diff --git a/notebooks/benchmark.py b/notebooks/benchmark.py index ef2868bfb..ec4644818 100644 --- a/notebooks/benchmark.py +++ b/notebooks/benchmark.py @@ -38,6 +38,35 @@ from pyod.models.cof import COF from pyod.models.sod import SOD + +from pyod.models.auto_encoder import AutoEncoder +from pyod.models.cd import CD +from pyod.models.copod import COPOD +from pyod.models.dif import DIF +from pyod.models.ecod import ECOD +from pyod.models.gmm import GMM +from pyod.models.kde import KDE +from pyod.models.lmdd import LMDD +from pyod.models.loci import LOCI #19S +from pyod.models.loda import LODA +from pyod.models.qmcd import QMCD +from pyod.models.sampling import Sampling +from pyod.models.sos import SOS + +from pyod.models.alad import ALAD #40s +from pyod.models.anogan import AnoGAN #151s +from pyod.models.inne import INNE +from pyod.models.kpca import KPCA +from pyod.models.lscp import LSCP +from pyod.models.lunar import LUNAR +from pyod.models.mad import MAD +from pyod.models.mo_gaal import MO_GAAL +from pyod.models.rgraph import RGraph #271S +from pyod.models.rod import ROD +from pyod.models.so_gaal import SO_GAAL +from pyod.models.sod import SOD +from pyod.models.vae import VAE + from pyod.utils.utility import standardizer from pyod.utils.utility import precision_n_scores from sklearn.metrics import roc_auc_score @@ -61,15 +90,19 @@ 'shuttle.mat', 'vertebral.mat', 'vowels.mat', - 'wbc.mat'] + 'wbc.mat' + ] # define the number of iterations -n_ite = 10 -n_classifiers = 10 +n_ite = 1 df_columns = ['Data', '#Samples', '# Dimensions', 'Outlier Perc', 'ABOD', 'CBLOF', 'FB', 'HBOS', 'IForest', 'KNN', 'LOF', - 'MCD', 'OCSVM', 'PCA'] + 'MCD', 'OCSVM', 'PCA', 'AutoEncoder', 'CD', 'COPOD', 'DIF', 'ECOD', + 'GMM', 'KDE', 'LODA', 'QMCD','Sampling', 'SOS', 'ALAD', 'AnoGAN ', + 'INNE', 'KPCA', 'LMDD', 'LOCI', 'LSCP', 'LUNAR', 'MO_GAAL', 'RGraph', 'SO_GAAL', 'SOD', 'VAE'] + +n_classifiers = len(df_columns)-4 # initialize the container for saving the results roc_df = pd.DataFrame(columns=df_columns) @@ -106,27 +139,71 @@ # standardizing data for processing X_train_norm, X_test_norm = standardizer(X_train, X_test) - classifiers = {'Angle-based Outlier Detector (ABOD)': ABOD( - contamination=outliers_fraction), + classifiers = { + 'Angle-based Outlier Detector (ABOD)': ABOD( + contamination=outliers_fraction), 'Cluster-based Local Outlier Factor': CBLOF( n_clusters=10, contamination=outliers_fraction, check_estimator=False, random_state=random_state), - 'Feature Bagging': FeatureBagging(contamination=outliers_fraction, - random_state=random_state), + 'Feature Bagging': FeatureBagging( + contamination=outliers_fraction, + random_state=random_state), 'Histogram-base Outlier Detection (HBOS)': HBOS( contamination=outliers_fraction), - 'Isolation Forest': IForest(contamination=outliers_fraction, - random_state=random_state), - 'K Nearest Neighbors (KNN)': KNN(contamination=outliers_fraction), + 'Isolation Forest': IForest( + contamination=outliers_fraction, + random_state=random_state), + 'K Nearest Neighbors (KNN)': KNN( + contamination=outliers_fraction), 'Local Outlier Factor (LOF)': LOF( contamination=outliers_fraction), 'Minimum Covariance Determinant (MCD)': MCD( - contamination=outliers_fraction, random_state=random_state), - 'One-class SVM (OCSVM)': OCSVM(contamination=outliers_fraction), + contamination=outliers_fraction, + random_state=random_state), + 'One-class SVM (OCSVM)': OCSVM( + contamination=outliers_fraction), 