update timegan & explanation

README.md

@@ -1,84 +1,145 @@
 # Time Series Generation
 Github Repo for timeseries generation (시계열 생성)
 
-## Purpose
+## 1. Purpose
 Time Series Generation can be used for multiple purposes
 
 For example :
 - Time Series Data Augmentations
 - Generating Simulations
 
 
-## How to use
-1. TimeGAN을 이용한 시계열 생성
+## 2. How to use
 
-```python
-python run_timegan.py
+- You can check detail about the argument at `4. Model Parameters`
+
+### 2.1. Time Series Generation using TimeGAN
 
+```python
+# Example
+python run_timegan.py --file_name test_data --cols_to_remove Time MNG_NO --time_gap 500 --emb_epochs 10 --sup_epochs 10 --gan_epochs 10 --window_size 5
 ```
 
-2. VRAE를 이용한 시계열 생성
+### 2.2. VRAE를 이용한 시계열 생성
 
 ```python
-python run_vrae.py
+
+# Example
+python run_vrae.py --file_name test_data --cols_to_remove Time MNG_NO --time_gap 500 --n_epochs 10 --window_size 5
 
 ```
 
-## Models Used
-### TimeGAN
+## 3. Models Used
+### 3.1. TimeGAN
 
 <img src = 'https://github.com/euisuk-chung/timeseries-generation/blob/main/image/TimeGAN_architecture.PNG?raw=true' width="650" height="400">
 
 - pyTorch implementation for `TimeGAN`
 - Code Reference : https://github.com/d9n13lt4n/timegan-pytorch
 
-### Variational Recurrent AutoEncoder (VRAE)
+### 3.2. Variational Recurrent AutoEncoder (VRAE)
 
 <img src = 'https://github.com/euisuk-chung/timeseries-generation/blob/main/image/LSTM_VAE_architecture.png?raw=true' width="650" height="400">
 
 - pyTorch implementation for `VRAE`
 - Code Reference : https://github.com/tejaslodaya/timeseries-clustering-vae
 
-## CAUTIONS!
+## 4. Model Arguments
+
+- Indeed, TimeGAN and VRAE have shared parameters, however there are also lot of parameters that are not shared.
+- Therefore, Arguments for each model is in `config_timegan.py` and `config_vrae.py`
+
+### 4.1. Shared Arguments
 
 Training method for each model are the same, which uses dataset that is loaded by moving sliding window(default=30) with certain stride(default=1).
 
 <img src = 'https://github.com/euisuk-chung/timeseries-generation/blob/main/image/train_test_image.png?raw=true' width="650" height="400">
 
-However, the generation method for each model are different! See below for more detail.
+There are few things you need to know before implementing our code : 
+
+- The generation method for each model are different :
+    - TimeGAN generates window sized timeseries from `random noise` (without any input)
+    - VRAE generates window sized timeseries from `given timeseries` and `trained latent space` (with input)
+
+- The query for train/test split in my code is currently used for my side-project.
+    - If you want to use train/test you need to go to `utils.custom_dataset` and change the query.
+    - For generation purpose, you don't have to worry about train/test `split (defalut = False)`
+
+Here are the following arguments:
+``` 
+--file_name file_name # 분석에 사용할 파일이름
+
+--cols_to_remove Var1 Var2 # 분석에서 제외할 변수이름 (Ex. time var, idx var) 
+
+--time_gap 500 # 데이터 수집 GAP(텀)
+
+--window_size 10 # 학습에 사용할 윈도우 크기
+
+```
+
+### 4.2. TimeGAN Arguments
+
+TimeGAN has following modes (for more check `config_timegan.py`) :
 
-### TimeGAN
-TimeGAN has 2 Modes, which is used to decide whether to train or generate :
 1. is_train (default = True) : train model with loaded train data (window_size=30, stride=1)
 2. is_generate (default = True) : generate multiple(num_generation) sequences of window (window_size=30)
 
 ```
-# Mode 1 : Train mode
---is_train # train timeGAN
+--is_train `True` or `False` # Train 데이터를 이용한 학습
+
+--num_generation 100 # 생성할 데이터 윈도우의 개수
+
+--is_generate `True` or `False` # 학습된 latent space를 바탕으로 데이터 생성
 
