58a6723eb1
* Add num target model parameter, which is configured from input labels. * Change elementwise metric and indexing for weights. * Add demo. * Add tests.
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
"""
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A demo for multi-output regression
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==================================
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The demo is adopted from scikit-learn:
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https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py
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"""
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import numpy as np
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import xgboost as xgb
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import argparse
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from matplotlib import pyplot as plt
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def plot_predt(y, y_predt, name):
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s = 25
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plt.scatter(y[:, 0], y[:, 1], c="navy", s=s,
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edgecolor="black", label="data")
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plt.scatter(y_predt[:, 0], y_predt[:, 1], c="cornflowerblue", s=s,
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edgecolor="black")
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plt.xlim([-1, 2])
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plt.ylim([-1, 2])
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plt.show()
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def main(plot_result: bool):
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"""Draw a circle with 2-dim coordinate as target variables."""
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rng = np.random.RandomState(1994)
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X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
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y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
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y[::5, :] += (0.5 - rng.rand(20, 2))
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y = y - y.min()
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y = y / y.max()
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# Train a regressor on it
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reg = xgb.XGBRegressor(tree_method="hist", n_estimators=64)
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reg.fit(X, y, eval_set=[(X, y)])
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y_predt = reg.predict(X)
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if plot_result:
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plot_predt(y, y_predt, 'multi')
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--plot", choices=[0, 1], type=int, default=1)
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args = parser.parse_args()
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main(args.plot == 1)
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