Is it possible to do incremental training on LimeTabularExplainer?

Hi, I have a data, I fit a model, store the model, later I get new data, I don’t want to retrain with full data, so I fit the new data, may I know is it possible to create explainer as a incremental fit for the new data.

data = [[1, 2], [0.5, 6], [0, 10], [1, 18]]
scaler = MinMaxScaler()
scaler.partial_fit(data)
sc_data = scaler.transform(data)
model1 = IForest(contamination=0.1).fit(sc_data)
explainer = lime.lime_tabular.LimeTabularExplainer(sc_data, 
                                                      mode='classification',
                                                      feature_names=feature_names,
                                                      kernel_width=5,
                                                      random_state=42,
                                                      discretize_continuous=False)

I store the model, scaler, explainer for serving purpose, after some time i get more data, so fit the new data to the same model, is it possible for explainer as well?

data2 = [[15, 12], [15, 16], [0, 11], [1, 18]]
scaler = load(scaler)
loaded_model = load(model1)

scaler.partial_fit(data2)
sc_data2 = scaler.transform(data2)
model2 = loaded_model.fit(sc_data2)
explainer = lime.lime_tabular.LimeTabularExplainer(????????????????)

Thanks in advance for the inputs.

[marcotcr]

You will have to have a separate object keeping track of the running averages for feature frequencies. I assume you’re not updating the discretizer every time, so everything else should stay the same. What you can do is apply the discretizer from the original explainer on the new data and update the frequencies.

[marcotcr]

You can use the training_data_stats parameter (description here). Best,