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Increased Robustness: Prevent crashes if dataset contains only identical elements #7

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Increased Robustness: Prevent crashes if dataset contains only identical elements #7

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fd159f2
Update beta_calibration.py
stheid Feb 12, 2024
d20b050
Update beta_calibration.py
stheid Feb 12, 2024
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11 changes: 6 additions & 5 deletions betacal/beta_calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,21 +30,22 @@ def _beta_calibration(df, y, sample_weight=None):
lr = LogisticRegression(C=99999999999)
lr.fit(x, y, sample_weight)
coefs = lr.coef_[0]
a = 0
a = None
b = coefs[0]
elif coefs[1] < 0:
x = x[:, 0].reshape(-1, 1)
lr = LogisticRegression(C=99999999999)
lr.fit(x, y, sample_weight)
coefs = lr.coef_[0]
a = coefs[0]
b = 0
b = None
else:
a = coefs[0]
b = coefs[1]
inter = lr.intercept_[0]

m = minimize_scalar(lambda mh: np.abs(b*np.log(1.-mh)-a*np.log(mh)-inter),
a_, b_ = a or 0, b or 0
m = minimize_scalar(lambda mh: np.abs(b_*np.log(1.-mh)-a_*np.log(mh)-inter),
bounds=[0, 1], method='Bounded').x
map = [a, b, m]
return map, lr
Expand Down Expand Up @@ -112,9 +113,9 @@ def predict(self, S):
x = np.hstack((df, 1. - df))
x = np.log(x)
x[:, 1] *= -1
if self.map_[0] == 0:
if self.map_[0] == None:
x = x[:, 1].reshape(-1, 1)
elif self.map_[1] == 0:
elif self.map_[1] == None:
x = x[:, 0].reshape(-1, 1)

return self.lr_.predict_proba(x)[:, 1]
Expand Down