Initial commit.

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# pycharm
+.idea/

LICENSE

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+MIT License
+
+Copyright (c) 2017 Yaguang Li
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
+![Diffusion Convolutional Recurrent Neural Network](figures/model_architecture.jpg "Model Architecture")
+
+This is a TensorFlow implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: \
+Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926).
+
+
+## Requirements
+- hyperopt>=0.1
+- scipy>=0.19.0
+- numpy>=1.12.1
+- pandas==0.19.2
+- tensorflow>=1.3.0
+- peewee>=2.8.8
+- python 2.7
+
+Dependency can be installed using the following command:
+```bash
+pip install -r requirements.txt
+```
+
+
+## Traffic Data
+The traffic data file for Los Angeles is available [here](https://drive.google.com/open?id=1tjf5aXCgUoimvADyxKqb-YUlxP8O46pb), and should be
+put into the `data/` folder.
+
+
+## Graph Construction
+ As the currently implementation is based on pre-calculated road network distances between sensors, it currently only
+ supports sensor ids in Los Angeles (see `data/sensor_graph/sensor_info_201206.csv`).
+
+```bash
+python gen_adj_mx.py  --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
+    --output_pkl_filename=data/sensor_graph/adj_mx.pkl
+```
+
+## Train the Model
+```bash
+python dcrnn_seq2seq_train.py --config_filename=data/model/dcrnn_config.json
+```
+
+
+## Run the Pre-trained Model
+
+```bash
+python run_demo.py
+```
+The generated prediction of DCRNN is in `data/results/dcrnn_predictions_[1-12].h5`.
+
+
+More details are being added ...
+
+## Citation
+
+If you find this repository useful in your research, please cite the following paper:
+```
+@article{li2017dcrnn_traffic,
+  title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
+  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
+  journal={arXiv preprint arXiv:1707.01926},
+  year={2017}
+}
+```

data/model/dcrnn_DR_2_h_12_64-64_lr_0.01_bs_64_d_0.00_sl_12_MAE_1207002222/checkpoint

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data/model/dcrnn_DR_2_h_12_64-64_lr_0.01_bs_64_d_0.00_sl_12_MAE_1207002222/config_100.json

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+{"verbose": 0, "num_rnn_layers": 2, "min_learning_rate": 2e-06, "epochs": 100, "patience": 50, "test_ratio": 0.2, "cl_decay_steps": 2000, "write_db": false, "epoch": 100, "max_diffusion_step": 2, "lr_decay_epoch": 20, "dropout": 0.0, "log_dir": "data/model/dcrnn_DR_2_h_12_64-64_lr_0.01_bs_64_d_0.00_sl_12_MAE_1207002222/", "validation_ratio": 0.1, "data_type": "ALL", "learning_rate": 0.01, "batch_size": 64, "filter_type": "dual_random_walk", "graph_pkl_filename": "data/sensor_graph/adj_mx.pkl", "max_grad_norm": 5.0, "model_filename": "data/model/dcrnn_DR_2_h_12_64-64_lr_0.01_bs_64_d_0.00_sl_12_MAE_1207002222/models-1.6253-35451", "global_step": 35451, "use_cpu_only": false, "l1_decay": 0.0, "loss_func": "MAE", "lr_decay": 0.1, "lr_decay_interval": 10, "test_every_n_epochs": 10, "horizon": 12, "null_val": 0.0, "use_curriculum_learning": true, "seq_len": 12, "base_dir": "data/model", "rnn_units": 64}

data/model/dcrnn_config.json

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+{
+  "verbose": 0,
+  "num_rnn_layers": 2,
+  "epochs": 100,
+  "patience": 50,
+  "test_ratio": 0.2,
+  "cl_decay_steps": 2000,
+  "graph_pkl_filename": "data/sensor_graph/adj_mx.pkl",
+  "global_step": 0,
+  "max_diffusion_step": 2,
+  "epoch": 0,
+  "lr_decay_epoch": 20,
+  "learning_rate": 0.01,
+  "validation_ratio": 0.1,
+  "data_type": "ALL",
+  "dropout": 0.0,
+  "batch_size": 64,
+  "max_grad_norm": 5.0,
+  "min_learning_rate": 2e-06,
+  "use_cpu_only": false,
+  "l1_decay": 0.0,
+  "loss_func": "MAE",
+  "write_db": false,
+  "lr_decay": 0.1,
+  "lr_decay_interval": 10,
+  "test_every_n_epochs": 10,
+  "horizon": 12,
+  "null_val": 0.0,
+  "use_curriculum_learning": true,
+  "seq_len": 12,
+  "rnn_units": 64,
+  "base_dir": "data/model",
+  "filter_type": "dual_random_walk"
+}

data/model/dcrnn_test_config.json

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+{
+  "verbose": 0,
+  "num_rnn_layers": 2,
+  "epochs": 100,
+  "patience": 50,
+  "test_ratio": 0.2,
+  "method_type": "GCRNN",
+  "cl_decay_steps": 2000,
+  "graph_pkl_filename": "data/sensor_graph/adj_mx.pkl",
+  "global_step": 0,
+  "max_diffusion_step": 2,
+  "epoch": 0,
+  "lr_decay_epoch": 20,
+  "learning_rate": 0.01,
+  "validation_ratio": 0.1,
+  "data_type": "ALL",
+  "dropout": 0.0,
+  "batch_size": 64,
+  "max_grad_norm": 5.0,
+  "min_learning_rate": 2e-06,
+  "use_cpu_only": false,
+  "l1_decay": 0.0,
+  "loss_func": "MAE",
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+  "horizon": 3,
+  "null_val": 0.0,
+  "use_curriculum_learning": true,
+  "seq_len": 3,
+  "rnn_units": 16,
+  "base_dir": "data/model",
+  "filter_type": "random_walk"
+}