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import torch
import config
def categorical_accuracy(preds, y):
"""
Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8
"""
max_preds = preds.argmax(
dim=1, keepdim=True) # get the index of the max probability
correct = max_preds.squeeze(1).eq(y)
return correct.sum() / torch.FloatTensor([y.shape[0]])
def label_encoder(x):
label_vec = {"0": 0, "1": 1, "-1": 2}
return label_vec[x.replace("__label__", "")]
def label_decoder(x):
label_vec = { 0:"U", 1:"P", 2:"N"}
return label_vec[x]
def label_full_decoder(x):
label_vec = { 0:"Neutral", 1:"Positive", 2:"Negative"}
return label_vec[x]
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