Problem using LIME with Keras

I train a model like so

model = Sequential() model.add(Dense(200, input_dim=11, kernel_initializer=‘normal’, activation=‘relu’)) model.add(Dropout(0.3)) model.add(Dense(200, activation=‘relu’)) model.add(Dropout(0.3)) model.add(Dense(200, activation=‘relu’)) model.add(Dropout(0.3)) model.add(Dense(1, activation=‘relu’)) model.compile(loss=‘mean_squared_error’, optimizer=‘adam’) # Fit the model model.fit(X_train, y_train, epochs=200, batch_size=512)`

Then I try to make a LIME prediction…

`import lime import lime.lime_tabular import pandas as pd

explainer = lime.lime_tabular.LimeTabularExplainer(df.as_matrix(), feature_names=df.columns, class_names=[‘Price’], verbose=True, mode=‘regression’)

exp = explainer.explain_instance(qc_reshape[0], model.predict, num_features=len(df.columns))

exp.show_in_notebook(show_table=True)

exp.as_list()`

After that I get the error…

`AssertionError Traceback (most recent call last) /anaconda3/envs/LIME/lib/python3.6/site-packages/lime/lime_tabular.py in explain_instance(self, data_row, predict_fn, labels, top_labels, num_features, num_samples, distance_metric, model_regressor) 300 try: –> 301 assert isinstance(yss, np.ndarray) and len(yss.shape) == 1 302 except AssertionError:

AssertionError:

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) <ipython-input-153-d3c739459480> in <module>() 8 verbose=True, mode=‘regression’) 9 —> 10 exp = explainer.explain_instance(qc_reshape[0], model.predict, num_features=len(df.columns)) 11 12 exp.show_in_notebook(show_table=True)

/anaconda3/envs/LIME/lib/python3.6/site-packages/lime/lime_tabular.py in explain_instance(self, data_row, predict_fn, labels, top_labels, num_features, num_samples, distance_metric, model_regressor) 302 except AssertionError: 303 raise ValueError(“Your model needs to output single-dimensional
–> 304 numpyarrays, not arrays of {} dimensions”.format(yss.shape)) 305 306 predicted_value = yss[0]

ValueError: Your model needs to output single-dimensional numpyarrays, not arrays of (5000, 1) dimensions `

Is there any way to make Keras output single dimensional arrays? I’ve looked around for a while and no luck 😦

Thanks in advance!

[EoinKenny]

Sure thing!

You might have to adjust one or two things though, I haven’t tested this pipeline specifically.

model = load_model('keras_model.h5')

# Previously loaded pandas df
qc = df.as_matrix()[1]
qc_reshape = qc.reshape(1,-1)

def predict(qc):
    global model
    qc = model.predict(qc)
    return qc.reshape(qc.shape[0])

import lime
import lime.lime_tabular
import pandas as pd

explainer = lime.lime_tabular.LimeTabularExplainer(df.as_matrix(), 
                                                   feature_names=df.columns, 
                                                   class_names=['Price'], 
                                                   verbose = True,
                                                   mode='regression')

exp = explainer.explain_instance(qc, predict, num_features=len(df.columns))

[hanzigs]

Hi @EoinKenny, I am linking to Lime issue https://github.com/marcotcr/lime/issues/376 I tried your solution

Keras_model.predict_proba(testData_for_model)
Out[73]: array([[0.6559619]], dtype=float32)

def predictKeras(testData_for_model):
    prediction_Class_1 = Keras_model.predict_proba(testData_for_model) 
    x = numpy.zeros((prediction_Class_1.shape[0], 1))
    probability = (x + 1) - prediction_Class_1
    final = numpy.append(probability,prediction_Class_1, axis=1)
    return final

The output of final is

final
Out[71]: array([[0.3440381‬, 0.6559619]])

Then I call

keras_explainer = lime.lime_tabular.LimeTabularExplainer(input_x, 
                                                 mode='classification',
                                                 feature_names=feature_names,
                                                 kernel_width=5,
                                                 random_state=42,
                                                 discretize_continuous=False) 
test_for_explainer = testData_for_model.reshape(testData_for_model.shape[1],)
exp = keras_explainer.explain_instance(test_for_explainer, predictKeras, num_features = 10)

Its Working Good

One question is, keras returns probability for Class 1, how should I mention class names in explainer, as exp.class_names return 0

exp.class_names
Out[85]: ['0']