update to the latest version

README.md

@@ -86,7 +86,7 @@ from data_generator import DataGenerator
 from myutils import Utils
 
 # one can use our already included datasets
-data_generator = DataGenerator(dataset='1_abalone.npz')
+data_generator = DataGenerator(dataset='6_cardio.npz')
 # specify the ratio of labeled anomalies to generate X and y
 # la could be any float number in [0.0, 1.0]
 data = data_generator.generator(la=0.1) 
@@ -113,7 +113,7 @@ result = utils.metric(y_true=data['y_test'], y_score=score)
 **_Angle II: Types of Anomalies_**
 ```python
 # For Angle II, different types of anomalies are generated as the following
-data_generator = DataGenerator(dataset='1_abalone.npz')
+data_generator = DataGenerator(dataset='6_cardio.npz')
 # the type of anomalies could be 'local', 'global', 'dependency' or 'cluster'.
 data = data_generator.generator(realistic_synthetic_mode='local')
 ```
@@ -122,7 +122,7 @@ data = data_generator.generator(realistic_synthetic_mode='local')
 **_Angle III: Model Robustness with Noisy and Corrupted Data_**
 ```python
 # For Angle III, different data noises and corruptions are added as the following
-data_generator = DataGenerator(dataset='1_abalone.npz')
+data_generator = DataGenerator(dataset='6_cardio.npz')
 # the type of anomalies could be 'duplicated_anomalies', 'irrelevant_features' or 'label_contamination'.
 data = data_generator.generator(noise_type='duplicated_anomalies')
 ```
@@ -146,7 +146,7 @@ Pretrained models are applied to extract data embedding from NLP and CV datasets
 Please see the [datasets](datasets) folder and our [paper]((https://arxiv.org/abs/2206.09426)) for detailed information.
 
 - We organize the above 57 datasets into user-friendly format. All the datasets are named as "number_data.npz" in the
-[datasets](datasets) folder. For example, one can evaluate AD algorithms on the abalone dataset by the following codes.
+[datasets](datasets) folder. For example, one can evaluate AD algorithms on the cardio dataset by the following codes.
 For multi-class dataset like CIFAR10, additional class numbers should be specified as "number_data_class.npz".
 Please see the folder for more details.
 
@@ -157,7 +157,7 @@ reproduce our procedures via the free GPUs. We hope this could be helpful for th
 
 ```python
 import numpy as np
-data = np.load('1_abalone.npz', allow_pickle=True)
+data = np.load('6_cardio.npz', allow_pickle=True)
 X, y = data['X'], data['y']
 ```
 

data_generator.py

@@ -32,6 +32,14 @@ def __init__(self, seed:int=42, dataset:str=None, test_size:float=0.3,
         self.generate_duplicates = generate_duplicates
         self.n_samples_threshold = n_samples_threshold
 
+        # dataset list
+        self.dataset_list_classical = [os.path.splitext(_)[0] for _ in os.listdir('datasets/Classical')
+                                       if os.path.splitext(_)[1] == '.npz'] # classical AD datasets
+        self.dataset_list_cv = [os.path.splitext(_)[0] for _ in os.listdir('datasets/CV(by ResNet-18)')
+                                if os.path.splitext(_)[1] == '.npz'] # CV datasets
+        self.dataset_list_nlp = [os.path.splitext(_)[0] for _ in os.listdir('datasets/NLP(by BERT)')
+                                 if os.path.splitext(_)[1] == '.npz'] # NLP datasets
+
         # myutils function
         self.utils = Utils()
 
