INNER CODE UNIT · Python

generate_data

ankonzoid/LearningX · advanced_ML/tree_ensembles/tree_ensembles.py:17

    def generate_data(func, x_range=(0, 10), N=100):
        X = np.linspace(x_range[0], x_range[1], N)
        y = np.vectorize(func)(X)
        X = X[:, None]
        return X, y
    func = lambda x: (x-1)*(x-4)*(x-8)*(x-8)
    X, y = generate_data(func, x_range=(0, 10), N=100)

    # Tree ensemble models
    from src.BaggedTrees import BaggedTreeRegressor
    from src.RandomForest import RandomForestTreeRegressor
    from src.GradientBoostedTree import GradientBoostedTreeRegressor

    # Train and plot models (pdf + png)
    def plot_model(tree_ensemble_model, X, y, plot_filename,
                   n_estimators_vec=[0, 10, 20], max_depth_vec=[1, 4, 8], name=""):
        from sklearn.metrics import mean_absolute_error
        print("Plotting '{}' model to '{}'...".format(name, plot_filename))

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