How to use a 1D-CNN model in Lime?

Question

I have a numeric health record dataset. I used a 1D CNN keras model for the classification step.

I am giving a reproductible example in Python:

import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Conv1D, Activation, Flatten, Dense
import numpy as np

a = np.array([[0,1,2,9,3], [0,5,1,33,6], [1, 12,1,8,9]])
train = np.reshape(a[:,1:],(a[:,1:].shape[0], a[:,1:].shape[1],1))
y_train = keras.utils.to_categorical(a[:,:1])

model = Sequential()
model.add(Conv1D(filters=2, kernel_size=2, strides=1, activation='relu', padding="same", input_shape=(train.shape[1], 1), kernel_initializer='he_normal'))

model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))

model.compile(loss=keras.losses.binary_crossentropy,
                 optimizer=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),
                 metrics=['accuracy'])

model.fit(train, y_train, epochs=3, verbose=1)

I am getting this error when I apply lime to my 1D CNN model

IndexError: boolean index did not match indexed array along dimension 1; dimension is 4 but corresponding boolean dimension is 1

import lime
import lime.lime_tabular

explainer = lime.lime_tabular.LimeTabularExplainer(train)

Is there a solution ?

Answer 1

I did some minor changes to your initial code (changed from keras to tensorflow.keras)

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv1D, Activation, Flatten, Dense
import numpy as np

a = np.array([[0,1,2,9,3], [0,5,1,33,6], [1, 12,1,8,9]])
train = np.reshape(a[:,1:],(a[:,1:].shape[0], a[:,1:].shape[1],1))
y_train = tf.keras.utils.to_categorical(a[:,:1])

model = Sequential()
model.add(Conv1D(filters=2, kernel_size=2, strides=1, activation='relu', 
     padding="same", input_shape=(train.shape[1], 1), 
kernel_initializer='he_normal'))

model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))

model.compile(loss=tf.keras.losses.binary_crossentropy,
             optimizer=tf.keras.optimizers.Adam(lr=0.001, beta_1=0.9, 
             beta_2=0.999, amsgrad=False),
             metrics=['accuracy'])

model.fit(train, y_train, epochs=3, verbose=1)

Then I added some test data because you don't want to train and test your LIME model on the same data

b = np.array([[1,4,5,3,2], [1,4,2,55,1], [7, 3,22,3,10]])
test = np.reshape(b[:,1:],(b[:,1:].shape[0], b[:,1:].shape[1],1))

Here I show how the RecurrentTabularExplainer can be trained

import lime
from lime import lime_tabular

explainer = lime_tabular.RecurrentTabularExplainer(train,training_labels=y_train, feature_names=["random clf"],
                                               discretize_continuous=False, feature_selection='auto', class_names=['class 1','class 2'])

Then you can run your LIME model on one of the examples in your test data:

exp = explainer.explain_instance(np.expand_dims(test[0],axis=0), model.predict, num_features=10)

and finally display the predictions

exp.show_in_notebook()

or just printing the prediction

print(exp.as_list())

Answer 2

You should try lime_tabular.RecurrentTabularExplainer instead of LimeTabularExplainer. It is an explainer for keras-style recurrent neural networks. Check out the examples in LIME documentation for better understanding. Good luck:)