def test_merge_nodes_2(self):
with open(os.path.join(script_dir, 'resource/sentence_for_merge_test.pkl'), 'rb') as f:
sentence = pickle.load(f)
bacterial_names = [name for name, ncbi_id in sentence.bacteria]
disease_names = [name for name, doid_id in sentence.diseases]
nutrient_names = [name for name, idname in sentence.nutrients]
food_names = [name for name, food_group in sentence.food]
merge_nodes(
tokenizer=stanford_tokenizer,
bacterial_names=bacterial_names,
disease_names=disease_names,
nutrient_names=nutrient_names,
food_names=food_names,
parser_output=sentence.parser_output
)
edges = sentence.parser_output.nx_graph.edges()
words = sentence.parser_output.words
tags = sentence.parser_output.tags
edges_expected = [(3, 4), (4, 1), (5, 4), (8, 5), (10, 11), (11, 5), (13, 5), (15, 5), (18, 5), (20, 22), (21, 22), (22, 5), (24, 5), (26, 28), (27, 28), (28, 5), (30, 5), (32, 33), (33, 5), (35, 5), (37, 41), (38, 41), (39, 41), (40, 41), (41, 5), (43, 5), (45, 46), (46, 5), (48, 5), (50, 52), (51, 52), (52, 5), (53, 52), (54, 56), (55, 56), (56, 52), (58, 5), (60, 61), (61, 5), (63, 5), (65, 66), (66, 5), (68, 5), (70, 72), (71, 72), (72, 5), (74, 5), (76, 78), (77, 78), (78, 5), (80, 5), (82, 86), (83, 86), (84, 86), (85, 86), (86, 5), (88, 89), (89, 5), (91, 93), (92, 93), (93, 5), (95, 5), (97, 102), (99, 102), (100, 102), (101, 102), (102, 5), (104, 5), (106, 110), (107, 110), (108, 110), (109, 110), (110, 5), (112, 5), (114, 117), (115, 117), (116, 117), (117, 5), (119, 5), (121, 125), (122, 125), (123, 125), (124, 125), (125, 5)]
words_expected = {1: 'NAFLD', 2: ':', 3: 'Non-alcoholic', 4: 'fatty', 5: 'liver disease', 7: ';', 8: 'NASH', 9: ':', 10: 'Non-alcoholic', 11: 'steatohepatitis', 12: ';', 13: 'HCC', 14: ':', 15: 'Hepatocellular carcinoma', 17: ';', 18: 'FXR', 19: ':', 20: 'Farnesoid', 21: 'X', 22: 'receptor', 23: ';', 24: 'SHP', 25: ':', 26: 'Small', 27: 'hetrodimer', 28: 'partner', 29: ';', 30: 'CA', 31: ':', 32: 'Cholic', 33: 'acid', 34: ';', 35: 'NTCP', 36: ':', 37: 'Na', 38: '/', 39: 'taurocholate', 40: 'cotransporter', 41: 'polypeptide', 42: ';', 43: 'CYP7A1', 44: ':', 45: '7', 46: 'alpha-hydroxylase', 47: ';', 48: 'CDCArg', 49: ':', 50: 'L-Arginine', 51: 'ethyl', 52: 'ester', 53: 'and', 54: 'chenodeoxycholic', 55: 'acid', 56: 'conjugate', 57: ';', 58: 'CDCA', 59: ':', 60: 'Chenodeoxycholic', 61: 'acid', 62: ';', 63: 'UDCA', 64: ':', 65: 'Urosodeoxycholic', 66: 'acid', 67: ';', 68: 'LFD', 69: ':', 70: 'Low', 71: 'fat', 72: 'diet', 73: ';', 74: 'HFD', 75: ':', 76: 'High', 77: 'fat', 78: 'diet', 79: ';', 80: 'TGR5', 81: ':', 82: 'G', 83: 'protein-coupled', 84: 'bile', 85: 'acid', 86: 'receptor', 87: ';', 88: 'SGPT', 89: 'ALT', 90: ':', 91: 'Serum', 92: 'glutamic-pyruvic', 93: 'transaminase', 94: ';', 95: 'SGOT', 96: ':', 97: 'AST', 98: ',', 99: 'Serum', 100: 'glutamic', 101: 'oxaloacetic', 102: 'transaminase', 103: ';', 104: 'PGC1α', 105: ':', 106: 'Proliferator-activated', 107: 'receptor', 108: 'gamma', 109: 'coactivator', 110: '1-alpha', 111: ';', 112: 'PPARα', 113: ':', 114: 'Peroxisome', 115: 'proliferator-activated', 116: 'receptor', 117: 'alpha', 118: ';', 119: 'SREBP1c', 120: ':', 121: 'Sterol', 122: 'regulatory', 123: 'element-binding', 124: 'protein', 125: '1c', 126: '.'}
