def testQuantileCalc(self):
MD = similarityMetrics.calcMahalanobis(self.trainData, self.testData)
quantiles = similarityMetrics.calcMahalanobisDistanceQuantiles(MD)
self.assertEqual(round(quantiles[0],3), round(1.1802994935528328,3))
self.assertEqual(round(quantiles[1],3), round(1.6345715553057074,3))
self.assertEqual(round(quantiles[2],3), round(2.316536184143136,3))
def testQuantileCalc(self):
MD = similarityMetrics.calcMahalanobis(self.trainData, self.testData)
quantiles = similarityMetrics.calcMahalanobisDistanceQuantiles(MD)
self.assertEqual(round(quantiles[0], 3), round(1.1802994935528328, 3))
self.assertEqual(round(quantiles[1], 3), round(1.6345715553057074, 3))
self.assertEqual(round(quantiles[2], 3), round(2.316536184143136, 3))
def testMahalanobis(self):
data = dataUtilities.DataTable(os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/trainData.tab"))
testData =dataUtilities.DataTable(os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/testData.tab"))
invCovMatFile =os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/SqrtInvCovMatrix.npy")
#invCovMatFile =os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/invCovMatrix.npy")
centerFile =os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/center.npy")
MahalanobisData =os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/mahalanobisDataTable.npy")
#MahalanobisData =os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/dataTable.npy")
domain = data.domain
MD1 = similarityMetrics.calcMahalanobis(data, testData)
MD2 = similarityMetrics.calcMahalanobis(data, testData, invCovMatFile, centerFile, MahalanobisData, domain)
expected = [{'_train_id_near1': 'XX', '_train_dist_near1': 8.5529724254000528e-15, '_train_dist_near2': 3.2469494012727824, '_train_id_near2': 'XX', '_train_dist_near3': 3.2731657154209062, '_train_av3nearest': 2.1733717055645658, '_train_SMI_near3': 'XXX', '_train_id_near3': 'XX', '_train_SMI_near1': 'XXX', '_train_SMI_near2': 'XXX', '_MD': 17.204748057165233}, {'_train_id_near1': 'XX', '_train_dist_near1': 5.115384476164845, '_train_dist_near2': 5.5191228149142137, '_train_id_near2': 'XX', '_train_dist_near3': 5.5944864806135319, '_train_av3nearest': 5.4096645905641978, '_train_SMI_near3': 'XXX', '_train_id_near3': 'XX', '_train_SMI_near1': 'XXX', '_train_SMI_near2': 'XXX', '_MD': 32.561505764913186}, {'_train_id_near1': 'XX', '_train_dist_near1': 4.2608827352786338e-15, '_train_dist_near2': 2.7885310210397281, '_train_id_near2': 'XX', '_train_dist_near3': 2.7885310210397281, '_train_av3nearest': 1.8590206806931533, '_train_SMI_near3': 'XXX', '_train_id_near3': 'XX', '_train_SMI_near1': 'XXX', '_train_SMI_near2': 'XXX', '_MD': 14.565865561707858}, {'_train_id_near1': 'XX', '_train_dist_near1': 5.1696950502912085e-15, '_train_dist_near2': 3.2469494012428579, '_train_id_near2': 'XX', '_train_dist_near3': 3.273165715391364, '_train_av3nearest': 2.1733717055447421, '_train_SMI_near3': 'XXX', '_train_id_near3': 'XX', '_train_SMI_near1': 'XXX', '_train_SMI_near2': 'XXX', '_MD': 17.204748056995847}, {'_train_id_near1': 'XX', '_train_dist_near1': 2.3478624106042396, '_train_dist_near2': 3.9525803005509639, '_train_id_near2': 'XX', '_train_dist_near3': 3.9997921263766512, '_train_av3nearest': 3.4334116125106182, '_train_SMI_near3': 'XXX', '_train_id_near3': 'XX', '_train_SMI_near1': 'XXX', '_train_SMI_near2': 'XXX', '_MD': 22.070533877619617}, {'_train_id_near1': 'XX', '_train_dist_near1': 3.2549006987559131e-15, '_train_dist_near2': 3.2469494012420288, '_train_id_near2': 'XX', '_train_dist_near3': 3.2731657153902853, '_train_av3nearest': 2.1733717055441057, '_train_SMI_near3': 'XXX', '_train_id_near3': 'XX', '_train_SMI_near1': 'XXX', '_train_SMI_near2': 'XXX', '_MD': 17.204748057000234}, {'_train_id_near1': 'XX', '_train_dist_near1': 1.887904134152689, '_train_dist_near2': 3.0871126343782667, '_train_id_near2': 'XX', '_train_dist_near3': 3.0950749249075327, '_train_av3nearest': 2.6900305644794962, '_train_SMI_near3': 'XXX', '_train_id_near3': 'XX', '_train_SMI_near1': 'XXX', '_train_SMI_near2': 'XXX', '_MD': 16.634324652887784}]
dists = ["_MD",'_train_dist_near1','_train_dist_near2','_train_dist_near3','_train_av3nearest']
