This Github is mainly used to introduce some commonly used methods, as well as some feature engineering methods independently researched and developed by SDSC staff. It unites and unifies methods from different packages like imblearn, sklearn, hyperopt and tsai. The common dataminig process and how to use the DataFactory for this is shown in our demos.
Go to the root directory and use the following code to create the test report:
python usersry_01_01_dash.py --datapath=./data/dataset_31_credit-g.csv --outputpath=./results/
We offer methods for data preprocessing. This includes label encoding, data balancing, sampling and dealing with NA values and outliers.
In addition to that, we provide functions for feature engineering. This includes unary, binary and multiple transformations.
We also provide a finetuning method based on hyperopt.
Here is a complete list of our supported models for time series:
| Model | String | Classification | Regression | Forecasting | Hyperparameters |
|---|---|---|---|---|---|
| Decision Tree | decision_tree | ✔️ | ✔️ | ❌ | C: see R: see |
| Random Forest | random_forest | ✔️ | ✔️ | ❌ | C: see R: see |
| AdaBoost | ada_boost | ✔️ | ✔️ | ❌ | C: see R: see |
| KNN | knn | ✔️ | ✔️ | ❌ | C: see R: see |
| GBDT | gbdt | ✔️ | ✔️ | ❌ | C: see R: see |
| Gaussian NB | gaussian_nb | ✔️ | ❌ | ❌ | see |
| SVM | svm | ✔️ | ✔️ | ❌ | C: see R: see |
| Bayesian Ridge | bayesian | ❌ | ✔️ | ❌ | see |
| LSTM | lstm | ✔️ | ✔️ | ✔️ | see |
| GRU | gru | ✔️ | ✔️ | ✔️ | see |
| MLP | mlp | ✔️ | ✔️ | ✔️ | see |
| FCN | fcn | ✔️ | ✔️ | ✔️ | see |
| ResNet | res_net | ✔️ | ✔️ | ✔️ | see |
| LSTM-FCN | lstm_fcn | ✔️ | ✔️ | ✔️ | see |
| GRU-FCN | gru_fcn | ✔️ | ✔️ | ✔️ | see |
| mWDN | mwdn | ✔️ | ✔️ | ✔️ | see |
| TCN | tcn | ✔️ | ✔️ | ✔️ | see |
| MLSTM-FCN | mlstm_fcn | ✔️ | ✔️ | ✔️ | see |
| InceptionTime | inception_time | ✔️ | ✔️ | ✔️ | see |
| InceptionTimePlus | inception_time_plus | ✔️ | ✔️ | ✔️ | see |
| XcetptionTime | xception_time | ✔️ | ✔️ | ✔️ | see |
| ResCNN | res_cnn | ✔️ | ✔️ | ✔️ | see |
| TabModel | tab_model | ✔️ | ✔️ | ✔️ | see |
| OmniScale | omni_scale | ✔️ | ✔️ | ✔️ | see |
| TST | tst | ✔️ | ✔️ | ✔️ | see |
| XCM | xcm | ✔️ | ✔️ | ✔️ | see |
(C: Classifiction, R: Regression, F: Forecasting)
Here is a complete list of our supported models for computer vision:
| Model | String | Classification | Hyperparameters |
|---|---|---|---|
| Decision Tree | decision_tree | ✔️ | see |
| Random Forest | random_forest | ✔️ | see |
| AdaBoost | ada_boost | ✔️ | see |
| KNN | knn | ✔️ | see |
| GBDT | gbdt | ✔️ | see |
| Gaussian NB | gaussian_nb | ✔️ | see |
| SVM | svm | ✔️ | see |
| ResNet/ResNeta | res_net | ✔️ | see/see |
| SEResNet | se_res_net | ✔️ | see |
| ResNeXt | res_next | ✔️ | see |
| AlexNet | alex_net | ✔️ | see |
| VGG | vgg | ✔️ | see |
| EfficientNet | efficient_net | ✔️ | see |
| WRN | wrn | ✔️ | see |
| RegNet | reg_net | ✔️ | see |
| SCNet | sc_net | ✔️ | see |
| PANSNet | pnas_net | ✔️ | see |