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Alejandro Moreo Fernandez 2024-02-14 14:39:37 +01:00
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README.md
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@ -14,7 +14,7 @@ for facilitating the analysis and interpretation of the experimental results.
### Last updates: ### Last updates:
* Version 0.1.8 is released! major changes can be consulted [here](CHANGE_LOG.txt). * Version 0.1.8 is released! major changes can be consulted [here](CHANGE_LOG.txt).
* A detailed documentation is now available [here](https://hlt-isti.github.io/QuaPy/) * A detailed wiki is available [here](https://github.com/HLT-ISTI/QuaPy/wiki)
* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html) * The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
### Installation ### Installation
@ -78,10 +78,12 @@ quantification methods based on structured output learning, HDy, QuaNet, quantif
* Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.). * Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
* Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.). * Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
* Datasets frequently used in quantification (textual and numeric), including: * Datasets frequently used in quantification (textual and numeric), including:
* 32 UCI Machine Learning datasets. * 32 UCI Machine Learning binary datasets.
* 5 UCI Machine Learning multiclass datasets (_new in v0.1.8!_).
* 11 Twitter quantification-by-sentiment datasets. * 11 Twitter quantification-by-sentiment datasets.
* 3 product reviews quantification-by-sentiment datasets. * 3 product reviews quantification-by-sentiment datasets.
* 4 tasks from LeQua competition (_new in v0.1.7!_) * 4 tasks from LeQua competition (_new in v0.1.7!_)
* IFCB dataset of plankton water samples (_new in v0.1.8!_).
* Native support for binary and single-label multiclass quantification scenarios. * Native support for binary and single-label multiclass quantification scenarios.
* Model selection functionality that minimizes quantification-oriented loss functions. * Model selection functionality that minimizes quantification-oriented loss functions.
* Visualization tools for analysing the experimental results. * Visualization tools for analysing the experimental results.
@ -95,6 +97,7 @@ quantification methods based on structured output learning, HDy, QuaNet, quantif
* tqdm * tqdm
* pandas, xlrd * pandas, xlrd
* matplotlib * matplotlib
* ucimlrepo
## Contributing ## Contributing
@ -102,7 +105,7 @@ In case you want to contribute improvements to quapy, please generate pull reque
## Documentation ## Documentation
The [developer API documentation](https://hlt-isti.github.io/QuaPy/build/html/modules.html) is available [here](https://hlt-isti.github.io/QuaPy/build/html/index.html). The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/index.html).
Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki), in which many examples Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki), in which many examples
are provided: are provided:
@ -117,4 +120,10 @@ are provided:
## Acknowledgments: ## Acknowledgments:
<img src="SoBigData.png" alt="SoBigData++" width="250"/>
<img src="logo/SoBigData.png" alt="SoBigData++" width="250"/>
<img src="logo/LogoQuaDaSh.png" alt="QuaDaSh" width="250"/>
<img src="logo/NextGenerationEU.jpg" alt="QuaDaSh" width="250"/>

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