308 lines
8.8 KiB
Python
308 lines
8.8 KiB
Python
import pdme.inputs
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import pdme.model
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import pdme.measurement.input_types
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import pdme.measurement.oscillating_dipole
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import pdme.util.fast_v_calc
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import pdme.util.fast_nonlocal_spectrum
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from typing import Sequence, Tuple, List
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import datetime
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import csv
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import multiprocessing
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import logging
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import numpy
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# TODO: remove hardcode
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CHUNKSIZE = 50
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# TODO: It's garbage to have this here duplicated from pdme.
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DotInput = Tuple[numpy.typing.ArrayLike, float]
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_logger = logging.getLogger(__name__)
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def get_a_result(input) -> int:
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discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
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sample_dipoles = discretisation.get_model().get_n_single_dipoles(
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monte_carlo_count, max_frequency
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)
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vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
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return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
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def get_a_result_using_pairs(input) -> int:
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(
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discretisation,
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dot_inputs,
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pair_inputs,
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local_lows,
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local_highs,
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nonlocal_lows,
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nonlocal_highs,
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monte_carlo_count,
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max_frequency,
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) = input
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sample_dipoles = discretisation.get_model().get_n_single_dipoles(
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monte_carlo_count, max_frequency
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)
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local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
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local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
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nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
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pair_inputs, sample_dipoles
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)
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nonlocal_matches = pdme.util.fast_v_calc.between(
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nonlocal_vals, nonlocal_lows, nonlocal_highs
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)
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combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
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return numpy.count_nonzero(combined_matches)
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class AltBayesRun:
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"""
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A single Bayes run for a given set of dots.
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Parameters
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----------
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dot_inputs : Sequence[DotInput]
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The dot inputs for this bayes run.
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discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
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The models to evaluate.
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actual_model_discretisation : pdme.model.Discretisation
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The discretisation for the model which is actually correct.
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filename_slug : str
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The filename slug to include.
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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__(
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self,
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dot_positions: Sequence[numpy.typing.ArrayLike],
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frequency_range: Sequence[float],
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discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
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actual_model: pdme.model.Model,
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filename_slug: str,
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run_count: int = 100,
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low_error: float = 0.9,
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high_error: float = 1.1,
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pairs_high_error=None,
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pairs_low_error=None,
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monte_carlo_count: int = 10000,
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monte_carlo_cycles: int = 10,
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target_success: int = 100,
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max_monte_carlo_cycles_steps: int = 10,
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max_frequency: float = 20,
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end_threshold: float = None,
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chunksize: int = CHUNKSIZE,
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use_pairs: bool = False,
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) -> None:
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self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
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dot_positions, frequency_range
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)
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self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
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self.dot_inputs
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)
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self.use_pairs = use_pairs
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self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(
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dot_positions, frequency_range
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)
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self.dot_pair_inputs_array = (
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pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
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)
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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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self.actual_model = actual_model
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self.model_count = len(self.discretisations)
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self.monte_carlo_count = monte_carlo_count
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self.monte_carlo_cycles = monte_carlo_cycles
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self.target_success = target_success
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self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
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self.run_count = run_count
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self.low_error = low_error
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self.high_error = high_error
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if pairs_low_error is None:
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self.pairs_low_error = self.low_error
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else:
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self.pairs_low_error = pairs_low_error
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if pairs_high_error is None:
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self.pairs_high_error = self.high_error
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else:
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self.pairs_high_error = pairs_high_error
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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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self.chunksize = chunksize
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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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self.probabilities = [1 / self.model_count] * self.model_count
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timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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if self.use_pairs:
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self.filename = f"{timestamp}-{filename_slug}.altbayes.pairs.csv"
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else:
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self.filename = f"{timestamp}-{filename_slug}.altbayes.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 < 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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_logger.info(f"Will abort early, at {self.end_threshold}.")
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else:
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raise ValueError(
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f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
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)
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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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writer.writeheader()
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for run in range(1, self.run_count + 1):
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rng = numpy.random.default_rng()
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frequency = rng.uniform(1, self.max_frequency)
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# Generate the actual dipoles
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actual_dipoles = self.actual_model.get_dipoles(frequency)
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dots = actual_dipoles.get_percent_range_dot_measurements(
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self.dot_inputs, self.low_error, self.high_error
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)
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(
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lows,
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highs,
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) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
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dots
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)
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pair_lows, pair_highs = (None, None)
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if self.use_pairs:
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pair_measurements = (
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actual_dipoles.get_percent_range_dot_pair_measurements(
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self.dot_pair_inputs,
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self.pairs_low_error,
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self.pairs_high_error,
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)
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)
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(
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pair_lows,
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pair_highs,
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) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
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pair_measurements
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)
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_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
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results = []
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_logger.debug("Going to iterate over discretisations now")
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for disc_count, discretisation in enumerate(self.discretisations):
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_logger.debug(f"Doing discretisation #{disc_count}")
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with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
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cycle_count = 0
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cycle_success = 0
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cycles = 0
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while (cycles < self.max_monte_carlo_cycles_steps) and (
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cycle_success <= self.target_success
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):
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_logger.debug(f"Starting cycle {cycles}")
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cycles += 1
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current_success = 0
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cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
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if self.use_pairs:
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current_success = sum(
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pool.imap_unordered(
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get_a_result_using_pairs,
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[
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(
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discretisation,
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self.dot_inputs_array,
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self.dot_pair_inputs_array,
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lows,
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highs,
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pair_lows,
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pair_highs,
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self.monte_carlo_count,
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self.max_frequency,
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)
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]
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* self.monte_carlo_cycles,
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self.chunksize,
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)
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)
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else:
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current_success = sum(
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pool.imap_unordered(
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get_a_result,
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[
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(
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discretisation,
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self.dot_inputs_array,
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lows,
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highs,
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self.monte_carlo_count,
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self.max_frequency,
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)
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]
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* self.monte_carlo_cycles,
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self.chunksize,
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)
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)
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cycle_success += current_success
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results.append((cycle_count, cycle_success))
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_logger.debug("Done, constructing output now")
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row = {
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"dipole_moment": actual_dipoles.dipoles[0].p,
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"dipole_location": actual_dipoles.dipoles[0].s,
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"dipole_frequency": actual_dipoles.dipoles[0].w,
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}
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successes: List[float] = []
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counts: List[int] = []
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for model_index, (name, (count, result)) in enumerate(
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zip(self.model_names, results)
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):
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row[f"{name}_success"] = result
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row[f"{name}_count"] = count
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successes.append(max(result, 0.5))
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counts.append(count)
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success_weight = sum(
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[
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(succ / count) * prob
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for succ, count, prob in zip(successes, counts, self.probabilities)
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]
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)
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new_probabilities = [
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(succ / count) * old_prob / success_weight
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for succ, count, old_prob in zip(successes, counts, self.probabilities)
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]
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self.probabilities = new_probabilities
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for name, probability in zip(self.model_names, self.probabilities):
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row[f"{name}_prob"] = probability
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_logger.info(row)
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(
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outfile, fieldnames=self.csv_fields, dialect="unix"
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)
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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(
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f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
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)
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break
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