easylearn 0.1.18a0

This project is designed for machine learning in resting-state fMRI field

Make machine learning easy!

Easylearn is designed for machine learning mainly in resting-state fMRI, radiomics and other fields (such as EEG). Easylearn is built on top of scikit-learn, pytorch and other packages. Easylearn can assist doctors and researchers who have limited coding experience to easily realize machine learning, e.g., (MR/CT/PET/EEG)imaging-marker- or other biomarker-based disease diagnosis and prediction, treatment response prediction, disease subtyping, dimensional decoding for transdiagnostic psychiatric diseases or other diseases, disease mechanism exploration and etc.

We focus on machine learning rather than data preprocessing. Many software, such as SPM, GRETNA, DPABI, REST, RESTPlus, CCS, FSL, Freesufer, nipy, nipype, nibabel, fmriprep and etc, can be used for data preprocessing.

Citing information:

If you think this software (or some function) is useful, citing the easylearn software in your paper or code would be greatly appreciated! Citing link: https://github.com/easylearn-fmri/easylearn

GUI preview

Main Interface

Data loading Interface

Feature engineering Interface (feature preprocessing)

Feature engineering Interface (dimension reduction)

Feature engineering Interface (feature selection)

Feature engineering Interface (unbalance treatment)

Machine learning Interface

Core Dependencies

The follows will be automatically install if you use "pip install -U easylearn" command

• sklearn
• numpy
• pandas
• python-dateutil
• pytz
• scikit-learn
• scipy
• six
• nibabel
• imbalanced-learn
• skrebate
• matplotlib

Install

pip install -U easylearn

Development

At present, the project is in the development stage. We hope you can join us!
Any contributions you make will be appreciated and announced.
Please refer to developer link for details.

Email: [e-mail]
Wechat: 13591648206

Initiators

Ke Xu

[e-mail]  
Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.  
Department of Radiology, The First Affiliated Hospital of China Medical University.

Chao Li

[e-mail]
Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.  

Mengshi Dong

[e-mail]  
Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.   

Supervisors/Consultants

Yanqing Tang

[e-mail]  
1 Brain Function Research Section, The First Affiliated Hospital of China Medical
University, Shenyang, Liaoning, PR China.  
2 Department of Psychiatry, The First Affiliated Hospital of China Medical University,
Shenyang, Liaoning, PR China.        

Yong He

[e-mail]  
1 National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China  
2 Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China  
3 IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China 

Maintainers

Vacancy 1

Contributors will first add to the contributors_list.md. Once your contribution is important or more than or equal to 1/4 of the total code, we will add you as a maintainer.

Vacancy 2

Contributors will first add to the contributors_list.md. Once your contribution is important or more than or equal to 1/4 of the total code, we will add you as a maintainer.

Contributors

The current contributors are in contributors_list.md. Once your contribution is important or more than or equal to 1/4 of the total code, we will add you as a maintainer.

Curent team members

The current team members are in current_team_members.md. If you contributed your code, please add yourself to the contributor list.

Sponsors

We welcome potential sponsors to sponsor our project! Please contact me for details <Wechat: QQ312214129>

Demo

Although, the GUI is under development, you can use simple commands to accomplish some machine learning tasks.
The simplest demo is in the eslearn/examples.
Below is a demo of training a model to classify insomnia patients using weighted functional connectivity strength as features (You can easily use other voxel-wise metrics as features, such as ReHo, ALFF). This demo use svc as classifier, Principal Component Analysis (PCA) as dimension reduction method and Recursive feature elimination (RFE) as feature selection method (inner cross-validation). In each fold, this program will upper-resampling the training dataset to balance the cases with +1 labels and 0 labels.

---

import numpy as np
import eslearn.machine_learning.classfication.pca_rfe_svc_cv as pca_rfe_svc

# =============================================================================
# All inputs
path_patients = r'D:\WorkStation_2018\Workstation_Old\WorkStation_2018-05_MVPA_insomnia_FCS\Degree\degree_gray_matter\Zdegree\Z_degree_patient\Weighted'  # All patients' image files, .nii format
path_HC = r'D:\WorkStation_2018\Workstation_Old\WorkStation_2018-05_MVPA_insomnia_FCS\Degree\degree_gray_matter\Zdegree\Z_degree_control\Weighted'  # All HCs' image files, .nii format
path_mask = r'G:\Softer_DataProcessing\spm12\spm12\tpm\Reslice3_TPM_greaterThan0.2.nii'  # Mask file for filter image
path_out = r'D:\WorkStation_2018\Workstation_Old\WorkStation_2018-05_MVPA_insomnia_FCS\Degree\degree_gray_matter\Zdegree'  # Directory for saving results
data_preprocess_method='StandardScaler'
data_preprocess_level='group'  # In which level to preprocess data 'subject' or 'group'
num_of_fold_outer=5  # How many folds to perform cross validation
is_dim_reduction=1  # Whether to perform dimension reduction, default is using PCA to reduce the dimension.
components=0.95   # How many percentages of the cumulatively explained variance to be retained. This is used to select the top principal components.
step=0.1  # RFE parameter: percentages or number of features removed each iteration.
num_fold_of_inner_rfeCV=5  # RFE parameter:  how many folds to perform inner RFE loop.
n_jobs=-1  # RFE parameter:  how many jobs (parallel works) to perform inner RFE loop.
is_showfig_finally=True  # Whether show results figure finally.
is_showfig_in_each_fold=False  # Whether show results in each fold.
# =============================================================================
***
clf = pca_rfe_svc.PcaRfeSvcCV(
        path_patients=path_patients,
        path_HC=path_HC,
        path_mask=path_mask,
        path_out=path_out,
        data_preprocess_method=data_preprocess_method,
        data_preprocess_level=data_preprocess_level,
        num_of_fold_outer=num_of_fold_outer,  # How many folds to perform cross validation (Default: 5-fold cross validation)
        is_dim_reduction=is_dim_reduction,  # Default is using PCA to reduce the dimension.
        components=components, 
        step=step,
        num_fold_of_inner_rfeCV=num_fold_of_inner_rfeCV,
        n_jobs=n_jobs,
        is_showfig_finally=is_showfig_finally,  # Whether show results figure finally.
        is_showfig_in_each_fold=is_showfig_in_each_fold  # Whether show results in each fold.
    )

results = clf.main_function()
results = results.__dict__

print(f"mean accuracy = {np.mean(results['accuracy'])}")
print(f"std of accuracy = {np.std(results['accuracy'])}")
print(f"mean sensitivity = {np.mean(results['sensitivity'])}")
print(f"std of sensitivity = {np.std(results['sensitivity'])}")
print(f"mean specificity = {np.mean(results['specificity'])}")
print(f"std of specificity = {np.std(results['specificity'])}")
print(f"mean AUC = {np.mean(results['AUC'])}")
print(f"std of AUC = {np.std(results['AUC'])}")

If the program runs successfully, some results file will be generated under the results folder (path_out), as follows:

Classification performances

Classification performances (text, each row are results of one fold of the 5-fold cross-validation)

Classfication weights (top 1%, BrainNet Viewer)

Predicted decision, predicted label and real label