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Code for Joint Multi-Modal Longitudinal Regression and Classification

jmmlrc 최신버젼 다운로드

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# jmmlrc

Joint Multi-Modal Longitudinal Regression and Classification (JMMLRC) Estimator

This repository contains the code for performing a classification and a regression task at the same time on longitudinal data. This work has been applied to the ADNI dataset and has shown state-of-the-art performance in predicting patients with and without Alzheimer's Disease.

## Getting Started using Conda

In order to set up your environment you must first install [conda](https://docs.conda.io/projects/conda/en/latest/index.html). Once this is done follow these commands to setup your environment.

```
cd jmmlrc
conda create --name jmmlrc python=3.7 --file requirements.txt 
conda activate jmmlrc
```

Once you have activated the `jmmlrc` conda environment you can run all associated tests with the following:

```
pytest test_jmmlrc_estimator.py
```

The test cases in `test_jmmlrc_estimator.py` show how the `JMMLRC` estimator can be used in practice (e.g. fit, predict, hyperparameter tuning, etc.)

## Experiment Hyperparameters

In our TMI submission titled "Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer’s Disease Prediction" we compare our method to the an array of machine learning algorithms with the following settings:

### Regression
```python
linear = LinearRegression(normalize=True, fit_intercept=False)
ridge = Ridge(alpha=1000)
lasso = Lasso(alpha=0.001)
mlp = MLPRegressor(alpha=1, activation='logistic', hidden_layer_sizes=(10,))
elm = ELM(neurons=100, func="rbf_l2")

jmmlrc_l21 = JMMLRC(gamma1 = 100, gamma2 = 0, gamma3 = 0)
jmmlrc_group = JMMLRC(gamma1 = 0, gamma2 = 100, gamma3 = 0)
jmmlrc_trace = JMMLRC(gamma1 = 0, gamma2 = 0, gamma3 = 100)

jmmlrc = JMMLRC(gamma1 = 1e-5, gamma2 = 0.01, gamma3 = 100)
```

### Classification
```python
log = LogisticRegression(C=0.1, penalty='l1')
tree = DecisionTreeClassifier(criterion='entropy')
svc = SVC(C=10, kernel='sigmoid')
knn = KNeighborsClassifier(weights='distance', algorithm='kd_tree', n_neighbors=20, p=1)
mlp = MLPClassifier(alpha=1, hidden_layer_sizes=(10,), activation='logistic')
sgd = SGDClassifier(alpha=0.01, loss='log', penalty='elasticnet', l1_ratio=0.5)
linearsvc = LinearSVC(C=0.001, loss='hinge')
elm = ELM(neurons=1000, func="tanh", classification="c")

jmmlrc_l21 = JMMLRC(gamma1 = 100, gamma2 = 0, gamma3 = 0)
jmmlrc_group = JMMLRC(gamma1 = 0, gamma2 = 1000, gamma3 = 0)
jmmlrc_trace = JMMLRC(gamma1 = 0, gamma2 = 0, gamma3 = 100)

jmmlrc = JMMLRC(gamma1 = 1e-5, gamma2 = .01, gamma3 = 100)
```

## Citation
If you find this code useful in your research, please consider citing:
```
@article{brand2019joint,
  title={Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer’s Disease Prediction},
  author={Brand, Lodewijk and Nichols, Kai and Wang, Hua and Shen, Li and Huang, Heng},
  journal={IEEE Transactions on Medical Imaging},
  volume={39},
  number={6},
  pages={1845--1855},
  year={2019},
  publisher={IEEE}
}
```

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