fix python modular example for new label system

shogun-toolbox · May 22, 2012 · c0954f1 · c0954f1
1 parent 74ec526
commit c0954f1
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diff --git a/examples/undocumented/python_modular/classifier_libsvmoneclass_modular.py b/examples/undocumented/python_modular/classifier_libsvmoneclass_modular.py
@@ -7,7 +7,7 @@
 parameter_list = [[traindat,testdat,2.2,1,1e-7],[traindat,testdat,2.1,1,1e-5]]
 
 def classifier_libsvmoneclass_modular (fm_train_real=traindat,fm_test_real=testdat,width=2.1,C=1,epsilon=1e-5):
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures
 	from shogun.Kernel import GaussianKernel
 	from shogun.Classifier import LibSVMOneClass
 

diff --git a/examples/undocumented/python_modular/classifier_mpdsvm_modular.py b/examples/undocumented/python_modular/classifier_mpdsvm_modular.py
@@ -9,7 +9,7 @@
 
 def classifier_mpdsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,C=1,epsilon=1e-5):
 
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures, BinaryLabels
 	from shogun.Kernel import GaussianKernel
 	from shogun.Classifier import MPDSVM
 
@@ -18,7 +18,7 @@ def classifier_mpdsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label
 	width=2.1
 	kernel=GaussianKernel(feats_train, feats_train, width)
 
-	labels=Labels(label_train_twoclass)
+	labels=BinaryLabels(label_train_twoclass)
 
 	svm=MPDSVM(C, kernel, labels)
 	svm.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_multiclass_ecoc.py b/examples/undocumented/python_modular/classifier_multiclass_ecoc.py
@@ -7,7 +7,7 @@
 
 import shogun.Classifier as Classifier
 from shogun.Classifier import ECOCStrategy
-from shogun.Features import RealFeatures, Labels
+from shogun.Features import RealFeatures, MulticlassLabels
 from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
 from shogun.Evaluation import MulticlassAccuracy
 
@@ -19,11 +19,11 @@
 
 fea_train = RealFeatures(traindat)
 fea_test  = RealFeatures(testdat)
-gnd_train = Labels(label_traindat)
+gnd_train = MulticlassLabels(label_traindat)
 if label_testdat is None:
     gnd_test = None
 else:
-    gnd_test = Labels(label_testdat)
+    gnd_test = MulticlassLabels(label_testdat)
 
 base_classifier = LibLinear(L2R_L2LOSS_SVC)
 base_classifier.set_bias_enabled(True)

diff --git a/examples/undocumented/python_modular/classifier_multiclass_ecoc_discriminant.py b/examples/undocumented/python_modular/classifier_multiclass_ecoc_discriminant.py
@@ -5,14 +5,14 @@
 parameter_list = [[traindat,testdat,label_traindat,label_testdat,2.1,1,1e-5],[traindat,testdat,label_traindat,label_testdat,2.2,1,1e-5]]
 
 def classifier_multiclasslinearmachine_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,lawidth=2.1,C=1,epsilon=1e-5):
-    from shogun.Features import RealFeatures, Labels
+    from shogun.Features import RealFeatures, MulticlassLabels
     from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
     from shogun.Classifier import ECOCStrategy, ECOCDiscriminantEncoder, ECOCHDDecoder
 
     feats_train = RealFeatures(fm_train_real)
     feats_test  = RealFeatures(fm_test_real)
 
-    labels = Labels(label_train_multiclass)
+    labels = MulticlassLabels(label_train_multiclass)
 
     classifier = LibLinear(L2R_L2LOSS_SVC)
     classifier.set_epsilon(epsilon)
@@ -32,7 +32,7 @@ def classifier_multiclasslinearmachine_modular (fm_train_real=traindat,fm_test_r
 
     if label_test_multiclass is not None:
         from shogun.Evaluation import MulticlassAccuracy
-        labels_test = Labels(label_test_multiclass)
+        labels_test = MulticlassLabels(label_test_multiclass)
         evaluator = MulticlassAccuracy()
         acc = evaluator.evaluate(label_pred, labels_test)
         print('Accuracy = %.4f' % acc)

