def test_containsOutliers5D(self):
d = 5
cluster_A = Cluster([(0.0, 3.0, 0.0, 2.0, 0.0),
(0.0, 4.0, 0.0, 2.0, 0.0),
(0.0, 4.0, 0.0, 1.0, 0.0),
(0.0, 3.0, 0.0, 1.0, 0.0)], d)
cluster_B = Cluster([(0.0, 3.0, 0.0, 5.0, 0.0),
(0.0, 5.0, 0.0, 5.0, 0.0),
(0.0, 4.0, 0.0, 6.0, 0.0)], d)
samples = SampleContainer([(0.0, 7.0, 0.0, 5.0, 0.0),
(0.0, 7.0, 0.0, 4.0, 0.0)], d)
resul = containsOutlier(mergeClusters(cluster_A, cluster_B), samples)
self.assertEqual(
False, resul,
"Dos clusters deberian ser mergeables si forman una componente convexa sin outliers en dimension %s"
% (d))
samples = SampleContainer([(0.0, 4.0, 0.0, 4.0, 0.0),
(0.0, 3.0, 0.0, 4.0, 0.0),
(0.0, 5.0, 0.0, 4.0, 0.0)], d)
resul = containsOutlier(mergeClusters(cluster_A, cluster_B), samples)
self.assertEqual(
True, resul,
"Dos clusters no deberian ser mergeables si forman una componente convexa con outliers %s"
% (d))
def test_createCluster2_onlyOneOutlier_2D(self):
d = 2
classA = SampleContainer([(0.0, 0.0), (0.0, 1.0), (0.0, 2.0),
(0.0, 3.0)], d)
classB = SampleContainer([(0.0, 1.5)], d)
clusters = createClusters(classA, classB)
for spl in classA.getSamples():
self.assertTrue(spl in clusters.getSamples().getSamples())
def test_createClusters2_onlyOneSampleForCluster_2D(self):
d = 2
s0_1, s0_2, s0_3, s0_4 = (0.0, 2.0), (0.0, 4.0), (0.0, 6.0), (0.0, 8.0)
s1_1, s1_2, s1_3, s1_4 = (0.0, 1.0), (0.0, 3.0), (0.0, 5.0), (0.0, 7.0)
classA = SampleContainer([s1_1, s1_2, s1_3, s1_4], d)
classB = SampleContainer([s0_1, s0_2, s0_3, s0_4], d)
clusters = createClusters(classA, classB)
for spl in classA.getSamples():
self.assertTrue(spl in clusters.getSamples().getSamples())
def test_createClusters2_DefineClusterNoneOutlierOnlyOneSample_2D(self):
d = 2
sA = Sample((0.0, 0.0))
classA = SampleContainer([sA], d)
classB = SampleContainer([(4.0, 0.0)], d)
clusters = createClusters2(classA, classB)
for spl in classA.getSamples():
self.assertTrue(spl in clusters.getSamples().getSamples())
def test_DefineClusterNoneOutlierOnlyOneSample_4D(self):
d = 4
sA = Sample((0.0, 0.0, 0.0, 0.0))
classA = SampleContainer([sA], d)
classB = SampleContainer([(0.0, 4.0, 0.0, 0.0)], d)
clusters = createClusters(classA, classB)
self.assertEquals(clusters.getSize(), 1,
"solo debe generarce un cluster")
self.assertTrue(sA in clusters.getClusters().pop().getSamples())
def test_onlyOneOutlier_3D(self):
d = 3
classA = SampleContainer([(0.0, 0.0, 0.0), (0.0, 1.0, 0.0),
(0.0, 2.0, 0.0), (0.0, 3.0, 0.0)], d)
classB = SampleContainer([(0.0, 1.5, 0.0)], d)
clusters = createClusters(classA, classB)
clusters_test = ClusterContainer([
Cluster([(0.0, 2.0, 0.0), (0.0, 3.0, 0.0)], d),
Cluster([(0.0, 0.0, 0.0), (0.0, 1.0, 0.0)], d)
], d)
self.assertEquals(
clusters, clusters_test,
"las muestras mergeables deben estar en el mismo cluster")
def test_createClusters2_severalOutliers2D(self):
d = 2
s1, s2, s3, s4, s5 = (3.0, 7.0), (3.0, 6.0), (10.0, 7.0), (10.0,
