def test_open_lit(self, set_up_sequential,
set_up_pandas_dataframe_and_columns, widget_render_mock):
pd_dataset, lit_columns = set_up_pandas_dataframe_and_columns
lit_dataset = create_lit_dataset(pd_dataset, lit_columns)
feature_types, label_types, saved_model_path = set_up_sequential
lit_model = create_lit_model(saved_model_path, feature_types,
label_types)
open_lit({"model": lit_model}, {"dataset": lit_dataset})
widget_render_mock.assert_called_once()
def test_create_lit_model_from_tensorflow_returns_model(
self, set_up_sequential):
feature_types, label_types, saved_model_path = set_up_sequential
lit_model = create_lit_model(saved_model_path, feature_types,
label_types)
test_inputs = [
{
"feature_1": 1.0,
"feature_2": 2.0
},
]
outputs = lit_model.predict_minibatch(test_inputs)
assert lit_model.input_spec() == dict(feature_types)
assert lit_model.output_spec() == dict(label_types)
assert len(outputs) == 1
for item in outputs:
assert item.keys() == {"label"}
assert len(item.values()) == 1
def test_create_lit_model_from_tensorflow_with_xai_returns_model(
self, set_up_sequential):
feature_types, label_types, saved_model_path = set_up_sequential
lit_model = create_lit_model(saved_model_path, feature_types,
label_types)
test_inputs = [
{
"feature_1": 1.0,
"feature_2": 2.0
},
]
outputs = lit_model.predict_minibatch(test_inputs)
assert lit_model.input_spec() == dict(feature_types)
assert lit_model.output_spec() == dict({
**label_types,
"feature_attribution":
lit_types.FeatureSalience(signed=True),
})
assert len(outputs) == 1
for item in outputs:
assert item.keys() == {"label", "feature_attribution"}
assert len(item.values()) == 2