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    "info": {
        "author": "Sa\u0161o Karakati\u010d",
        "author_email": "karakatic@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
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            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3.9",
            "Programming Language :: Python :: 3.10",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development"
        ],
        "description": "# EvoPreprocess\n\nEvoPreprocess is a Python toolkit for sampling datasets, instance weighting, and feature selection. It is compatible with [scikit-learn](http://scikit-learn.org/stable/) and [imbalanced-learn](https://imbalanced-learn.readthedocs.io/en/stable/). It is based on [NiaPy](https://github.com/NiaOrg/NiaPy) library for the implementation of nature-inspired algorithms and is distributed under GNU General Public License v3.0 license.\n\n## Getting Started\n\nThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.\n\n### Requirements\n- Python 3.6+\n- PIP\n\n### Dependencies\nEvoSampling requires:\n\n- numpy\n- scipy\n- scikit-learn\n- imbalanced-learn\n- NiaPy(>=2.0.0)\n\n### Installation\nInstall EvoPreprocess with pip:\n\n```sh\n$ pip install evopreprocess\n```\nTo install EvoPreprocess on Fedora:\n\n```sh\n$ dnf install python-EvoPreprocess\n```\n\nOr directly from the source code:\n\n```sh\n$ git clone https://github.com/karakatic/EvoPreprocess.git\n$ cd evopreprocess\n$ python setup.py install\n```\n\n# Usage\n\nAfter installation, the package can be imported:\n\n```sh\n$ python\n>>> import evopreprocess\n>>> evopreprocess.__version__\n```\n\n## Data sampling\n\n### Simple data sampling example\n\n```python\nfrom sklearn.datasets import load_breast_cancer\nfrom evopreprocess.data_sampling import EvoSampling\n\n# Load classification data\ndataset = load_breast_cancer()\n\n# Print the size of dataset\nprint(dataset.data.shape, len(dataset.target))\n\n# Sample instances of dataset with default settings with EvoSampling\nX_resampled, y_resampled = EvoSampling().fit_resample(dataset.data, dataset.target)\n\n# Print the size of dataset after sampling\nprint(X_resampled.shape, len(y_resampled))\n```\n\n### Data sampling for regression with custom nature-inspired algorithm and other custom settings\n\n```python\nimport niapy.algorithms.basic as nia\nfrom sklearn.datasets import load_boston\nfrom sklearn.tree import DecisionTreeRegressor\nfrom evopreprocess.data_sampling import EvoSampling\n\n# Load regression data\ndataset = load_boston()\n\n# Print the size of dataset\nprint(dataset.data.shape, len(dataset.target))\n\n# Sample instances of dataset with custom settings and regression with EvoSampling\nX_resampled, y_resampled = EvoSampling(\n    evaluator=DecisionTreeRegressor(),\n    optimizer=nia.EvolutionStrategyMpL,\n    n_folds=5,\n    n_runs=5,\n    n_jobs=4\n).fit_resample(dataset.data, dataset.target)\n\n# Print the size of dataset after sampling\nprint(X_resampled.shape, len(y_resampled))\n```\n\n### Data sampling with scikit-learn\n\n```python\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom evopreprocess.data_sampling import EvoSampling\n\n# Set the random seed for the reproducibility\nrandom_seed = 1111\n\n# Load classification data\ndataset = load_breast_cancer()\n\n# Split the dataset to training and testing set\nX_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target,\n                                                    test_size=0.33,\n                                                    random_state=random_seed)\n\n# Train the decision tree model\ncls = DecisionTreeClassifier(random_state=random_seed)\ncls.fit(X_train, y_train)\n\n# Print the results: shape of the original dataset and the accuracy of decision tree classifier on original data\nprint(X_train.shape, accuracy_score(y_test, cls.predict(X_test)), sep=': ')\n\n# Sample the data with random_seed set\nevo = EvoSampling(n_folds=3, random_seed=random_seed)\nX_resampled, y_resampled = evo.fit_resample(X_train, y_train)\n\n# Fit the decision tree model\ncls.fit(X_resampled, y_resampled)\n\n# Print the results: shape of the original dataset and the accuracy of decision tree classifier on original data\nprint(X_resampled.shape, accuracy_score(y_test, cls.predict(X_test)), sep=': ')\n```\n\n## Instance weighting\n\n### Simple instance weighting example\n\n```python\nfrom sklearn.datasets import load_breast_cancer\nfrom evopreprocess.data_weighting import EvoWeighting\n\n# Load classification data\ndataset = load_breast_cancer()\n\n# Get weights for the instances\ninstance_weights = EvoWeighting(random_seed=123).reweight(dataset.data, dataset.target)\n\n# Print the weights for instances\nprint(instance_weights)\n```\n\n### Instance weighting with scikit-learn\n\n```python\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom evopreprocess.data_weighting import EvoWeighting\n\n# Set the random seed for the reproducibility\nrandom_seed = 1234\n\n# Load classification data\ndataset = load_breast_cancer()\n\n# Split the dataset to training and testing set\nX_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target,\n                                                    test_size=0.33,\n                                                    random_state=random_seed)\n\n# Train the decision tree model with custom instance weights\ncls = DecisionTreeClassifier(random_state=random_seed)\ncls.fit(X_train, y_train)\n\n# Print the results: shape of the original dataset and the accuracy of decision tree classifier on original data\nprint(X_train.shape, accuracy_score(y_test, cls.predict(X_test)), sep=': ')\n\n# Get