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