How does python calculate the auc indicator?

1. Install scikit-learn

1.1 Scikit-learn depends on

• ##Python (>= 2.6 or >= 3.3) ,

• NumPy (>= 1.6.1),

• SciPy (>= 0.9).

Check the versions of the above three dependencies respectively,

python -V Result: Python 2.7.3

python -c 'import scipy; print scipy.version.version' scipy version result: 0.9.0
python -c "import numpy; print numpy.version.version" numpy result: 1.10.2
1.2 Scikit-learn installation
If you have installed NumPy, SciPy and python and all meet the requirements If the conditions required in 1.1 are met, you can directly run sudo
pip install -U scikit-learn Perform the installation.

2. Calculate auc indicator

   sklearn.metrics  y_true = np.array([0, 0, 1, 1 y_scores = np.array([0.1, 0.4, 0.35, 0.8 roc_auc_score(y_true, y_scores)

输出：0.75

 <br>

3. Calculate roc curve

   sklearn  y = np.array([1, 1, 2, 2 scores = np.array([0.1, 0.4, 0.35, 0.8 fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2    thresholds

输出：
array([ 0. ,  0.5,  0.5,  1. ])
array([ 0.5,  0.5,  1. ,  1. ])
array([ 0.8 ,  0.4 ,  0.35,  0.1 ])

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