# ISED: Information-based smoothness-enhanced embedding learning model
ISED (Information-based smoothness-enhanced embedding learning model) is a Python library that provides temporal smoothness embedding learning through a unique approach for dimensionality reduction of high-dimensional dynamical systems in order to attain smoothned trajectories.
## Features
- **Intrinsic Dimension Estimation**: Uses local PCA to estimate the intrinsic dimensionality of the data.
- **Dominant Period Calculation**: Calculates the dominant period to guide sliding window selection.
- **Sliding Window Embedding**: Allows customizable window lengths and latent dimensions for embedding high-dimensional data.
- **Integration with State-of-the-Art Techniques**: Works seamlessly with embedding techniques such as UMAP, PHATE, and others.
## Installation
To use ISED, clone the repository and ensure that the required dependencies are installed. Dependencies include NumPy, SciPy, Scikit-learn, and Matplotlib, among others.
```sh
# Clone this repository
git clone https://github.com/LeonBai/ISED_learner.git
# Install required packages
pip install -r requirements.txt
```
### Requirements
- Python 3.6+
- NumPy >= 1.18.0
- TensorFlow >= 2.0.0
- SciPy >= 1.4.0
- scikit-learn >= 0.22.0
- scikit-dimension >= 0.3.4
- scikit-image >= 0.16.0
- GPy >= 1.9.0
- PHATE >= 1.0.0
- tphate >= 0.1.0
## Notebook (`Test_run.ipynb`)
The notebook `Test_run.ipynb` provides a structured example to test the ISED model workflow:
- **Loading Functions**: Import all relevant functions from the `ISED.py` script.
- **Loading Data**: Simulation data is loaded, and a preprocessing method is applied.
- **Determine Latent Dimension**: The latent dimension (`latent_dim`) is set for the analysis.
- **Training and Evaluation**: Train the `ISEDModel` and analyze the embedding and decoded dynamics.
## Usage
Below is a simple code snippet demonstrating how to use the ISED model with pre-processing method:
```python
from ISED_learner import ISED
from sklearn import preprocessing
# Load simulation data file
data_file = './ISED/data/Simulation_data/Xs.pkl' ## Or your self-defined data path, currently accepting both .pkl and .npy files
# Preprocess data, normalization, and subsequencing with opted subsequence methods (choose from sliding window, buffering and appending methods)
processor = ISED.DataProcessor(filepath=data_file, alpha=0.5)## alpha = 0, 0.5, 1 === [0,1,2]
x_train, x_test, x = processor.load_and_preprocess_data(method = 'buffering')
# Instanitate ISED model
model = ISED.ISEDModel(
input_dim=x_train.shape[2],
seq_length=x_train.shape[1],
latent_dim=latent_dim, ## self-defined latent dimension
batch_size=50,
epochs=300,
encoder_layers=[(50, 'relu'), (latent_dim, 'relu')],
rnn_layers=[(30, 'gru'), (latent_dim, 'gru')],
decoder_layers=[(latent_dim, 'relu'), (30, 'relu')],
optimizer='adam',
use_early_stopping=True,
loss_weight = None, ## ISED-sublearner choice
verbose = 0 ### tensorflow and keras print-out options: set to 1 or 2 for full results printout during training
)
# Train ISEDModel on train data
model.fit(x_train,x[:500])
# Attain trajectories on test data
length = 320
z_y = model.transform(x_test[:length])
encoded_data = preprocessing.MinMaxScaler().fit_transform(z_y) ## Learned trajectories
decoded_data = preprocessing.MinMaxScaler().fit_transform(model.decode(encoded_data)) ## Reconstructed feature dynamics
```
## License
This project is licensed under the MIT License.
## Author
- Wenjun Bai (wjbai@atr.jp)
For more details, please visit the [GitHub Repository](https://github.com/LeonBai/ISED).
## Contributing
Contributions are welcome! Please submit a pull request or file an issue to help improve ISED.
