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Documentation for Cox data #24

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Documentation for Cox data #24

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17ef1ca
Add README for customizing Cox dataset
xyang23 Sep 10, 2022
e4f0b76
Update README.md
xyang23 Sep 10, 2022
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4 changes: 2 additions & 2 deletions examples/cox_regression.py
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from lassonet import LassoNetCoxRegressor, plot_path

data = Path(__file__).parent / "data"
X = np.genfromtxt(data / "hnscc_x.csv", delimiter=",", skip_header=1)
y = np.genfromtxt(data / "hnscc_y.csv", delimiter=",", skip_header=1)
X = np.genfromtxt(data / "my_dataset_x.csv", delimiter=",", skip_header=1)
y = np.genfromtxt(data / "my_dataset_y.csv", delimiter=",", skip_header=1)


X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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12 changes: 12 additions & 0 deletions examples/data/README.md
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# Customizing datasets for Cox experiments

You can customize your dataset to use LassoNet on survival analysis.

## Usage
- Put the input matrix in `my_dataset_x.csv` and outcome matrix in `my_dataset_y.csv` under the `data/` folder.
- `my_dataset_x.csv`: For N samples and T features, the input matrix should be of size [N, T].
- `my_dataset_y.csv`: The outcome matrix should be of size [N, 2] with the first column as time and the second column as event (1 for uncensored and 0 otherwise).
- You can optionally include the feature name as the header of csv file by setting `skip_header=1` in `cox_regression.py`.
- To run LassoNet using customized datasets:
```python cox_regression.py```

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