@@ -210,17 +218,16 @@ def generator(self, X=None, y=None, minmax=True,
         # load dataset
         if self.dataset is None:
             assert X is not None and y is not None, "For customized dataset, you should provide the X and y!"
-        # datasets from https://github.com/GuansongPang/ADRepository-Anomaly-detection-datasets/tree/main/numerical%20data/DevNet%20datasets
-        elif self.dataset in ['bank-additional-full_normalised', 'celeba_baldvsnonbald_normalised',
-                              'census-income-full-mixed-binarized', 'creditcardfraud_normalised',
-                              'KDD2014_donors_10feat_nomissing_normalised', 'UNSW_NB15_traintest_backdoor']:
-            data = pd.read_csv(os.path.join('datasets', self.dataset+'.csv'))
-            X = data.drop(['class'], axis=1).values
-            y = data['class'].values
-
-            minmax = False
         else:
-            data = np.load(os.path.join('datasets', self.dataset+'.npz'), allow_pickle=True)
+            if self.dataset in self.dataset_list_classical:
+                data = np.load(os.path.join('datasets', 'Classical', self.dataset + '.npz'), allow_pickle=True)
+            elif self.dataset in self.dataset_list_cv:
+                data = np.load(os.path.join('datasets', 'CV(by ResNet-18)', self.dataset + '.npz'), allow_pickle=True)
+            elif self.dataset in self.dataset_list_nlp:
+                data = np.load(os.path.join('datasets', 'NLP(by BERT)', self.dataset + '.npz'), allow_pickle=True)
+            else:
+                raise NotImplementedError
+
             X = data['X']
             y = data['y']
 
@@ -264,6 +271,7 @@ def generator(self, X=None, y=None, minmax=True,
                     X = data_dependency['X']; y = data_dependency['y']
 
                 except:
+                    # raise NotImplementedError
                     print(f'Generating dependency anomalies...')
                     X, y = self.generate_realistic_synthetic(X, y,
                                                              realistic_synthetic_mode=realistic_synthetic_mode,

other_utils/utils.py

@@ -211,7 +211,7 @@ def filter_names(name):
              linewidth=linewidth)
         if labels:
             # text(textspace + 0.3, chei - 0.075, format(ssums[i], '.4f'), ha="right", va="center", size=10)
-            text(textspace + 0.7, chei - 0.075, format(avmetrics[i], '.4f'), ha="right", va="center", size=12) # by bug, show the average metric
+            text(textspace + 0.6, chei - 0.075, format(avmetrics[i], '.2f'), ha="right", va="center", size=12) # by bug, show the average metric
         text(textspace - 0.2, chei, filter_names(nnames[i]), ha="right", va="center", size=16)
 
     for i in range(math.ceil(k / 2), k):
@@ -222,7 +222,7 @@ def filter_names(name):
              linewidth=linewidth)
         if labels:
             # text(textspace + scalewidth - 0.3, chei - 0.075, format(ssums[i], '.4f'), ha="left", va="center", size=10)
-            text(textspace + scalewidth - 0.7, chei - 0.075, format(avmetrics[i], '.4f'), ha="left", va="center", size=12) # by bug, show the average metric
+            text(textspace + scalewidth - 0.6, chei - 0.075, format(avmetrics[i], '.2f'), ha="left", va="center", size=12) # by bug, show the average metric
         text(textspace + scalewidth + 0.2, chei, filter_names(nnames[i]),
              ha="left", va="center", size=16)
 

run.py

@@ -132,8 +132,13 @@ def __init__(self, suffix:str=None, mode:str='rla', parallel:str=None,
     # dataset filter for delelting those datasets that do not satisfy the experimental requirement
     def dataset_filter(self):
         # dataset list in the current folder
-        dataset_list_org = [os.path.splitext(_)[0] for _ in os.listdir('datasets') if os.path.splitext(_)[1] in ['.npz', '.csv']]
-        # dataset_list_org = [_ for _ in dataset_list_org if not _.split('_')[0].isdigit()]
+        dataset_list_org = [os.path.splitext(_)[0] for _ in os.listdir('datasets/Classical')
+                            if os.path.splitext(_)[1] == '.npz'] # classical AD datasets
+        dataset_list_org.extend([os.path.splitext(_)[0] for _ in os.listdir('datasets/CV(by ResNet-18)')
+                                 if os.path.splitext(_)[1] == '.npz']) # CV datasets
+        dataset_list_org.extend([os.path.splitext(_)[0] for _ in os.listdir('datasets/NLP(by BERT)')
+                                 if os.path.splitext(_)[1] == '.npz']) # NLP datasets
+
 
         dataset_list = []
         dataset_size = []