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cognitivefactory-features-maximization-metric-0.1.1
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Implementation of Features Maximization Metric, an unbiased metric aimed at estimate the quality of an unsupervised classification.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cognitivefactory-features-maximization-metric-0.1.1 |
| نام | cognitivefactory-features-maximization-metric |
| نسخه کتابخانه | 0.1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Erwan Schild <erwan.schild@e-i.com> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/cognitivefactory-features-maximization-metric/ |
| مجوز | CECILL-C |
# Features Maximization Metric
[](https://github.com/cognitivefactory/features-maximization-metric/actions?query=workflow%3Aci)
[](https://cognitivefactory.github.io/features-maximization-metric/)
[](https://pypi.org/project/cognitivefactory-features-maximization-metric/)
[](https://doi.org/10.5281/zenodo.7646382)
Implementation of _Features Maximization Metric_, an unbiased metric aimed at estimate the quality of an unsupervised classification.
## <a name="Description"></a> Quick description
_Features Maximization_ (`FMC`) is a features selection method described in `Lamirel J.-C., Cuxac P., Hajlaoui K., A new approach for feature selection based on quality metric, Advances in Knowledge Discovery and Management, 6 (665), Springer.`
This metric is computed by applying the following steps:
1. Compute the ***Features F-Measure*** metric (based on ***Features Recall*** and ***Features Predominance*** metrics).
> (a) The ***Features Recall*** `FR[f][c]` for a given class `c` and a given feature `f` is the ratio between
> the sum of the vectors weights of the feature `f` for data in class `c`
> and the sum of all vectors weights of feature `f` for all data.
> It answers the question: "_Can the feature `f` distinguish the class `c` from other classes `c'` ?_"
> (b) The ***Features Predominance*** `FP[f][c]` for a given class `c` and a given feature `f` is the ratio between
> the sum of the vectors weights of the feature `f` for data in class `c`
> and the sum of all vectors weights of all feature `f'` for data in class `c`.
> It answers the question: "_Can the feature `f` better identify the class `c` than the other features `f'` ?_"
> (c) The ***Features F-Measure*** `FM[f][c]` for a given class `c` and a given feature `f` is
> the harmonic mean of the ***Features Recall*** (a) and the ***Features Predominance*** (c).
> It answers the question: "_How much information does the feature `f` contain about the class `c` ?_"
2. Compute the ***Features Selection*** (based on ***F-Measure Overall Average*** comparison).
> (d) The ***F-Measure Overall Average*** is the average of ***Features F-Measure*** (c) for all classes `c` and for all features `f`.
> It answers the question: "_What are the mean of information contained by features in all classes ?_"
> (e) A feature `f` is ***Selected*** if and only if it exist at least one class `c` for which the ***Features F-Measure*** (c) `FM[f][c]` is bigger than the ***F-Measure Overall Average*** (d).
> It answers the question: "_What are the features which contain more information than the mean of information in the dataset ?_"
> (f) A Feature `f` is ***Deleted*** if and only if the ***Features F-Measure*** (c) `FM[f][c]` is always lower than the ***F-Measure Overall Average*** (d) for each class `c`.
> It answers the question: "_What are the features which do not contain more information than the mean of information in the dataset ?_"
3. Compute the ***Features Contrast*** and ***Features Activation*** (based on ***F-Measure Marginal Averages*** comparison).
> (g) The ***F-Measure Marginal Averages*** for a given feature `f` is the average of ***Features F-Measure*** (c) for all classes `c` and for the given feature `f`.
> It answers the question: "_What are the mean of information contained by the feature `f` in all classes ?_"
> (h) The ***Features Contrast*** `FC[f][c]` for a given class `c` and a given selected feature `f` is the ratio between
> the ***Features F-Measure*** (c) `FM[f][c]`
> and the ***F-Measure Marginal Averages*** (g) for selected feature f
> put to the power of an ***Amplification Factor***.
> It answers the question: "_How relevant is the feature `f` to distinguish the class `c` ?_"
> (i) A selected Feature `f` is ***Active*** for a given class `c` if and only if the ***Features Contrast*** (h) `FC[f][c]` is bigger than `1.0`.
> It answers the question : "_For which classes a selected feature `f` is relevant ?_"
This metric is an **efficient method** to:
- **identify relevant features** of a dataset modelization;
- **describe association** between vectors features and data classes;
- **increase contrast** between data classes.
## <a name="Documentation"></a> Documentation
- [Main documentation](https://cognitivefactory.github.io/features-maximization-metric/)
## <a name="Installation"></a> Installation
Features Maximization Metric requires [`Python`](https://www.python.org/) 3.8 or above.
To install with [`pip`](https://github.com/pypa/pip):
```bash
# install package
python3 -m pip install cognitivefactory-features-maximization-metric
```
To install with [`pipx`](https://github.com/pypa/pipx):
```bash
# install pipx
python3 -m pip install --user pipx
# install package
pipx install --python python3 cognitivefactory-features-maximization-metric
```
## <a name="Development"></a> Development
To work on this project or contribute to it, please read:
- the [Copier PDM](https://pawamoy.github.io/copier-pdm/) template documentation ;
- the [Contributing](https://cognitivefactory.github.io/features-maximization-metric/contributing/) page for environment setup and development help ;
- the [Code of Conduct](https://cognitivefactory.github.io/features-maximization-metric/code_of_conduct/) page for contribution rules.
## <a name="References"></a> References
- **Features Maximization Metric**: `Lamirel J.-C., Cuxac P., Hajlaoui K., A new approach for feature selection based on quality metric, Advances in Knowledge Discovery and Management, 6 (665), Springer.`
- **V-Measure**: `Rosenberg, Andrew & Hirschberg, Julia. (2007). V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure. 410-420.`
## <a name="How to cite"></a> How to cite
`Schild, E. (2023). cognitivefactory/features-maximization-metric. Zenodo. https://doi.org/10.5281/zenodo.7646382.`
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy>=1.22.2 |
| - | scikit-learn>=0.24.1 |
| - | scipy>=1.7.3 |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl cognitivefactory-features-maximization-metric-0.1.1:
pip install cognitivefactory-features-maximization-metric-0.1.1.whl
نصب پکیج tar.gz cognitivefactory-features-maximization-metric-0.1.1:
pip install cognitivefactory-features-maximization-metric-0.1.1.tar.gz