added kwargs tests and code to raise warning for extra kwarg

dianna/__init__.py

@@ -23,6 +23,7 @@
 import importlib
 import logging
 from . import utils
+import warnings 
 
 logging.getLogger(__name__).addHandler(logging.NullHandler())
 
@@ -77,6 +78,10 @@ def explain_image(model_or_function, input_image, method, labels, **kwargs):
     explain_image_kwargs = utils.get_kwargs_applicable_to_function(
         explainer.explain, kwargs
     )
+    for key in explain_image_kwargs.keys():
+        kwargs.pop(key)
+    if kwargs:
+        warnings.warn(message = f'Please note the following kwargs are not being used: {kwargs}')
     return explainer.explain(
         model_or_function, input_image, labels, **explain_image_kwargs
     )
@@ -154,4 +159,6 @@ def _get_explainer(method, kwargs, modality):
     method_kwargs = utils.get_kwargs_applicable_to_function(
         method_class.__init__, kwargs
     )
+    for key in method_kwargs.keys():
+        kwargs.pop(key)
     return method_class(**method_kwargs)

tests/test_init.py

@@ -0,0 +1,60 @@
+from unittest import TestCase
+import pytest
+import dianna
+from tests.test_onnx_runner import generate_data
+import numpy as np
+
+class initialize_method(TestCase):
+
+    def test_lime_image_correct_kwargs(self):
+        model_filename = 'tests/test_data/mnist_model.onnx'
+        input_data = generate_data(batch_size=1)[0].astype(np.float32)
+        axis_labels = ('channels', 'y', 'x')
+        labels = [1]
+
+        dianna.explain_image(model_filename,
+                                input_data,
+                                method='LIME',
+                                labels=labels,
+                                kernel=None,
+                                kernel_width=25,
+                                verbose=False,
+                                feature_selection='auto',
+                                random_state=None,
+                                axis_labels=axis_labels,
+                                preprocess_function=None,
+                                top_labels=None,
+                                num_features=10,
+                                num_samples=5000,
+                                return_masks=True,
+                                positive_only=False,
+                                hide_rest=True,
+                                )
+
+    def test_lime_image_extra_kwarg(self):
+        model_filename = 'tests/test_data/mnist_model.onnx'
+        input_data = generate_data(batch_size=1)[0].astype(np.float32)
+        axis_labels = ('channels', 'y', 'x')
+        labels = [1]
+
+        with self.assertWarns(Warning):
+            dianna.explain_image(model_filename,
+                                    input_data,
+                                    method='LIME',
+                                    labels=labels,
+                                    kernel=None,
+                                    kernel_width=25,
+                                    verbose=False,
+                                    feature_selection='auto',
+                                    random_state=None,
+                                    axis_labels=axis_labels,
+                                    preprocess_function=None,
+                                    top_labels=None,
+                                    num_features=10,
+                                    num_samples=5000,
+                                    return_masks=True,
+                                    positive_only=False,
+                                    hide_rest=True,
+                                    extra_kwarg=None
+                                    )
+