Metadata-Version: 2.1
Name: data-complexity-profiler
Version: 0.2.0
Summary: Data Complexity Profiler
Author-Email: Iraitz Montalban <iraitzm@gmail.com>
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Project-URL: Repository, https://github.com/IraitzM/data-complexity-profiler
Project-URL: Issues, https://github.com/IraitzM/data-complexity-profiler/issues
Project-URL: Changelog, https://github.com/IraitzM/data-complexity-profiler/blob/main/CHANGELOG.md
Requires-Python: <3.13,>=3.12
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Provides-Extra: tests
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Description-Content-Type: text/markdown

[![SQAaaS badge shields.io](https://github.com/EOSC-synergy/data-complexity-profiler.assess.sqaaas/raw/main/.badge/status_shields.svg)](https://sqaaas.eosc-synergy.eu/#/full-assessment/report/https://raw.githubusercontent.com/eosc-synergy/data-complexity-profiler.assess.sqaaas/main/.report/assessment_output.json)


# Data Complexity

The Data Complexity Measures in pure Python.

## Install

```bash
pip install data-complexity-profiler
```

## How it works

One can import the model and use the common _.fit()_ and
_.transform()_ functions (sklearn-like interface)

```python
import dcp
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data
y = iris.target

model = dcp.ComplexityProfile()
model.fit(X, y)
model.transform()
```

Complexity profile takes different inputs from none to
specific measures to be obtained.

## References

[1] How Complex is your classification problem? A survey on measuring
classification complexity, [ArXiv](https://arxiv.org/abs/1808.03591)

[2] The Extended Complexity Library (ECoL),
[github repo](https://github.com/lpfgarcia/ECoL)