for idx,x in enumerate(expected):
for d in dists:
self.assert_(abs(MD2[idx][d]-x[d]) < 0.00001, "MD2: idx "+str(idx) +" diff = "+str(MD2[idx][d]-x[d]))
for idx,x in enumerate(expected):
for d in dists:
self.assert_(abs(MD1[idx][d]-x[d]) < 0.00001, "MD1: idx "+str(idx) +" diff = "+str(MD1[idx][d]-x[d]))
def testMahalanobis(self):
data = dataUtilities.DataTable(
os.path.join(AZOC.AZORANGEHOME,
"tests/source/data/Mahalanobis/trainData.tab"))
testData = dataUtilities.DataTable(
os.path.join(AZOC.AZORANGEHOME,
"tests/source/data/Mahalanobis/testData.tab"))
invCovMatFile = os.path.join(
AZOC.AZORANGEHOME,
"tests/source/data/Mahalanobis/SqrtInvCovMatrix.npy")
#invCovMatFile =os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/invCovMatrix.npy")
centerFile = os.path.join(AZOC.AZORANGEHOME,
"tests/source/data/Mahalanobis/center.npy")
MahalanobisData = os.path.join(
AZOC.AZORANGEHOME,
"tests/source/data/Mahalanobis/mahalanobisDataTable.npy")
#MahalanobisData =os.path.join(AZOC.AZORANGEHOME,"tests/source/data/Mahalanobis/dataTable.npy")
domain = data.domain
MD1 = similarityMetrics.calcMahalanobis(data, testData)
MD2 = similarityMetrics.calcMahalanobis(data, testData, invCovMatFile,
centerFile, MahalanobisData,
domain)
expected = [{
'_train_id_near1': 'XX',
'_train_dist_near1': 8.5529724254000528e-15,
'_train_dist_near2': 3.2469494012727824,
'_train_id_near2': 'XX',
'_train_dist_near3': 3.2731657154209062,
'_train_av3nearest': 2.1733717055645658,
'_train_SMI_near3': 'XXX',
'_train_id_near3': 'XX',
'_train_SMI_near1': 'XXX',
'_train_SMI_near2': 'XXX',
'_MD': 17.204748057165233
}, {
'_train_id_near1': 'XX',
'_train_dist_near1': 5.115384476164845,
'_train_dist_near2': 5.5191228149142137,
'_train_id_near2': 'XX',
'_train_dist_near3': 5.5944864806135319,
'_train_av3nearest': 5.4096645905641978,
'_train_SMI_near3': 'XXX',
'_train_id_near3': 'XX',
'_train_SMI_near1': 'XXX',
'_train_SMI_near2': 'XXX',
'_MD': 32.561505764913186
}, {
'_train_id_near1': 'XX',
'_train_dist_near1': 4.2608827352786338e-15,
'_train_dist_near2': 2.7885310210397281,
'_train_id_near2': 'XX',
'_train_dist_near3': 2.7885310210397281,
'_train_av3nearest': 1.8590206806931533,
'_train_SMI_near3': 'XXX',
'_train_id_near3': 'XX',
'_train_SMI_near1': 'XXX',
'_train_SMI_near2': 'XXX',
'_MD': 14.565865561707858
}, {
'_train_id_near1': 'XX',
'_train_dist_near1': 5.1696950502912085e-15,
'_train_dist_near2': 3.2469494012428579,
'_train_id_near2': 'XX',
'_train_dist_near3': 3.273165715391364,
'_train_av3nearest': 2.1733717055447421,
'_train_SMI_near3': 'XXX',
'_train_id_near3': 'XX',
'_train_SMI_near1': 'XXX',
'_train_SMI_near2': 'XXX',
'_MD': 17.204748056995847
}, {
'_train_id_near1': 'XX',
'_train_dist_near1': 2.3478624106042396,
'_train_dist_near2': 3.9525803005509639,
'_train_id_near2': 'XX',
'_train_dist_near3': 3.9997921263766512,
'_train_av3nearest': 3.4334116125106182,
'_train_SMI_near3': 'XXX',
'_train_id_near3': 'XX',
'_train_SMI_near1': 'XXX',
'_train_SMI_near2': 'XXX',
'_MD': 22.070533877619617
}, {
'_train_id_near1': 'XX',
'_train_dist_near1': 3.2549006987559131e-15,
'_train_dist_near2': 3.2469494012420288,
'_train_id_near2': 'XX',
'_train_dist_near3': 3.2731657153902853,
'_train_av3nearest': 2.1733717055441057,
'_train_SMI_near3': 'XXX',
'_train_id_near3': 'XX',
'_train_SMI_near1': 'XXX',
'_train_SMI_near2': 'XXX',
'_MD': 17.204748057000234
}, {
'_train_id_near1': 'XX',
'_train_dist_near1': 1.887904134152689,
'_train_dist_near2': 3.0871126343782667,
'_train_id_near2': 'XX',
'_train_dist_near3': 3.0950749249075327,
'_train_av3nearest': 2.6900305644794962,
'_train_SMI_near3': 'XXX',
'_train_id_near3': 'XX',
'_train_SMI_near1': 'XXX',
'_train_SMI_near2': 'XXX',
'_MD': 16.634324652887784
}]
dists = [
"_MD", '_train_dist_near1', '_train_dist_near2',
'_train_dist_near3', '_train_av3nearest'
]
for idx, x in enumerate(expected):
for d in dists:
self.assert_(
abs(MD2[idx][d] - x[d]) < 0.00001, "MD2: idx " + str(idx) +
" diff = " + str(MD2[idx][d] - x[d]))
for idx, x in enumerate(expected):
for d in dists:
self.assert_(
abs(MD1[idx][d] - x[d]) < 0.00001, "MD1: idx " + str(idx) +
" diff = " + str(MD1[idx][d] - x[d]))
