Python 3.x 如何绘制多类数据的ROC曲线并从混淆矩阵中测量MAUC
我在一个数据集上使用了反向传播,该数据集有3个类:L、B、R。在制作神经网络之后,我还制作了一个混淆矩阵 实际类数组:Python 3.x 如何绘制多类数据的ROC曲线并从混淆矩阵中测量MAUC,python-3.x,machine-learning,roc,confusion-matrix,Python 3.x,Machine Learning,Roc,Confusion Matrix,我在一个数据集上使用了反向传播,该数据集有3个类:L、B、R。在制作神经网络之后,我还制作了一个混淆矩阵 实际类数组: sample_test = array([0, 1, 0, 2, 0, 2, 1, 1, 0, 1, 1, 1], dtype=int64) 预测类数组: yp = array([0, 1, 0, 2, 0, 2, 0, 1, 0, 1, 1, 1], dtype=int64) 混淆矩阵代码: from sklearn import svm, datasets from s
sample_test = array([0, 1, 0, 2, 0, 2, 1, 1, 0, 1, 1, 1], dtype=int64)
预测类数组:
yp = array([0, 1, 0, 2, 0, 2, 0, 1, 0, 1, 1, 1], dtype=int64)
混淆矩阵代码:
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
class_names = ['B','R','L']
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
classes = [0, 1, 2]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax
np.set_printoptions(precision=2)
# Plot non-normalized confusion matrix
plot_confusion_matrix(sample_test, yp, classes=class_names,
title='Confusion matrix, without normalization')
# Plot normalized confusion matrix
plot_confusion_matrix(sample_test, yp, classes=class_names , normalize=True,
title='Normalized confusion matrix')
plt.show()
输出:
现在我想画出这个的ROC曲线,并计算MAUC。我看到了这张照片,但不知道该怎么办
如果有人能为我提供一些建议,我将非常感激。提前感谢。ROC是按班级计算的-将每个班级视为“积极”班级,其他班级视为“消极”班级。注意-首先,您必须使用predict_proba()-获得每个类的预测概率。大概是这样的:
import seaborn as sns
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.metrics import roc_auc_score
iris = sns.load_dataset('iris')
X = iris.drop('species',axis=1)
y = iris['species']
X_train, X_test, y_train, y_test = train_test_split(X,y)
le = preprocessing.LabelEncoder()
le.fit(y_train)
le.transform(y_train)
model = DecisionTreeClassifier(max_depth=1)
model.fit(X_train,le.transform(y_train))
predictions =pd.DataFrame(model.predict_proba(X_test),columns=list(le.inverse_transform(model.classes_)))
print(roc_auc_score((y_test == 'versicolor').astype(float), predictions['versicolor']))
ROC按类别计算——将每个类别视为“积极”类别,其他类别视为“消极”类别。注意-首先,您必须使用predict_proba()-获得每个类的预测概率。大概是这样的:
import seaborn as sns
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.metrics import roc_auc_score
iris = sns.load_dataset('iris')
X = iris.drop('species',axis=1)
y = iris['species']
X_train, X_test, y_train, y_test = train_test_split(X,y)
le = preprocessing.LabelEncoder()
le.fit(y_train)
le.transform(y_train)
model = DecisionTreeClassifier(max_depth=1)
model.fit(X_train,le.transform(y_train))
predictions =pd.DataFrame(model.predict_proba(X_test),columns=list(le.inverse_transform(model.classes_)))
print(roc_auc_score((y_test == 'versicolor').astype(float), predictions['versicolor']))
此值适用于1类。我说得对吗?那么我如何才能得到其他类的auc值,以及如何组合它们来计算MAUC?你能给我提供一些文件吗?谢谢你的帮助。看(方程式3),你可以用公式组合成对的AUC。这个值适用于1类。我说得对吗?那么我如何才能得到其他类的auc值,以及如何组合它们来计算MAUC?你能给我提供一些文件吗?谢谢你的帮助。看(方程式3),你可以使用公式组合成对AUC