-# Mode 2 : Generation mode
---is_generate # generate window size sequences
---num_generation # number of sequences to make
+--emb_epochs 3000 # AutoEncoder 학습 Epoch 수
+
+--sup_epochs 3000 # Supervisor 학습 Epoch 수
+
+--gan_epochs 3000 # GAN 모델 학습 Epoch 수
 
 ```
 
-### Variational Recurrent AutoEncoder (VRAE)
-VRAE has 3 Modes, which is used to decide whether to train or generate(train) or generate(test) :
-1. is_train (default = True) : train model with loaded train data (window_size=30, stride=1)
-2. is_generate_train (default = True) : generate train dataset loaded sequentially (window_size=stride)
-3. is_generate_test (default = False) : generate test dataset loaded sequentially (window_size=stride)
+### 4.3. Variational Recurrent AutoEncoder (VRAE) Arguments
 
-✨ The query for train/test split in my code is currently used for my side-project.
-✨ If you want to use train/test you need to go to `utils.custom_dataset` and change the query.
+VRAE has following modes (for more check `config_vrae.py`) :
+
+1. is_train (default = True) : train model with loaded train data (window_size=30, stride=1)
+2. is_generate_train (default = True) : generate train dataset loaded sequentially (stride=window_size)
+3. is_generate_test (default = False) : generate test dataset loaded sequentially (stride=window_size)
 
 ```
-# Mode 1 : Train mode
---is_train # train VRAE
+--is_train `True` or `False` # Train 데이터를 이용한 학습
+
+--is_generate_train `True` or `False` # 학습된 latent space와 Train Data를 바탕으로 데이터 생성 
 
-# Mode 2 : Train Generation mode
---is_generate_train # generate train dataset
+--is_generate_test `True` or `False` # 학습된 latent space와 Test 데이터 생성 (실험용 : 데이터 생성 시 사용할 필요 X)
 
