def main():
args, extra = parse_args(sys.argv[1:])
# Pretrained Alibi explainer
alibi_model = None
keras_model = None
predict_fn = construct_predict_fn(
predictor_host=args.predictor_host,
model_name=args.model_name,
protocol=Protocol(args.protocol),
tf_data_type=args.tf_data_type,
)
if args.storage_uri is not None:
# we assume here that model is local
path = args.storage_uri
if is_persisted_explainer(path):
alibi_model = get_persisted_explainer(predict_fn=predict_fn,
dirname=path)
if is_persisted_keras(path):
keras_model = get_persisted_keras(path)
explainer = AlibiExplainer(
name=args.model_name,
predict_fn=predict_fn,
method=ExplainerMethod(args.command),
config=extra,
explainer=alibi_model,
protocol=Protocol(args.protocol),
keras_model=keras_model,
)
explainer.load()
ExplainerServer(args.http_port).start(explainer)
def main():
args, extra = parse_args(sys.argv[1:])
# Pretrained Alibi explainer
alibi_model = None
keras_model = None
if args.storage_uri is not None:
path = kfserving.Storage.download(args.storage_uri)
alibi_model = os.path.join(path, EXPLAINER_FILENAME)
if os.path.exists(alibi_model):
with open(alibi_model, "rb") as f:
logging.info("Loading Alibi model")
alibi_model = dill.load(f)
else:
keras_path = os.path.join(path, KERAS_MODEL)
if os.path.exists(keras_path):
with open(keras_path, "rb") as f:
logging.info("Loading Keras model")
keras_model = keras.models.load_model(keras_path)
explainer = AlibiExplainer(args.model_name, args.predictor_host,
ExplainerMethod(args.command), extra,
alibi_model, Protocol(args.protocol),
args.tf_data_type, keras_model)
explainer.load()
ExplainerServer(args.http_port).start(explainer)
def test_anchor_images_parser():
args = [
"--predictor_host",
PREDICTOR_HOST,
"AnchorImages",
"--p_sample",
str(P_SAMPLE),
]
parser, _ = parse_args(args)
assert parser.predictor_host == PREDICTOR_HOST
assert parser.explainer.p_sample == P_SAMPLE
def test_integrated_gradients_parser():
args = [
"--predictor_host", PREDICTOR_HOST, "IntegratedGradients", "--method",
str(METHOD), "--layer",
str(LAYER), "--n_steps",
str(N_STEPS), "--internal_batch_size",
str(INTERNAL_BATCH_SIZE)
]
parser, _ = parse_args(args)
assert parser.predictor_host == PREDICTOR_HOST
assert parser.explainer.method == METHOD
assert parser.explainer.layer == LAYER
assert parser.explainer.n_steps == N_STEPS
assert parser.explainer.internal_batch_size == INTERNAL_BATCH_SIZE
def test_shared_explainer_args():
args = [
"--predictor_host",
PREDICTOR_HOST,
"AnchorTabular",
"--threshold",
str(THRESHOLD),
"--delta",
str(DELTA),
"--tau",
str(TAU),
"--batch_size",
str(BATCH_SIZE),
"--coverage_samples",
str(COVERAGE_SAMPLES),
"--beam_size",
str(BEAM_SIZE),
"--stop_on_first",
str(STOP_ON_FIRST),
"--max_anchor_size",
str(MAX_ANCHOR_SIZE),
"--max_samples_start",
str(MAX_SAMPLES_START),
"--n_covered_ex",
str(N_COVERED_EX),
"--binary_cache_size",
str(BINARY_CACHE_SIZE),
"--cache_margin",
str(CACHE_MARGIN),
"--verbose",
str(VERBOSE),
"--verbose_every",
str(VERBOSE_EVERY),
]
parser, _ = parse_args(args)
assert parser.explainer.threshold == THRESHOLD
assert parser.explainer.delta == DELTA
assert parser.explainer.tau == TAU
assert parser.explainer.batch_size == BATCH_SIZE
assert parser.explainer.coverage_samples == COVERAGE_SAMPLES
assert parser.explainer.beam_size == BEAM_SIZE
assert parser.explainer.stop_on_first == STOP_ON_FIRST
assert parser.explainer.max_anchor_size == MAX_ANCHOR_SIZE
assert parser.explainer.max_samples_start == MAX_SAMPLES_START
assert parser.explainer.n_covered_ex == N_COVERED_EX
assert parser.explainer.binary_cache_size == BINARY_CACHE_SIZE
assert parser.explainer.cache_margin == CACHE_MARGIN
assert parser.explainer.verbose == VERBOSE
assert parser.explainer.verbose_every == VERBOSE_EVERY
def main():
args, extra = parse_args(sys.argv[1:])
# Pretrained Alibi explainer
alibi_model = None
if args.storage_uri is not None:
alibi_model = os.path.join(
kfserving.Storage.download(args.storage_uri), EXPLAINER_FILENAME)
with open(alibi_model, "rb") as f:
logging.info("Loading Alibi model")
alibi_model = dill.load(f)
explainer = AlibiExplainer(
args.model_name,
args.predictor_host,
ExplainerMethod(args.command),
extra,
alibi_model,
)
explainer.load()
kfserving.KFServer().start(models=[explainer])
def test_anchor_text_parser():
args = [
"--predictor_host",
PREDICTOR_HOST,
"AnchorText",
"--use_unk",
str(USE_UNK),
"--use_similarity_proba",
str(USE_SIMILARITY_PROBA),
"--sample_proba",
str(SAMPLE_PROBA),
"--top_n",
str(TOP_N),
"--temperature",
str(TEMPERATURE),
]
parser, _ = parse_args(args)
assert parser.predictor_host == PREDICTOR_HOST
assert parser.explainer.use_unk == USE_UNK
assert parser.explainer.use_similarity_proba == USE_SIMILARITY_PROBA
assert parser.explainer.sample_proba == SAMPLE_PROBA
assert parser.explainer.top_n == TOP_N
assert parser.explainer.temperature == TEMPERATURE
def test_basic_args():
args = ["--predictor_host", PREDICTOR_HOST]
parser, _ = parse_args(args)
assert parser.predictor_host == PREDICTOR_HOST
