我喜欢使用Plotly来可视化所有内容,我正在尝试通过Plotly来可视化混淆矩阵,这是我的代码:
def plot_confusion_matrix(y_true, y_pred, class_names):
confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
confusion_matrix = confusion_matrix.astype(int)
layout = {
"title": "Confusion Matrix",
"xaxis": {"title": "Predicted value"},
"yaxis": {"title": "Real value"}
}
fig = go.Figure(data=go.Heatmap(z=confusion_matrix,
x=class_names,
y=class_names,
hoverongaps=False),
layout=layout)
fig.show()
结果是
您可以使用带注释的热图ff.create_annotated_heatmap()
来获得此信息:
完整的代码:
import plotly.figure_factory as ff
z = [[0.1, 0.3, 0.5, 0.2],
[1.0, 0.8, 0.6, 0.1],
[0.1, 0.3, 0.6, 0.9],
[0.6, 0.4, 0.2, 0.2]]
x = ['healthy', 'multiple diseases', 'rust', 'scab']
y = ['healthy', 'multiple diseases', 'rust', 'scab']
# change each element of z to type string for annotations
z_text = [[str(y) for y in x] for x in z]
# set up figure
fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, colorscale='Viridis')
# add title
fig.update_layout(title_text='<i><b>Confusion matrix</b></i>',
#xaxis = dict(title='x'),
#yaxis = dict(title='x')
)
# add custom xaxis title
fig.add_annotation(dict(font=dict(color="black",size=14),
x=0.5,
y=-0.15,
showarrow=False,
text="Predicted value",
xref="paper",
yref="paper"))
# add custom yaxis title
fig.add_annotation(dict(font=dict(color="black",size=14),
x=-0.35,
y=0.5,
showarrow=False,
text="Real value",
textangle=-90,
xref="paper",
yref="paper"))
# adjust margins to make room for yaxis title
fig.update_layout(margin=dict(t=50, l=200))
# add colorbar
fig['data'][0]['showscale'] = True
fig.show()
@ClementViricel函数是
ff.create_annotated_heatmaps()
。在代码片段中。并且代码段是完全可复制的。自己动手。好吧,我确实尝试过了,而且行得通。它只是用于创建注释的for循环。我的错。
我只是认为,对于新手来说,提供诸如def plot ..这样的简单代码并解释其实际作用可能会更清楚。
@ClementViricel好的。我在答案的开头包含了ff.create_annotated_heatmaps(),以使所有不阅读代码段的人都可以清楚地知道如何解决该问题。您愿意收回您的否决票吗?毕竟,很久以前,该建议已被OP标记为可接受的答案