-# Mode 3 : Test Generation mode
---is_generate_test # generate test dataset
+--n_epochs 2000 # 모델 학습할 Epoch 수
 
 ```
+
+## Repository Structure
+```
+├── data
+│   ├── Data You Want to Use (in pkl)
+├── gen_data_gan
+│   └── where GAN Generated Data are saved
+├── gen_data_vae
+│   └── where VAE Generated Data are saved
+├── models
+│   ├── TimeGAN.py
+│   └── vrae.py
+├── save_model
+│   └── where model parameters get saved 
+├── utils
+│   ├── TSTR.py # TSTR(TRTS) code
+│   ├── custom_dataset.py # dataloading code
+│   ├── utils_timegan.py # util function for timegan
+│   ├── utils_vrae.py # util function for vrae
+│   ├── visual_timegan.py # visualization function for timegan
+│   └── visual_vrae.py # visualization function for vrae
+├── run_timegan.py
+├── run_vrae.py
+├── config_timegan.py
+├── config_vrae.py
+```

config_timegan.py

@@ -30,6 +30,21 @@ def parser_setting(parser):
         '--file_name',
         default='netis',
         type=str)
+    parser.add_argument(
+        '--cols_to_remove',
+        default='Time',
+        type=str,
+        nargs='*',
+        help = 'Columns to Remove')
+    parser.add_argument(
+        "--split",
+        type=str2bool,
+        default=False,
+        help = 'Argument for Train/Test split')
+    parser.add_argument(
+        '--time_gap',
+        default=100,
+        type=int)
 
     # train/generate argument
     parser.add_argument(
@@ -113,7 +128,7 @@ def parser_setting(parser):
         default=1e-3,
         type=float)
     parser.add_argument(
-        '--model_path',
+        '--dload',
         default="save_model",
         type=str)
     return parser

run_timegan.py

@@ -28,31 +28,40 @@
 from utils.custom_dataset import *
 from utils.utils_timegan import *
 
-args = config.get_config() # argument 호출
-fix_seed(args.seed) # seed 고정
+# argument 호출
+args = config.get_config() 
+
+# seed 고정
+fix_seed(args.seed)
+
 file_name = args.file_name # 데이터 파일명
 WINDOW_SIZE = args.window_size # Window size
 scale_type = args.scale_type # Scaler Type 설정 ('Standard' or 'MinMax' or 'Robust')
 undo = args.undo # reconstruction 시 unscale 수행여부
+cols_to_remove = args.cols_to_remove # 제거할 변수
+split = args.split # TRAIN/TEST Split 여부
+time_gap = args.time_gap # 데이터수집단위
+
 
 # Load & Scale data
-TRAIN_DF, TEST_DF, TRAIN_SCALED, TEST_SCALED, TRAIN_Time, TEST_Time, cols, scaler = load_gen_data(file_name = file_name, scale_type = scale_type)
+TRAIN_DF, TEST_DF, TRAIN_SCALED, TEST_SCALED, TRAIN_Time, TEST_Time, cols, scaler = load_gen_data(file_name = file_name, \
+                                                                                                  scale_type = scale_type,\
+                                                                                                  cols_to_remove = cols_to_remove,\
+                                                                                                  split = split)
 
 # under custom_dataset.py
 ## Train dataset with stride
-train_dataset = NetisDataset(data = TRAIN_SCALED, timestamps = TRAIN_Time, window_size = WINDOW_SIZE, stride =1)
-
-## Test dataset with no window collapse (for generation window size must be WINDOW_SIZE)
-test_dataset = NetisDataset(data = TEST_SCALED, timestamps = TEST_Time, window_size = WINDOW_SIZE, stride = WINDOW_SIZE)
+train_dataset = TimeSeriesDataset(data = TRAIN_SCALED, timestamps = TRAIN_Time, window_size = WINDOW_SIZE, stride =1, time_gap = time_gap)
 
 # SET DEVICE
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
 # SET ARGUMENTS
 args.feature_dim = train_dataset[0].size(1)
 args.Z_dim = train_dataset[0].size(1)
-args.model_path = "save_model"
+args.dload = "./save_model"
 
+# model_path
 # DEFINE MODEl
 model = TimeGAN(args)
 model = model.to(device)

run_vrae.py

@@ -54,7 +54,7 @@
 train_dataset = TimeSeriesDataset(data = TRAIN_SCALED, timestamps = TRAIN_Time, window_size = WINDOW_SIZE, stride =1, time_gap = time_gap)
 
 # SET ARGUMENTS
-args.dload = "./save_model_test" # CHANGE BACK TO NORMAL AFTER TESTING
+args.dload = "./save_model"
 args.sequence_length = WINDOW_SIZE
 args.number_of_features = train_dataset[0].shape[1]
 

utils/utils_timegan.py

@@ -235,9 +235,9 @@ def timegan_trainer(model, dataset, args):
 
     # Save model, args, and hyperparameters
     print('SAVING TRAINED MODEL...')
-    torch.save(args, f"{args.model_path}/args.pickle")
-    torch.save(model.state_dict(), f"{args.model_path}/model.pt")
-    print(f"\nSaved at path: {args.model_path}")
+    torch.save(args, f"{args.dload}/args.pickle")
+    torch.save(model.state_dict(), f"{args.dload}/model.pt")
+    print(f"\nSaved at path: {args.dload}")
 
 # timeGAN generator
 def timegan_generator(model, Num, args):
@@ -250,14 +250,14 @@ def timegan_generator(model, Num, args):
         - generated_data (np.ndarray): The synthetic data generated by the model
     """
     # Load model for inference
-    if not os.path.exists(args.model_path):
+    if not os.path.exists(args.dload):
         raise ValueError(f"Model directory not found...")
 
     # Load arguments and model
-    with open(f"{args.model_path}/args.pickle", "rb") as fb:
+    with open(f"{args.dload}/args.pickle", "rb") as fb:
         args = torch.load(fb)
 
-    model.load_state_dict(torch.load(f"{args.model_path}/model.pt"))
+    model.load_state_dict(torch.load(f"{args.dload}/model.pt"))
 
     print("\nGenerating Data...")
     # Initialize model to evaluation mode and run without gradients