diff --git a/examples/undocumented/python_modular/classifier_multiclass_ecoc_ovr.py b/examples/undocumented/python_modular/classifier_multiclass_ecoc_ovr.py
@@ -22,14 +22,14 @@
 parameter_list = [[traindat,testdat,label_traindat,label_testdat,2.1,1,1e-5],[traindat,testdat,label_traindat,label_testdat,2.2,1,1e-5]]
 
 def classifier_multiclasslinearmachine_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,lawidth=2.1,C=1,epsilon=1e-5):
-    from shogun.Features import RealFeatures, Labels
+    from shogun.Features import RealFeatures, MulticlassLabels
     from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
     from shogun.Classifier import ECOCStrategy, ECOCOVREncoder, ECOCHDDecoder, MulticlassOneVsRestStrategy
 
     feats_train = RealFeatures(fm_train_real)
     feats_test  = RealFeatures(fm_test_real)
 
-    labels = Labels(label_train_multiclass)
+    labels = MulticlassLabels(label_train_multiclass)
 
     classifier = LibLinear(L2R_L2LOSS_SVC)
     classifier.set_epsilon(epsilon)
@@ -54,7 +54,7 @@ def classifier_multiclasslinearmachine_modular (fm_train_real=traindat,fm_test_r
 
     if label_test_multiclass is not None:
         from shogun.Evaluation import MulticlassAccuracy
-        labels_test = Labels(label_test_multiclass)
+        labels_test = MulticlassLabels(label_test_multiclass)
         evaluator = MulticlassAccuracy()
         acc_mc = evaluator.evaluate(label_mc, labels_test)
         acc_ecoc = evaluator.evaluate(label_ecoc, labels_test)

diff --git a/examples/undocumented/python_modular/classifier_multiclass_ecoc_random.py b/examples/undocumented/python_modular/classifier_multiclass_ecoc_random.py
@@ -22,14 +22,14 @@
 parameter_list = [[traindat,testdat,label_traindat,label_testdat,2.1,1,1e-5],[traindat,testdat,label_traindat,label_testdat,2.2,1,1e-5]]
 
 def classifier_multiclasslinearmachine_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,lawidth=2.1,C=1,epsilon=1e-5):
-    from shogun.Features import RealFeatures, Labels
+    from shogun.Features import RealFeatures, MulticlassLabels
     from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
     from shogun.Classifier import ECOCStrategy, ECOCRandomSparseEncoder, ECOCRandomDenseEncoder, ECOCHDDecoder
 
     feats_train = RealFeatures(fm_train_real)
     feats_test  = RealFeatures(fm_test_real)
 
-    labels = Labels(label_train_multiclass)
+    labels = MulticlassLabels(label_train_multiclass)
 
     classifier = LibLinear(L2R_L2LOSS_SVC)
     classifier.set_epsilon(epsilon)
@@ -50,7 +50,7 @@ def classifier_multiclasslinearmachine_modular (fm_train_real=traindat,fm_test_r
 
     if label_test_multiclass is not None:
         from shogun.Evaluation import MulticlassAccuracy
-        labels_test = Labels(label_test_multiclass)
+        labels_test = MulticlassLabels(label_test_multiclass)
         evaluator = MulticlassAccuracy()
         acc_dense = evaluator.evaluate(label_dense, labels_test)
         acc_sparse = evaluator.evaluate(label_sparse, labels_test)

diff --git a/examples/undocumented/python_modular/classifier_multiclassliblinear_modular.py b/examples/undocumented/python_modular/classifier_multiclassliblinear_modular.py
@@ -5,13 +5,13 @@
 parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
 
 def classifier_multiclassliblinear_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures, MulticlassLabels
 	from shogun.Classifier import MulticlassLibLinear
 
 	feats_train=RealFeatures(fm_train_real)
 	feats_test=RealFeatures(fm_test_real)
 
