二分类模型评价指标-Python实现

  Sklearn的metrics模块下有多个计算模型评价指标的函数，本文只介绍二分类的指标函数。

1.准确率

1.1参数说明

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

计算公式如下，详情参考​​http://scikit-learn.org/dev/modules/model_evaluation.html#accuracy-score​​

1.2 应用

import pandas as pd
import numpy as np
from sklearn import metrics
y_pred = [0,1,0,1]
y_true = [0,0,0,0]

## 返回正确率
metrics.accuracy_score(y_pred,y_true)

0.5

## 返回正确个数
metrics.accuracy_score(y_pred,y_true,normalize=False)

2

2.混淆矩阵

2.1 参数说明

2.2 应用

y_true = [2, 0, 2, 0, 0, 1]
y_pred = [0, 0, 2, 2, 3, 2]
metrics.confusion_matrix(y_true, y_pred)

array([[1, 0, 1, 1],
       [0, 0, 1, 0],
       [1, 0, 1, 0],
       [0, 0, 0, 0]], dtype=int64)

  简单说明:y_true和y_pred的并集为[0,1,2,3]，对角线为每一类的预测准确率。[1,3]第一行第三例为1，意为实际为2，正确预测为2的样本有1个。这样看还是不直观。官网上有一个可视化混淆矩阵的函数，可以参看​​http://scikit-learn.org/dev/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py​​

import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()

y_test = [1, 1, 1, 0]
y_pred = [1, 1, 0, 0]
# Compute confusion matrix
# #注意混淆矩阵要求预测结果在第一个位置
cnf_matrix = confusion_matrix(y_test, y_pred)
## 限制两位小数
np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=[0, 1], title='Confusion matrix, without normalization')
plt.show()

Confusion matrix, without normalization
[[1 0]
 [1 2]]

# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(
    cnf_matrix,
    classes=[0, 1],
    normalize=True,
    title='Normalized confusion matrix')

plt.show()

Normalized confusion matrix
[[ 1.    0.  ]
 [ 0.33  0.67]]



<matplotlib.figure.Figure at 0xc2022b0>

3. Recall&Precision

3.1 参数说明

3.2 应用

  要注意y_true和y_pred的位置顺序。

y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']
print(metrics.classification_report(y_true, y_pred))

             precision    recall  f1-score   support

          0       0.50      1.00      0.67         1
          1       0.00      0.00      0.00         1
          2       1.00      0.67      0.80         3

avg / total       0.70      0.60      0.61         5

print(metrics.classification_report(y_true, y_pred, target_names=target_names))

             precision    recall  f1-score   support

    class 0       0.50      1.00      0.67         1
    class 1       0.00      0.00      0.00         1
    class 2       1.00      0.67      0.80         3

avg / total       0.70      0.60      0.61         5

4.Roc&Auc

  计算AUC，画ROC曲线的貌似没有直接的函数。

4.1 参数

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

0.75

                         2018-07-16 于南京市建邺区新城科技园