Merge pull request 'pairs' (#5) from pairs into master
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gitea-physics/deepdog/pipeline/head This commit looks good
Reviewed-on: #5
This commit is contained in:
commit
604916a829
@ -1,6 +1,9 @@
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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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@ -26,6 +29,17 @@ def get_a_result(input) -> int:
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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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discretisation, dot_inputs, pair_inputs, local_lows, local_highs, nonlocal_lows, nonlocal_highs, monte_carlo_count, max_frequency = input
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sample_dipoles = discretisation.get_model().get_n_single_dipoles(monte_carlo_count, max_frequency)
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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(pair_inputs, sample_dipoles)
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nonlocal_matches = pdme.util.fast_v_calc.between(nonlocal_vals, nonlocal_lows, nonlocal_highs)
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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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@ -43,18 +57,34 @@ class AltBayesRun():
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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, low_error: float = 0.9, high_error: float = 1.1, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, max_frequency: float = 20, end_threshold: float = None, chunksize: int = CHUNKSIZE) -> None:
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self.dot_inputs = dot_inputs
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self.dot_inputs_array = pdme.measurement.oscillating_dipole.dot_inputs_to_array(dot_inputs)
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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:
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self.dot_inputs = pdme.inputs.inputs_with_frequency_range(dot_positions, frequency_range)
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self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(self.dot_inputs)
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self.use_pairs = use_pairs
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self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(dot_positions, frequency_range)
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self.dot_pair_inputs_array = pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_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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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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@ -64,7 +94,10 @@ class AltBayesRun():
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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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self.filename = f"{timestamp}-{filename_slug}.altbayes.csv"
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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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@ -89,7 +122,13 @@ class AltBayesRun():
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actual_dipoles = self.actual_model.get_dipoles(frequency)
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dots = actual_dipoles.get_percent_range_dot_measurements(self.dot_inputs, self.low_error, self.high_error)
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lows, highs = pdme.measurement.oscillating_dipole.dot_range_measurements_low_high_arrays(dots)
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lows, highs = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(dots)
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pair_lows, pair_highs = (None, None)
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if self.use_pairs:
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pair_measurements = actual_dipoles.get_percent_range_dot_pair_measurements(self.dot_pair_inputs, self.pairs_low_error, self.pairs_high_error)
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pair_lows, pair_highs = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(pair_measurements)
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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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@ -97,9 +136,25 @@ class AltBayesRun():
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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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results.append(sum(
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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)
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))
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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 (cycle_success <= self.target_success):
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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(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)
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)
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else:
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current_success = sum(
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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)
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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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@ -109,12 +164,12 @@ class AltBayesRun():
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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, result) in enumerate(zip(self.model_names, results)):
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for model_index, (name, (count, result)) in enumerate(zip(self.model_names, results)):
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row[f"{name}_success"] = result
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row[f"{name}_count"] = self.monte_carlo_count * self.monte_carlo_cycles
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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(self.monte_carlo_count * self.monte_carlo_cycles)
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counts.append(count)
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success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
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new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]
