Metadata-Version: 2.1
Name: table15
Version: 0.1.0
Summary: Table 1.5 is a Python application that can generate a table that is adjunct to a typical Table 1 (association statistics). Table 1.5 goes beyond static association by analyzing the impact that a change in each single feature has to changes in the outcome.
Author-email: Rajas Kale <rajas.kale@carelon.com>
License: The MIT License (MIT)
        
        Copyright (c) 2020 Fernando C. Souza
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of
        this software and associated documentation files (the "Software"), to deal in
        the Software without restriction, including without limitation the rights to
        use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
        the Software, and to permit persons to whom the Software is furnished to do so,
        subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
        FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
        COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
        IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
        CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Project-URL: Homepage, https://https://github.com/KaleRP/table15
Keywords: Table1.5
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# Table1.5

Table 1.5 is a Python application that can generate a table that is adjunct to a typical Table 1 (association statistics). Table 1.5 goes beyond static association by analyzing the impact that a change in each single feature has to changes in the outcome.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Table1.5.

```bash
pip install table15
```

## Code Structure
```
table15
├── LICENSE
├── README.md
│       ├── __init__.py
│       ├── magec_sensitivity.py
│       ├── mimic_queries.py
│       ├── mimic_utils.py
│       ├── rbo.py
│       └── table15
│           ├── __init__.py
│           ├── __main__.py
│           ├── magec_utils.py
│           ├── pima_utils.py
│           ├── pipeline_utils.py
│           ├── runner.py
│           ├── utils
│           │   ├── __init__.py
│           │   ├── data_utils.py
│           │   ├── magec_utils.py
│           │   ├── model_utils.py
│           │   ├── pima_utils.py
│           │   └── pipeline_utils.py
│           └── viewer.py
├── pyproject.toml
├── src
│   ├── __init__.py
│   ├── data
│   │   ├── diabetes.csv
│   │   ├── healthcare-dataset-stroke-data.csv
│   │   └── linear_data.csv
│   ├── table15
│   │   ├── __init__.py
│   │   ├── __main__.py
│   │   ├── configs
│   │   │   ├── data_configs
│   │   │   │   ├── pima_full.yaml
│   │   │   │   ├── pima_lite.yaml
│   │   │   │   └── stroke_full.yaml
│   │   │   ├── model_configs
│   │   │   │   ├── deep_models_configs
│   │   │   │   │   └── multi_layer_perceptron_1.yaml
│   │   │   │   ├── ensemble_configs
│   │   │   │   │   ├── random_forrest_cc_1.yaml
│   │   │   │   │   └── voting_classifier_1.yaml
│   │   │   │   ├── linear_model_configs
│   │   │   │   │   ├── lr_1.yaml
│   │   │   │   │   ├── lr_2.yaml
│   │   │   │   │   └── lr_cv_1.yaml
│   │   │   │   └── svm_configs
│   │   │   │       ├── linear_svm_cc_1.yaml
│   │   │   │       └── svm_1.yaml
│   │   │   └── pipeline_configs
│   │   │       ├── linear.yaml
│   │   │       ├── pima.yaml
│   │   │       ├── stroke.yaml
│   │   │       └── synth_data.yaml
│   │   ├── configs.py
│   │   ├── models
│   │   │   ├── __init__.py
│   │   │   ├── deep_models.py
│   │   │   ├── ensemble_models.py
│   │   │   ├── linear_models.py
│   │   │   ├── model.py
│   │   │   ├── model_factory.py
│   │   │   ├── svm_models.py
│   │   │   └── test_linear_model.py
│   │   ├── perturbations
│   │   │   ├── __init__.py
│   │   │   ├── group_perturbation.py
│   │   │   ├── perturbation.py
│   │   │   └── z_perturbation.py
│   │   ├── runner.py
│   │   └── utils
│   │       ├── __init__.py
│   │       ├── data_tables.py
│   │       ├── magec_utils.py
│   │       ├── models_container.py
│   │       └── pipeline_utils.py
│   └── table15.egg-info
└── tests
    ├── configs
    │   └── t_configs.yaml
    ├── test_perturbations.py
    └── test_pipeline_utils.py
```

## Usage
To generate Table 1.5, the main entry point is through `runner.py`. This takes a single parameter `pipeline_configs_path`, which contains arguments to run the pipeline, as well as references to other configs (Data Configs, Model Configs) that are necessary to run the pipeline.
Generate a Pipeline Configs yaml (example at src/table15/configs/pipeline_configs/stroke.yaml) that contains general parameters for the pipeline that are meant to be changed frequently for different runs.
Reference a Data Configs yaml (example at src/table15/configs/pipeline_configs/stroke.yaml) that references configs related to data and are meant to be more static (ie, we don't change the data arguments very often)
Also reference a set of Model Configs yamls (example at src/table15/configs/model_configs/linear_model_configs/lr_1.yaml), which almost never change except for tuning and when implementing a new model.
Together, these can be used to generate Table 1.5

```python
import table15
table15.runner.run(`path_to_pipeline_configs_yaml`)
```

## Support
Issues and support can be directed to @KaleRP

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.

## Authors and acknowledgment
Author of this project is @KaleRP.
Special thanks to @gstef80 for creating the original project this application was forked from.
Another special thanks to @beaunorgeot for originally conceiving this project.


## License

[MIT](https://choosealicense.com/licenses/mit/)
