How to extract the actual values from the shap summary plot

Joe 0
Viewed 52

How to go about extracting the numerical values for the shap summary plot so that the data can be viewed in a dataframe?:

enter image description here

Here is a MWE:

from sklearn.datasets import make_classification
from shap import Explainer, waterfall_plot, Explanation
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Generate noisy Data
X, y = make_classification(n_samples=1000, 
                          n_features=50, 
                          n_informative=9, 
                          n_redundant=0, 
                          n_repeated=0, 
                          n_classes=10, 
                          n_clusters_per_class=1,
                          class_sep=9,
                          flip_y=0.2,
                          random_state=17)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

model = RandomForestClassifier()
model.fit(X_train, y_train)

explainer = Explainer(model)
sv = explainer.shap_values(X_test)

shap.summary_plot(shap_values, X_train, plot_type="bar")

I tried

np.abs(shap_values.values).mean(axis=0)

but I get a shape of (50,10). How do I get just the aggerated value for each feature to then sort for the feature importance?

shap python-3.x
1 Answers

You've done this:

from sklearn.datasets import make_classification
from shap import Explainer, waterfall_plot, Explanation
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from shap import summary_plot


# Generate noisy Data
X, y = make_classification(n_samples=1000, 
                          n_features=50, 
                          n_informative=9, 
                          n_redundant=0, 
                          n_repeated=0, 
                          n_classes=10, 
                          n_clusters_per_class=1,
                          class_sep=9,
                          flip_y=0.2,
                          random_state=17)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

model = RandomForestClassifier()
model.fit(X_train, y_train)

explainer = Explainer(model)
sv = explainer.shap_values(X_test)

summary_plot(sv, X_train, plot_type="bar")

enter image description here

Note, you have features 3, 29, 34 and so on at the top.

If you do:

np.abs(sv).shape

(10, 250, 50)

You'll find out you've got 10 classes for 250 datapoints for 50 features.

If you aggregate, you'll get everything you need:

aggs = np.abs(sv).mean(1)
aggs.shape

(10, 50)

You can draw it:

sv_df = pd.DataFrame(aggs.T)
sv_df.plot(kind="barh",stacked=True)

enter image description here

And if it still doesn't look familiar, you can rearrange and filter:

sv_df.loc[sv_df.sum(1).sort_values(ascending=True).index[-10:]].plot(kind="barh",stacked=True)

enter image description here

Conclusion:

sv_df are aggregated SHAP values, as in summary plot, arranged as features per row and classes per column.

Does it help?

Sergey Bushmanov0
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