def test_tl_transform_wt_fit():
"""Test either if an error is raised when transform is called before
fitting"""
# Create the object
tl = TomekLinks(random_state=RND_SEED)
assert_raises(RuntimeError, tl.transform, X, Y)
def test_tl_fit_single_class():
"""Test either if an error when there is a single class"""
# Create the object
tl = TomekLinks(random_state=RND_SEED)
# Resample the data
# Create a wrong y
y_single_class = np.zeros((X.shape[0], ))
assert_raises(RuntimeError, tl.fit, X, y_single_class)
def test_rest(x, y):
print('Random under-sampling')
US = UnderSampler(indices_support=indices_support, verbose=verbose)
usx, usy, idx_tmp = US.fit_transform(x, y)
print ('Indices selected')
print(idx_tmp)
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, indices_support=indices_support, verbose=verbose)
nm1x, nm1y, idx_tmp = NM1.fit_transform(x, y)
print ('Indices selected')
print(idx_tmp)
print('NearMiss-2')
NM2 = NearMiss(version=2, indices_support=indices_support, verbose=verbose)
nm2x, nm2y, idx_tmp = NM2.fit_transform(x, y)
print ('Indices selected')
print(idx_tmp)
print('NearMiss-3')
NM3 = NearMiss(version=3, indices_support=indices_support, verbose=verbose)
nm3x, nm3y, idx_tmp = NM3.fit_transform(x, y)
print ('Indices selected')
print(idx_tmp)
print('Neighboorhood Cleaning Rule')
NCR = NeighbourhoodCleaningRule(indices_support=indices_support, verbose=verbose)
ncrx, ncry, idx_tmp = NCR.fit_transform(x, y)
print ('Indices selected')
print(idx_tmp)
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 test_tl_fit_transform():
"""Test the fit transform routine"""
# Resample the data
tl = TomekLinks(random_state=RND_SEED)
X_resampled, y_resampled = tl.fit_transform(X, Y)
currdir = os.path.dirname(os.path.abspath(__file__))
X_gt = np.load(os.path.join(currdir, 'data', 'tl_x.npy'))
y_gt = np.load(os.path.join(currdir, 'data', 'tl_y.npy'))
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_tl_fit():
"""Test the fitting method"""
# Create the object
tl = TomekLinks(random_state=RND_SEED)
# Fit the data
tl.fit(X, Y)
# Check if the data information have been computed
assert_equal(tl.min_c_, 0)
assert_equal(tl.maj_c_, 1)
assert_equal(tl.stats_c_[0], 500)
assert_equal(tl.stats_c_[1], 4500)
def test_tl_init():
"""Test the initialisation of the object"""
# Define a ratio
verbose = True
tl = TomekLinks(random_state=RND_SEED, verbose=verbose)
assert_equal(tl.n_jobs, -1)
assert_equal(tl.rs_, RND_SEED)
assert_equal(tl.verbose, verbose)
assert_equal(tl.min_c_, None)
assert_equal(tl.maj_c_, None)
assert_equal(tl.stats_c_, {})
def test_tl_fit_transform_with_indices():
"""Test the fit transform routine with indices support"""
# Resample the data
tl = TomekLinks(return_indices=True, random_state=RND_SEED)
X_resampled, y_resampled, idx_under = tl.fit_transform(X, Y)
currdir = os.path.dirname(os.path.abspath(__file__))
X_gt = np.load(os.path.join(currdir, 'data', 'tl_x.npy'))
y_gt = np.load(os.path.join(currdir, 'data', 'tl_y.npy'))
idx_gt = np.load(os.path.join(currdir, 'data', 'tl_idx.npy'))
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
assert_array_equal(idx_under, idx_gt)
def balance_data_undersampling_tomek_links(self):
'''
Balance data clustering centroids.
'''
x = self.X
y = self.y
y.shape = (len(self.y))
verbose = True
TL = TomekLinks(verbose=verbose)
tlx, tly = TL.fit_transform(x, y)
self.X = tlx
self.y = tly
self.y.shape = (len(self.y), 1)
def balance_data_oversampling(self, ratio = 2, balance_type = "OverSampler"):
'''
Balance data.
'''
verbose = True
if balance_type == "OverSampler":
sm = OverSampler(verbose = verbose, ratio = ratio)
elif balance_type == 'SMOTE_borderline1':
sm = SMOTE(kind = 'borderline1', verbose = verbose, ratio = ratio)
elif balance_type == 'SMOTE_regular':
sm = SMOTE(kind = 'regular', verbose = verbose, ratio = ratio)
elif balance_type == 'SMOTE_borderline2':
sm = SMOTE(kind = 'borderline2', verbose = verbose, ratio = ratio)
else:
sm = TomekLinks(verbose = verbose)
self.train_x, self.train_y = sm.fit_transform(self.train_x, self.train_y)
def __get_sample_transformed_examples(sample_type, train_x, train_y, ratio):
sampler = None
verbose = True
if sample_type == SMOTE_REG:
sampler = SMOTE(kind='regular', verbose=verbose, ratio=ratio, k=15)
elif sample_type == SMOTE_SVM:
# TODO: Make this configurable?
svm_args = {'class_weight': 'balanced'}
sampler = SMOTE(kind='svm',
ratio=ratio,
verbose=verbose,
k=15,
**svm_args)
elif sample_type == SMOTE_BORDERLINE_1:
sampler = SMOTE(kind='borderline1', ratio=ratio, verbose=verbose)
elif sample_type == SMOTE_BORDERLINE_2:
sampler = SMOTE(kind='borderline2', ratio=ratio, verbose=verbose)
elif sample_type == SMOTE_ENN:
sampler = SMOTEENN(ratio=ratio, verbose=verbose, k=15)
elif sample_type == SMOTE_TOMEK:
sampler = SMOTETomek(ratio=ratio, verbose=verbose, k=15)
elif sample_type == UNDERSAMPLER:
sampler = UnderSampler(ratio=ratio,
verbose=verbose,
replacement=False,
random_state=17)
elif sample_type == ADASYN_SAMPLER:
sampler = ADASYN(k=15, imb_threshold=0.6, ratio=ratio)
elif sample_type == TOMEK_LINKS:
sampler = TomekLinks()
elif sample_type == CLUSTER_CENTROIDS:
sampler = ClusterCentroids(ratio=ratio)
elif sample_type == NEARMISS:
sampler = NearMiss(ratio=ratio)
else:
print "Unrecoqnized sample technique: " + sample_type
print "Returning original data"
return train_x, train_y
return sampler.fit_transform(train_x, train_y)
weights=[0.1, 0.9],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=5000,
random_state=10)
# Instanciate a PCA object for the sake of easy visualisation
pca = PCA(n_components=2)
# Fit and transform x to visualise inside a 2D feature space
X_vis = pca.fit_transform(X)
# Apply the random under-sampling
tl = TomekLinks()
X_resampled, y_resampled = tl.fit_transform(X, y)
X_res_vis = pca.transform(X_resampled)
# Two subplots, unpack the axes array immediately
f, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(X_vis[y == 0, 0],
X_vis[y == 0, 1],
label="Class #0",
alpha=0.5,
edgecolor=almost_black,
facecolor=palette[0],
linewidth=0.15)
ax1.scatter(X_vis[y == 1, 0],
X_vis[y == 1, 1],