6.0), (6.5,
6.5)
samplesA = SampleContainer([s1, s2, s3, s4, s5], d)
samplesB = SampleContainer([(6.0, 7.0), (6.0, 6.0), (7.0, 6.0),
(7.0, 7.0), (6.0, 6.5), (7.0, 6.5)], d)
c1 = Cluster([s1, s2], d)
c2 = Cluster([s4, s3], d)
c3 = Cluster([s5], d)
clusters = createClusters(samplesA, samplesB)
for spl in samplesA.getSamples():
self.assertTrue(spl in clusters.getSamples().getSamples())
def test_createClusters2_allSamplesInTheSameCluster_2D(self):
d = 2
s0_1, s0_2, s0_3 = Sample((3.0, 3.0)), Sample((4.0, 4.0)), Sample(
(3.0, 4.0))
s1_1, s1_2, s1_3, s1_4, s1_5, s1_6 = Sample((0.0, 1.0)), Sample(
(0.0, 2.0)), Sample((0.0, 3.0)), Sample((1.0, 0.0)), Sample(
(1.0, 1.0)), Sample((1.0, 2.0))
class0 = SampleContainer([s0_1, s0_2, s0_3], d)
class1 = SampleContainer([s1_1, s1_2, s1_3, s1_4, s1_5, s1_6], d)
clusters = createClusters2(class1, class0)
for spl in class1.getSamples():
self.assertTrue(spl in clusters.getSamples().getSamples())
def test_onlyOneSampleForCluster_2D(self):
d = 2
s0_1, s0_2, s0_3, s0_4 = (0.0, 2.0), (0.0, 4.0), (0.0, 6.0), (0.0, 8.0)
s1_1, s1_2, s1_3, s1_4 = (0.0, 1.0), (0.0, 3.0), (0.0, 5.0), (0.0, 7.0)
classA = SampleContainer([s1_1, s1_2, s1_3, s1_4], d)
classB = SampleContainer([s0_1, s0_2, s0_3, s0_4], d)
clusters = createClusters(classA, classB)
clusters_test = ClusterContainer([
Cluster([s1_1], d),
Cluster([s1_2], d),
Cluster([s1_3], d),
Cluster([s1_4], d)
], d)
self.assertEquals(clusters, clusters_test,
"debe generarce un cluster para cada muestra")
def test_createClusters_allSamplesInTheSameCluster_2D(self):
d = 2
s0_1, s0_2, s0_3 = Sample((3.0, 3.0)), Sample((4.0, 4.0)), Sample(
(3.0, 4.0))
s1_1, s1_2, s1_3, s1_4, s1_5, s1_6 = Sample((0.0, 1.0)), Sample(
(0.0, 2.0)), Sample((0.0, 3.0)), Sample((1.0, 0.0)), Sample(
(1.0, 1.0)), Sample((1.0, 2.0))
class0 = SampleContainer([s0_1, s0_2, s0_3], d)
class1 = SampleContainer([s1_1, s1_2, s1_3, s1_4, s1_5, s1_6], d)
clusters_test = ClusterContainer(
[Cluster([s1_1, s1_2, s1_3, s1_4, s1_5, s1_6], d)], d)
clusters = createClusters(class1, class0)
self.assertEquals(
clusters_test, clusters,
"todas las muestras deben estar en un unico cluster")
def test_createClusters_severalOutliers2D(self):
d = 2
s1, s2, s3, s4, s5 = (3.0, 7.0), (3.0, 6.0), (10.0, 7.0), (10.0,
6.0), (6.5,
6.5)
samplesA = SampleContainer([s1, s2, s3, s4, s5], d)
samplesB = SampleContainer([(6.0, 7.0), (6.0, 6.0), (7.0, 6.0),
(7.0, 7.0), (6.0, 6.5), (7.0, 6.5)], d)
c1 = Cluster([s1, s2], d)
c2 = Cluster([s4, s3], d)
c3 = Cluster([s5], d)
container_test = ClusterContainer([c1, c2, c3], d)
container = createClusters(samplesA, samplesB)
self.assertEquals(
container, container_test,
"Deben definirse los clusters: [s1,s2],[s3,s4],[s5]")
def test_containsOutliers2D(self):
d = 2
cluster_A = Cluster([(0.0, 2.0), (0.0, 4.0)], d)
cluster_B = Cluster([(4.0, 2.0), (4.0, 4.0)], d)
samples = SampleContainer([(6.0, 3.0)], d)