tags_expected = {1: 'NNP', 2: 'NO_TAG', 3: 'JJ', 4: 'NN', 5: 'DISEASE', 7: 'NO_TAG', 8: 'NNP', 9: 'NO_TAG', 10: 'JJ', 11: 'NNS', 12: 'NO_TAG', 13: 'NNP', 14: 'NO_TAG', 15: 'DISEASE', 17: 'NO_TAG', 18: 'NNP', 19: 'NO_TAG', 20: 'NNP', 21: 'NNP', 22: 'NNP', 23: 'NO_TAG', 24: 'NNP', 25: 'NO_TAG', 26: 'JJ', 27: 'NN', 28: 'NN', 29: 'NO_TAG', 30: 'NNP', 31: 'NO_TAG', 32: 'JJ', 33: 'NN', 34: 'NO_TAG', 35: 'NNP', 36: 'NO_TAG', 37: 'NNP', 38: 'NNP', 39: 'JJ', 40: 'NN', 41: 'NN', 42: 'NO_TAG', 43: 'NNP', 44: 'NO_TAG', 45: 'CD', 46: 'NN', 47: 'NO_TAG', 48: 'NNP', 49: 'NO_TAG', 50: 'JJ', 51: 'NN', 52: 'NN', 53: 'CC', 54: 'JJ', 55: 'NN', 56: 'NN', 57: 'NO_TAG', 58: 'NNP', 59: 'NO_TAG', 60: 'JJ', 61: 'NN', 62: 'NO_TAG', 63: 'NNP', 64: 'NO_TAG', 65: 'JJ', 66: 'NN', 67: 'NO_TAG', 68: 'NNP', 69: 'NO_TAG', 70: 'NNP', 71: 'NUTRIENT', 72: 'NN', 73: 'NO_TAG', 74: 'NNP', 75: 'NO_TAG', 76: 'JJ', 77: 'NUTRIENT', 78: 'NN', 79: 'NO_TAG', 80: 'NNP', 81: 'NO_TAG', 82: 'CD', 83: 'JJ', 84: 'JJ', 85: 'NN', 86: 'NN', 87: 'NO_TAG', 88: 'NNP', 89: 'NNP', 90: 'NO_TAG', 91: 'NNP', 92: 'JJ', 93: 'NN', 94: 'NO_TAG', 95: 'NNP', 96: 'NO_TAG', 97: 'NNS', 98: 'NO_TAG', 99: 'NNP', 100: 'JJ', 101: 'NN', 102: 'NN', 103: 'NO_TAG', 104: 'NNS', 105: 'NO_TAG', 106: 'JJ', 107: 'NN', 108: 'NN', 109: 'NN', 110: 'NNS', 111: 'NO_TAG', 112: 'NN', 113: 'NO_TAG', 114: 'JJ', 115: 'JJ', 116: 'NN', 117: 'NNS', 118: 'NO_TAG', 119: 'NNS', 120: 'NO_TAG', 121: 'JJ', 122: 'JJ', 123: 'JJ', 124: 'NN', 125: 'NNS', 126: 'NO_TAG'}
self.assertListEqual(edges_expected, edges)
self.assertEqual(words_expected, words)
self.assertEqual(tags_expected, tags)
def test_merge_nodes_3(self):
sentence_text = 'The study found that • children reported greater liking of the high-sugar cereals and consumed almost twice the amount per eating occasion compared with those served the low-sugar cereals (61.3 vs. 34.6 g); • children offered low-sugar cereals added more table sugar than those eating high-sugar cereals, and the total sugar content (from cereal and added sugar) was almost twice as high as with the high-sugar cereals (24.4 vs. 12.5 g; < 0.001); • there was no difference in the amount of milk consumed with the 2 types of cereals, nor the total energy consumed at the breakfast meal; and • children in the low-sugar group were more likely to put fresh fruit on their breakfast cereal compared with the high-sugar cereal condition (54% vs. 8%; = 0.05).'
