어떤 레몬이 문제인지 찾기: ML로 비즈니스 가치를 평가하고 최적화하기

inp,preds,targs,out = learn.get_preds(with_input=True, with_decoded=True)
inp.shape, preds.shape, targs.shape, out.shape

imgs = [wandb.Image(t.permute(1,2,0)) for t in inp] # we need to put as channels last for wandb.Image 
pred_proba = preds[:,1].numpy().tolist()
targets = targs.numpy().tolist()
predictions = out.numpy().tolist()

preds_df = pd.DataFrame(list(zip(imgs, pred_proba, predictions, targets)),
                        columns =['image', 'probability', 'prediction', 'target'])

run.log({'predictions_table': wandb.Table(dataframe=preds_df)})
run.finish()

import wandb
import pandas as pd
from fastcore.all import Path

def val_pred_table(run_id, entity='wandb_course', proj='lemon-project'):
    "Get prediction table on the validation set for the lemon project."
    api = wandb.Api()
    path = api.artifact(f'{entity}/{proj}/run-{run_id}-predictions_table:v0').download()
    preds = (Path(path)/'predictions_table.table.json').read_json()
    return pd.DataFrame([{'pred':p, 'label':t}  for _,p,_,t in preds['data']])

if __name__ == '__main__':
    preds = val_pred_table('zxum4ef0')
    preds.to_excel(Path('Validtation_Predictions.xlsx')