[英]Plot confusion matrix sklearn with multiple labels
I am plotting a confusion matrix for a multiple labelled data, where labels look like:我正在为多标签数据绘制混淆矩阵,其中标签如下所示:
label1: 1, 0, 0, 0
标签 1: 1, 0, 0, 0
label2: 0, 1, 0, 0
标签 2: 0, 1, 0, 0
label3: 0, 0, 1, 0
标签 3: 0, 0, 1, 0
label4: 0, 0, 0, 1
标签 4: 0, 0, 0, 1
I am able to classify successfully using the below code.我能够使用以下代码成功分类。 I only need some help to plot confusion matrix.
我只需要一些帮助来绘制混淆矩阵。
for i in range(4):
y_train= y[:,i]
print('Train subject %d, class %s' % (subject, cols[i]))
lr.fit(X_train[::sample,:],y_train[::sample])
pred[:,i] = lr.predict_proba(X_test)[:,1]
I used the following code to print confusion matrix, but it always return a 2X2 matrix我使用以下代码打印混淆矩阵,但它总是返回一个 2X2 矩阵
prediction = lr.predict(X_train)
print(confusion_matrix(y_train, prediction))
I found a function that can plot the confusion matrix which generated from sklearn
.我找到了一个可以绘制从
sklearn
生成的混淆矩阵的sklearn
。
import numpy as np
def plot_confusion_matrix(cm,
target_names,
title='Confusion matrix',
cmap=None,
normalize=True):
"""
given a sklearn confusion matrix (cm), make a nice plot
Arguments
---------
cm: confusion matrix from sklearn.metrics.confusion_matrix
target_names: given classification classes such as [0, 1, 2]
the class names, for example: ['high', 'medium', 'low']
title: the text to display at the top of the matrix
cmap: the gradient of the values displayed from matplotlib.pyplot.cm
see http://matplotlib.org/examples/color/colormaps_reference.html
plt.get_cmap('jet') or plt.cm.Blues
normalize: If False, plot the raw numbers
If True, plot the proportions
Usage
-----
plot_confusion_matrix(cm = cm, # confusion matrix created by
# sklearn.metrics.confusion_matrix
normalize = True, # show proportions
target_names = y_labels_vals, # list of names of the classes
title = best_estimator_name) # title of graph
Citiation
---------
http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
"""
import matplotlib.pyplot as plt
import numpy as np
import itertools
accuracy = np.trace(cm) / float(np.sum(cm))
misclass = 1 - accuracy
if cmap is None:
cmap = plt.get_cmap('Blues')
plt.figure(figsize=(8, 6))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
if target_names is not None:
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=45)
plt.yticks(tick_marks, target_names)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 1.5 if normalize else cm.max() / 2
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
if normalize:
plt.text(j, i, "{:0.4f}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
else:
plt.text(j, i, "{:,}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
plt.show()
This works the best for me :这对我来说最有效:
from sklearn.metrics import multilabel_confusion_matrix
y_unique = y_test.unique()
mcm = multilabel_confusion_matrix(y_test, y_pred, labels = y_unique)
mcm
I see this is still an open issue in sklearn
's repository:我看到这仍然是
sklearn
存储库中的一个悬而未决的问题:
https://github.com/scikit-learn/scikit-learn/issues/3452 https://github.com/scikit-learn/scikit-learn/issues/3452
However there have been some attempts at implementing it.然而,已经有一些实施它的尝试。 From the same #3452 thread issue:
来自相同的 #3452 线程问题:
https://github.com/Magellanea/scikit-learn/commit/514287c1d5dad2f0ab4918dc4da5cf7053fe6734#diff-b04acd877dd793f28ae7be13a999ed88R187 https://github.com/Magellanea/scikit-learn/commit/514287c1d5dad2f0ab4918dc4da5cf7053fe6734#diff-b04acd877dd793f28ae7be13a999ed88R187
You can check the code proposed in the function and see if that fits your needs.您可以检查函数中建议的代码,看看它是否符合您的需要。
from sklearn.metrics import multilabel_confusion_matrix
mul_c = multilabel_confusion_matrix(
test_Y,
pred_k,
labels=["benign", "dos","probe","r2l","u2r"])
mul_c
I found an easy solution with sklearn and seaborn libraries.我用 sklearn 和 seaborn 库找到了一个简单的解决方案。
from sklearn.metrics import confusion_matrix, classification_report
from matplotlib import pyplot as plt
import seaborn as sns
def plot_confusion_matrix(y_test,y_scores, classNames):
y_test=np.argmax(y_test, axis=1)
y_scores=np.argmax(y_scores, axis=1)
classes = len(classNames)
cm = confusion_matrix(y_test, y_scores)
print("**** Confusion Matrix ****")
print(cm)
print("**** Classification Report ****")
print(classification_report(y_test, y_scores, target_names=classNames))
con = np.zeros((classes,classes))
for x in range(classes):
for y in range(classes):
con[x,y] = cm[x,y]/np.sum(cm[x,:])
plt.figure(figsize=(40,40))
sns.set(font_scale=3.0) # for label size
df = sns.heatmap(con, annot=True,fmt='.2', cmap='Blues',xticklabels= classNames , yticklabels= classNames)
df.figure.savefig("image2.png")
classNames = ['A', 'B', 'C', 'D', 'E']
plot_confusion_matrix(y_test,y_scores, classNames)
#y_test is your ground truth
#y_scores is your predicted probabilities
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