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"description": "# ISED: Information-based smoothness-enhanced embedding learning model\n\nISED (Information-based smoothness-enhanced embedding learning model) is a Python library that provides temporal smoothness embedding learning through a unique approach for dimensionality reduction of high-dimensional dynamical systems in order to attain smoothned trajectories.\n\n## Features\n- **Intrinsic Dimension Estimation**: Uses local PCA to estimate the intrinsic dimensionality of the data.\n- **Dominant Period Calculation**: Calculates the dominant period to guide sliding window selection.\n- **Sliding Window Embedding**: Allows customizable window lengths and latent dimensions for embedding high-dimensional data.\n- **Integration with State-of-the-Art Techniques**: Works seamlessly with embedding techniques such as UMAP, PHATE, and others.\n\n## Installation\n\nTo use ISED, clone the repository and ensure that the required dependencies are installed. Dependencies include NumPy, SciPy, Scikit-learn, and Matplotlib, among others.\n\n```sh\n# Clone this repository\ngit clone https://github.com/LeonBai/ISED_learner.git\n\n# Install required packages\npip install -r requirements.txt\n```\n\n\n### Requirements\n- Python 3.6+\n- NumPy >= 1.18.0\n- TensorFlow >= 2.0.0\n- SciPy >= 1.4.0\n- scikit-learn >= 0.22.0\n- scikit-dimension >= 0.3.4\n- scikit-image >= 0.16.0\n- GPy >= 1.9.0\n- PHATE >= 1.0.0\n- tphate >= 0.1.0\n\n\n## Notebook (`Test_run.ipynb`)\n\nThe notebook `Test_run.ipynb` provides a structured example to test the ISED model workflow:\n- **Loading Functions**: Import all relevant functions from the `ISED.py` script.\n- **Loading Data**: Simulation data is loaded, and a preprocessing method is applied.\n- **Determine Latent Dimension**: The latent dimension (`latent_dim`) is set for the analysis.\n- **Training and Evaluation**: Train the `ISEDModel` and analyze the embedding and decoded dynamics.\n\n\n## Usage\n\nBelow is a simple code snippet demonstrating how to use the ISED model with pre-processing method:\n\n```python\nfrom ISED_learner import ISED\nfrom sklearn import preprocessing\n\n# Load simulation data file\ndata_file = './ISED/data/Simulation_data/Xs.pkl' ## Or your self-defined data path, currently accepting both .pkl and .npy files\n\n# Preprocess data, normalization, and subsequencing with opted subsequence methods (choose from sliding window, buffering and appending methods)\n\nprocessor = ISED.DataProcessor(filepath=data_file, alpha=0.5)## alpha = 0, 0.5, 1 === [0,1,2]\nx_train, x_test, x = processor.load_and_preprocess_data(method = 'buffering')\n\n# Instanitate ISED model\nmodel = ISED.ISEDModel(\n input_dim=x_train.shape[2],\n seq_length=x_train.shape[1],\n latent_dim=latent_dim, ## self-defined latent dimension\n batch_size=50,\n epochs=300,\n encoder_layers=[(50, 'relu'), (latent_dim, 'relu')],\n rnn_layers=[(30, 'gru'), (latent_dim, 'gru')],\n decoder_layers=[(latent_dim, 'relu'), (30, 'relu')],\n optimizer='adam',\n use_early_stopping=True,\n loss_weight = None, ## ISED-sublearner choice\n verbose = 0 ### tensorflow and keras print-out options: set to 1 or 2 for full results printout during training\n)\n# Train ISEDModel on train data\n\nmodel.fit(x_train,x[:500])\n\n# Attain trajectories on test data\nlength = 320\n\nz_y = model.transform(x_test[:length])\n\nencoded_data = preprocessing.MinMaxScaler().fit_transform(z_y) ## Learned trajectories\n\ndecoded_data = preprocessing.MinMaxScaler().fit_transform(model.decode(encoded_data)) ## Reconstructed feature dynamics\n```\n\n\n## License\nThis project is licensed under the MIT License.\n\n## Author\n- Wenjun Bai (wjbai@atr.jp)\n\nFor more details, please visit the [GitHub Repository](https://github.com/LeonBai/ISED).\n\n## Contributing\nContributions are welcome! Please submit a pull request or file an issue to help improve ISED.\n",
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