无依赖的多类混淆矩阵
# A Simple Confusion Matrix Implementation
def confusionmatrix(actual, predicted, normalize = False):
"""
Generate a confusion matrix for multiple classification
@params:
actual - a list of integers or strings for known classes
predicted - a list of integers or strings for predicted classes
normalize - optional boolean for matrix normalization
@return:
matrix - a 2-dimensional list of pairwise counts
"""
unique = sorted(set(actual))
matrix = [[0 for _ in unique] for _ in unique]
imap = {key: i for i, key in enumerate(unique)}
# Generate Confusion Matrix
for p, a in zip(predicted, actual):
matrix[imap[p]][imap[a]] += 1
# Matrix Normalization
if normalize:
sigma = sum([sum(matrix[imap[i]]) for i in unique])
matrix = [row for row in map(lambda i: list(map(lambda j: j / sigma, i)), matrix)]
return matrix
这里的方法是将actual 向量中的唯一类配对成一个二维列表。从那里，我们只需遍历压缩的 actual 和 predicted 向量并使用索引填充计数以访问矩阵位置。
用法
cm = confusionmatrix(
[1, 1, 2, 0, 1, 1, 2, 0, 0, 1], # actual
[0, 1, 1, 0, 2, 1, 2, 2, 0, 2] # predicted
)
# And The Output
print(cm)
[[2, 1, 0], [0, 2, 1], [1, 2, 1]]
注意：actual 类位于列中，predicted 类位于行中。
# Actual
# 0 1 2
# # #
[[2, 1, 0], # 0
[0, 2, 1], # 1 Predicted
[1, 2, 1]] # 2
类名可以是字符串或整数
cm = confusionmatrix(
["B", "B", "C", "A", "B", "B", "C", "A", "A", "B"], # actual
["A", "B", "B", "A", "C", "B", "C", "C", "A", "C"] # predicted
)
# And The Output
print(cm)
[[2, 1, 0], [0, 2, 1], [1, 2, 1]]
您还可以返回具有比例的矩阵（归一化）
cm = confusionmatrix(
["B", "B", "C", "A", "B", "B", "C", "A", "A", "B"], # actual
["A", "B", "B", "A", "C", "B", "C", "C", "A", "C"], # predicted
normalize = True
)
# And The Output
print(cm)
[[0.2, 0.1, 0.0], [0.0, 0.2, 0.1], [0.1, 0.2, 0.1]]
更强大的解决方案
自从写这篇文章以来，我已经将我的库实现更新为一个在内部使用混淆矩阵表示来计算统计数据的类，此外还可以漂亮地打印混淆矩阵本身。看到这个Gist。
示例用法
# Actual & Predicted Classes
actual = ["A", "B", "C", "C", "B", "C", "C", "B", "A", "A", "B", "A", "B", "C", "A", "B", "C"]
predicted = ["A", "B", "B", "C", "A", "C", "A", "B", "C", "A", "B", "B", "B", "C", "A", "A", "C"]
# Initialize Performance Class
performance = Performance(actual, predicted)
# Print Confusion Matrix
performance.tabulate()
输出：
===================================
Aᴬ Bᴬ Cᴬ
Aᴾ 3 2 1
Bᴾ 1 4 1
Cᴾ 1 0 4
Note: classᴾ = Predicted, classᴬ = Actual
===================================
对于归一化矩阵：
# Print Normalized Confusion Matrix
performance.tabulate(normalized = True)
使用标准化输出：
===================================
Aᴬ Bᴬ Cᴬ
Aᴾ 17.65% 11.76% 5.88%
Bᴾ 5.88% 23.53% 5.88%
Cᴾ 5.88% 0.00% 23.53%
Note: classᴾ = Predicted, classᴬ = Actual
===================================