ea850ecd20
* All Linux tests are now in Jenkins CI * Tests are now de-coupled from builds. We can now build XGBoost with one version of CUDA/JDK and test it with another version of CUDA/JDK * Builds (compilation) are significantly faster because 1) They use C5 instances with faster CPU cores; and 2) build environment setup is cached using Docker containers
11 lines
432 B
Python
11 lines
432 B
Python
from sklearn.datasets import load_iris
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import numpy as np
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import pandas
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X, y = load_iris(return_X_y=True)
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y = y.astype(np.int)
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df = pandas.DataFrame(data=X, columns=['sepal length', 'sepal width', 'petal length', 'petal width'])
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class_id_to_name = {0:'Iris-setosa', 1:'Iris-versicolor', 2:'Iris-virginica'}
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df['class'] = np.vectorize(class_id_to_name.get)(y)
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df.to_csv('./iris.csv', float_format='%.1f', header=False, index=False)
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