Python Calculates AUC metrics

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1. Installing Scikit-learn

1.1scikit-learn Dependency

· Python (>= 2.6 or >= 3.3),

· NumPy (>= 1.6.1),

· SciPy (>= 0.9).

View each of the three dependent versions above,

Python-v Result: Python 2.7.3

Python-c ' Import scipy; Print scipy.version.version ' scipy version results: 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 required conditions in 1.1, you can run sudo directly

Pip Install-u Scikit-learn

Perform the installation.

2. Calculate the AUC indicator

1 Import numpy as NP

2 from Sklearn.metrics import Roc_auc_score

3 Y_true = NP. Array ([0, 0, 1, 1])

4 Y_scores = NP. Array ([0.1, 0.4, 0.35, 0.8])

5 Roc_auc_score (Y_true, Y_scores)

Output: 0.75

3. Calculate the ROC curve

1 Import numpy as NP

2 from Sklearn Import metrics

3 y = np. Array ([1, 1, 2, 2]) #实际值

4 scores = NP. Array ([0.1, 0.4, 0.35, 0.8]) #预测值

5 FPR, TPR, thresholds = Metrics.roc_curve (y, scores, pos_label=2) #pos_label = 2, representing the actual value of 2 as a positive sample

6 Print FPR

7 Print TPR

8 Print thresholds

Output:

Array ([0., 0.5, 0.5, 1.])

Array ([0.5, 0.5, 1., 1.])

Array ([0.8, 0.4, 0.35, 0.1])

Source: Blog Park

Python Calculates AUC metrics