Plotly: How to make an annotated confusion matrix using a heatmap?

Question

I like to use Plotly to visualize everything, I'm trying to visualize a confusion matrix by Plotly, this is my code:

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()

and the result is

How can I show the number inside corresponding cell instead of hovering, like this

Answer 1

My code:

import plotly.express as px
from sklearn.metrics import confusion_matrix

def display_confusion_matrix(y_true, y_pred, labels=[0, 1]):
    cm = confusion_matrix(y_true, y_pred)
    
    dims = [str(l) for l in labels]

    fig = px.imshow(cm, 
                    x=dims, 
                    y=dims, 
                    color_continuous_scale='Reds', 
                    aspect="auto")

    fig.update_traces(text=cm, texttemplate="%{text}")

    fig.update_layout(title="Confusion matrix",
                      xaxis_title='Predicted',
                      yaxis_title='True',
                      dragmode='select', 
                      width=500, 
                      height=500, 
                      hovermode='closest',
                      template='seaborn')
    fig.show()

Answer 2

I found @vestland's strategy to be the most useful.

However, unlike a traditional confusion matrix, the correct model predictions are along the upper-right diagonal, not the upper-left.

This can easily be fixed by inverting all index values of the confusion matrix such as shown below:

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]]

# invert z idx values
z = z[::-1]

x = ['healthy', 'multiple diseases', 'rust', 'scab']
y =  x[::-1].copy() # invert idx values of x

# 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()

Answer 3

You can use annotated heatmaps with ff.create_annotated_heatmap() to get this:

Complete code:

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()

Answer 4

As @vestland say you can annotate figure with plotly. The heatmap works as any kind of plotly Figure. Here's a code for plotting heatmap from a confusion matrix (basically just a 2-d vector with numbers).

def plot_confusion_matrix(cm, labels, title):
# cm : confusion matrix list(list)
# labels : name of the data list(str)
# title : title for the heatmap
data = go.Heatmap(z=cm, y=labels, x=labels)
annotations = []
for i, row in enumerate(cm):
    for j, value in enumerate(row):
        annotations.append(
            {
                "x": labels[i],
                "y": labels[j],
                "font": {"color": "white"},
                "text": str(value),
                "xref": "x1",
                "yref": "y1",
                "showarrow": False
            }
        )
layout = {
    "title": title,
    "xaxis": {"title": "Predicted value"},
    "yaxis": {"title": "Real value"},
    "annotations": annotations
}
fig = go.Figure(data=data, layout=layout)
return fig