-	labels=Labels(label_train_multiclass)
+	labels=MulticlassLabels(label_train_multiclass)
 
 	classifier = MulticlassLibLinear(C,feats_train,labels)
 	classifier.train()

diff --git a/examples/undocumented/python_modular/classifier_multiclasslibsvm_modular.py b/examples/undocumented/python_modular/classifier_multiclasslibsvm_modular.py
@@ -5,15 +5,15 @@
 parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
 
 def classifier_multiclasslibsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures, MulticlassLabels
 	from shogun.Kernel import GaussianKernel
 	from shogun.Classifier import MulticlassLibSVM
 
 	feats_train=RealFeatures(fm_train_real)
 	feats_test=RealFeatures(fm_test_real)
 	kernel=GaussianKernel(feats_train, feats_train, width)
 
-	labels=Labels(label_train_multiclass)
+	labels=MulticlassLabels(label_train_multiclass)
 
 	svm=MulticlassLibSVM(C, kernel, labels)
 	svm.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_multiclasslinearmachine_modular.py b/examples/undocumented/python_modular/classifier_multiclasslinearmachine_modular.py
@@ -5,13 +5,13 @@
 parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
 
 def classifier_multiclasslinearmachine_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures, MulticlassLabels
 	from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine, MulticlassOneVsOneStrategy, MulticlassOneVsRestStrategy
 
 	feats_train = RealFeatures(fm_train_real)
 	feats_test  = RealFeatures(fm_test_real)
 
-	labels = Labels(label_train_multiclass)
+	labels = MulticlassLabels(label_train_multiclass)
 
 	classifier = LibLinear(L2R_L2LOSS_SVC)
 	classifier.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_multiclassmachine_modular.py b/examples/undocumented/python_modular/classifier_multiclassmachine_modular.py
@@ -5,15 +5,15 @@
 parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
 
 def classifier_multiclassmachine_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures, MulticlassLabels
 	from shogun.Kernel import GaussianKernel
 	from shogun.Classifier import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy
 
 	feats_train=RealFeatures(fm_train_real)
 	feats_test=RealFeatures(fm_test_real)
 	kernel=GaussianKernel(feats_train, feats_train, width)
 
-	labels=Labels(label_train_multiclass)
+	labels=MulticlassLabels(label_train_multiclass)
 
 	classifier = LibSVM(C, kernel, labels)
 	classifier.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_multiclassocas_modular.py b/examples/undocumented/python_modular/classifier_multiclassocas_modular.py
@@ -5,13 +5,13 @@
 parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
 
 def classifier_multiclassocas_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures, MulticlassLabels
 	from shogun.Classifier import MulticlassOCAS
 
 	feats_train=RealFeatures(fm_train_real)
 	feats_test=RealFeatures(fm_test_real)
 
-	labels=Labels(label_train_multiclass)
+	labels=MulticlassLabels(label_train_multiclass)
 
 	classifier = MulticlassOCAS(C,feats_train,labels)
 	classifier.train()

diff --git a/examples/undocumented/python_modular/classifier_perceptron_modular.py b/examples/undocumented/python_modular/classifier_perceptron_modular.py
@@ -8,13 +8,13 @@
 parameter_list = [[traindat,testdat,label_traindat,1.,1000,1],[traindat,testdat,label_traindat,1.,1000,1]]
 
 def classifier_perceptron_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,learn_rate=1.,max_iter=1000,num_threads=1):
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures, BinaryLabels
 	from shogun.Classifier import Perceptron
 
 	feats_train=RealFeatures(fm_train_real)
 	feats_test=RealFeatures(fm_test_real)
 
-	labels=Labels(label_train_twoclass)
+	labels=BinaryLabels(label_train_twoclass)
 
 	perceptron=Perceptron(feats_train, labels)
 	perceptron.set_learn_rate(learn_rate)

diff --git a/examples/undocumented/python_modular/classifier_qda_modular.py b/examples/undocumented/python_modular/classifier_qda_modular.py
@@ -9,13 +9,13 @@
 		  [traindat, testdat, label_traindat, 1e-4, True]]
 
 def classifier_qda_modular (fm_train_real=traindat, fm_test_real=testdat, label_train_twoclass=label_traindat, tolerance=1e-4, store_covs=False):
-	from shogun.Features import RealFeatures, Labels
+	from shogun.Features import RealFeatures, MulticlassLabels
 	from shogun.Classifier import QDA
 
 	feats_train = RealFeatures(fm_train_real)
 	feats_test  = RealFeatures(fm_test_real)
 
