def test_anchor_tabular():
skmodel = SKLearnServer(IRIS_MODEL_URI)
skmodel.load()
with tempfile.TemporaryDirectory() as alibi_model_dir:
make_anchor_tabular(alibi_model_dir)
alibi_model = load_explainer(predictor=skmodel.predict,
path=alibi_model_dir)
anchor_tabular = AnchorTabular(alibi_model)
test_data = np.array([[5.964, 4.006, 2.081, 1.031]])
explanation = anchor_tabular.explain(test_data)
explanation_json = json.loads(explanation.to_json())
assert explanation_json["meta"]["name"] == "AnchorTabular"
def test_anchor_tabular():
alibi_model = os.path.join(
kfserving.Storage.download(ADULT_EXPLAINER_URI), EXPLAINER_FILENAME
)
with open(alibi_model, "rb") as f:
skmodel = SKLearnServer(ADULT_MODEL_URI)
skmodel.load()
alibi_model = dill.load(f)
anchor_tabular = AnchorTabular(skmodel.predict, alibi_model)
adult = fetch_adult()
X_test = adult.data[30001:, :]
np.random.seed(0)
explanation = anchor_tabular.explain(X_test[0:1].tolist())
exp_json = json.loads(explanation.to_json())
assert exp_json["data"]["anchor"][0] == "Marital Status = Never-Married"
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 __init__(self,
name: str,
predict_url: str,
protocol: Protocol,
method: ExplainerMethod,
config: Mapping,
explainer: object = None):
super().__init__(name)
self.predict_url = predict_url
self.protocol = protocol
self.method = method
if self.method is ExplainerMethod.anchor_tabular:
self.wrapper = AnchorTabular(self._predict_fn,explainer,**config)
else:
raise NotImplementedError
def test_anchor_tabular():
os.environ.clear()
alibi_model = os.path.join(kserve.Storage.download(ADULT_EXPLAINER_URI),
EXPLAINER_FILENAME)
with open(alibi_model, "rb") as f:
skmodel = SKLearnModel("adult", ADULT_MODEL_URI)
skmodel.load()
predictor = Predictor(skmodel)
alibi_model = dill.load(f)
anchor_tabular = AnchorTabular(predictor.predict_fn, alibi_model)
adult = fetch_adult()
X_test = adult.data[30001:, :]
np.random.seed(0)
explanation = anchor_tabular.explain(X_test[0:1].tolist())
exp_json = json.loads(explanation.to_json())
assert exp_json["data"]["anchor"][0] == "Relationship = Own-child" or \
exp_json["data"]["anchor"][0] == "Age <= 28.00"
def __init__(self,
name: str,
predict_url: str,
protocol: Protocol,
method: ExplainerMethod,
training_data_url: str = None):
super().__init__(name)
self.predict_url = predict_url
self.protocol = protocol
self.method = method
self.training_data_url = training_data_url
if self.method is ExplainerMethod.anchor_tabular:
self.explainer: ExplainerMethodImpl = AnchorTabular(
self._predict_fn)
self.explainer.validate(self.training_data_url)
else:
raise NotImplementedError
class AlibiExplainer(kfserving.KFModel):
def __init__(self,
name: str,
predict_url: str,
protocol: Protocol,
method: ExplainerMethod,
config: Mapping,
explainer: object = None):
super().__init__(name)
self.predict_url = predict_url
self.protocol = protocol
self.method = method
if self.method is ExplainerMethod.anchor_tabular:
self.wrapper = AnchorTabular(self._predict_fn,explainer,**config)
else:
raise NotImplementedError
def load(self):
pass
def _predict_fn(self, arr: np.ndarray) -> np.ndarray:
if self.protocol == Protocol.seldon_http:
payload = seldon.create_request(arr, seldon.SeldonPayload.NDARRAY)
response_raw = requests.post(self.predict_url, json=payload)
if response_raw.status_code == 200:
rh = SeldonRequestHandler(response_raw.json())
response_list = rh.extract_request()
return np.array(response_list)
else:
raise Exception("Failed to get response from model return_code:%d" % response_raw.status_code)
else:
raise NotImplementedError
def explain(self, inputs: List) -> Any:
if self.method is ExplainerMethod.anchor_tabular:
explaination = self.wrapper.explain(inputs)
return json.loads(json.dumps(explaination, cls=NumpyEncoder))
else:
raise NotImplementedError
def __init__( # pylint:disable=too-many-arguments
self,
name: str,
predictor_host: str,
method: ExplainerMethod,
config: Mapping,
explainer: object = None,
):
super().__init__(name)
self.predictor_host = predictor_host
logging.info("Predict URL set to %s", self.predictor_host)
self.method = method
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)
else:
raise NotImplementedError
def __init__(self,
name: str,
predictor_host: str,
protocol: Protocol,
method: ExplainerMethod,
config: Mapping,
explainer: object = None):
super().__init__(name)
self.protocol = protocol
if self.protocol == Protocol.tensorflow_http:
self.predict_url = PREDICTOR_URL_FORMAT.format(predictor_host, name)
else:
self.predict_url = SELDON_PREDICTOR_URL_FORMAT.format(predictor_host)
logging.info("Predict URL set to %s", self.predict_url)
self.method = method
if self.method is ExplainerMethod.anchor_tabular:
self.wrapper = 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)
else:
raise NotImplementedError
