Kaggle competition: RSNA STR Pulmonary Embolism Detection
<SOS> metric: weightedmeancolumnwiselogloss Kaggle data source: rsna-str-pulmonary-embolism-detection<sort_values>
!pip install -q /kaggle/input/timm-0-1-30/timm-0.1.30-py3-none-any.whl
!pip install -q /kaggle/input/omegaconf/omegaconf-2.0.2-py3-none-any.whl
!cp /kaggle/input/gdcm-conda-install/gdcm.tar.
 !tar -xvzf gdcm.tar
!conda install --offline./gdcm/gdcm-2.8.9-py37h71b2a6d_0.tar.bz2
train.isnull().sum().sort_values(ascending=False )<count_missing_values>
def check_consistency(sub, test):

 for i in test['StudyInstanceUID'].unique() :
df_tmp = sub.loc[sub.id.str.contains(i, regex = False)].reset_index(drop = True)
 df_tmp['StudyInstanceUID'] = df_tmp['id'].str.split('_' ).str[0]
df_tmp['label_type'] = df_tmp['id'].str.split('_' ).str[1:].apply(lambda x: '_'.join(x))
del df_tmp['id']
if i == test['StudyInstanceUID'].unique() [0]:
df = df_tmp.copy()
else:
df = pd.concat([df, df_tmp], axis = 0)
 df_exam = df.pivot(index = 'StudyInstanceUID', columns = 'label_type', values = 'label')
 df_image = sub.loc[sub.id.isin(test.SOPInstanceUID)].reset_index(drop = True)
 df_image = df_image.merge(test, how = 'left', left_on = 'id', right_on = 'SOPInstanceUID')
 df_image.rename(columns = {"label": "pe_present_on_image"}, inplace = True)
 del df_image['id']
df = df_exam.merge(df_image, how = 'left', on = 'StudyInstanceUID')
 ids = ['StudyInstanceUID', 'SeriesInstanceUID', 'SOPInstanceUID']
labels = [c for c in df.columns if c not in ids]
df = df[ids + labels]
df['positive_images_in_exam'] = df['StudyInstanceUID'].map(df.groupby(['StudyInstanceUID'] ).pe_present_on_image.max())
 df_pos = df.loc[df.positive_images_in_exam > 0.5]
df_neg = df.loc[df.positive_images_in_exam <= 0.5]
rule1a = df_pos.loc[(( df_pos.rv_lv_ratio_lt_1 > 0.5)& 
(df_pos.rv_lv_ratio_gte_1 > 0.5)) | 
(( df_pos.rv_lv_ratio_lt_1 <= 0.5)& 
(df_pos.rv_lv_ratio_gte_1 <= 0.5)) ].reset_index(drop = True)
 rule1a['broken_rule'] = '1a'
rule1b = df_pos.loc[(df_pos.central_pe <= 0.5)& 
(df_pos.rightsided_pe <= 0.5)& 
(df_pos.leftsided_pe <= 0.5)].reset_index(drop = True)
 rule1b['broken_rule'] = '1b'
rule1c = df_pos.loc[(df_pos.acute_and_chronic_pe > 0.5)& 
(df_pos.chronic_pe > 0.5)].reset_index(drop = True)
 rule1c['broken_rule'] = '1c'
rule1d = df_pos.loc[(df_pos.indeterminate > 0.5)| 
(df_pos.negative_exam_for_pe > 0.5)].reset_index(drop = True)
 rule1d['broken_rule'] = '1d'
rule2a = df_neg.loc[(( df_neg.indeterminate > 0.5)& 
(df_neg.negative_exam_for_pe > 0.5)) | 
(( df_neg.indeterminate <= 0.5)& 
(df_neg.negative_exam_for_pe <= 0.5)) ].reset_index(drop = True)
 rule2a['broken_rule'] = '2a'
rule2b = df_neg.loc[(df_neg.rv_lv_ratio_lt_1 > 0.5)| 
(df_neg.rv_lv_ratio_gte_1 > 0.5)| 
(df_neg.central_pe > 0.5)| 
(df_neg.rightsided_pe > 0.5)| 
(df_neg.leftsided_pe > 0.5)| 
(df_neg.acute_and_chronic_pe > 0.5)| 
(df_neg.chronic_pe > 0.5)].reset_index(drop = True)
 rule2b['broken_rule'] = '2b'
errors = pd.concat([rule1a, rule1b, rule1c, rule1d, rule2a, rule2b], axis = 0)
 print('Found', len(errors), 'inconsistent predictions')
 return errors
test.isnull().sum().sort_values(ascending=False )<feature_engineering>
DO_FULL = False
FAST_COMMIT = path.exists('/kaggle/input/rsna-str-pulmonary-embolism-detection/train')and not DO_FULL
train.loc[train['id'] == 16,'revenue'] = 192864
train.loc[train['id'] == 90,'budget'] = 30000000
