def keans_smote(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
sm = KMeansSMOTE(random_state=42)
X_res, y_res = sm.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def random_over_sampler(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
ros = RandomOverSampler(random_state=42)
X_res, y_res = ros.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def instance_hardness_thresold(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
iht = InstanceHardnessThreshold(random_state=42)
X_res, y_res = iht.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def adasyn(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
ada = ADASYN(random_state=42)
X_res, y_res = ada.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def condensed_nearest_neighbour(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
cnn = CondensedNearestNeighbour(random_state=42)
X_res, y_res = cnn.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def repeated_edited_nearest_neighbours(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
renn = RepeatedEditedNearestNeighbours()
X_res, y_res = renn.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def one_sided_selection(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
oss = OneSidedSelection(random_state=42)
X_res, y_res = oss.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def cluster_centroids(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
cc = ClusterCentroids(random_state=42)
X_res, y_res = cc.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def near_miss(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
nm = NearMiss()
X_res, y_res = nm.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def tomeklinks(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
tl = TomekLinks()
X_res, y_res = tl.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
def aiiknn(X,
y,
visualize=False,
pca2d=True,
pca3d=True,
tsne=True,
pie_evr=True):
allknn = AllKNN()
X_res, y_res = allknn.fit_resample(X, y)
if visualize == True:
hist_over_and_undersampling(y_res)
pca_general(X_res, y_res, d2=pca2d, d3=pca3d, pie_evr=pie_evr)
return X_res, y_res
