def update_scene(self):
self.clear_scene()
self.mo_info = ""
self.bv_info = ""
scores = None
if self.__results is not None:
data = self.__results.transformed_data
pred = self.__results.predictions
base = self.__results.base_value
values, _, labels, ranges = prepare_force_plot_data(
self.__results.values, data, pred, self.target_index)
index = 0
HIGH, LOW = 0, 1
plot_data = PlotData(high_values=values[index][HIGH],
low_values=values[index][LOW][::-1],
high_labels=labels[index][HIGH],
low_labels=labels[index][LOW][::-1],
value_range=ranges[index],
model_output=pred[index][self.target_index],
base_value=base[self.target_index])
self.setup_plot(plot_data)
self.mo_info = f"Model prediction: {_str(plot_data.model_output)}"
self.bv_info = f"Base value: {_str(plot_data.base_value)}"
assert isinstance(self.__results.values, list)
scores = self.__results.values[self.target_index][0, :]
names = [a.name for a in data.domain.attributes]
scores = self.create_scores_table(scores, names)
self.Outputs.scores.send(scores)
def test_prepare_force_plot_no_top_n_features(self):
shap_values = [
np.random.random((3, 4)),
np.array([[1, -2, 6, 5], [-2, -3, -1, -5], [1, 2, 4, 5]]),
]
predictions = np.array([[1, 2], [1, 3], [1, 4]])
shaps, segments, labels, ranges = prepare_force_plot_data(
shap_values, self.iris[:4], predictions, 1)
self.assertEqual(len(shaps), 3)
self.assertEqual(len(shaps[0][0]), 3)
self.assertEqual(len(shaps[0][1]), 1)
self.assertEqual(len(shaps[1][0]), 0)
self.assertEqual(len(shaps[1][1]), 4)
self.assertEqual(len(shaps[2][0]), 4)
self.assertEqual(len(shaps[2][1]), 0)
self.assertEqual(len(segments), 3)
self.assertEqual(len(segments[0][0]), 3)
self.assertEqual(len(segments[0][1]), 1)
self.assertEqual(len(segments[1][0]), 0)
self.assertEqual(len(segments[1][1]), 4)
self.assertEqual(len(segments[2][0]), 4)
self.assertEqual(len(segments[2][1]), 0)
self.assertEqual(len(labels), 3)
self.assertEqual(len(labels[0][0]), 3)
self.assertEqual(len(labels[0][1]), 1)
self.assertEqual(len(labels[1][0]), 0)
self.assertEqual(len(labels[1][1]), 4)
self.assertEqual(len(labels[2][0]), 4)
self.assertEqual(len(labels[2][1]), 0)
self.assertEqual(len(ranges), 3)
def test_prepare_force_plot_data_zero_shap(self):
"""
prepare_force_plot_data should remove all values and variables that
have SHAP values 0. Test if it works
"""
shap_values = [
np.random.random((3, 4)),
np.array([[1, -2, 6, 0], [-2, -3, 0, -5], [1, 0, 4, 5]]),
]
predictions = np.array([[1, 2], [1, 3], [1, 4]])
shaps, segments, labels, ranges = prepare_force_plot_data(
shap_values, self.iris[:4], predictions, 1)
self.assertListEqual([([6, 1], [-2]), ([], [-5, -3, -2]),
([5, 4, 1], [])], shaps)
self.assertListEqual(
[
([(2, -4), (-4, -5)], [(2, 4)]),
([], [(3, 8), (8, 11), (11, 13)]),
([(4, -1), (-1, -5), (-5, -6)], []),
],
segments,
)
self.assertListEqual(
[
(
[("petal length", 1.4), ("sepal length", 5.1)],
[("sepal width", 3.5)],
),
(
[],
[
("petal width", 0.2),
("sepal width", 3.0),
("sepal length", 4.9),
],
),
(
[
("petal width", 0.2),
("petal length", 1.3),
("sepal length", 4.7),
],
[],
),
],
labels,
)
self.assertListEqual([(-5, 4), (3, 13), (-6, 4)], ranges)
def test_prepare_force_plot_data_target_1(self):
# for target class 1
shap_values = [
np.random.random((3, 4)),
np.array([[1, -2, 6, 5], [-2, -3, -1, -5], [1, 2, 4, 5]]),
]
predictions = np.array([[1, 2], [1, 3], [1, 4]])
shaps, segments, labels, ranges = prepare_force_plot_data(
shap_values, self.iris[:4], predictions, 1, top_n_features=3)
self.assertListEqual([([6, 5], [-2]), ([], [-5, -3, -2]),
([5, 4, 2], [])], shaps)
self.assertListEqual(
[
([(2, -4), (-4, -9)], [(2, 4)]),
([], [(3, 8), (8, 11), (11, 13)]),
([(4, -1), (-1, -5), (-5, -7)], []),
],
segments,
)
self.assertListEqual(
[
(
[("petal length", 1.4), ("petal width", 0.2)],
[("sepal width", 3.5)],
),
(
[],
[
("petal width", 0.2),
("sepal width", 3.0),
("sepal length", 4.9),
],
),
(
[
("petal width", 0.2),
("petal length", 1.3),
("sepal width", 3.2),
],
[],
),
],
labels,
)
self.assertListEqual([(-9, 4), (3, 13), (-7, 4)], ranges)