def assign_labels(data, centroids):
from shogun.Classifier import KNN
from numpy import arange
labels = Labels(arange(1., 11.))
fea = RealFeatures(data)
fea_centroids = RealFeatures(centroids)
distance = EuclidianDistance(fea_centroids, fea_centroids)
knn = KNN(1, distance, labels)
knn.train()
return knn.apply(fea)
def converter_multidimensionalscaling_modular(data): from shogun.Features import RealFeatures from shogun.Converter import MultidimensionalScaling from shogun.Distance import EuclidianDistance features = RealFeatures(data) distance_before = EuclidianDistance() distance_before.init(features,features) converter = MultidimensionalScaling() converter.set_target_dim(2) converter.set_landmark(False) embedding =converter.apply(features) distance_after = EuclidianDistance() distance_after.init(embedding,embedding) distance_matrix_after = distance_after.get_distance_matrix() distance_matrix_before = distance_before.get_distance_matrix() return numpy.linalg.norm(distance_matrix_after-distance_matrix_before)/numpy.linalg.norm(distance_matrix_before)
def run_clustering(data, k):
from shogun.Clustering import KMeans
from shogun.Mathematics import Math_init_random
Math_init_random(42)
fea = RealFeatures(data)
distance = EuclidianDistance(fea, fea)
kmeans = KMeans(k, distance)
print("Running clustering...")
kmeans.train()
return kmeans.get_cluster_centers()
def bench_shogun(X, y, T, valid):
#
# .. Shogun ..
#
from shogun.Distance import EuclidianDistance
from shogun.Features import RealFeatures
from shogun.Clustering import KMeans
start = datetime.now()
feat = RealFeatures(X.T)
distance = EuclidianDistance(feat, feat)
clf = KMeans(n_components, distance)
clf.train()
delta = datetime.now() - start
return inertia(X, clf.get_cluster_centers().T), delta
def distance_euclidian_modular(fm_train_real=traindat, fm_test_real=testdat):
from shogun.Features import RealFeatures
from shogun.Distance import EuclidianDistance
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
distance = EuclidianDistance(feats_train, feats_train)
dm_train = distance.get_distance_matrix()
distance.init(feats_train, feats_test)
dm_test = distance.get_distance_matrix()
return distance, dm_train, dm_test
def clustering_hierarchical_modular(fm_train=traindat, merges=3):
from shogun.Distance import EuclidianDistance
from shogun.Features import RealFeatures
from shogun.Clustering import Hierarchical
feats_train = RealFeatures(fm_train)
distance = EuclidianDistance(feats_train, feats_train)
hierarchical = Hierarchical(merges, distance)
hierarchical.train()
out_distance = hierarchical.get_merge_distances()
out_cluster = hierarchical.get_cluster_pairs()
return hierarchical, out_distance, out_cluster
def kernel_multiquadric_modular (fm_train_real=traindat,fm_test_real=testdat, shift_coef=1.0): from shogun.Features import RealFeatures from shogun.Kernel import MultiquadricKernel from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance(feats_train, feats_train) kernel=MultiquadricKernel(feats_train, feats_train, shift_coef, distance) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_cauchy_modular (fm_train_real=traindat,fm_test_real=testdat, sigma=1.0): from shogun.Features import RealFeatures from shogun.Kernel import CauchyKernel from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance(feats_train, feats_train) kernel=CauchyKernel(feats_train, feats_train, sigma, distance) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_tstudent_modular (fm_train_real=traindat,fm_test_real=testdat, degree=2.0): from shogun.Features import RealFeatures from shogun.Kernel import TStudentKernel from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance(feats_train, feats_train) kernel=TStudentKernel(feats_train, feats_train, degree, distance) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_distance_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.7): from shogun.Kernel import DistanceKernel from shogun.Features import RealFeatures from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance() kernel=DistanceKernel(feats_train, feats_test, width, distance) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_exponential_modular (fm_train_real=traindat,fm_test_real=testdat, tau_coef=1.0): from shogun.Features import RealFeatures from shogun.Kernel import ExponentialKernel from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance = EuclidianDistance(feats_train, feats_train) kernel=ExponentialKernel(feats_train, feats_train, tau_coef, distance, 10) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def classifier_knn_modular(fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat, k=3 ): from shogun.Features import RealFeatures, Labels from shogun.Classifier import KNN from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance(feats_train, feats_train) labels=Labels(label_train_multiclass) knn=KNN(k, distance, labels) knn_train = knn.train() output=knn.apply(feats_test).get_labels() multiple_k=knn.classify_for_multiple_k() return knn,knn_train,output,multiple_k
def clustering_kmeans_modular(fm_train=traindat, k=3):
from shogun.Distance import EuclidianDistance
from shogun.Features import RealFeatures
from shogun.Clustering import KMeans
from shogun.Mathematics import Math_init_random
Math_init_random(17)
feats_train = RealFeatures(fm_train)
distance = EuclidianDistance(feats_train, feats_train)
kmeans = KMeans(k, distance)
kmeans.train()
out_centers = kmeans.get_cluster_centers()
kmeans.get_radiuses()
return out_centers, kmeans