-	labels = Labels(label_train_twoclass)
+	labels = MulticlassLabels(label_train_twoclass)
 
 	qda = QDA(feats_train, labels, tolerance, store_covs)
 	qda.train()

diff --git a/examples/undocumented/python_modular/classifier_subgradientsvm_modular.py b/examples/undocumented/python_modular/classifier_subgradientsvm_modular.py
@@ -9,7 +9,7 @@
 def classifier_subgradientsvm_modular(fm_train_real, fm_test_real,
 		label_train_twoclass, C, epsilon, max_train_time):
 
-	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
+	from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels
 	from shogun.Classifier import SubGradientSVM
 
 	realfeat=RealFeatures(fm_train_real)
@@ -19,7 +19,7 @@ def classifier_subgradientsvm_modular(fm_train_real, fm_test_real,
 	feats_test=SparseRealFeatures()
 	feats_test.obtain_from_simple(realfeat)
 
-	labels=Labels(label_train_twoclass)
+	labels=BinaryLabels(label_train_twoclass)
 
 	svm=SubGradientSVM(C, feats_train, labels)
 	svm.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_svmlight_batch_linadd_modular.py b/examples/undocumented/python_modular/classifier_svmlight_batch_linadd_modular.py
@@ -11,7 +11,7 @@
 def classifier_svmlight_batch_linadd_modular(fm_train_dna, fm_test_dna,
 		label_train_dna, degree, C, epsilon, num_threads):
 
-	from shogun.Features import StringCharFeatures, Labels, DNA
+	from shogun.Features import StringCharFeatures, BinaryLabels, DNA
 	from shogun.Kernel import WeightedDegreeStringKernel, MSG_DEBUG
 	try:
 		from shogun.Classifier import SVMLight
@@ -28,7 +28,7 @@ def classifier_svmlight_batch_linadd_modular(fm_train_dna, fm_test_dna,
 
 	kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
 
-	labels=Labels(label_train_dna)
+	labels=BinaryLabels(label_train_dna)
 
 	svm=SVMLight(C, kernel, labels)
 	svm.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_svmlight_linear_term_modular.py b/examples/undocumented/python_modular/classifier_svmlight_linear_term_modular.py
@@ -29,7 +29,7 @@ def classifier_svmlight_linear_term_modular(fm_train_dna=traindna,fm_test_dna=te
                                                 label_train_dna=label_traindna,degree=3, \
                                                 C=10,epsilon=1e-5,num_threads=1):
 
-    from shogun.Features import StringCharFeatures, Labels, DNA
+    from shogun.Features import StringCharFeatures, BinaryLabels, DNA
     from shogun.Kernel import WeightedDegreeStringKernel
     from shogun.Classifier import SVMLight
 
@@ -40,7 +40,7 @@ def classifier_svmlight_linear_term_modular(fm_train_dna=traindna,fm_test_dna=te
 
     kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
 
-    labels=Labels(label_train_dna)
+    labels=BinaryLabels(label_train_dna)
 
     svm=SVMLight(C, kernel, labels)
     svm.set_qpsize(3)

diff --git a/examples/undocumented/python_modular/classifier_svmlight_modular.py b/examples/undocumented/python_modular/classifier_svmlight_modular.py
@@ -8,7 +8,7 @@
 parameter_list = [[traindat,testdat,label_traindat,1.1,1e-5,1],[traindat,testdat,label_traindat,1.2,1e-5,1]]
 
 def classifier_svmlight_modular (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,C=1.2,epsilon=1e-5,num_threads=1):
-	from shogun.Features import StringCharFeatures, Labels, DNA
+	from shogun.Features import StringCharFeatures, BinaryLabels, DNA
 	from shogun.Kernel import WeightedDegreeStringKernel
 	try:
 		from shogun.Classifier import SVMLight
@@ -24,7 +24,7 @@ def classifier_svmlight_modular (fm_train_dna=traindat,fm_test_dna=testdat,label
 
