'Shap - The color bar is not displayed in the summary plot

When displaying summary_plot, the color bar does not show.

shap.summary_plot(shap_values, X_train)

Summary plot

I have tried changing plot_size. When the plot is higher the color bar appears, but it is very small - doesn't look like it should.

shap.summary_plot(shap_values, X_train, plot_size=0.7)

Higher summary plot

Here is an example of a proper looking color bar. Proper summary plot

Does anyone know if this can be fixed somehow?

How to reproduce:

import pandas as pd
import shap
import sklearn
from sklearn.ensemble import RandomForestRegressor

# a classic housing price dataset
X,y = shap.datasets.boston()

# a simple linear model
model = RandomForestRegressor(max_depth=6, random_state=0, n_estimators=10)
model.fit(X, y)
shap_values = shap.TreeExplainer(model).shap_values(X)
shap.summary_plot(shap_values, X)

In this case, the color bar is displayed, but it is very small. I have chosen such an example to make it easy to retrieve the data.

Solution 1:[1]

I had the same problem as you did, and I found that the solution was to downgrade matplotlib to 3.4.3.. It appears SHAP isn't optimized for matplotlib 3.5.1 yet.

Solution 2:[2]

I had a similar issue and I was using max_display=10. The below code solved my problem:


Solution 3:[3]

As mentioned above, it seems that the handling of colorbar or box aspect ratio has been changed in matplotlib.pyplot version 3.5. However, you can correct that.

  • Use shap.summary_plot(..., show=False) to allow altering the plot
  • As mentioned above, set the aspect of the colorbar with plt.gcf().axes[-1].set_aspect(1000)
  • Then set also the aspect of the color bar's box plt.gcf().axes[-1].set_box_aspect(1000)

This gives you the old result back. If you want to make the colorbar thicker, set the aspect to 100.

Solution 4:[4]

just use 'auto' aspect to do the trick:

# As mentioned, smaller "box_aspect" value to make colorbar thicker

shap version 0.40.0; matplotlib version 3.5.1

Solution 5:[5]

Calling plt.colorbar() explicitly will do:

import xgboost
import shap

X, y = shap.datasets.adult()
model = xgboost.XGBClassifier().fit(X, y)

explainer = shap.Explainer(model, X)
shap_values = explainer(X)

shap.plots.beeswarm(shap_values, show=False, color_bar=False)

enter image description here


This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution Source
Solution 1 ethan tenison
Solution 2 Tushar Tyagi
Solution 3 NegatedObjectIdentity
Solution 4 HairyCat
Solution 5