feat: Adds end threshold for early abort
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@ -41,7 +41,7 @@ class BayesRun():
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run_count: int
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The number of runs to do.
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'''
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def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, max_frequency: float = None) -> None:
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def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, max_frequency: float = None, end_threshold: float = None) -> None:
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self.dot_inputs = dot_inputs
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self.discretisations = [disc for (_, disc) in discretisations_with_names]
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self.model_names = [name for (name, _) in discretisations_with_names]
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@ -50,7 +50,6 @@ class BayesRun():
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self.run_count = run_count
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self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
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self.compensate_zeros = True
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for name in self.model_names:
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self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
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@ -60,6 +59,13 @@ class BayesRun():
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self.filename = f"{timestamp}-{filename_slug}.csv"
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self.max_frequency = max_frequency
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if end_threshold is not None:
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if 0 < self.end_threshold < 1:
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self.end_threshold: float = end_threshold
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self.use_end_threshold = True
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else:
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raise ValueError(f"{end_threshold} should be between 0 and 1")
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def go(self) -> None:
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
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@ -111,3 +117,9 @@ class BayesRun():
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
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writer.writerow(row)
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if self.use_end_threshold:
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max_prob = max(self.probabilities)
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if max_prob > self.end_threshold:
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_logger.info(f"Aborting early, because {max_prob} is greater than {self.end_threshold}")
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break
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