'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)
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)
Here is an example of a proper looking color bar.
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:
plt.gcf().axes[-1].set_aspect(100)
plt.gcf().axes[-1].set_box_aspect(100)
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:
plt.gcf().axes[-1].set_aspect('auto')
plt.tight_layout()
# As mentioned, smaller "box_aspect" value to make colorbar thicker
plt.gcf().axes[-1].set_box_aspect(50)
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)
plt.colorbar()
plt.show()
Sources
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 |