def __init__(self,
name: str,
predictor_host: str,
method: ExplainerMethod,
config: Mapping,
explainer: object = None,
protocol: Protocol = Protocol.seldon_grpc,
tf_data_type: str = None,
keras_model: keras.Model = None):
super().__init__(name)
self.predictor_host = predictor_host
logging.info("Predict URL set to %s", self.predictor_host)
self.method = method
self.protocol = protocol
self.tf_data_type = tf_data_type
logging.info("Protocol is %s", str(self.protocol))
# Add type for first value to help pass mypy type checks
if self.method is ExplainerMethod.anchor_tabular:
self.wrapper: ExplainerWrapper = AnchorTabular(
self._predict_fn, explainer, **config)
elif self.method is ExplainerMethod.anchor_images:
self.wrapper = AnchorImages(self._predict_fn, explainer, **config)
elif self.method is ExplainerMethod.anchor_text:
self.wrapper = AnchorText(self._predict_fn, explainer, **config)
elif self.method is ExplainerMethod.kernel_shap:
self.wrapper = KernelShap(self._predict_fn, explainer, **config)
elif self.method is ExplainerMethod.integrated_gradients:
self.wrapper = IntegratedGradients(keras_model, **config)
elif self.method is ExplainerMethod.tree_shap:
self.wrapper = TreeShap(explainer, **config)
else:
raise NotImplementedError
def test_integrated_gradients():
with tempfile.TemporaryDirectory() as model_dir:
download_from_gs(IMDB_KERAS_MODEL_URI, model_dir)
keras_model_path = os.path.join(model_dir, KERAS_MODEL_FILENAME)
keras_model = keras.models.load_model(keras_model_path)
integrated_gradients = IntegratedGradients(keras_model, layer=1)
max_features = 10000
maxlen = 100
(x_train, y_train), (x_test,
y_test) = imdb.load_data(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
explanation = integrated_gradients.explain(x_test[0:1].tolist())
attrs = explanation["attributions"]
assert len(attrs) > 0
def test_integrated_gradients():
keras_model_path = os.path.join(
kfserving.Storage.download(IMDB_KERAS_MODEL_URI), KERAS_MODEL_FILENAME)
keras_model = keras.models.load_model(keras_model_path)
integrated_gradients = IntegratedGradients(keras_model, layer=1)
max_features = 10000
maxlen = 100
(x_train, y_train), (x_test,
y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
explanation = integrated_gradients.explain(x_test[0:1].tolist())
attrs = explanation["attributions"]
assert len(attrs) > 0
print(explanation)