56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
import numpy as np
|
|
|
|
|
|
def from_name(err_name):
|
|
assert err_name in ERROR_NAMES, f"unknown error {err_name}"
|
|
callable_error = globals()[err_name]
|
|
return callable_error
|
|
|
|
|
|
# def f1(prev):
|
|
# # https://github.com/dice-group/gerbil/wiki/Precision,-Recall-and-F1-measure
|
|
# if prev[0] == 0 and prev[1] == 0 and prev[2] == 0:
|
|
# return 1.0
|
|
# elif prev[0] == 0 and prev[1] > 0 and prev[2] == 0:
|
|
# return 0.0
|
|
# elif prev[0] == 0 and prev[1] == 0 and prev[2] > 0:
|
|
# return float('NaN')
|
|
# else:
|
|
# recall = prev[0] / (prev[0] + prev[1])
|
|
# precision = prev[0] / (prev[0] + prev[2])
|
|
# return 2 * (precision * recall) / (precision + recall)
|
|
|
|
|
|
def f1(prev):
|
|
den = (2 * prev[3]) + prev[1] + prev[2]
|
|
if den == 0:
|
|
return 0.0
|
|
else:
|
|
return (2 * prev[3]) / den
|
|
|
|
|
|
def f1e(prev):
|
|
return 1 - f1(prev)
|
|
|
|
|
|
def acc(prev: np.ndarray) -> float:
|
|
return (prev[0] + prev[3]) / np.sum(prev)
|
|
|
|
|
|
def accd(true_prevs: np.ndarray, estim_prevs: np.ndarray) -> np.ndarray:
|
|
vacc = np.vectorize(acc, signature="(m)->()")
|
|
a_tp = vacc(true_prevs)
|
|
a_ep = vacc(estim_prevs)
|
|
return np.abs(a_tp - a_ep)
|
|
|
|
|
|
def maccd(true_prevs: np.ndarray, estim_prevs: np.ndarray) -> float:
|
|
return accd(true_prevs, estim_prevs).mean()
|
|
|
|
|
|
ACCURACY_ERROR = {maccd}
|
|
ACCURACY_ERROR_SINGLE = {accd}
|
|
ACCURACY_ERROR_NAMES = {func.__name__ for func in ACCURACY_ERROR}
|
|
ACCURACY_ERROR_SINGLE_NAMES = {func.__name__ for func in ACCURACY_ERROR_SINGLE}
|
|
ERROR_NAMES = ACCURACY_ERROR_NAMES | ACCURACY_ERROR_SINGLE_NAMES
|