SHAP TreeExplainer for RandomForest multiclass: what is shap_values[i]?

Question

I am trying to plot SHAP This is my code rnd_clf is a RandomForestClassifier:

import shap 
explainer = shap.TreeExplainer(rnd_clf) 
shap_values = explainer.shap_values(X) 
shap.summary_plot(shap_values[1], X) 

I understand that shap_values[0] is negative and shap_values[1] is positive.

But what about for multiple class RandomForestClassifier? I have the rnd_clf classifying one of:

['Gusto','Kestrel 200 SCI Older Road Bike', 'Vilano Aluminum Road Bike 21 Speed Shimano', 'Fixie'].

How do I determine which index of shap_values[i] corresponds to which class of my output?

Answer 1

How do I determine which index of shap_values[i] corresponds to which class of my output?

shap_values[i] are SHAP values for i'th class. What is an i'th class is more a question of an encoding schema you use: LabelEncoder, pd.factorize, etc.

You may try the following as a clue:

from sklearn.preprocessing import LabelEncoder

labels = [
    "Gusto",
    "Kestrel 200 SCI Older Road Bike",
    "Vilano Aluminum Road Bike 21 Speed Shimano",
    "Fixie",
]
le = LabelEncoder()
y = le.fit_transform(labels)
encoding_scheme = dict(zip(y, labels))
pprint(encoding_scheme)

---

{0: 'Fixie',
 1: 'Gusto',
 2: 'Kestrel 200 SCI Older Road Bike',
 3: 'Vilano Aluminum Road Bike 21 Speed Shimano'}

So, eg shap_values[3] for this particular case is for 'Vilano Aluminum Road Bike 21 Speed Shimano'

To further understand how to interpret SHAP values let's prepare a synthetic dataset for multiclass classification with 100 features and 10 classes:

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from shap import TreeExplainer
from shap import summary_plot

X, y = make_classification(1000, 100, n_informative=8, n_classes=10)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
print(X_train.shape)

---

(750, 100)

At this point we have train dataset with 750 rows, 100 features, and 10 classes.

Let's train RandomForestClassifier and feed it to TreeExplainer:

clf = RandomForestClassifier(n_estimators=100, max_depth=3)
clf.fit(X_train, y_train)
explainer = TreeExplainer(clf)
shap_values = np.array(explainer.shap_values(X_train))
print(shap_values.shape)

---

(10, 750, 100)

10 : number of classes. All SHAP values are organized into 10 arrays, 1 array per class.
750 : number of datapoints. We have local SHAP values per datapoint.
100 : number of features. We have SHAP value per every feature.

For example, for Class 3 you'll have:

print(shap_values[3].shape)

---

(750, 100)

750: SHAP values for every datapoint
100: SHAP value contributions for every feature

Finally, you can run a sanity check to make it sure real predictions from model are the same as those predicted by shap.

To do so, we'll (1) swap the first 2 dimensions of shap_values, (2) sum up SHAP values per class for all features, (3) add SHAP values to base values:

shap_values_ = shap_values.transpose((1,0,2))

np.allclose(
    clf.predict_proba(X_train),
    shap_values_.sum(2) + explainer.expected_value
)

---

True

Then you may proceed to summary_plot that will show feature rankings based on SHAP values on a per class basis. For class 3 this will be:

summary_plot(shap_values[3],X_train)

Which is interpreted as follows:

• For class 3 most influential features based on SHAP contributions are 44, 64, 17

• For features 64 and 17 lower values tend to result in higher SHAP values (hence higher probability of the class label)

• Features 92, 6, 53 are least influential out of 20 displayed