train.loc[train['id'] == 118,'budget'] = 60000000
train.loc[train['id'] == 149,'budget'] = 18000000
train.loc[train['id'] == 313,'revenue'] = 12000000
train.loc[train['id'] == 451,'revenue'] = 12000000
train.loc[train['id'] == 464,'budget'] = 20000000
train.loc[train['id'] == 470,'budget'] = 13000000
train.loc[train['id'] == 513,'budget'] = 930000
train.loc[train['id'] == 797,'budget'] = 8000000
train.loc[train['id'] == 819,'budget'] = 90000000
train.loc[train['id'] == 850,'budget'] = 90000000
train.loc[train['id'] == 1007,'budget'] = 2
train.loc[train['id'] == 1112,'budget'] = 7500000
train.loc[train['id'] == 1131,'budget'] = 4300000
train.loc[train['id'] == 1359,'budget'] = 10000000
train.loc[train['id'] == 1542,'budget'] = 1
train.loc[train['id'] == 1570,'budget'] = 15800000
train.loc[train['id'] == 1571,'budget'] = 4000000
train.loc[train['id'] == 1714,'budget'] = 46000000
train.loc[train['id'] == 1721,'budget'] = 17500000
train.loc[train['id'] == 1865,'revenue'] = 25000000
train.loc[train['id'] == 1885,'budget'] = 12
train.loc[train['id'] == 2091,'budget'] = 10
train.loc[train['id'] == 2268,'budget'] = 17500000
train.loc[train['id'] == 2491,'budget'] = 6
train.loc[train['id'] == 2602,'budget'] = 31000000
train.loc[train['id'] == 2612,'budget'] = 15000000
train.loc[train['id'] == 2696,'budget'] = 10000000
train.loc[train['id'] == 2801,'budget'] = 10000000
train.loc[train['id'] == 335,'budget'] = 2
train.loc[train['id'] == 348,'budget'] = 12
train.loc[train['id'] == 470,'budget'] = 13000000
train.loc[train['id'] == 513,'budget'] = 1100000
train.loc[train['id'] == 640,'budget'] = 6
train.loc[train['id'] == 696,'budget'] = 1
train.loc[train['id'] == 797,'budget'] = 8000000
train.loc[train['id'] == 850,'budget'] = 1500000
train.loc[train['id'] == 1199,'budget'] = 5
train.loc[train['id'] == 1282,'budget'] = 9
train.loc[train['id'] == 1347,'budget'] = 1
train.loc[train['id'] == 1755,'budget'] = 2
train.loc[train['id'] == 1801,'budget'] = 5
train.loc[train['id'] == 1918,'budget'] = 592
train.loc[train['id'] == 2033,'budget'] = 4
train.loc[train['id'] == 2118,'budget'] = 344
train.loc[train['id'] == 2252,'budget'] = 130
train.loc[train['id'] == 2256,'budget'] = 1
train.loc[train['id'] == 2696,'budget'] = 10000000
test.loc[test['id'] == 6733,'budget'] = 5000000
test.loc[test['id'] == 3889,'budget'] = 15000000
test.loc[test['id'] == 6683,'budget'] = 50000000
test.loc[test['id'] == 5704,'budget'] = 4300000
test.loc[test['id'] == 6109,'budget'] = 281756
test.loc[test['id'] == 7242,'budget'] = 10000000
test.loc[test['id'] == 7021,'budget'] = 17540562
test.loc[test['id'] == 5591,'budget'] = 4000000
test.loc[test['id'] == 4282,'budget'] = 20000000
test.loc[test['id'] == 3033,'budget'] = 250
test.loc[test['id'] == 3051,'budget'] = 50
test.loc[test['id'] == 3084,'budget'] = 337
test.loc[test['id'] == 3224,'budget'] = 4
test.loc[test['id'] == 3594,'budget'] = 25
test.loc[test['id'] == 3619,'budget'] = 500
test.loc[test['id'] == 3831,'budget'] = 3
test.loc[test['id'] == 3935,'budget'] = 500
test.loc[test['id'] == 4049,'budget'] = 995946
test.loc[test['id'] == 4424,'budget'] = 3
test.loc[test['id'] == 4460,'budget'] = 8
test.loc[test['id'] == 4555,'budget'] = 1200000
test.loc[test['id'] == 4624,'budget'] = 30
test.loc[test['id'] == 4645,'budget'] = 500
test.loc[test['id'] == 4709,'budget'] = 450
test.loc[test['id'] == 4839,'budget'] = 7
