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AutoX is an efficient automl tool, mainly aimed at data mining competitions with tabular data.
Its features include:
AutoX covers following interpretable machine learning methods:
1. git clone https://github.com/4paradigm/autox.git
2. cd autox
3. python setup.py install
├── autox
│ ├── ensemble
│ ├── feature_engineer
│ ├── feature_selection
│ ├── file_io
│ ├── join_tables
│ ├── metrics
│ ├── models
│ ├── process_data
│ └── util.py
│ ├── CONST.py
│ ├── autox.py
├── run_oneclick.py
└── demo
└── test
├── setup.py
├── README.md
from autox import AutoX
path = data_dir
autox = AutoX(target = 'loss', train_name = 'train.csv', test_name = 'test.csv',
id = ['id'], path = path)
sub = autox.get_submit()
sub.to_csv("submission.csv", index = False)
index | data_type | data_name(link) | metric | AutoX | AutoGluon | H2o |
---|---|---|---|---|---|---|
1 | regression | zhidemai | mse | 1.1231 | 1.9466 | 1.1927 |
2 | regression | Tabular Playground Series - Aug 2021 | rmse | 7.87731 | 10.3944 | 7.8895 |
3 | regression | House Prices | rmse | 0.13043 | 0.13104 | 0.13161 |
4 | binary classification | Titanic | accuracy | 0.77751 | 0.78229 | 0.79186 |
1.1 Read data
1.2 Concat train and test
1.3 Identify columns type in data
1.4 Data preprocess
Every feature engineer class inclues the following features:
1. auto select columns which will be executed with current operation
2. review the selected columns
3. modify the columns
4. execute the operation, and return features whose samples' number and order are consistent with orginal table.
Combine the raw features and derived features, and return wide table.
Split the wide table into train and test.
Filter the features according to the distribution of train and test.
Inputs of models are filtered features.
model class inclues the following features:
1. get the default parameters
2. model training
3. parameters tuning
4. get the features importance
5. prediction
- extract year, month, day, hour, weekday info from time columns
- delete invalid(nunique equal to 1) features
- delete invalid (label is nan) samples
target encoding feature
shift feature
AutoX supports fellowing models:
1. Lightgbm
2. Xgboost
3. Tabnet
AutoX supports two ensemble methods(Bagging will be used in default).
1. Stacking;
2. Bagging。
competition | magics |
---|---|
kaggle criteo | |
zhidemai |
Log | Solution |
---|
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