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 apply_sampling(X_data, Y_data, sampling, n_states, maxlen):
ratio = float(np.count_nonzero(Y_data == 1)) / \
float(np.count_nonzero(Y_data == 0))
X_data = np.reshape(X_data, (len(X_data), n_states * maxlen))
# 'Random over-sampling'
if sampling == 'OverSampler':
OS = OverSampler(ratio=ratio, verbose=True)
# 'Random under-sampling'
elif sampling == 'UnderSampler':
OS = UnderSampler(verbose=True)
# 'Tomek under-sampling'
elif sampling == 'TomekLinks':
OS = TomekLinks(verbose=True)
# Oversampling
elif sampling == 'SMOTE':
OS = SMOTE(ratio=1, verbose=True, kind='regular')
# Oversampling - Undersampling
elif sampling == 'SMOTETomek':
OS = SMOTETomek(ratio=ratio, verbose=True)
# Undersampling
elif sampling == 'OneSidedSelection':
OS = OneSidedSelection(verbose=True)
# Undersampling
elif sampling == 'CondensedNearestNeighbour':
OS = CondensedNearestNeighbour(verbose=True)
# Undersampling
elif sampling == 'NearMiss':
OS = NearMiss(version=1, verbose=True)
# Undersampling
elif sampling == 'NeighbourhoodCleaningRule':
OS = NeighbourhoodCleaningRule(verbose=True)
# ERROR: WRONG SAMPLER, TERMINATE
else:
print('Wrong sampling variable you have set... Exiting...')
sys.exit()
# print('shape ' + str(X.shape))
X_data, Y_data = OS.fit_transform(X_data, Y_data)
return X_data, Y_data
def _sample_values(X, y, method=None, ratio=1, verbose=False):
"""Perform any kind of sampling(over and under).
Parameters
----------
X : array, shape = [n_samples, n_features]
Data.
y : array, shape = [n_samples]
Target.
method : str, optional default: None
Over or under smapling method.
ratio: float
Unbalanced class ratio.
Returns
-------
X, y : tuple
Sampled X and y.
"""
if method == 'SMOTE':
sampler = SMOTE(ratio=ratio, verbose=verbose)
elif method == 'SMOTEENN':
ratio = ratio * 0.3
sampler = SMOTEENN(ratio=ratio, verbose=verbose)
elif method == 'random_over_sample':
sampler = OverSampler(ratio=ratio, verbose=verbose)
elif method == 'random_under_sample':
sampler = UnderSampler(verbose=verbose)
elif method == 'TomekLinks':
sampler = TomekLinks(verbose=verbose)
return sampler.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
# Plot the two classes
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)