resul = containsOutlier(mergeClusters(cluster_A, cluster_B), samples)
self.assertEqual(
False, resul,
"Dos clusters deberian ser mergeables si forman una componente convexa sin outliers"
)
samples = SampleContainer([(2.3, 3.0)], d)
resul = containsOutlier(mergeClusters(cluster_A, cluster_B), samples)
self.assertEqual(
True, resul,
"Dos clusters no deberian ser mergeables si forman una componente convexa con outliers"
)
pass
def testisMergeableEmptyOutliers(self):
d = 2
cluster_A = Cluster([(0.0, 2.0), (0.0, 4.0)], d)
cluster_B = Cluster([(4.0, 2.0), (4.0, 4.0)], d)
samples = SampleContainer([], d)
resul = containsOutlier(mergeClusters(cluster_A, cluster_B), samples)
self.assertEqual(
False, resul,
"Dos clusters deberian ser mergeables si no hay otras muestras")
def main():
d = 2
k = 1
c0, c1 = Importer.readSample(
"/home/pandari/Escritorio/Tesis-Classification/Resources/R2/t1-ConjuntosDisjuntos.csv"
)
'''
train0 = [(-0.1663, -0.208), (-1.4265, 1.2276), (6.8148, -0.6143), (-0.7036, 1.0372), (0.2668, -1.6665), (0.2529, -1.9605)]
train1 = [(-0.1663, -0.208), (-1.4265, 1.2276), (-0.7036, 1.0372), (0.2668, -1.6665), (0.2529, -1.9605)]
test0 = [(-0.1663, -0.208), (-1.4265, 1.2276), (6.8148, -0.6143), (-0.7036, 1.0372), (0.2668, -1.6665), (0.2529, -1.9605)]
test1 = [(-0.1663, -0.208), (-1.4265, 1.2276), (-0.7036, 1.0372), (0.2668, -1.6665), (0.2529, -1.9605)]
'''
train0, test0 = divideProportionally(c0, 0.9)
train1, test1 = divideProportionally(c1, 0.9)
train0 = SampleContainer(train0, d)
train1 = SampleContainer(train1, d)
t0 = "rojo"
t1 = "azul"
clasifier = Classifier(train0, train1, t0, t1, d, k)
clasifier.train()
TP, FP, TN, FN = ConfuseMatrix.generateConfuseMatrix(
clasifier, test0, test1, t0, t1)
metC0 = MetricsClassifier(0, TP, FP, TN, FN)
metC1 = MetricsClassifier(1, FN, TN, FP, TP)
TITLE_CASETEST = "Titulo del testsss"
ACCURACY = "\nAccuracy: {}\n"
CONFUSE_MATRIX = "Matrix Confuse:\n|{}, {}|\n|{}, {}|\n"
HEADER_METRIC = "Report:\n\tClass\tPresicion\tRecall\t\tF1-Score\tSupport\n"
print(TITLE_CASETEST)
print(ACCURACY.format(metC0.getAccuracy()))
print(CONFUSE_MATRIX.format(int(TP), int(FP), int(TN), int(FN)))
print(HEADER_METRIC)
print(metC0.showMetrics())
print(metC1.showMetrics())
def test_createScrollTuple(self):
d = 2
samples = SampleContainer([(2.0, 3.0), (-2.0, 3.0), (-2.0, -2.0),
(3.0, -2.0), (-4.0, 4.0), (-4.0, -2.0),
(1.0, -3.0)], d)
gb = createScrollSample(samples, d)
for i in range(d):
print(gb.getFeature(i))
self.assertEquals(Sample((4.0, 3.0)), createScrollSample(samples, d))
def __init__(self, c0,c1,t0,t1,d,k):
'''
Un Classifier se compone de
'''
self.regions = []
self.__dimension = d
self.__num_groups = k
self.__class1 = c1
self.__class0 = c0
self.__tag0 = t0
self.__tag1 = t1
self.__displace_sample = createDisplaceSample(SampleContainer(self.__class0.getSamples().union(self.__class1.getSamples()),d), self.__dimension)
