Adding path_save and path_load arguments

1_train_predictor.py

@@ -56,6 +56,10 @@
                     help='save interval')
 parser.add_argument('--save_fig', action='store_true',
                     help='save figure')
+parser.add_argument('--path_save', type=str, default='./result/nyc_taxi',
+                    help='file path to save data')
+parser.add_argument('--path_load', type=str, default='./result/nyc_taxi',
+                    help='file path to load dataset from')
 parser.add_argument('--resume','-r',
                     help='use checkpoint model parameters as initial parameters (default: False)',
                     action="store_true")
@@ -72,7 +76,7 @@
 ###############################################################################
 # Load data
 ###############################################################################
-TimeseriesData = preprocess_data.PickleDataLoad(data_type=args.data, filename=args.filename,
+TimeseriesData = preprocess_data.PickleDataLoad(pathVar=args.path_load, data_type=args.data, filename=args.filename,
                                                 augment_test_data=args.augment)
 train_dataset = TimeseriesData.batchify(args,TimeseriesData.trainData, args.batch_size)
 test_dataset = TimeseriesData.batchify(args,TimeseriesData.testData, args.eval_batch_size)
@@ -167,7 +171,7 @@ def generate_output(args,epoch, model, gen_dataset, disp_uncertainty=True,startP
         plt.legend()
         plt.tight_layout()
         plt.text(startPoint-500+10, target.min(), 'Epoch: '+str(epoch),fontsize=15)
-        save_dir = Path('result',args.data,args.filename).with_suffix('').joinpath('fig_prediction')
+        save_dir = Path(args.path_save+'/result',args.data,args.filename).with_suffix('').joinpath('fig_prediction')
         save_dir.mkdir(parents=True,exist_ok=True)
         plt.savefig(save_dir.joinpath('fig_epoch'+str(epoch)).with_suffix('.png'))
         #plt.show()
@@ -296,7 +300,7 @@ def evaluate(args, model, test_dataset):
 # Loop over epochs.
 if args.resume or args.pretrained:
     print("=> loading checkpoint ")
-    checkpoint = torch.load(Path('save', args.data, 'checkpoint', args.filename).with_suffix('.pth'))
+    checkpoint = torch.load(Path(args.path_save+'/save', args.data, 'checkpoint', args.filename).with_suffix('.pth'))
     args, start_epoch, best_val_loss = model.load_checkpoint(args,checkpoint,feature_dim)
     optimizer.load_state_dict((checkpoint['optimizer']))
     del checkpoint
@@ -358,4 +362,4 @@ def evaluate(args, model, test_dataset):
                     'covs': covs
                     }
 model.save_checkpoint(model_dictionary, True)
-print('-' * 89)
+print('-' * 89)

2_anomaly_detection.py

@@ -21,6 +21,10 @@
                     help='filename of the dataset')
 parser.add_argument('--save_fig', action='store_true',
                     help='save results as figures')
+parser.add_argument('--path_save', type=str, default='./result/nyc_taxi',
+                    help='file path to save data')
+parser.add_argument('--path_load', type=str, default='./result/nyc_taxi',
+                    help='file path to load dataset from')
 parser.add_argument('--compensate', action='store_true',
                     help='compensate anomaly score using anomaly score esimation')
 parser.add_argument('--beta', type=float, default=1.0,
@@ -30,7 +34,7 @@
 args_ = parser.parse_args()
 print('-' * 89)
 print("=> loading checkpoint ")
-checkpoint = torch.load(str(Path('save',args_.data,'checkpoint',args_.filename).with_suffix('.pth')))
+checkpoint = torch.load(str(Path(args_.path_save+'/save',args_.data,'checkpoint',args_.filename).with_suffix('.pth')))
 args = checkpoint['args']
 args.prediction_window_size= args_.prediction_window_size
 args.beta = args_.beta
@@ -46,7 +50,7 @@
 ###############################################################################
 # Load data
 ###############################################################################
-TimeseriesData = preprocess_data.PickleDataLoad(data_type=args.data,filename=args.filename, augment_test_data=False)
+TimeseriesData = preprocess_data.PickleDataLoad(pathVar=args_.path_load, data_type=args.data,filename=args.filename, augment_test_data=False)
 train_dataset = TimeseriesData.batchify(args,TimeseriesData.trainData[:TimeseriesData.length], bsz=1)
 test_dataset = TimeseriesData.batchify(args,TimeseriesData.testData, bsz=1)
 