'Principal Component Analysis (PCA)': PCA( - contamination=outliers_fraction, random_state=random_state), + contamination=outliers_fraction, + random_state=random_state), + 'AutoEncoder': AutoEncoder( + contamination=outliers_fraction), + 'CD': CD( + contamination=outliers_fraction), + 'COPOD': COPOD( + contamination=outliers_fraction), + 'DIF': DIF( + contamination=outliers_fraction), + 'ECOD': ECOD( + contamination=outliers_fraction), + 'GMM': GMM( + contamination=outliers_fraction), + 'KDE': KDE( + contamination=outliers_fraction), + + 'LODA': LODA( + contamination=outliers_fraction), + 'QMCD': QMCD( + contamination=outliers_fraction), + 'Sampling': Sampling( + contamination=outliers_fraction), + 'SOS': SOS( + contamination=outliers_fraction), + # 'ALAD': ALAD( + # contamination=outliers_fraction), + # 'AnoGAN':AnoGAN( + # contamination=outliers_fraction), + 'INNE': INNE(contamination=outliers_fraction), + 'KPCA': KPCA(contamination=outliers_fraction), + 'LMDD': LMDD(contamination=outliers_fraction), + # 'LOCI': LOCI(contamination=outliers_fraction), + 'LUNAR': LUNAR(contamination=outliers_fraction), + 'MO_GAAL': MO_GAAL(contamination=outliers_fraction), + # 'RGraph': RGraph(contamination=outliers_fraction), + # 'SO_GAAL': SO_GAAL(contamination=outliers_fraction), + 'SOD': SOD(contamination=outliers_fraction), + } classifiers_indices = { 'Angle-based Outlier Detector (ABOD)': 0, @@ -139,12 +216,47 @@ 'Minimum Covariance Determinant (MCD)': 7, 'One-class SVM (OCSVM)': 8, 'Principal Component Analysis (PCA)': 9, + 'AutoEncoder': 10, + 'CD': 11, + 'COPOD': 12, + 'DIF': 13, + 'ECOD': 14, + 'GMM': 15, + 'KDE': 16, + 'LODA': 17, + 'QMCD': 18, + 'Sampling': 19, + 'SOS': 20, + 'ALAD': 21, + 'AnoGAN': 22, + 'INNE': 23, + 'KPCA': 24, + 'LMDD': 25, + 'LOCI': 26, + 'LUNAR': 27, + 'MO_GAAL': 28, + 'RGraph': 29, + 'SO_GAAL': 30, + 'SOD': 31, + + + } + for clf_name, clf in classifiers.items(): t0 = time() clf.fit(X_train_norm) test_scores = clf.decision_function(X_test_norm) + + # Handle NaN values in test_scores + test_scores = np.nan_to_num(test_scores, + nan=0.0, + posinf=np.nanmax(test_scores), + neginf=np.nanmin(test_scores)) + # Handle NaN values in y_test + y_test = np.nan_to_num(y_test, nan=0.0, posinf=0.0, neginf=0.0) + t1 = time() duration = round(t1 - t0, ndigits=4) @@ -177,4 +289,4 @@ # Save the results for each run time_df.to_csv('time.csv', index=False, float_format='%.3f') roc_df.to_csv('roc.csv', index=False, float_format='%.3f') - prn_df.to_csv('prc.csv', index=False, float_format='%.3f') + prn_df.to_csv('prc.csv', index=False, float_format='%.3f') \ No newline at end of file diff --git a/notebooks/prc.csv b/notebooks/prc.csv new file mode 100644 index 000000000..dcba90798 --- /dev/null +++ b/notebooks/prc.csv @@ -0,0 +1,18 @@ +Data,#Samples,# Dimensions,Outlier Perc,ABOD,CBLOF,FB,HBOS,IForest,KNN,LOF,MCD,OCSVM,PCA,AutoEncoder,CD,COPOD,DIF,ECOD,GMM,KDE,LODA,QMCD,Sampling,SOS,ALAD,AnoGAN ,INNE,KPCA,LMDD,LOCI,LSCP,LUNAR,MO_GAAL,RGraph,SO_GAAL,SOD,VAE +arrhythmia,452,274,14.6018,0.38076000000000004,0.4585699999999999,0.42641,0.51108,0.49556999999999995,0.44636999999999993,0.4334300000000001,0.39952,0.4614,0.46129,0.41970999999999997,0.0,0.46495,0.47433,0.49446,0.36323000000000005,0.41471,0.43984999999999996,0.049729999999999996,0.45327,0.30998000000000003,0.0,0.0,0.36761000000000005,0.42781,0.47909000000000007,0.0,0.48439,0.31001,0.0,0.0,0.36889,0.0,0.0 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