diseases = []
nutrients = [('table sugar', 'Sucrose'), ('sugar', 'Sucrose'), ('sugar', 'Sucrose')]
bacteria = []
food = [('cereals', 'Breakfast Cereals'), ('cereals', 'Breakfast Cereals'), ('cereals', 'Breakfast Cereals'), ('cereals', 'Breakfast Cereals'), ('cereals', 'Breakfast Cereals'), ('milk', 'Dairy and Egg Products'), ('cereals', 'Breakfast Cereals')]
parser_output = sentence_parser.parse_sentence(sentence_text)
bacterial_names = [name for name, ncbi_id in bacteria]
disease_names = [name for name, doid_id in diseases]
nutrient_names = [name for name, idname in nutrients]
food_names = [name for name, food_group in food]
merge_nodes(
tokenizer=stanford_tokenizer,
bacterial_names=bacterial_names,
disease_names=disease_names,
nutrient_names=nutrient_names,
food_names=food_names,
parser_output=parser_output
)
edges = parser_output.nx_graph.edges()
words = parser_output.words
tags = parser_output.tags
edges_expected = [(1, 2), (2, 3), (4, 7), (5, 6), (6, 7), (7, 3), (8, 9), (9, 7), (10, 13), (11, 13), (12, 13), (13, 9), (14, 7), (15, 7), (16, 17), (17, 15), (18, 19), (19, 15), (20, 21), (21, 19), (22, 21), (23, 25), (24, 25), (25, 21), (26, 25), (27, 29), (28, 29), (29, 26), (31, 29), (32, 31), (33, 34), (34, 31), (37, 38), (38, 39), (39, 3), (40, 41), (41, 42), (42, 39), (43, 44), (44, 42), (46, 50), (47, 50), (48, 50), (49, 50), (50, 42), (52, 39), (53, 56), (54, 56), (55, 56), (56, 68), (58, 62), (59, 62), (60, 59), (61, 59), (62, 56), (64, 68), (65, 66), (66, 68), (67, 68), (68, 39), (69, 73), (70, 73), (71, 73), (72, 73), (73, 68), (75, 73), (76, 78), (77, 78), (78, 75), (80, 78), (81, 80), (84, 86), (85, 86), (86, 3), (87, 88), (88, 86), (89, 91), (90, 91), (91, 88), (92, 93), (93, 91), (94, 93), (95, 98), (96, 98), (97, 98), (98, 94), (99, 100), (100, 98), (102, 91), (103, 105), (104, 105), (105, 91), (106, 105), (107, 110), (108, 110), (109, 110), (110, 106), (112, 3), (113, 114), (114, 121), (115, 118), (116, 118), (117, 118), (118, 114), (119, 121), (120, 121), (121, 3), (122, 123), (123, 121), (124, 125), (125, 123), (126, 129), (127, 129), (128, 129), (129, 123), (130, 135), (131, 135), (132, 135), (133, 135), (134, 135), (135, 123), (137, 138), (138, 135), (139, 141), (140, 141), (141, 138), (143, 144), (144, 141)]
