Metadata-Version: 2.4
Name: explaining-confident-black-box-predictions
Version: 0.1.0
Summary: Research project for rule list and checklist mining experiments
Author: Anonymized
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: matplotlib>=3.3.0
Requires-Dist: tqdm>=4.60.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: scikit-learn>=1.0.0
Provides-Extra: dev
Requires-Dist: pytest>=6.0; extra == "dev"
Requires-Dist: pytest-cov>=2.0; extra == "dev"
Requires-Dist: black>=21.0; extra == "dev"
Requires-Dist: flake8>=3.8; extra == "dev"
Requires-Dist: mypy>=0.800; extra == "dev"

## Code Repository for Paper: Explaining Confident Black-Box Predictions

To run the results from the paper, run the file `run_results.py` with appropriate command-line arguments. See the file itself for details. 

To mine for rule lists or checklist rules on a dataset, see the following code snippets (duplicated from `run_results.py`)

```
dataset: Dataset
rulelist_params: RuleListMinerParams
target_support: float

miner = RuleListMiner(dataset, rulelist_params, False)
rule = miner.run(target_support=target_support)
```

You must implement your dataset as a subclass of `Dataset`, specify the rule list params and the target support level. 

For checklist rules: 

```
dataset: Dataset
checklist_params: ChecklistMinerParams
target_support: float

miner = ChecklistMiner(dataset, checklist_params, False)
rule = miner.run(target_support=target_support)
```