test.loc[test['id'] == 3125,'budget'] = 25
test.loc[test['id'] == 3142,'budget'] = 1
test.loc[test['id'] == 3201,'budget'] = 450
test.loc[test['id'] == 3222,'budget'] = 6
test.loc[test['id'] == 3545,'budget'] = 38
test.loc[test['id'] == 3670,'budget'] = 18
test.loc[test['id'] == 3792,'budget'] = 19
test.loc[test['id'] == 3881,'budget'] = 7
test.loc[test['id'] == 3969,'budget'] = 400
test.loc[test['id'] == 4196,'budget'] = 6
test.loc[test['id'] == 4221,'budget'] = 11
test.loc[test['id'] == 4222,'budget'] = 500
test.loc[test['id'] == 4285,'budget'] = 11
test.loc[test['id'] == 4319,'budget'] = 1
test.loc[test['id'] == 4639,'budget'] = 10
test.loc[test['id'] == 4719,'budget'] = 45
test.loc[test['id'] == 4822,'budget'] = 22
test.loc[test['id'] == 4829,'budget'] = 20
test.loc[test['id'] == 4969,'budget'] = 20
test.loc[test['id'] == 5021,'budget'] = 40
test.loc[test['id'] == 5035,'budget'] = 1
test.loc[test['id'] == 5063,'budget'] = 14
test.loc[test['id'] == 5119,'budget'] = 2
test.loc[test['id'] == 5214,'budget'] = 30
test.loc[test['id'] == 5221,'budget'] = 50
test.loc[test['id'] == 4903,'budget'] = 15
test.loc[test['id'] == 4983,'budget'] = 3
test.loc[test['id'] == 5102,'budget'] = 28
test.loc[test['id'] == 5217,'budget'] = 75
test.loc[test['id'] == 5224,'budget'] = 3
test.loc[test['id'] == 5469,'budget'] = 20
test.loc[test['id'] == 5840,'budget'] = 1
test.loc[test['id'] == 5960,'budget'] = 30
test.loc[test['id'] == 6506,'budget'] = 11
test.loc[test['id'] == 6553,'budget'] = 280
test.loc[test['id'] == 6561,'budget'] = 7
test.loc[test['id'] == 6582,'budget'] = 218
test.loc[test['id'] == 6638,'budget'] = 5
test.loc[test['id'] == 6749,'budget'] = 8
test.loc[test['id'] == 6759,'budget'] = 50
test.loc[test['id'] == 6856,'budget'] = 10
test.loc[test['id'] == 6858,'budget'] = 100
test.loc[test['id'] == 6876,'budget'] = 250
test.loc[test['id'] == 6972,'budget'] = 1
test.loc[test['id'] == 7079,'budget'] = 8000000
test.loc[test['id'] == 7150,'budget'] = 118
test.loc[test['id'] == 6506,'budget'] = 118
test.loc[test['id'] == 7225,'budget'] = 6
test.loc[test['id'] == 7231,'budget'] = 85
test.loc[test['id'] == 5222,'budget'] = 5
test.loc[test['id'] == 5322,'budget'] = 90
test.loc[test['id'] == 5350,'budget'] = 70
test.loc[test['id'] == 5378,'budget'] = 10
test.loc[test['id'] == 5545,'budget'] = 80
test.loc[test['id'] == 5810,'budget'] = 8
test.loc[test['id'] == 5926,'budget'] = 300
test.loc[test['id'] == 5927,'budget'] = 4
test.loc[test['id'] == 5986,'budget'] = 1
test.loc[test['id'] == 6053,'budget'] = 20
test.loc[test['id'] == 6104,'budget'] = 1
test.loc[test['id'] == 6130,'budget'] = 30
test.loc[test['id'] == 6301,'budget'] = 150
test.loc[test['id'] == 6276,'budget'] = 100
test.loc[test['id'] == 6473,'budget'] = 100
test.loc[test['id'] == 6842,'budget'] = 30
test.loc[test['release_date'].isnull() == True, 'release_date'] = '01/01/98'<categorify>
cfg = OmegaConf.load('/kaggle/input/rsna-str-pe-src/configs/predict/predict107.yaml')
 cfg.local_rank = 0
cfg.data.num_workers = 0
cfg.submission = '/kaggle/working/submission.csv'
predict_full(cfg, runtime=True, bsize=128, final_sub_only=True, fast_commit=FAST_COMMIT, use_vtk=True, debug=False )
<define_variables><EOS> 
sub = pd.read_csv('/kaggle/working/submission.csv')
 test = pd.read_csv('/kaggle/input/rsna-str-pulmonary-embolism-detection/test.csv')
 errors = check_consistency(sub, test)
 assert len(errors)== 0