weights for the instances\ninstance_weights = EvoWeighting(random_seed=random_seed).reweight(X_train, y_train)\n\n# Fit the decision tree model\ncls.fit(X_train, y_train, sample_weight=instance_weights)\n\n# Print the results: shape of the original dataset and the accuracy of decision tree classifier on original data\nprint(X_train.shape, accuracy_score(y_test, cls.predict(X_test)), sep=': ')\n```\n\n## Feature selection\n\n### Simple feature selection example\n\n```python\nfrom sklearn.datasets import load_breast_cancer\nfrom evopreprocess.feature_selection import EvoFeatureSelection\n\n# Load classification data\ndataset = load_breast_cancer()\n\n# Print the size of dataset\nprint(dataset.data.shape)\n\n# Run feature selection with EvoFeatureSelection\nX_new = EvoFeatureSelection().fit_transform(\n    dataset.data,\n    dataset.target)\n\n# Print the size of dataset after feature selection\nprint(X_new.shape)\n```\n\n### Feature selection for regression with custom nature-inspired algorithm and other custom settings\n\n```python\nfrom sklearn.datasets import load_boston\nfrom sklearn.tree import DecisionTreeRegressor\nimport niapy.algorithms.basic as nia\nfrom evopreprocess.feature_selection import EvoFeatureSelection\n\n# Load regression data\ndataset = load_boston()\n\n# Print the size of dataset\nprint(dataset.data.shape)\n\n# Run feature selection with custom settings and regression with EvoFeatureSelection\nX_new = EvoFeatureSelection(\n    evaluator=DecisionTreeRegressor(max_depth=2),\n    optimizer=nia.DifferentialEvolution,\n    random_seed=1,\n    n_runs=5,\n    n_folds=5,\n    n_jobs=4\n).fit_transform(dataset.data, dataset.target)\n\n# Print the size of dataset after feature selection\nprint(X_new.shape)\n```\n\n### Feature selection with scikit-learn\n\n```python\nfrom sklearn.datasets import load_boston\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeRegressor\nfrom evopreprocess.feature_selection import EvoFeatureSelection\n\n# Set the random seed for the reproducibility\nrandom_seed = 654\n\n# Load regression data\ndataset = load_boston()\n\n# Split the dataset to training and testing set\nX_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target,\n                                                    test_size=0.33,\n                                                    random_state=random_seed)\n\n# Train the decision tree model\nmodel = DecisionTreeRegressor(random_state=random_seed)\nmodel.fit(X_train, y_train)\n\n# Print the results: shape of the original dataset and the accuracy of decision tree regressor on original data\nprint(X_train.shape, mean_squared_error(y_test, model.predict(X_test)), sep=': ')\n\n# Sample the data with random_seed set\nevo = EvoFeatureSelection(evaluator=model, random_seed=random_seed)\nX_train_new = evo.fit_transform(X_train, y_train)\n\n# Fit the decision tree model\nmodel.fit(X_train_new, y_train)\n\n# Keep only selected feature on test set\nX_test_new = evo.transform(X_test)\n\n# Print the results: shape of the original dataset and the MSE of decision tree regressor on original data\nprint(X_train_new.shape, mean_squared_error(y_test, model.predict(X_test_new)), sep=': ')\n```\n\n## EvoPreprocess as a part of the pipeline (from imbalanced-learn)\n\n```python\nfrom imblearn.pipeline import Pipeline\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom evopreprocess.data_sampling import EvoSampling\nfrom evopreprocess.feature_selection import EvoFeatureSelection\n\n# Set the random seed for the reproducibility\nrandom_seed = 1111\n\n# Load classification data\ndataset = load_breast_cancer()\n\n# Split the dataset to training and testing set\nX_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target,\n                                                    test_size=0.33,\n                                                    random_state=random_seed)\n\n# Train the decision tree model\ncls = DecisionTreeClassifier(random_state=random_seed)\ncls.fit(X_train, y_train)\n\n# Print the results: shape of the original dataset and the accuracy of decision tree classifier on original data\nprint(X_train.shape, accuracy_score(y_test, cls.predict(X_test)), sep=': ')\n\n# Make scikit-learn pipeline with feature selection and data sampling\npipeline = Pipeline(steps=[\n    ('feature_selection', EvoFeatureSelection(n_folds=10, random_seed=random_seed)),\n    ('data_sampling', EvoSampling(n_folds=10, random_seed=random_seed)),\n    ('classifier', DecisionTreeClassifier(random_state=random_seed))])\n\n# Fit the pipeline\npipeline.fit(X_train, y_train)\n\n# Print the results: the accuracy of the pipeline\nprint(accuracy_score(y_test, pipeline.predict(X_test)))\n```\n\nFor more examples please look at **Examples** folder.\n\n# Author\n\nEvoPreprocess was programmed and is maintained by Saso Karakatic from University of Maribor.\n\n# Citing\n\n## Plain format\n```\nKarakatic, S., (2020).\nEvoPreprocess - Data Preprocessing Framework with Nature-Inspired Optimization Algorithms.\nMathematics, 8(6), p.900. <https://doi.org/10.3390/math8060900>\n```\n\n## Bibtex format\n```\n@article{karakativc2020evopreprocess,\n  title={EvoPreprocess\u2014Data Preprocessing Framework with Nature-Inspired Optimization Algorithms},\n  author={Karakati{\\v{c}}, Sa{\\v{s}}o},\n  journal={Mathematics},\n  volume={8},\n  number={6},\n  pages={900},\n  year={2020},\n  publisher={Multidisciplinary Digital Publishing Institute}\n}\n```\n\n## License\n\nThis project is licensed under the GNU General Public License v3.0 License - see <http://www.opensource.org/licenses/GNU General Public 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