def prepare_logistic(self, normalized_dist: dist.Logistic) -> dist.Logistic:
"""
Transform a single logistic distribution by clipping the
parameters and adding scale information as needed for submission to
Metaculus. The loc and scale have to be within a certain range
for the Metaculus API to accept the prediction.
:param dist: a (normalized) logistic distribution
:return: a transformed logistic distribution
"""
if hasattr(normalized_dist, "base_dist"):
normalized_dist = normalized_dist.base_dist # type: ignore
if normalized_dist.s <= 0:
raise ValueError("logistic_params.scale must be greater than 0")
clipped_loc = min(normalized_dist.loc, max_loc)
clipped_scale = float(onp.clip(normalized_dist.s, min_scale, max_scale)) # type: ignore
if self.low_open:
low = float(onp.clip(normalized_dist.cdf(0), min_open_low, max_open_low,))
else:
low = 0
if self.high_open:
high = float(
onp.clip(normalized_dist.cdf(1), min_open_high + low, max_open_high,)
)
else:
high = 1
return dist.Logistic(
clipped_loc, clipped_scale, Scale(0, 1), {"low": low, "high": high}
)
def get_logistic_from_json(logistic_json: Dict) -> dist.Logistic:
return dist.Logistic(
logistic_json["x0"],
logistic_json["s"],
metadata={
"low": logistic_json["low"],
"high": logistic_json["high"]
},
)
def get_logistic_from_json(self, logistic_json: Dict) -> dist.Logistic:
bounds = self.get_bounds()
normed_bounds = {}
if bounds.floor is not None:
normed_bounds["floor"] = self.scale.normalize_point(bounds.floor)
if bounds.ceiling is not None:
normed_bounds["ceiling"] = self.scale.normalize_point(bounds.ceiling)
return dist.Truncate( # type: ignore
dist.Logistic(logistic_json["x0"], logistic_json["s"], normalized=True),
**normed_bounds,
)