Feature importance in deep learning

Deep Learning
64

FYI, here’s how I got at least the model-agnostic KernelExplainer from shap to run in a notebook without errors on a tabular learner (model/data on gpu with both categorical and continuous variables):

# learn = tabular_learner(...)
# learn.fit_one_cycle(...)

import numpy as np
import pandas as pd
import shap
shap.initjs()

def pred(data):
    device = learn.data.device
    cat_cols = len(learn.data.train_ds.x.cat_names)
    cont_cols = len(learn.data.train_ds.x.cont_names)
    x_cat = torch.from_numpy(data[:, :cat_cols]).to(device, torch.int64)
    x_cont = torch.from_numpy(data[:, -cont_cols:]).to(device, torch.float32)
    pred_proba = learn.model(x_cat, x_cont).detach().to('cpu').numpy()
    return pred_proba

def shap_data(data):    
    X_train, y_train = data.one_batch(denorm=False, cpu=False)
    X_test, y_test = data.one_batch(denorm=False, cpu=False)
    cols = data.train_ds.x.col_names
    X_train = pd.DataFrame(np.concatenate([v.to('cpu').numpy() for v in X_train], axis=1), columns=cols)
    X_test = pd.DataFrame(np.concatenate([v.to('cpu').numpy() for v in X_test], axis=1), columns=cols)
    return X_train, X_test

X_train, X_test = shap_data(learn.data)
e = shap.KernelExplainer(pred, X_train)
shap_values = e.shap_values(X_test, nsamples=100, l1_reg=False)
shap.force_plot(e.expected_value[0], shap_values[0], X_test)

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

This grabs two batches from the training set as X_train and X_test for shap.

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