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
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 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
