Source code for wimarka.tasks.scoring

import pandas as pd
import wimarka
from wimarka.utils.model import load_model
from wimarka.utils.helper import get_column
import numpy as np
import torch
import sys

sys.modules['__main__'].get_column = wimarka.utils.helper.get_column



def classify(score):
    if score >= 0.81:
        return "Very High"
    elif score >= 0.61:
        return "High"
    elif score >=0.41:
        return "Average"
    elif score>=0.21:
        return "Poor"
    return "Very Poor"




def scoring(source, target, errors):

    model = load_model('regression')
    data = {
        'Source': [source],
        'Original Translation': [target],
        'Errors': [errors]
    }

    X_new = pd.DataFrame(data)
    predictions = model.predict(X_new)
    tensor = torch.tensor(predictions)
    predictions = tensor.tolist()

    scores = []

    for i,score in enumerate(predictions[0]):
        scores.append(f"{classify(score)} ({round(score, 2)})")

    return scores[0], scores[1], scores[2]