def test_displace(self):
def isInFirstQuandrant(sample):
for i in range(sample.getDimension()):
if sample.getFeature(i) < 0:
return False
return True
d = 2
samples = SampleContainer([(2.0, 3.0), (-2.0, 3.0), (-2.0, -2.0),
(3.0, -2.0), (-4.0, 4.0), (-4.0, -2.0),
(1.0, -3.0)], d)
scroll = Sample((4.0, 3.0))
test = displace(samples, scroll)
for sample in test.getSamples():
self.assertTrue(isInFirstQuandrant(sample))
def main():
d = 2
k = 1
c0, c1 = Importer.readSample(
"/home/javier/Documentos/Repositorios Git/Tesis-Classification/Resources/R2/t7-DiagonalIntercalada.csv"
)
split_samples_0 = map(lambda cluster: divideProportionally(cluster, 0.7),
c0)
split_samples_1 = map(lambda cluster: divideProportionally(cluster, 0.7),
c1)
train0 = [
item for sublist in map(lambda s: s[0], split_samples_0)
for item in sublist
]
test0 = [
item for sublist in map(lambda s: s[1], split_samples_0)
for item in sublist
]
train1 = [
item for sublist in map(lambda s: s[0], split_samples_1)
for item in sublist
]
test1 = [
item for sublist in map(lambda s: s[1], split_samples_1)
for item in sublist
]
train0 = SampleContainer(train0, d)
train1 = SampleContainer(train1, d)
t0 = "rojo"
t1 = "azul"
clasifier = Classifier(train0, train1, t0, t1, d, k)
clasifier.train(createClustersMethod=createClusters)
TP, FP, TN, FN = ConfuseMatrix.generateConfuseMatrix(
clasifier, test0, test1, t0, t1)
metC0 = MetricsClassifier(0, TP, FP, FN, TN)
metC1 = MetricsClassifier(1, TN, FN, FP, TP)
TITLE_CASETEST = "Titulo del testsss"
ACCURACY = "\nAccuracy: {}\n"
CONFUSE_MATRIX = "Matrix Confuse:\n|{}, {}|\n|{}, {}|\n"
HEADER_METRIC = "Report:\n\tClass\tPresicion\tRecall\t\tF1-Score\tSupport\n"
print(TITLE_CASETEST)
print(ACCURACY.format(metC0.getAccuracy()))
print(CONFUSE_MATRIX.format(int(TP), int(FP), int(FN), int(TN)))
print(HEADER_METRIC)
print(metC0.showMetrics())
print(metC1.showMetrics())
clasifier.export(
"/home/javier/Documents/LiClipse Workspace/Ploteo/TEST/solution", d)
clasifier.exportRegion(
"/home/javier/Documents/LiClipse Workspace/Ploteo/TEST/solutionPrimeraRegion",
d, clasifier.regions.pop())
print("vector de desplazamiento: " +
str(clasifier.getDisplaceSample().getData()))
print("DONE")
def displace(samples, scrollSample):
d = samples.getDimension()
return SampleContainer(map(lambda spl: sampleSum(spl,scrollSample,d) , samples.getSamples()),d)
def getSamples(self):
ret = set()
for c in self.getClusters():
ret = ret | c.getSamples()
return SampleContainer(ret, self.getDimension())
def getOutliers(eVar, clusters):
return SampleContainer(
filter(lambda spl: eVar[spl] > 1,
clusters.getSamples().getSamples()), clusters.getDimension())
def displace(samples, scrollSample):
d = samples.getDimension()
def sampleSum(s1,s2):
return Sample(tuple(map(lambda i:s1.getFeature(i) + s2.getFeature(i) , range(d))))
return SampleContainer(map(lambda spl: sampleSum(spl,scrollSample) , samples.getSamples()),d)
