Concept_Norm/cn_analysis_share.py at main · Machine-Learning-for-Medical-Language/Concept_Norm · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
import os
from collections import Counter

import matplotlib.pyplot as plt
import numpy as np
import sklearn.metrics as metrics
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix

import process_funtions as process
import read_files as read


def analyze_cn_top1(dev, cui_file_path, input_file_path):

    # semantic_type = read.read_from_json(
    #     "data/umls/cui_st_term_snomed_rxnorm_dict_all")
    # semantic_type['CUI-less'] = ['CUI_less']

    cui_synonyms = read.read_from_json(
        "data/share/umls/ontology_concept_synonyms")
    cui_synonyms['CUI-less'] = ['CUI_less']

    if dev == True:
        train_input = read.read_from_tsv("data/share/processed/data/train.tsv")
    else:
        train_input = read.read_from_tsv(
            "data/share/processed/data/train.tsv") + read.read_from_tsv(
                "data/share/processed/data/dev.tsv")

    train_cui = {}
    for item in train_input:
        train_cui = read.add_dict(train_cui, item[1], item[2])
    # train_cui = [item[1] for item in train_input]

    dev_pre = read.textfile2list(cui_file_path)
    dev_pre = [item.split(" ") for item in dev_pre]

    # dev_input = read.read_from_tsv(input_file_path)

    dev_input = read.textfile2list(input_file_path)

    dev_input = [item.split("\t") for item in dev_input]

    count_st = 0

    count_all = len(dev_input)
    count = 0

    count_see = 0
    count_see_all = 0

    count_unsee = 0
    count_unsee_pre_seen = 0
    count_unsee_all = 0

    count_cuiless = 0
    count_cuiless_all = 0

    count_st = 0

    output = []

    for pre_cui, input in zip(dev_pre, dev_input):
        # st, cui, mention = input
        mention, cui = input
        pre_cui = pre_cui[0]
        # st_pre = '_'.join(
        #     process.get_st_cui(semantic_type, pre_cui).split(' '))

        # if st_pre == st:
        #     count_st += 1

        if cui == pre_cui:
            count += 1
            # print(cui, st, mention)
            # print()
            # print(cui_synonyms[cui])
            # print()
            # print(pre_cui, st_pre, cui_synonyms[pre_cui])
            # print()
            # print()
            # print()
        else:
            print(cui, mention)
            print()
            if cui in cui_synonyms:
                print(cui_synonyms[cui])
            print()
            print(pre_cui, cui_synonyms[pre_cui])
            print()
            print()
            print("________________")
        if cui == 'CUI-less':
            count_cuiless_all += 1
            if cui == pre_cui:
                count_cuiless += 1

        else:

            if cui in train_cui:
                count_see_all += 1
                if cui == pre_cui:
                    count_see += 1

            else:
                count_unsee_all += 1
                if cui == pre_cui:
                    count_unsee += 1

                    # print(cui, st, mention)
                    # print()
                    # print(cui_synonyms[cui])
                    # print()
                    # print(pre_cui, st_pre, cui_synonyms[pre_cui])
                    # print()
                    # print()
                    # print()
                else:
                    if pre_cui in train_cui:
                        count_unsee_pre_seen += 1
                    #     print("special notification......")
                    #     print(train_cui[pre_cui])

                    # print(cui, st, mention)
                    # print()
                    # print(list(set(cui_synonyms[cui])))
                    # print()
                    # print(pre_cui, st_pre, list(set(cui_synonyms[pre_cui])))
                    # print()
                    # print()
                    # print()

                    #     print(cui, st, mention)
                    #     print()
                    #     print(cui_synonyms[cui])
                    #     print()
                    #     print(pre_cui, st_pre, cui_synonyms[pre_cui])
                    #     print()
                    #     print()
                    #     print()
    print(
        "acc",
        count / count_all,
        "cuiless",
        # )
        count_cuiless / count_cuiless_all)
    print("seen", count_see / count_see_all, "unseen",
          count_unsee / count_unsee_all, "unseen gold truth but seen pred",
          count_unsee_pre_seen / count_unsee_all)
    print("st", count_st / (count_all - count_cuiless))


cui_file_path = "data/share/outputs/1_triplet_ontology+train_all_e20_b400_seq16_5e5_sc45_m0.35/st_joint_test_predictions.txt"
input_file_path = "data/share/raw/test.txt"
analyze_cn_top1(True, cui_file_path, input_file_path)

# cui_file_path = "data/n2c2/models/e20_b16_s128_5e5/"
# input_file_path = "data/n2c2/processed/input_joint/st/test.tsv"
# analyze_cn_top1(False, cui_file_path, input_file_path)