# miml: Multi-Instance Multi-Label Learning Library for Python
The aim of the library is to ease the development, testing, and comparison of classification algorithms for multi-instance multi-label learning (MIML).
## Table of Contents
- [Installation](#installation)
- [Documentation](#documentation)
- [Usage](#usage)
- [License](#license)
### Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install miml.
```bash
$ pip install mimllearning
```
#### Requirements
The requirement packages for miml library are: numpy and scikit-learn.
Installing miml with the package manager does not install the package dependencies.
So install them with the package manager manually if not already downloaded.
$ pip install numpy
$ pip install scikit-learn
### Documentation
We can find the documentation of the project in this link: [Documentation](https://p82maavd.github.io/MIML/)
### Usage
#### Datasets
``` python
from miml.data.load_datasets import load_dataset
dataset_train = load_dataset("miml_birds_random_80train.arff", from_library=True)
dataset_test = load_dataset("C:/Users/Damián/Desktop/miml_birds_random_20test.arff")
```
#### Classifier
``` python
from miml.classifier import MIMLtoMIBRClassifier, AllPositiveAPRClassifier
classifier_mi = MIMLtoMIBRClassifier(AllPositiveAPRClassifier())
classifier_mi.fit(dataset_train)
results_mi=classifier_mi.evaluate(dataset_test)
probs_mi = classifier_mi.predict_proba(dataset_test)
```
#### Report
``` python
from miml.report import Report
report = Report(results_mi, probs_mi, dataset_test)
report.to_string()
print("")
report.to_csv()
```
### License
MIML library is released under the GNU General Public License [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html).
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"description": "# miml: Multi-Instance Multi-Label Learning Library for Python\nThe aim of the library is to ease the development, testing, and comparison of classification algorithms for multi-instance multi-label learning (MIML). \n\n## Table of Contents\n\n- [Installation](#installation)\n- [Documentation](#documentation)\n- [Usage](#usage)\n- [License](#license)\n\n### Installation\n\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install miml.\n\n```bash\n$ pip install mimllearning\n```\n#### Requirements\nThe requirement packages for miml library are: numpy and scikit-learn.\nInstalling miml with the package manager does not install the package dependencies.\nSo install them with the package manager manually if not already downloaded.\n\n $ pip install numpy\n $ pip install scikit-learn\n\n### Documentation\n\nWe can find the documentation of the project in this link: [Documentation](https://p82maavd.github.io/MIML/)\n\n\n### Usage\n\n\n#### Datasets\n\n``` python\nfrom miml.data.load_datasets import load_dataset\n\ndataset_train = load_dataset(\"miml_birds_random_80train.arff\", from_library=True)\ndataset_test = load_dataset(\"C:/Users/Dami\u00e1n/Desktop/miml_birds_random_20test.arff\")\n```\n\n#### Classifier\n\n``` python\nfrom miml.classifier import MIMLtoMIBRClassifier, AllPositiveAPRClassifier\n\nclassifier_mi = MIMLtoMIBRClassifier(AllPositiveAPRClassifier())\nclassifier_mi.fit(dataset_train)\nresults_mi=classifier_mi.evaluate(dataset_test)\nprobs_mi = classifier_mi.predict_proba(dataset_test)\n```\n\n#### Report\n\n``` python\nfrom miml.report import Report\n\nreport = Report(results_mi, probs_mi, dataset_test)\nreport.to_string()\nprint(\"\")\nreport.to_csv()\n```\n\n### License\nMIML library is released under the GNU General Public License [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html).\n",
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