words_expected = {1: 'The', 2: 'study', 3: 'found', 4: 'that', 5: '•', 6: 'children', 7: 'reported', 8: 'greater', 9: 'liking', 10: 'of', 11: 'the', 12: 'high-sugar', 13: 'cereals', 14: 'and', 15: 'consumed', 16: 'almost', 17: 'twice', 18: 'the', 19: 'amount', 20: 'per', 21: 'eating', 22: 'occasion', 23: 'compared', 24: 'with', 25: 'those', 26: 'served', 27: 'the', 28: 'low-sugar', 29: 'cereals', 30: '-LRB-', 31: '61.3', 32: 'vs.', 33: '34.6', 34: 'g', 35: '-RRB-', 36: ';', 37: '•', 38: 'children', 39: 'offered', 40: 'low-sugar', 41: 'cereals', 42: 'added', 43: 'more', 44: 'table sugar', 46: 'than', 47: 'those', 48: 'eating', 49: 'high-sugar', 50: 'cereals', 51: ',', 52: 'and', 53: 'the', 54: 'total', 55: 'sugar', 56: 'content', 57: '-LRB-', 58: 'from', 59: 'cereal', 60: 'and', 61: 'added', 62: 'sugar', 63: '-RRB-', 64: 'was', 65: 'almost', 66: 'twice', 67: 'as', 68: 'high', 69: 'as', 70: 'with', 71: 'the', 72: 'high-sugar', 73: 'cereals', 74: '-LRB-', 75: '24.4', 76: 'vs.', 77: '12.5', 78: 'g', 79: ';', 80: '<', 81: '0.001', 82: '-RRB-', 83: ';', 84: '•', 85: 'there', 86: 'was', 87: 'no', 88: 'difference', 89: 'in', 90: 'the', 91: 'amount', 92: 'of', 93: 'milk', 94: 'consumed', 95: 'with', 96: 'the', 97: '2', 98: 'types', 99: 'of', 100: 'cereals', 101: ',', 102: 'nor', 103: 'the', 104: 'total', 105: 'energy', 106: 'consumed', 107: 'at', 108: 'the', 109: 'breakfast', 110: 'meal', 111: ';', 112: 'and', 113: '•', 114: 'children', 115: 'in', 116: 'the', 117: 'low-sugar', 118: 'group', 119: 'were', 120: 'more', 121: 'likely', 122: 'to', 123: 'put', 124: 'fresh', 125: 'fruit', 126: 'on', 127: 'their', 128: 'breakfast', 129: 'cereal', 130: 'compared', 131: 'with', 132: 'the', 133: 'high-sugar', 134: 'cereal', 135: 'condition', 136: '-LRB-', 137: '54', 138: '%', 139: 'vs.', 140: '8', 141: '%', 142: ';', 143: '=', 144: '0.05', 145: '-RRB-', 146: '.'}
tags_expected = {1: 'DT', 2: 'NN', 3: 'VBD', 4: 'IN', 5: 'JJ', 6: 'NNS', 7: 'VBD', 8: 'JJR', 9: 'NN', 10: 'IN', 11: 'DT', 12: 'JJ', 13: 'FOOD', 14: 'CC', 15: 'VBD', 16: 'RB', 17: 'RB', 18: 'DT', 19: 'NN', 20: 'IN', 21: 'VBG', 22: 'NN', 23: 'VBN', 24: 'IN', 25: 'DT', 26: 'VBN', 27: 'DT', 28: 'JJ', 29: 'FOOD', 30: 'NO_TAG', 31: 'CD', 32: 'CC', 33: 'CD', 34: 'NN', 35: 'NO_TAG', 36: 'NO_TAG', 37: 'JJ', 38: 'NNS', 39: 'VBD', 40: 'JJ', 41: 'FOOD', 