add performance check file

examples/perf_check.py

@@ -0,0 +1,62 @@
+# -*- coding: utf-8 -*-
+"""Example of combining multiple base outlier scores. Four combination
+frameworks are demonstrated:
+
+1. Average: take the average of all base detectors
+2. maximization : take the maximum score across all detectors as the score
+3. Average of Maximum (AOM)
+4. Maximum of Average (MOA)
+"""
+# Author: Yue Zhao <[email protected]>
+# License: BSD 2 clause
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+# temporary solution for relative imports in case pyod is not installed
+# if pyod is installed, no need to use the following line
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
+
+import numpy as np
+from sklearn.model_selection import train_test_split
+from scipy.io import loadmat
+
+from pyod.models.knn import KNN
+from pyod.models.combination import aom, moa, average, maximization, median
+from pyod.utils.utility import standardizer
+from pyod.utils.data import generate_data
+from pyod.utils.data import evaluate_print
+
+if __name__ == "__main__":
+
+    file_list = ['arrhythmia.mat', 'cardio.mat', 'ionosphere.mat',
+                 'letter.mat', 'pima.mat']
+    # Define data file and read X and y
+    # Generate some data if the source data is missing
+
+    for mat_file in file_list:
+        # mat_file = 'cardio.mat'
+        # mat_file = 'letter.mat'
+        try:
+            mat = loadmat(os.path.join('data', mat_file))
+
+        except TypeError:
+            print('{data_file} does not exist. Use generated data'.format(
+                data_file=mat_file))
+            X, y = generate_data(train_only=True)  # load data
+        except IOError:
+            print('{data_file} does not exist. Use generated data'.format(
+                data_file=mat_file))
+            X, y = generate_data(train_only=True)  # load data
+        else:
+            X = mat['X']
+            y = mat['y'].ravel()
+
+        clf = KNN()
+        clf.fit(X)
+
+        evaluate_print(mat_file, y, clf.decision_scores_)