File size: 1,934 Bytes
add142e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import numpy as np
import pandas as pd


def sub_to_dict(file):
    """
    Converts a submission file to a dict with predictions.

    Args:
        truths (str): Path to submission file.

    Returns:
        dict: Dictionary containing {study: predictions} pairs.
    """
    sub = pd.read_csv(file)
    sub['study'] = sub['row_id'].apply(lambda x: x.split('_')[0])
    sub['tgt'] = sub['row_id'].apply(lambda x: x.split('_', 1)[-1])

    cols = [
        "spinal_canal_stenosis_l1_l2",
        "spinal_canal_stenosis_l2_l3",
        "spinal_canal_stenosis_l3_l4",
        "spinal_canal_stenosis_l4_l5",
        "spinal_canal_stenosis_l5_s1",
        "left_neural_foraminal_narrowing_l1_l2",
        "left_neural_foraminal_narrowing_l2_l3",
        "left_neural_foraminal_narrowing_l3_l4",
        "left_neural_foraminal_narrowing_l4_l5",
        "left_neural_foraminal_narrowing_l5_s1",
        "right_neural_foraminal_narrowing_l1_l2",
        "right_neural_foraminal_narrowing_l2_l3",
        "right_neural_foraminal_narrowing_l3_l4",
        "right_neural_foraminal_narrowing_l4_l5",
        "right_neural_foraminal_narrowing_l5_s1",
        "left_subarticular_stenosis_l1_l2",
        "left_subarticular_stenosis_l2_l3",
        "left_subarticular_stenosis_l3_l4",
        "left_subarticular_stenosis_l4_l5",
        "left_subarticular_stenosis_l5_s1",
        "right_subarticular_stenosis_l1_l2",
        "right_subarticular_stenosis_l2_l3",
        "right_subarticular_stenosis_l3_l4",
        "right_subarticular_stenosis_l4_l5",
        "right_subarticular_stenosis_l5_s1",
    ]

    preds_dict = {}
    for study, dfs in sub.groupby('study'):
        # display(dfs)
        dfs = dfs[["tgt", "normal_mild", "moderate", "severe"]].set_index("tgt")
        preds = np.zeros((25, 3))
        for i, c in enumerate(cols):
            preds[i] = dfs.loc[c].values
        preds_dict[int(study)] = preds
    return preds_dict