CODEWITHSUNDEEP
pythondataframemachine-learningclassificationshap
The Great
The Great

Reputation: 7743

SHAP plotting waterfall using an index value in dataframe

I am working on a binary classification using random forest algorithm

Currently, am trying to explain the model predictions using SHAP values.

So, I referred this useful post here and tried the below.

from shap import TreeExplainer, Explanation
from shap.plots import waterfall
sv = explainer(ord_test_t)
exp = Explanation(sv.values[:,:,1], 
                  sv.base_values[:,1], 
                  data=ord_test_t.values, 
                  feature_names=ord_test_t.columns)
idx = 20
waterfall(exp[idx])

I like the above approach as it allows to display the feature values along with waterfall plot. So, I wish to use this approach

However, this doesn't help me get the waterfall for a specific row in ord_test_t (test data).

For example, let's consider that ord_test_t.Index.tolist() returns 3,5,8,9 etc...

Now, I want to plot the waterfall plot for ord_test_t.iloc[[9]] but when I pass exp[9], it just gets the 9th row but not the index named as 9.

When I try exp.iloc[[9]] it throws error as explanation object doesnt have iloc.

Can help me with this please?

Upvotes: 2

Views: 1853

Answers (1)

Sergey Bushmanov
Sergey Bushmanov

Reputation: 25249

My suggestion is as following:

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from shap import TreeExplainer, Explanation
from shap.plots import waterfall

import shap

print(shap.__version__)

X, y = load_breast_cancer(return_X_y=True, as_frame=True)

idx = 9
model = RandomForestClassifier(max_depth=5, n_estimators=100).fit(X, y)
explainer = TreeExplainer(model)
sv = explainer(X.loc[[idx]])    # corrected, pass the row of interest as df
exp = Explanation(
    sv.values[:, :, 1],         # class to explain
    sv.base_values[:, 1],
    data=X.loc[[idx]].values,   # corrected, pass the row of interest as df
    feature_names=X.columns,
)
waterfall(exp[0])               # pretend you have only 1 data point which is 0th 

0.40.0

enter image description here

Proof:

model.predict_proba(X.loc[[idx]]) # corrected

array([[0.95752656, 0.04247344]])

Upvotes: 1

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