example #4
0
  def BuildModel(self, data, labels, options):
    mVote = MajorityVote()
    randomForest = RandomForest(self.form, self.numTrees)
    randomForest.set_combination_rule(mVote)
    randomForest.set_labels(labels)
    randomForest.train(data)

    return randomForest
def multiclass_randomforest_modular(train=traindat,test=testdat,labels=label_traindat,ft=feattypes):
	try:
		from modshogun import RealFeatures, MulticlassLabels, CSVFile, RandomForest, MajorityVote
	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))

	# Random Forest formation
	rand_forest=RandomForest(feats_train,train_labels,20,1)
	rand_forest.set_feature_types(ft)
	rand_forest.set_combination_rule(MajorityVote())
	rand_forest.train()

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

	return rand_forest,output
def regression_randomforest_modular(num_train=500,
                                    num_test=50,
                                    x_range=15,
                                    noise_var=0.2,
                                    ft=feattypes):
    try:
        from modshogun import RealFeatures, RegressionLabels, CSVFile, RandomForest, MeanRule, PT_REGRESSION
    except ImportError:
        print("Could not import Shogun 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])

    # Random Forest formation
    rand_forest = RandomForest(feats_train, train_labels, 20, 1)
    rand_forest.set_feature_types(ft)
    rand_forest.set_machine_problem_type(PT_REGRESSION)
    rand_forest.set_combination_rule(MeanRule())
    rand_forest.train()

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

    return rand_forest, output