NormTab/normtab_tf_eval.py at main · mahadi-nahid/NormTab · 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
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
import sqlite3
from utils.preprocess import *
from utils.normalizer import *
from utils.llm_agent import *
from utils.all_prompts import *
import time
import csv


# ---------------------------------------------------------------

def get_ans_sql(prompt):
    response = None
    while response is None:
        try:
            response = get_completion(prompt, temperature=0)
        except:
            time.sleep(2)
            pass
    return response
# ---------------------------------------------------------------

if __name__ == "__main__":

    norm_table_path = "analysis/normtab_unique_output_tf.csv"
    tab_group_path = "datasets/table_group_tf.json"

    with open(tab_group_path, "r") as file:
        table_qa_group = json.load(file)

    # print(table_qa_group)

    df = pd.read_csv(norm_table_path)

    number_of_tables = df.shape[0]
    print()

    tf_path = "datasets/tabfact_small_test.jsonl"

    unique_tabs = [x for x in range(0, number_of_tables)]

    conn = sqlite3.connect('table.db')

    start = 5
    end = 20

    # ---------------------------------------Write outputs---------------------------
    fw = open(f'outputs_C/normtab_t_eval_june10_tf_a.jsonl', 'a')
    # fw.write(json.dumps(tmp) + '\n')
    # ---------------------------------------------------------------
    f = open('outputs_C/normtab_t_eval_june10_tf_a.csv', 'a')
    writer = csv.writer(f)
    # header = ['id', 'statemet', 'answer', 'prediction', 'sql', 'response']
    # writer.writerow(header)
    # -------------------------------------------------------------------------------

    t_correct = 0
    gt_samples = 0


    for index, row in df.iterrows():
        if index in unique_tabs[start:end]:
            id = row['id']
            norm_tab = row['norm_table']
            norm_tab = str(norm_tab).strip().split('=')[1]

            try:
                df = parse_table(norm_tab)
                print('ids: ', id, '\nTable:\n', df.to_markdown(index=False))

                df.to_sql('T', conn, if_exists='replace', index=False)

                col = df.columns

                tab_col = ""
                for c in col:
                    tab_col += c + ", "
                tab_col = tab_col.strip().strip(',')
                print('Table Column: ', tab_col)

                # Execute select query
                query = "SELECT * FROM T limit 3"
                three_rows = pd.read_sql_query(query, conn)
                three_rows = table_linearization(three_rows, style='pipe')
            except:
                print("error\n")
                continue

            # Display the result
            print("DataFrame from database (3 rows) :\n")
            print(three_rows)

            table_ids = table_qa_group[id]
            # table_ids = [int(item.replace('nu-', '')) for item in table_ids]
            print("table_ids: ", table_ids)

            print('\nmain_table:', id, 'table_qa_group: ', table_ids, 'number of questions: ', len(table_ids))
            # table_ids = [0,1]
            correct = 0
            t_samples = 0

            with open(tf_path, encoding='utf-8') as f1:
                for i, l in enumerate(f1):
                    if i in table_ids:
                        dic = json.loads(l)
                        ids = dic['table_id']
                        title = dic['table_caption']
                        # table = dic['table_text']
                        statement = dic['statement']
                        label = dic['label']

                        print('\n\nid: ', ids, ' S: ', statement, ' label: ', label)

                        prompt = generate_sql_prompt_tf(title, tab_col, statement, three_rows)

                        response = ""
                        output_ans = ""

                        try:
                            answer_sql = get_ans_sql(prompt)
                            print("answer_sql; ", answer_sql)

                            prediction = pd.read_sql_query(answer_sql, conn)
                            print("\n\nprediction: ", prediction, '\nGold answer: ', label)

                            if prediction.shape == (1, 1):
                                result_list = prediction.values.tolist()

                                output_ans = ""
                                for row in result_list:
                                    for coll in row:
                                        output_ans += str(coll) + " "
                                        # print(coll)
                                response = "direct ans"
                                output_ans = output_ans.lower()
                                print('\nDirect ans: ', output_ans, 'Gold: ', label)


                            output_ans = output_ans.lower()
                            print('\nGen output: ', output_ans, 'Gold: ', label)

                            if output_ans.strip() == str(label):
                                correct += 1
                                t_correct += 1
                                print("correct: ", correct)

                            t_samples += 1
                            gt_samples += 1

                            print('Correcet: ', correct, 'total: ', t_samples, "Accuracy: ", correct / (t_samples + 0.0001))
                            print("\n-----------------------------------------------\n")

                        except:
                            print("error: ", ids)
                            continue

                        tmp = {'key': ids, 'statement': statement, 'prediction': output_ans,
                               'answer': label}
                        fw.write(json.dumps(tmp) + '\n')
                        # #
                        data = [ids, statement, label, output_ans.strip(), answer_sql, prediction.to_markdown(index=False)]
                        writer.writerow(data)


                print("***********************************************************\n")

                print('Tablewise: Correcet: ', correct, 'total: ', t_samples, "Accuracy: ", correct / (t_samples + 0.0001))
                print("\n-----------------------------------------------\n")

            print('Cumilitive: Correcet: ', t_correct, 'total: ', gt_samples, "Accuracy: ", t_correct / (gt_samples + 0.0001))
            print("\n-----------------------------------------------\n")

            print("***********************************************************\n")

    f.close()
    fw.close()
    conn.close()