def test_rest(x, y):
print('Random under-sampling')
US = UnderSampler(verbose=verbose)
usx, usy = US.fit_transform(x, y)
print('Tomek links')
TL = TomekLinks(verbose=verbose)
tlx, tly = TL.fit_transform(x, y)
print('Clustering centroids')
CC = ClusterCentroids(verbose=verbose)
ccx, ccy = CC.fit_transform(x, y)
print('NearMiss-1')
NM1 = NearMiss(version=1, verbose=verbose)
nm1x, nm1y = NM1.fit_transform(x, y)
print('NearMiss-2')
NM2 = NearMiss(version=2, verbose=verbose)
nm2x, nm2y = NM2.fit_transform(x, y)
print('NearMiss-3')
NM3 = NearMiss(version=3, verbose=verbose)
nm3x, nm3y = NM3.fit_transform(x, y)
print('Neighboorhood Cleaning Rule')
NCR = NeighbourhoodCleaningRule(verbose=verbose)
ncrx, ncry = NCR.fit_transform(x, y)
print('Random over-sampling')
OS = OverSampler(verbose=verbose)
ox, oy = OS.fit_transform(x, y)
print('SMOTE Tomek links')
STK = SMOTETomek(verbose=verbose)
stkx, stky = STK.fit_transform(x, y)
print('SMOTE ENN')
SENN = SMOTEENN(verbose=verbose)
sennx, senny = SENN.fit_transform(x, y)
print('EasyEnsemble')
EE = EasyEnsemble(verbose=verbose)
eex, eey = EE.fit_transform(x, y)
def tomek_links(self):
TL = TomekLinks(verbose=self.verbose)
tlx, tly = TL.fit_transform(self.x, self.y)
print "TomekLins Transformed"
return tlx, tly
plt.scatter(x_vis[y==0, 0], x_vis[y==0, 1], label="Class #0", alpha=0.5,
edgecolor=almost_black, facecolor='red', linewidth=0.15)
plt.scatter(x_vis[y==1, 0], x_vis[y==1, 1], label="Class #1", alpha=0.5,
edgecolor=almost_black, facecolor='blue', linewidth=0.15)
plt.legend()
plt.show()
# Generate the new dataset using under-sampling method
verbose = False
# 'Random under-sampling'
US = UnderSampler(verbose=verbose)
usx, usy = US.fit_transform(x, y)
# 'Tomek links'
TL = TomekLinks(verbose=verbose)
tlx, tly = TL.fit_transform(x, y)
# 'Clustering centroids'
CC = ClusterCentroids(verbose=verbose)
ccx, ccy = CC.fit_transform(x, y)
# 'NearMiss-1'
NM1 = NearMiss(version=1, verbose=verbose)
nm1x, nm1y = NM1.fit_transform(x, y)
# 'NearMiss-2'
NM2 = NearMiss(version=2, verbose=verbose)
nm2x, nm2y = NM2.fit_transform(x, y)
# 'NearMiss-3'
NM3 = NearMiss(version=3, verbose=verbose)
nm3x, nm3y = NM3.fit_transform(x, y)
# 'Condensed Nearest Neighbour'
CNN = CondensedNearestNeighbour(size_ngh=51, n_seeds_S=51, verbose=verbose)
cnnx, cnny = CNN.fit_transform(x, y)
