Kaggle competition: TReNDS Neuroimaging
<SOS> metric: WMAE Kaggle data source: trends-neuroimaging<create_dataframe>
import numpy as np
import pandas as pd
import os
xnli = nlp.load_dataset(path='xnli' )<create_dataframe>
svr = pd.read_csv('/kaggle/input/rapids-svm-on-trends-neuroimaging/submission.csv')
 svr
buffer = {
'premise': [],
'hypothesis': [],
'label': [],
'lang_abv': []
}
for x in xnli['validation']:
label = x['label']
for idx, lang in enumerate(x['hypothesis']['language']):
hypothesis = x['hypothesis']['translation'][idx]
premise = x['premise'][lang]
buffer['premise'].append(premise)
 buffer['hypothesis'].append(hypothesis)
 buffer['label'].append(label)
 buffer['lang_abv'].append(lang)
 xnli_valid_df = pd.DataFrame(buffer)
 xnli_valid_df = xnli_valid_df[['premise', 'hypothesis', 'label', 'lang_abv']]<load_pretrained>
nn = pd.read_csv('/kaggle/input/trends-tabular-nn-0-159/submission_10kfold_blended.csv')
 nn
response = requests.get('https://miro.medium.com/max/750/0*cZwrV8EyfJSeunag.png')
 image_bytes = io.BytesIO(response.content)
 img = PIL.Image.open(image_bytes)
 img<load_pretrained>
submission = nn.append(svr ).groupby('Id' ).mean().reset_index()
submission
<install_modules><EOS> 
submission.to_csv("submission_svr_nn.csv", index=False )