@@ -139,7 +143,7 @@
 
 
         if args.save_fig:
-            save_dir = Path('result',args.data,args.filename).with_suffix('').joinpath('fig_detection')
+            save_dir = Path(args_.path_save+'/result',args.data,args.filename).with_suffix('').joinpath('fig_detection')
             save_dir.mkdir(parents=True,exist_ok=True)
             plt.plot(precision.cpu().numpy(),label='precision')
             plt.plot(recall.cpu().numpy(),label='recall')
@@ -191,7 +195,7 @@
 
 
 print('=> saving the results as pickle extensions')
-save_dir = Path('result', args.data, args.filename).with_suffix('')
+save_dir = Path(args_.path_save+'/result', args.data, args.filename).with_suffix('')
 save_dir.mkdir(parents=True, exist_ok=True)
 pickle.dump(targets, open(str(save_dir.joinpath('target.pkl')),'wb'))
 pickle.dump(mean_predictions, open(str(save_dir.joinpath('mean_predictions.pkl')),'wb'))
@@ -202,4 +206,4 @@
 pickle.dump(precisions, open(str(save_dir.joinpath('precision.pkl')),'wb'))
 pickle.dump(recalls, open(str(save_dir.joinpath('recall.pkl')),'wb'))
 pickle.dump(f_betas, open(str(save_dir.joinpath('f_beta.pkl')),'wb'))
-print('-' * 89)
+print('-' * 89)

model/model.py

@@ -96,13 +96,13 @@ def repackage_hidden(self,h):
     def save_checkpoint(self,state, is_best):
         print("=> saving checkpoint ..")
         args = state['args']
-        checkpoint_dir = Path('save',args.data,'checkpoint')
+        checkpoint_dir = Path(args.path_save+'/save',args.data,'checkpoint')
         checkpoint_dir.mkdir(parents=True,exist_ok=True)
         checkpoint = checkpoint_dir.joinpath(args.filename).with_suffix('.pth')
 
         torch.save(state, checkpoint)
         if is_best:
-            model_best_dir = Path('save',args.data,'model_best')
+            model_best_dir = Path(args.path_save+'/save',args.data,'model_best')
             model_best_dir.mkdir(parents=True,exist_ok=True)
 
             shutil.copyfile(checkpoint, model_best_dir.joinpath(args.filename).with_suffix('.pth'))

preprocess_data.py

@@ -18,10 +18,10 @@ def reconstruct(seqData,mean,std):
     return seqData*std+mean
 
 class PickleDataLoad(object):
-    def __init__(self, data_type, filename, augment_test_data=True):
+    def __init__(self, pathVar, data_type, filename, augment_test_data=True):
         self.augment_test_data=augment_test_data
-        self.trainData, self.trainLabel = self.preprocessing(Path('dataset',data_type,'labeled','train',filename),train=True)
-        self.testData, self.testLabel = self.preprocessing(Path('dataset',data_type,'labeled','test',filename),train=False)
+        self.trainData, self.trainLabel = self.preprocessing(Path(pathVar+'/dataset',data_type,'labeled','train',filename),train=True)
+        self.testData, self.testLabel = self.preprocessing(Path(pathVar+'/dataset',data_type,'labeled','test',filename),train=False)
 
     def augmentation(self,data,label,noise_ratio=0.05,noise_interval=0.0005,max_length=100000):
         noiseSeq = torch.randn(data.size())