89 lines
1.7 KiB
Python
89 lines
1.7 KiB
Python
from sklearn.metrics import f1_score
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import numpy as np
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SAMPLE_SIZE = None
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def f1e(y_true, y_pred):
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return 1. - f1_score(y_true, y_pred, average='macro')
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def acce(y_true, y_pred):
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return 1. - (y_true == y_pred).mean()
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def mae(prevs, prevs_hat):
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return ae(prevs, prevs_hat).mean()
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def ae(p, p_hat):
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assert p.shape == p_hat.shape, 'wrong shape'
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return abs(p_hat-p).mean(axis=-1)
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def mse(prevs, prevs_hat):
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return se(prevs, prevs_hat).mean()
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def se(p, p_hat):
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return ((p_hat-p)**2).mean(axis=-1)
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def mkld(prevs, prevs_hat):
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return kld(prevs, prevs_hat).mean()
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def kld(p, p_hat, eps=None):
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eps = __check_eps(eps)
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sp = p+eps
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sp_hat = p_hat + eps
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return (sp*np.log(sp/sp_hat)).sum(axis=-1)
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def mnkld(prevs, prevs_hat):
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return nkld(prevs, prevs_hat).mean()
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def nkld(p, p_hat, eps=None):
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ekld = np.exp(kld(p, p_hat, eps))
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return 2. * ekld / (1 + ekld) - 1.
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def mrae(p, p_hat, eps=None):
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return rae(p, p_hat, eps).mean()
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def rae(p, p_hat, eps=None):
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eps = __check_eps(eps)
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p = smooth(p, eps)
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p_hat = smooth(p_hat, eps)
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return (abs(p-p_hat)/p).mean(axis=-1)
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def smooth(p, eps):
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n_classes = p.shape[-1]
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return (p+eps)/(eps*n_classes + 1)
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def __check_eps(eps):
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if eps is None:
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if SAMPLE_SIZE is None:
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raise ValueError('eps was not defined, and qp.error.SAMPLE_SIZE was not set')
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else:
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eps = 1. / (2. * SAMPLE_SIZE)
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return eps
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CLASSIFICATION_ERROR = {f1e, acce}
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QUANTIFICATION_ERROR = {mae, mrae, mse, mkld, mnkld}
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f1_error = f1e
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acc_error = acce
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mean_absolute_error = mae
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absolute_error = ae
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mean_relative_absolute_error = mrae
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relative_absolute_error = rae
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