Hi @<1787291173992271872:profile|BlandCormorant75> , how are you logging those plots? Can you provide a stand alone snippet that reproduces your behaviour?

This should work:

from clearml import Task

task = Task.init(project_name="examples", task_name="shap example")
import xgboost
import shap

# train an XGBoost model
X, y = shap.datasets.california()
model = xgboost.XGBRegressor().fit(X, y)

# explain the model's predictions using SHAP
# (same syntax works for LightGBM, CatBoost, scikit-learn, transformers, Spark, etc.)
explainer = shap.Explainer(model)
shap_values = explainer(X)

# visualize the first prediction's explanation
shap.plots.waterfall(shap_values[0])

Hey, good day and thank you for your quick replies! So this is the code snippet I was using to create the plots (see appended image). I also tried removing the plt.savefig() part or the plt.show() part or manually adding the report_matplotlib_figure-part for the task but nothing seems to make a difference.

"plt" comes from matplotlib.pyplot and as I understand the clearML documentation, matplotlib plots are logged automatically. In other scripts, this works just fine but not with these SHAP-plots that are just displayed as empty plots:

Hmm Could you check if it makes a difference importing ClearML before shap ?
If this changes nothing, could you put a standalone script to reproduce the issue ?

Hey Martin, unfortunately the adapted import order did not change anything. Thank you anyways 🙂 I will try to provide some standalone code so the issue can be reproduced

another though, see what happens if you remove the .save and .close and stay with the .show, maybe the close call somehow interfere's with it ?
Otherwise, if you can test one of the shaps examples and see maybe they fail in your setup that's another avenue to take for reproducing the issue