matplotlib - How to customize matplotlib plots using gcf() or gca()?

Question

I am using a package called shap which has a integrated plot function. However i want to adjust some things like the labels, legend, coloring, size etc.

apparently due to the developer thats possible via using plt.gcf().

I call the plot like this, this will give a figure object but i am not sure how to use it:

fig = shap.summary_plot(shap_values_DT, data_train,color=plt.get_cmap("tab10"), show=False)
ax = plt.subplot()

UPDATE / SOLUTION Finally i got everything adjusted as i wanted it by doing the following:

shap.summary_plot(shap_values_DT, data_train, color=plt.get_cmap("tab10"), show=False)
fig = plt.gcf()
fig.set_figheight(12)
fig.set_figwidth(14)
ax = plt.gca()
ax.set_xlabel(r'durchschnittliche SHAP Werte $\vert\sigma_{ij}\vert$', fontsize=16)
ax.set_ylabel('Inputparameter', fontsize=16)
ylabels = string_latexer([tick.get_text() for tick in ax.get_yticklabels()])
ax.set_yticklabels(ylabels)
leg = ax.legend()
for l in leg.get_texts(): l.set_text(l.get_text().replace('Class', 'Klasse'))
plt.show()

Answer 1

最后，我通过执行以下操作将所有内容调整为我想要的：

shap.summary_plot(shap_values_DT, data_train, color=plt.get_cmap("tab10"), show=False)
fig = plt.gcf()
fig.set_figheight(12)
fig.set_figwidth(14)
ax = plt.gca()
ax.set_xlabel(r'durchschnittliche SHAP Werte $\vert\sigma_{ij}\vert$', fontsize=16)
ax.set_ylabel('Inputparameter', fontsize=16)
ylabels = string_latexer([tick.get_text() for tick in ax.get_yticklabels()])
ax.set_yticklabels(ylabels)
leg = ax.legend()
for l in leg.get_texts(): l.set_text(l.get_text().replace('Class', 'Klasse'))
plt.show()

Answer 2

我还没用过shap，但也许你可以通过以下方式修改：

shap.summary_plot(shap_values_DT, data_train,color=plt.get_cmap("tab10"), show=False)
plt.title('my custom title')
plt.savefig('test.png')

更新 从官方文档，我读到

import xgboost
import shap

# load JS visualization code to notebook
shap.initjs()

# train XGBoost model
X,y = shap.datasets.boston()
model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)

# explain the model's predictions using SHAP values
# (same syntax works for LightGBM, CatBoost, and scikit-learn models)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)

# visualize the first prediction's explanation (use matplotlib=True to avoid Javascript)
shap.force_plot(explainer.expected_value, shap_values[0,:], X.iloc[0,:])

我很快尝试了这个例子，如果你添加matplotlib=True选项，它似乎可以工作。然而，并非所有功能似乎都支持它......