{
    "info": {
        "author": "Arthur Amstutz",
        "author_email": "arthur.amstutz@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "## Pylearning: python machine learning library\n\n[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/amstuta/pylearning/blob/master/LICENSE.md)\n[![PyPI](https://img.shields.io/pypi/pyversions/pylearning.svg)]()\n\nPylearning is a high-level machine learning package designed to easily prototype\nand implement data analysis programs.\n\nThe library includes the following algorithms:\n- Regression:\n    - Decision tree regressor\n    - Random forest regressor\n    - Nearest neighbours regressor\n- Classification:\n    - Decision tree classifier\n    - Random forest classifier\n    - Nearest neighbours classifier\n- Clustering:\n    - K-means\n    - DBSCAN (density-based clustering)\n\nThe two random forests algorithms use multithreading to train the trees in a\nparallelized fashion.\nThis package is compatible with Python3+.\n\n### Basic usage\n\nAll the algorithms available use the same simple interface described in the\nexamples below.\n\n```python\n# Basic regression example using a random forest\n\nfrom pylearning.ensembles import RandomForestRegressor\n\n# Load the training dataset\nfeatures, targets = ...\n\nrf = RandomForestRegressor(nb_trees=10, nb_samples=100, max_depth=20)\nrf.fit(features, targets)\n\n# Load a testing sample\ntest_feature, test_target = ...\n\nvalue_predicted = rf.predict(test_feature, test_target)\n```\n\n```python\n# Clustering example using DBSCAN algorithm\n\nimport matplotlib.pyplot as plt\nfrom pylearning.clustering import DBSCAN\nfrom sklearn.datasets import make_circles\n\n# Load a dataset composed of two circles\ndata = make_circles(n_samples=1000, noise=0.05, factor=0.3)[0]\n\ncl = DBSCAN(epsilon=0.2)\ncl.fit(data)\n\nlabels_data = {i: ([],[]) for i in range(-1, 2)}\nfor ex, label in zip(data, cl.labels):\n    labels_data[label][0].append(ex[0])\n    labels_data[label][1].append(ex[1])\n\ncolors = ['g','b']\nfor label, values in labels_data.items():\n    if label == -1:\n        plt.scatter(values[0], values[1], color='black')\n    else:\n        plt.scatter(values[0], values[1], color=colors[label], s=50)\n\nplt.show()\n```\n\n![Alt text](resources/dbscan.png?raw=true)\n\nA complete documentation of the API is available [here](https://pylearning.arthuramstutz.com/).\n\n### Installation\n\nPylearning requires to have numpy installed. It can be installed simply using Pypy:\n```sh\n# for the stable version\npip3 install pylearning\n\n# for the latest version\npip3 install git+https://github.com/amstuta/pylearning.git\n```\n\n### Further improvements\n\nThe core functionalities of the different algorithms are\nimplemented in this project, however there are many possible improvements:\n- gini criterion for splitting nodes (Decision trees)\n- pruning (Decision trees)\n- ability to split a node into an arbitrary number of child nodes (Decision trees)\n- optimizations to reduce time and memory consumption\n- better compatibility with pandas DataFrame\n- addition of new algorithms (density-based clustering, SVM, neural networks, ...)\n\nIf you wish, you're welcome to participate in the project or to make suggestions !\nTo do so, you can simply open an issue or fork the project and then create a pull\nrequest.\n\n\n",
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        "keywords": "machine learning data decision trees random forest nearest neighbours kmeans clustering",
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