 	kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
 
-	labels=Labels(label_train_dna)
+	labels=BinaryLabels(label_train_dna)
 
 	svm=SVMLight(C, kernel, labels)
 	svm.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_svmlin_modular.py b/examples/undocumented/python_modular/classifier_svmlin_modular.py
@@ -8,7 +8,7 @@
 parameter_list = [[traindat,testdat,label_traindat,0.9,1e-5,1],[traindat,testdat,label_traindat,0.8,1e-5,1]]
 
 def classifier_svmlin_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,C=0.9,epsilon=1e-5,num_threads=1):
-	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
+	from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels
 	from shogun.Classifier import SVMLin
 
 	realfeat=RealFeatures(fm_train_real)
@@ -18,7 +18,7 @@ def classifier_svmlin_modular (fm_train_real=traindat,fm_test_real=testdat,label
 	feats_test=SparseRealFeatures()
 	feats_test.obtain_from_simple(realfeat)
 
-	labels=Labels(label_train_twoclass)
+	labels=BinaryLabels(label_train_twoclass)
 
 	svm=SVMLin(C, feats_train, labels)
 	svm.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_svmocas_modular.py b/examples/undocumented/python_modular/classifier_svmocas_modular.py
@@ -9,7 +9,7 @@
 
 def classifier_svmocas_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,C=0.9,epsilon=1e-5,num_threads=1):
 
-	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
+	from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels
 	from shogun.Classifier import SVMOcas
 
 	realfeat=RealFeatures(fm_train_real)
@@ -19,7 +19,7 @@ def classifier_svmocas_modular (fm_train_real=traindat,fm_test_real=testdat,labe
 	feats_test=SparseRealFeatures()
 	feats_test.obtain_from_simple(realfeat)
 
-	labels=Labels(label_train_twoclass)
+	labels=BinaryLabels(label_train_twoclass)
 
 	svm=SVMOcas(C, feats_train, labels)
 	svm.set_epsilon(epsilon)

diff --git a/examples/undocumented/python_modular/classifier_svmsgd_modular.py b/examples/undocumented/python_modular/classifier_svmsgd_modular.py
@@ -9,7 +9,7 @@
 
 def classifier_svmsgd_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,C=0.9,num_threads=1,num_iter=5):
 
-	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
+	from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels
 	from shogun.Classifier import SVMSGD
 
 	realfeat=RealFeatures(fm_train_real)
@@ -19,7 +19,7 @@ def classifier_svmsgd_modular (fm_train_real=traindat,fm_test_real=testdat,label
 	feats_test=SparseRealFeatures()
 	feats_test.obtain_from_simple(realfeat)
 
-	labels=Labels(label_train_twoclass)
+	labels=BinaryLabels(label_train_twoclass)
 
 	svm=SVMSGD(C, feats_train, labels)
 	svm.set_epochs(num_iter)

diff --git a/examples/undocumented/python_modular/evaluation_clustering.py b/examples/undocumented/python_modular/evaluation_clustering.py
@@ -3,7 +3,7 @@
 
 from shogun.Distance import EuclidianDistance
 from shogun.Features import RealFeatures
-from shogun.Features import Labels
+from shogun.Features import MulticlassLabels
 from shogun.Evaluation import ClusteringAccuracy
 from shogun.Evaluation import ClusteringMutualInformation
 
@@ -49,7 +49,7 @@ def assign_labels(data, centroids):
     from shogun.Classifier import KNN
     from numpy import arange
 
-    labels = Labels(arange(1.,11.))
+    labels = MulticlassLabels(arange(0.,11.))
     fea = RealFeatures(data)
     fea_centroids = RealFeatures(centroids)
     distance = EuclidianDistance(fea_centroids, fea_centroids)
@@ -61,7 +61,7 @@ def assign_labels(data, centroids):
     (fea, gnd_raw) = prepare_data()
     centroids = run_clustering(fea, 10)
     gnd_hat = assign_labels(fea, centroids)
-    gnd = Labels(gnd_raw)
+    gnd = MulticlassLabels(gnd_raw)
 
     AccuracyEval = ClusteringAccuracy()
     AccuracyEval.best_map(gnd_hat, gnd)