gradio/test/test_interpretation.py

import unittest
import gradio.interpretation
import gradio.test_data
from gradio.processing_utils import decode_base64_to_image, encode_array_to_base64
from gradio import Interface
import numpy as np


class TestDefault(unittest.TestCase):
    def test_default_text(self):
        max_word_len = lambda text: max([len(word) for word in text.split(" ")])
        text_interface = Interface(max_word_len, "textbox", "label", interpretation="default")
        interpretation = text_interface.interpret(["quickest brown fox"])[0][0]
        self.assertGreater(interpretation[0][1], 0)  # Checks to see if the first word has >0 score.
        self.assertEqual(interpretation[-1][1], 0)  # Checks to see if the last word has 0 score.        

class TestShapley(unittest.TestCase):
    def test_shapley_text(self):
        max_word_len = lambda text: max([len(word) for word in text.split(" ")])
        text_interface = Interface(max_word_len, "textbox", "label", interpretation="shapley")
        interpretation = text_interface.interpret(["quickest brown fox"])[0][0]
        self.assertGreater(interpretation[0][1], 0)  # Checks to see if the first word has >0 score.
        self.assertEqual(interpretation[-1][1], 0)  # Checks to see if the last word has 0 score.        

class TestCustom(unittest.TestCase):
    def test_custom_text(self):
        max_word_len = lambda text: max([len(word) for word in text.split(" ")])
        custom = lambda text: [(char, 1) for char in text]
        text_interface = Interface(max_word_len, "textbox", "label", interpretation=custom)
        result = text_interface.interpret(["quickest brown fox"])[0][0]
        self.assertEqual(result[0][1], 1)  # Checks to see if the first letter has score of 1.

    def test_custom_img(self):
        max_pixel_value = lambda img: img.max()
        custom = lambda img: img.tolist()
        img_interface = Interface(max_pixel_value, "image", "label", interpretation=custom)
        result = img_interface.interpret([gradio.test_data.BASE64_IMAGE])[0][0]
        expected_result = np.asarray(decode_base64_to_image(gradio.test_data.BASE64_IMAGE).convert('RGB')).tolist()
        self.assertEqual(result, expected_result)
        

class TestHelperMethods(unittest.TestCase):
    def test_diff(self):
        diff = gradio.interpretation.diff(13, "2")
        self.assertEquals(diff, 11)
        diff = gradio.interpretation.diff("cat", "dog")
        self.assertEquals(diff, 1)
        diff = gradio.interpretation.diff("cat", "cat")
        self.assertEquals(diff, 0)

    def test_quantify_difference_with_textbox(self):
        iface = Interface(lambda text: text, ["textbox"], ["textbox"])
        diff = gradio.interpretation.quantify_difference_in_label(iface, ["test"], ["test"])
        self.assertEquals(diff, 0)
        diff = gradio.interpretation.quantify_difference_in_label(iface, ["test"], ["test_diff"])
        self.assertEquals(diff, 1)

    def test_quantify_difference_with_label(self):
        iface = Interface(lambda text: len(text), ["textbox"], ["label"])
        diff = gradio.interpretation.quantify_difference_in_label(iface, ["3"], ["10"])
        self.assertEquals(diff, -7)
        diff = gradio.interpretation.quantify_difference_in_label(iface, ["0"], ["100"])
        self.assertEquals(diff, -100)

    def test_quantify_difference_with_confidences(self):
        iface = Interface(lambda text: len(text), ["textbox"], ["label"])
        output_1 = {
            "cat": 0.9,
            "dog": 0.1
        }
        output_2 = {
            "cat": 0.6,
            "dog": 0.4
        }
        output_3 = {
            "cat": 0.1,
            "dog": 0.6
        }
        diff = gradio.interpretation.quantify_difference_in_label(iface, [output_1], [output_2])
        self.assertAlmostEquals(diff, 0.3)
        diff = gradio.interpretation.quantify_difference_in_label(iface, [output_1], [output_3])
        self.assertAlmostEquals(diff, 0.8)

    def test_get_regression_value(self):
        iface = Interface(lambda text: text, ["textbox"], ["label"])
        output_1 = {
            "cat": 0.9,
            "dog": 0.1
        }
        output_2 = {
            "cat": float("nan"),
            "dog": 0.4
        }
        output_3 = {
            "cat": 0.1,
            "dog": 0.6
        }
        diff = gradio.interpretation.get_regression_or_classification_value(iface, [output_1], [output_2])
        self.assertEquals(diff, 0)
        diff = gradio.interpretation.get_regression_or_classification_value(iface, [output_1], [output_3])
        self.assertAlmostEquals(diff, 0.1)

    def test_get_classification_value(self):
        iface = Interface(lambda text: text, ["textbox"], ["label"])
        diff = gradio.interpretation.get_regression_or_classification_value(iface, ["cat"], ["test"])
        self.assertEquals(diff, 1)
        diff = gradio.interpretation.get_regression_or_classification_value(iface, ["test"], ["test"])
        self.assertEquals(diff, 0)

if __name__ == '__main__':
    unittest.main()