如何在Python中编写混淆矩阵? [英] How to write a confusion matrix in Python?
问题描述
我用Python编写了一个混淆矩阵计算代码:
I wrote a confusion matrix calculation code in Python:
def conf_mat(prob_arr, input_arr):
# confusion matrix
conf_arr = [[0, 0], [0, 0]]
for i in range(len(prob_arr)):
if int(input_arr[i]) == 1:
if float(prob_arr[i]) < 0.5:
conf_arr[0][1] = conf_arr[0][1] + 1
else:
conf_arr[0][0] = conf_arr[0][0] + 1
elif int(input_arr[i]) == 2:
if float(prob_arr[i]) >= 0.5:
conf_arr[1][0] = conf_arr[1][0] +1
else:
conf_arr[1][1] = conf_arr[1][1] +1
accuracy = float(conf_arr[0][0] + conf_arr[1][1])/(len(input_arr))
prob_arr是我的分类代码返回的数组，并且示例数组如下:
prob_arr is an array that my classification code returned and a sample array is like this:
[1.0, 1.0, 1.0, 0.41592955657342651, 1.0, 0.0053405015805891975, 4.5321494433440449e-299, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.70943426182688163, 1.0, 1.0, 1.0, 1.0]
input_arr是数据集的原始类标签，它是这样的:
input_arr is the original class labels for a dataset and it is like this:
[2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1]
我的代码试图做的是:我得到prob_arr和input_arr，对于每个类(1和2)，我检查它们是否被错误分类.
What my code is trying to do is: i get prob_arr and input_arr and for each class (1 and 2) I check if they are misclassified or not.
但是我的代码仅适用于两个类.如果我为多个分类的数据运行此代码，它将无法正常工作.如何针对多个班级进行此操作?
But my code only works for two classes. If I run this code for a multiple classed data, it doesn't work. How can I make this for multiple classes?
例如，对于具有三个类的数据集，它应返回:[[21,7,3],[3,38,6],[5,4,19]]
For example, for a data set with three classes, it should return: [[21,7,3],[3,38,6],[5,4,19]]
推荐答案
Scikit-Learn提供了confusion_matrix函数
Scikit-Learn provides a confusion_matrix function
from sklearn.metrics import confusion_matrix
y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
confusion_matrix(y_actu, y_pred)
输出一个Numpy数组
which output a Numpy array
array([[3, 0, 0],
[0, 1, 2],
[2, 1, 3]])
但是您也可以使用Pandas创建混淆矩阵:
But you can also create a confusion matrix using Pandas:
import pandas as pd
y_actu = pd.Series([2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2], name='Actual')
y_pred = pd.Series([0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2], name='Predicted')
df_confusion = pd.crosstab(y_actu, y_pred)
您将获得一个(贴有标签的)Pandas DataFrame:
You will get a (nicely labeled) Pandas DataFrame:
Predicted 0 1 2
Actual
0 3 0 0
1 0 1 2
2 2 1 3
如果您添加margins=True喜欢
df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)
您还将获得每一行和每一列的总和:
you will get also sum for each row and column:
Predicted 0 1 2 All
Actual
0 3 0 0 3
1 0 1 2 3
2 2 1 3 6
All 5 2 5 12
您还可以使用以下方法获得归一化的混淆矩阵:
You can also get a normalized confusion matrix using:
df_conf_norm = df_confusion / df_confusion.sum(axis=1)
Predicted 0 1 2
Actual
0 1.000000 0.000000 0.000000
1 0.000000 0.333333 0.333333
2 0.666667 0.333333 0.500000
您可以使用
import matplotlib.pyplot as plt
def plot_confusion_matrix(df_confusion, title='Confusion matrix', cmap=plt.cm.gray_r):
plt.matshow(df_confusion, cmap=cmap) # imshow
#plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(df_confusion.columns))
plt.xticks(tick_marks, df_confusion.columns, rotation=45)
plt.yticks(tick_marks, df_confusion.index)
#plt.tight_layout()
plt.ylabel(df_confusion.index.name)
plt.xlabel(df_confusion.columns.name)
plot_confusion_matrix(df_confusion)
或使用以下方法绘制归一化混淆矩阵:
Or plot normalized confusion matrix using:
plot_confusion_matrix(df_conf_norm)
您可能对此项目也很感兴趣 https://github.com/pandas-ml/pandas-ml 及其Pip包 https://pypi.python.org/pypi/pandas_ml
You might also be interested by this project https://github.com/pandas-ml/pandas-ml and its Pip package https://pypi.python.org/pypi/pandas_ml
有了这个软件包，混乱矩阵可以被漂亮地打印，绘制. 您可以对混淆矩阵进行二值化处理，获取类统计信息，例如TP，TN，FP，FN，ACC，TPR，FPR，FNR，TNR(SPC)，LR +，LR-，DOR，PPV，FDR，FOR，NPV等统计
With this package confusion matrix can be pretty-printed, plot. You can binarize a confusion matrix, get class statistics such as TP, TN, FP, FN, ACC, TPR, FPR, FNR, TNR (SPC), LR+, LR-, DOR, PPV, FDR, FOR, NPV and some overall statistics
In [1]: from pandas_ml import ConfusionMatrix
In [2]: y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
In [3]: y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
In [4]: cm = ConfusionMatrix(y_actu, y_pred)
In [5]: cm.print_stats()
Confusion Matrix:
Predicted 0 1 2 __all__
Actual
0 3 0 0 3
1 0 1 2 3
2 2 1 3 6
__all__ 5 2 5 12
Overall Statistics:
Accuracy: 0.583333333333
95% CI: (0.27666968568210581, 0.84834777019156982)
No Information Rate: ToDo
P-Value [Acc > NIR]: 0.189264302376
Kappa: 0.354838709677
Mcnemar's Test P-Value: ToDo
Class Statistics:
Classes 0 1 2
Population 12 12 12
P: Condition positive 3 3 6
N: Condition negative 9 9 6
Test outcome positive 5 2 5
Test outcome negative 7 10 7
TP: True Positive 3 1 3
TN: True Negative 7 8 4
FP: False Positive 2 1 2
FN: False Negative 0 2 3
TPR: (Sensitivity, hit rate, recall) 1 0.3333333 0.5
TNR=SPC: (Specificity) 0.7777778 0.8888889 0.6666667
PPV: Pos Pred Value (Precision) 0.6 0.5 0.6
NPV: Neg Pred Value 1 0.8 0.5714286
FPR: False-out 0.2222222 0.1111111 0.3333333
FDR: False Discovery Rate 0.4 0.5 0.4
FNR: Miss Rate 0 0.6666667 0.5
ACC: Accuracy 0.8333333 0.75 0.5833333
F1 score 0.75 0.4 0.5454545
MCC: Matthews correlation coefficient 0.6831301 0.2581989 0.1690309
Informedness 0.7777778 0.2222222 0.1666667
Markedness 0.6 0.3 0.1714286
Prevalence 0.25 0.25 0.5
LR+: Positive likelihood ratio 4.5 3 1.5
LR-: Negative likelihood ratio 0 0.75 0.75
DOR: Diagnostic odds ratio inf 4 2
FOR: False omission rate 0 0.2 0.4285714
我注意到一个名为 PyCM 的有关Confusion Matrix的新Python库已经发布:也许您可以看看
I noticed that a new Python library about Confusion Matrix named PyCM is out: maybe you can have a look.
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