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)
