physics/deepdog
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feat: Adds dynamic cycle count increases to help reach minimum success count
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This commit is contained in:
Deepak Mallubhotla 2022-03-28 15:46:40 -05:00
parent 31e6cfaf51
commit ec7b4cac39
Signed by: deepak
GPG Key ID: BEBAEBF28083E022
1 changed files with 25 additions and 12 deletions
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37
deepdog/alt_bayes_run.py
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@ -57,7 +57,7 @@ class AltBayesRun():
run_count: int
The number of runs to do.
'''
def __init__(self, dot_positions: Sequence[numpy.typing.ArrayLike], frequency_range: Sequence[float], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int = 100, low_error: float = 0.9, high_error: float = 1.1, pairs_high_error=None, pairs_low_error=None, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, max_frequency: float = 20, end_threshold: float = None, chunksize: int = CHUNKSIZE, use_pairs: bool = False) -> None:
def __init__(self, dot_positions: Sequence[numpy.typing.ArrayLike], frequency_range: Sequence[float], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int = 100, low_error: float = 0.9, high_error: float = 1.1, pairs_high_error=None, pairs_low_error=None, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, target_success: int = 100, max_monte_carlo_cycles_steps: int = 10, max_frequency: float = 20, end_threshold: float = None, chunksize: int = CHUNKSIZE, use_pairs: bool = False) -> None:
self.dot_inputs = pdme.inputs.inputs_with_frequency_range(dot_positions, frequency_range)
self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(self.dot_inputs)
@ -72,6 +72,8 @@ class AltBayesRun():
self.model_count = len(self.discretisations)
self.monte_carlo_count = monte_carlo_count
self.monte_carlo_cycles = monte_carlo_cycles
self.target_success = target_success
self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
self.run_count = run_count
self.low_error = low_error
self.high_error = high_error
@ -134,14 +136,25 @@ class AltBayesRun():
for disc_count, discretisation in enumerate(self.discretisations):
_logger.debug(f"Doing discretisation #{disc_count}")
with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
if self.use_pairs:
results.append(sum(
pool.imap_unordered(get_a_result_using_pairs, [(discretisation, self.dot_inputs_array, self.dot_pair_inputs_array, lows, highs, pair_lows, pair_highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
))
else:
results.append(sum(
pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
))
cycle_count = 0
cycle_success = 0
cycles = 0
while (cycles < self.max_monte_carlo_cycles_steps) and (cycle_success <= self.target_success):
_logger.debug(f"Starting cycle {cycles}")
cycles += 1
current_success = 0
cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
if self.use_pairs:
current_success = sum(
pool.imap_unordered(get_a_result_using_pairs, [(discretisation, self.dot_inputs_array, self.dot_pair_inputs_array, lows, highs, pair_lows, pair_highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
)
else:
current_success = sum(
pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
)
cycle_success += current_success
results.append((cycle_count, cycle_success))
_logger.debug("Done, constructing output now")
row = {
@ -151,12 +164,12 @@ class AltBayesRun():
}
successes: List[float] = []
counts: List[int] = []
for model_index, (name, result) in enumerate(zip(self.model_names, results)):
for model_index, (name, (count, result)) in enumerate(zip(self.model_names, results)):
row[f"{name}_success"] = result
row[f"{name}_count"] = self.monte_carlo_count * self.monte_carlo_cycles
row[f"{name}_count"] = count
successes.append(max(result, 0.5))
counts.append(self.monte_carlo_count * self.monte_carlo_cycles)
counts.append(count)
success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]