Random forest for exoplanets
Project description
BEM : beyond the exoplanet mass-radius relation with random forest
Predicting the radius of exoplanets based on its planetary and stellar parameters
Branca Edmée Marques
A portuguese scientist who worked on nuclear physics in France with Marie Curie
To install bem
pip install bem
or
git clone https://github.com/soleneulmer/bem.git
cd bem
python setup.py install
A simple decision tree
to predict exoplanet radius
How to run bem:
1. Load dataset and model
# Load exoplanet and solar system planets dataset dataset = bem.load_dataset() # Plot the dataset radius as a function of mass and equilibrium temperature bem.plot_dataset(dataset)
# Build the random forest model and predict radius of the dataset regr, y_test_predict, _, train_test_sets = bem.random_forest_regression(dataset)
2. Predict the radius of your planet
my_planet = [planetary_mass, semi major axis, eccentricity, stellar radius, stellar effective temperature, stellar mass]
or with error bars
my_planet = [planetary_mass, error, semi major axis, error eccentricity, error, stellar radius, error, stellar effective temperature, error, stellar mass, error]
# Predict a new radius radius, my_pred_planet = bem.predict_radius(my_planet=np.array([[1.63, 0.034, 0.02, 0.337, 3505.0, 0.342]]), my_name=np.array(['GJ 357 b']), regr=regr, jupiter_mass=False, error_bar=False) # If error_bar is True # print('Radius: ', radius[0][0], '+-', radius[1])
3. Compute error bars for the radius predictions
# Load exoplanet and solar system planets dataset with uncertainties dataset_errors = bem.load_dataset_errors() # Compute the error bars for the test set planets radii_test_output_error, _ = bem.computing_errorbars(regr, dataset_errors, train_test_sets) # Plot the test set, true radius versus RF predicted radius bem.plot_true_predicted(train_test_sets, y_test_predict, radii_test_output_error)
4. Radial velocity dataset
# Load the radial velocity dataset dataset_rv = bem.load_dataset_RV() # Predict the radius of the RV dataset radii_RV_RF = regr.predict(dataset_rv) # Plot the predictions of the RV dataset bem.plot_dataset(dataset_rv, predicted_radii=radii_RV_RF, rv=True)
5. Diagnostic plots
# Plot the learning curve bem.plot_learning_curve(regr, dataset) # Plot the validation curves bem.plot_validation_curves(regr, dataset, name='features') bem.plot_validation_curves(regr, dataset, name='tree') bem.plot_validation_curves(regr, dataset, name='depth')
6. LIME explanations
see their github
# Explain the RF predictions # of the exoplanets from the test set bem.plot_LIME_predictions(regr, dataset, train_test_sets) # LIME explanation for your planet # in this case GJ 357 b bem.plot_LIME_predictions(regr, dataset, train_test_sets, my_pred_planet=my_pred_planet, my_true_radius=1.166)
Project details
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bem-1.0.0.tar.gz
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