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64
poetry.lock
generated
64
poetry.lock
generated
@ -305,7 +305,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
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[[package]]
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name = "pdme"
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version = "0.5.4"
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version = "0.6.1"
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description = "Python dipole model evaluator"
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category = "main"
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optional = false
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@ -313,7 +313,7 @@ python-versions = ">=3.8,<3.10"
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[package.dependencies]
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numpy = ">=1.21.1,<2.0.0"
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scipy = ">=1.5,<1.6"
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scipy = ">=1.8,<1.9"
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[[package]]
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name = "pkginfo"
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@ -533,14 +533,14 @@ idna2008 = ["idna"]
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[[package]]
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name = "scipy"
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version = "1.5.4"
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version = "1.8.0"
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description = "SciPy: Scientific Library for Python"
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category = "main"
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optional = false
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python-versions = ">=3.6"
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python-versions = ">=3.8,<3.11"
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[package.dependencies]
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numpy = ">=1.14.5"
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numpy = ">=1.17.3,<1.25.0"
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[[package]]
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name = "secretstorage"
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@ -698,7 +698,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
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[metadata]
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lock-version = "1.1"
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python-versions = "^3.8,<3.10"
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content-hash = "e553e861684fb28f832738e60e0ed24234343e83bd5d26ffb6f092fd2d739fa0"
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content-hash = "2911e2f292dbc9f6a87bfbb0b534695e2f0ae1e1a260aecca10955e685d6f846"
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[metadata.files]
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atomicwrites = [
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@ -956,8 +956,8 @@ packaging = [
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||||
{file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"},
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]
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pdme = [
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{file = "pdme-0.5.4-py3-none-any.whl", hash = "sha256:90ba75efbec04f5a505c9c5228824538510149e218372e758c15150d5967d92b"},
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{file = "pdme-0.6.1-py3-none-any.whl", hash = "sha256:c73edeeab13a5518a016f5ac1fdf576f328030530a49991ec822f0ca0c311ff3"},
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{file = "pdme-0.6.1.tar.gz", hash = "sha256:c857a905d5fbc03ac24777d8fd652e1c5531d0817088748ce443ad1b378124f9"},
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]
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pkginfo = [
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@ -1028,31 +1028,29 @@ rfc3986 = [
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|
||||
{file = "scipy-1.8.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:92b2c2af4183ed09afb595709a8ef5783b2baf7f41e26ece24e1329c109691a7"},
|
||||
{file = "scipy-1.8.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a279e27c7f4566ef18bab1b1e2c37d168e365080974758d107e7d237d3f0f484"},
|
||||
{file = "scipy-1.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad5be4039147c808e64f99c0e8a9641eb5d2fa079ff5894dcd8240e94e347af4"},
|
||||
{file = "scipy-1.8.0-cp38-cp38-win32.whl", hash = "sha256:3d9dd6c8b93a22bf9a3a52d1327aca7e092b1299fb3afc4f89e8eba381be7b59"},
|
||||
{file = "scipy-1.8.0-cp38-cp38-win_amd64.whl", hash = "sha256:5e73343c5e0d413c1f937302b2e04fb07872f5843041bcfd50699aef6e95e399"},
|
||||
{file = "scipy-1.8.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:de2e80ee1d925984c2504812a310841c241791c5279352be4707cdcd7c255039"},
|
||||
{file = "scipy-1.8.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:c2bae431d127bf0b1da81fc24e4bba0a84d058e3a96b9dd6475dfcb3c5e8761e"},
|
||||
{file = "scipy-1.8.0-cp39-cp39-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:723b9f878095ed994756fa4ee3060c450e2db0139c5ba248ee3f9628bd64e735"},
|
||||
{file = "scipy-1.8.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:011d4386b53b933142f58a652aa0f149c9b9242abd4f900b9f4ea5fbafc86b89"},
|
||||
{file = "scipy-1.8.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6f0cd9c0bd374ef834ee1e0f0999678d49dcc400ea6209113d81528958f97c7"},
|
||||
{file = "scipy-1.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3720d0124aced49f6f2198a6900304411dbbeed12f56951d7c66ebef05e3df6"},
|
||||
{file = "scipy-1.8.0-cp39-cp39-win32.whl", hash = "sha256:3d573228c10a3a8c32b9037be982e6440e411b443a6267b067cac72f690b8d56"},
|
||||
{file = "scipy-1.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:bb7088e89cd751acf66195d2f00cf009a1ea113f3019664032d9075b1e727b6c"},
|
||||
{file = "scipy-1.8.0.tar.gz", hash = "sha256:31d4f2d6b724bc9a98e527b5849b8a7e589bf1ea630c33aa563eda912c9ff0bd"},
|
||||
]
|
||||
secretstorage = [
|
||||
{file = "SecretStorage-3.3.1-py3-none-any.whl", hash = "sha256:422d82c36172d88d6a0ed5afdec956514b189ddbfb72fefab0c8a1cee4eaf71f"},
|
||||
|
||||
@ -6,7 +6,7 @@ authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.8,<3.10"
|
||||
pdme = "^0.5.4"
|
||||
pdme = "^0.6.1"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
pytest = ">=6"
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user