42: 'VBD', 43: 'JJR', 44: 'NUTRIENT', 46: 'IN', 47: 'DT', 48: 'JJ', 49: 'JJ', 50: 'FOOD', 51: 'NO_TAG', 52: 'CC', 53: 'DT', 54: 'JJ', 55: 'NUTRIENT', 56: 'NN', 57: 'NO_TAG', 58: 'IN', 59: 'NN', 60: 'CC', 61: 'VBN', 62: 'NUTRIENT', 63: 'NO_TAG', 64: 'VBD', 65: 'RB', 66: 'RB', 67: 'RB', 68: 'JJ', 69: 'RB', 70: 'IN', 71: 'DT', 72: 'JJ', 73: 'FOOD', 74: 'NO_TAG', 75: 'CD', 76: 'IN', 77: 'CD', 78: 'NN', 79: 'NO_TAG', 80: 'NNP', 81: 'CD', 82: 'NO_TAG', 83: 'NO_TAG', 84: 'RB', 85: 'EX', 86: 'VBD', 87: 'DT', 88: 'NN', 89: 'IN', 90: 'DT', 91: 'NN', 92: 'IN', 93: 'FOOD', 94: 'VBN', 95: 'IN', 96: 'DT', 97: 'CD', 98: 'NNS', 99: 'IN', 100: 'FOOD', 101: 'NO_TAG', 102: 'CC', 103: 'DT', 104: 'JJ', 105: 'NN', 106: 'VBN', 107: 'IN', 108: 'DT', 109: 'NN', 110: 'NN', 111: 'NO_TAG', 112: 'CC', 113: 'JJ', 114: 'NNS', 115: 'IN', 116: 'DT', 117: 'JJ', 118: 'NN', 119: 'VBD', 120: 'RBR', 121: 'JJ', 122: 'TO', 123: 'VB', 124: 'JJ', 125: 'NN', 126: 'IN', 127: 'PRP$', 128: 'NN', 129: 'NN', 130: 'VBN', 131: 'IN', 132: 'DT', 133: 'JJ', 134: 'NN', 135: 'NN', 136: 'NO_TAG', 137: 'CD', 138: 'NN', 139: 'IN', 140: 'CD', 141: 'NN', 142: 'NO_TAG', 143: 'SYM', 144: 'CD', 145: 'NO_TAG', 146: 'NO_TAG'}
self.assertListEqual(edges_expected, edges)
self.assertEqual(words_expected, words)
self.assertEqual(tags_expected, tags)
def test_merge_nodes_1(self):
bacterial_names = ['bb cc dd']
disease_names = ['bb aa']
nx_graph = nx.DiGraph()
nx_graph.add_nodes_from([1, 2, 3, 4, 5])
nx_graph.add_edges_from([
(1, 2, {'rel': '1 -> 2'}),
(1, 4, {'rel': '1 -> 4'}),
(2, 3, {'rel': '2 -> 3'}),
(2, 4, {'rel': '2 -> 4'}),
(4, 5, {'rel': '4 -> 5'})]
)
words = {1: 'bb', 2: 'aa', 3: 'bb', 4: 'cc', 5: 'dd'}
tags = {1: 'T1', 2: 'T2', 3: 'T3', 4: 'T4', 5: 'T5'}
parser_output = ParserOutput("a text", nx_graph, words, tags)
merge_nodes(
tokenizer = stanford_tokenizer,
bacterial_names = bacterial_names,
disease_names = disease_names,
nutrient_names = [],
food_names = [],
parser_output = parser_output
)
edges = parser_output.nx_graph.edges()
words = parser_output.words
tags = parser_output.tags
edges_expected = [(1, 3)]
words_expected = {1: 'bb aa', 3: 'bb cc dd'}
tags_expected = {1: 'DISEASE', 3: 'BACTERIUM'}
self.assertEqual(edges_expected, edges)
self.assertEqual(words_expected, words)
self.assertEqual(tags_expected, tags)
