eddings / fae

FeAture Explorer

FAE

Feature Analysis Explorer (FAE) can help researchers develop a classification model with comparison among different methods. This project was inspired on the Radiomics, and provides a GUI to help analyze the feature matrix, including feature matrix pre-process, model development, and results visualization.

If you publish any work which uses this package, I will appreciate that you could give the following link (https://github.com/salan668/FAE)

Welcome any issues and PR.

Release

The Windows 64 version was release here https://drive.google.com/open?id=1htts7YsfaxKtN1NeDcNU4iksXfjr_XyK (Alternative link is: https://pan.baidu.com/s/1ha66TajeoT6dA-a4Qdt8fA)

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Pre-install

The below modules must be installed first to make the FAE work.

- imbalanced-learn
- matplotlib (seaborn)
- numpy
- pandas
- pdfdocument(https://github.com/salan668/pdfdocument.git)
- pymrmr
- PyQt5
- PyQtGraph
- pyradiomics
- reportlab
- scikit-learn
- scipy

Installing

Just clone it by typing in:

git clone https://github.com/salan668/FAE.git

If you would like to use FAE in any project, please add the path in your system envirement. A trick method is to create a .pth file in the site-packages folder (\Lib\site-packages) and add a path that point to the root folder of the FAE.

Architecture of Project

• DataContainer
  • DataContainer. The structure to contain the data, which also includes methods like saving/loading.
  • DataSeparate. Including functions to separate data into training part and testing part.
  • DataBalance, The class to deal with data imbalance. Now we provided Under-sampling, Over-sampling, and SMOTE method.
• Feature Analysis
  • Normalization. To Normalize the data
  • DimensionReduction. To reduce the dimension, including PCA.
  • Classifier. The classifier to develop the model, including SVM, AE, Random Forests, LDA.
  • CrossValidation. The CV model to estimate the model. Return the metrics
  • FeatureSelector. The class to select features, which including 1) remove non-useful features, e.g. the VolumnNum; 2) different method to select features, like ANOVA, RFE, Relief.
  • FeturePipeline. The class to estimate the model with different feature selected method and classifier.
• Image2Feature
  • RadiomicsFeatureExtractor. This class help extract features from image and ROI with batch process. This class should be more "smart" in the future.
• Visulization.
  • DrawDoubleLine. This function helps draw doulbe-y plot. e.g. plot accuracy and error against the number of iterations.
  • DrawROCList. This function helps draw different ROC curves. AUC will be calculated automaticly and labeled on the legend.
  • FeatureRelationship. This function helps draw the distribution of the values of different features. I can only show at most 3 features in one figure.
  • FeatureSort. This function helps draw the features and the weights of them on the classifiction model.
  • PlotMetricVsFeatureNumber. This function helps draw the AUC / Accuracy / other metrics against the number of chosen features. This can help find the adaptive number of the features.
• Report
  • To Generate the report with PDF format.

Document

TODO

Author

• Yang Song
• Jing Zhang
• Guang Yang

License

This project is licensed under the GPL 3.0 License - see the LICENSE.md file for details

Acknowledge

• Contributor:
  • Yi-lai Pei
  • jmtaysom
  • Zhiyong Zhao
• Demo data support.
  • Yu-dong Zhang, Xu Yan.