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        "description": "# XGP Python package\n\n[![PyPI version](https://badge.fury.io/py/xgp.svg)](https://badge.fury.io/py/xgp)\n[![Travis build status](https://travis-ci.org/MaxHalford/xgp-python.svg?branch=master)](https://travis-ci.org/MaxHalford/xgp-python)\n[![Coverage Status](https://coveralls.io/repos/github/MaxHalford/xgp-python/badge.svg?branch=master)](https://coveralls.io/github/MaxHalford/xgp-python?branch=master)\n\nThis repository contains Python bindings to the [XGP library](https://github.com/MaxHalford/xgp). It is a simple wrapper that calls the XGP dynamic shared library and exposes a scikit-learn interface.\n\n## Documentation\n\nPlease refer to the [Python section of the XGP website](https://maxhalford.github.io/xgp/python/).\n\n## Installation\n\nInstallation instructions are available [here](https://maxhalford.github.io/xgp/cli/#installation).\n\n## Quick start\n\n```python\n>>> from sklearn import datasets\n>>> from sklearn import metrics\n>>> from sklearn import model_selection\n>>> import xgp\n\n>>> X, y = datasets.load_boston(return_X_y=True)\n>>> X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, random_state=42)\n\n>>> model = xgp.XGPRegressor(\n...    flavor='boosting',\n...    loss_metric='mse',\n...    funcs='add,sub,mul,div',\n...    n_individuals=50,\n...    n_generations=20,\n...    parsimony_coefficient=0.01,\n...    n_rounds=8,\n...    random_state=42,\n... )\n\n>>> model = model.fit(X_train, y_train, eval_set=(X_test, y_test), verbose=True)\n\n>>> metrics.mean_squared_error(y_train, model.predict(X_train))  # doctest: +ELLIPSIS\n17.794685...\n\n>>> metrics.mean_squared_error(y_test, model.predict(X_test))  # doctest: +ELLIPSIS\n17.337693...\n\n```\n\nThis will also produce the following output in the shell:\n\n```sh\n00:00:00 -- train mse: 42.06567 -- val mse: 33.80606 -- round 1\n00:00:00 -- train mse: 24.20662 -- val mse: 22.73832 -- round 2\n00:00:00 -- train mse: 22.06328 -- val mse: 18.90887 -- round 3\n00:00:00 -- train mse: 20.25549 -- val mse: 18.45531 -- round 4\n00:00:00 -- train mse: 18.86693 -- val mse: 18.22908 -- round 5\n00:00:00 -- train mse: 17.79469 -- val mse: 17.33769 -- round 6\n00:00:01 -- train mse: 17.62692 -- val mse: 22.67012 -- round 7\n00:00:01 -- train mse: 17.24799 -- val mse: 22.77802 -- round 8\n```\n\n\n",
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