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