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"description": "## BEM : beyond the exoplanet mass-radius relation with random forest\nPredicting the radius of exoplanets based on its planetary and stellar parameters\n\n![](\"https://github.com/soleneulmer/bem/raw/master/figures/Bem.png\")
\n\n[](https://travis-ci.org/soleneulmer/bem)\n[](https://github.com/soleneulmer/bem/LICENSE)\n[](https://badge.fury.io/py/bem)\n[](https://arxiv.org/abs/1909.07392)\n\n### Branca Edm\u00e9e Marques\nA portuguese scientist who worked on nuclear physics in France with Marie Curie\n\n\n### To install bem\n```bash\npip install bem\n```\nor\n```bash\ngit clone https://github.com/soleneulmer/bem.git\ncd bem\npython setup.py install\n```\n\n### A simple decision tree\n#### to predict exoplanet radius\n\n![](\"https://github.com/soleneulmer/bem/raw/master/figures/decision_tree.png\")
\n\n### How to run bem:\n#### 1. Load dataset and model\n```bash\n# Load exoplanet and solar system planets dataset\ndataset = bem.load_dataset()\n# Plot the dataset radius as a function of mass and equilibrium temperature\nbem.plot_dataset(dataset)\n```\n```bash\n# Build the random forest model and predict radius of the dataset\nregr, y_test_predict, _, train_test_sets = bem.random_forest_regression(dataset)\n```\n#### 2. Predict the radius of your planet\n\nmy_planet = [planetary_mass,\n semi major axis,\n eccentricity,\n stellar radius,\n stellar effective temperature,\n stellar mass]\n\nor with error bars\n\nmy_planet = [planetary_mass, error,\n semi major axis, error\n eccentricity, error,\n stellar radius, error,\n stellar effective temperature, error,\n stellar mass, error]\n```bash\n# Predict a new radius\nradius, my_pred_planet = bem.predict_radius(my_planet=np.array([[1.63,\n\t\t\t\t\t\t\t\t 0.034,\n \t\t 0.02,\n \t\t 0.337,\n \t\t 3505.0,\n \t\t 0.342]]),\n \t\t my_name=np.array(['GJ 357 b']),\n \t\t regr=regr,\n \t\t jupiter_mass=False,\n\t\t\t\t\t error_bar=False)\n# If error_bar is True\n# print('Radius: ', radius[0][0], '+-', radius[1])\n```\n\n#### 3. Compute error bars for the radius predictions\n```bash\n# Load exoplanet and solar system planets dataset with uncertainties\ndataset_errors = bem.load_dataset_errors()\n# Compute the error bars for the test set planets\nradii_test_output_error, _ = bem.computing_errorbars(regr,\n dataset_errors,\n train_test_sets)\n# Plot the test set, true radius versus RF predicted radius\nbem.plot_true_predicted(train_test_sets,\n y_test_predict,\n radii_test_output_error)\n\n```\n\n#### 4. Radial velocity dataset\n```bash\n# Load the radial velocity dataset\ndataset_rv = bem.load_dataset_RV()\n# Predict the radius of the RV dataset\nradii_RV_RF = regr.predict(dataset_rv)\n# Plot the predictions of the RV dataset\nbem.plot_dataset(dataset_rv, predicted_radii=radii_RV_RF, rv=True)\n```\n\n#### 5. Diagnostic plots\n```bash\n# Plot the learning curve\nbem.plot_learning_curve(regr, dataset)\n# Plot the validation curves\nbem.plot_validation_curves(regr, dataset, name='features')\nbem.plot_validation_curves(regr, dataset, name='tree')\nbem.plot_validation_curves(regr, dataset, name='depth')\n```\n\n#### 6. LIME explanations \nsee their [github](https://github.com/marcotcr/lime)\n```bash\n# Explain the RF predictions\n# of the exoplanets from the test set\nbem.plot_LIME_predictions(regr, dataset, train_test_sets)\n# LIME explanation for your planet\n# in this case GJ 357 b\nbem.plot_LIME_predictions(regr, dataset, train_test_sets,\n my_pred_planet=my_pred_planet,\n my_true_radius=1.166)\n```",
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