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multi #8

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deepak merged 16 commits from multi into master 2022-05-22 00:24:08 +00:00
Conversation 0 Commits 16 Files Changed 7 +233 -567
7 changed files with 233 additions and 567 deletions
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8
deepdog/__init__.py
View File

@ -1,9 +1,7 @@
import logging import logging
from deepdog.meta import __version__ from deepdog.meta import __version__
from deepdog.bayes_run import BayesRun from deepdog.bayes_run import BayesRun
from deepdog.alt_bayes_run import AltBayesRun from deepdog.bayes_run_simulpairs import BayesRunSimulPairs
from deepdog.alt_bayes_run_simulpairs import AltBayesRunSimulPairs
from deepdog.diagnostic import Diagnostic
def get_version(): def get_version():
@ -13,9 +11,7 @@ def get_version():
__all__ = [ __all__ = [
"get_version", "get_version",
"BayesRun", "BayesRun",
"AltBayesRun", "BayesRunSimulPairs",
"AltBayesRunSimulPairs",
"Diagnostic",
] ]

307
deepdog/alt_bayes_run.py
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@ -1,307 +0,0 @@
import pdme.inputs
import pdme.model
import pdme.measurement.input_types
import pdme.measurement.oscillating_dipole
import pdme.util.fast_v_calc
import pdme.util.fast_nonlocal_spectrum
from typing import Sequence, Tuple, List
import datetime
import csv
import multiprocessing
import logging
import numpy
# TODO: remove hardcode
CHUNKSIZE = 50
# TODO: It's garbage to have this here duplicated from pdme.
DotInput = Tuple[numpy.typing.ArrayLike, float]
_logger = logging.getLogger(__name__)
def get_a_result(input) -> int:
discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
sample_dipoles = discretisation.get_model().get_n_single_dipoles(
monte_carlo_count, max_frequency
)
vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
def get_a_result_using_pairs(input) -> int:
(
discretisation,
dot_inputs,
pair_inputs,
local_lows,
local_highs,
nonlocal_lows,
nonlocal_highs,
monte_carlo_count,
max_frequency,
) = input
sample_dipoles = discretisation.get_model().get_n_single_dipoles(
monte_carlo_count, max_frequency
)
local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
pair_inputs, sample_dipoles
)
nonlocal_matches = pdme.util.fast_v_calc.between(
nonlocal_vals, nonlocal_lows, nonlocal_highs
)
combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
return numpy.count_nonzero(combined_matches)
class AltBayesRun:
"""
A single Bayes run for a given set of dots.
Parameters
----------
dot_inputs : Sequence[DotInput]
The dot inputs for this bayes run.
discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
The models to evaluate.
actual_model_discretisation : pdme.model.Discretisation
The discretisation for the model which is actually correct.
filename_slug : str
The filename slug to include.
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,
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
)
self.use_pairs = use_pairs
self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(
dot_positions, frequency_range
)
self.dot_pair_inputs_array = (
pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
)
self.discretisations = [disc for (_, disc) in discretisations_with_names]
self.model_names = [name for (name, _) in discretisations_with_names]
self.actual_model = actual_model
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
if pairs_low_error is None:
self.pairs_low_error = self.low_error
else:
self.pairs_low_error = pairs_low_error
if pairs_high_error is None:
self.pairs_high_error = self.high_error
else:
self.pairs_high_error = pairs_high_error
self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
self.compensate_zeros = True
self.chunksize = chunksize
for name in self.model_names:
self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
self.probabilities = [1 / self.model_count] * self.model_count
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
if self.use_pairs:
self.filename = f"{timestamp}-{filename_slug}.altbayes.pairs.csv"
else:
self.filename = f"{timestamp}-{filename_slug}.altbayes.csv"
self.max_frequency = max_frequency
if end_threshold is not None:
if 0 < end_threshold < 1:
self.end_threshold: float = end_threshold
self.use_end_threshold = True
_logger.info(f"Will abort early, at {self.end_threshold}.")
else:
raise ValueError(
f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
)
def go(self) -> None:
with open(self.filename, "a", newline="") as outfile:
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
writer.writeheader()
for run in range(1, self.run_count + 1):
rng = numpy.random.default_rng()
frequency = rng.uniform(1, self.max_frequency)
# Generate the actual dipoles
actual_dipoles = self.actual_model.get_dipoles(frequency)
dots = actual_dipoles.get_percent_range_dot_measurements(
self.dot_inputs, self.low_error, self.high_error
)
(
lows,
highs,
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
dots
)
pair_lows, pair_highs = (None, None)
if self.use_pairs:
pair_measurements = (
actual_dipoles.get_percent_range_dot_pair_measurements(
self.dot_pair_inputs,
self.pairs_low_error,
self.pairs_high_error,
)
)
(
pair_lows,
pair_highs,
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
pair_measurements
)
_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
results = []
_logger.debug("Going to iterate over discretisations now")
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:
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 = {
"dipole_moment": actual_dipoles.dipoles[0].p,
"dipole_location": actual_dipoles.dipoles[0].s,
"dipole_frequency": actual_dipoles.dipoles[0].w,
}
successes: List[float] = []
counts: List[int] = []
for model_index, (name, (count, result)) in enumerate(
zip(self.model_names, results)
):
row[f"{name}_success"] = result
row[f"{name}_count"] = count
successes.append(max(result, 0.5))
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)
]
self.probabilities = new_probabilities
for name, probability in zip(self.model_names, self.probabilities):
row[f"{name}_prob"] = probability
_logger.info(row)
with open(self.filename, "a", newline="") as outfile:
writer = csv.DictWriter(
outfile, fieldnames=self.csv_fields, dialect="unix"
)
writer.writerow(row)
if self.use_end_threshold:
max_prob = max(self.probabilities)
if max_prob > self.end_threshold:
_logger.info(
f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
)
break

222
deepdog/bayes_run.py
View File

@ -1,17 +1,19 @@
import pdme.inputs
import pdme.model import pdme.model
import pdme.measurement.input_types
import pdme.measurement.oscillating_dipole
import pdme.util.fast_v_calc
import pdme.util.fast_nonlocal_spectrum
from typing import Sequence, Tuple, List from typing import Sequence, Tuple, List
import datetime import datetime
import itertools
import csv import csv
import multiprocessing
import logging import logging
import numpy import numpy
import scipy.optimize
import multiprocessing
# TODO: remove hardcode # TODO: remove hardcode
COST_THRESHOLD = 1e-10 CHUNKSIZE = 50
# TODO: It's garbage to have this here duplicated from pdme. # TODO: It's garbage to have this here duplicated from pdme.
DotInput = Tuple[numpy.typing.ArrayLike, float] DotInput = Tuple[numpy.typing.ArrayLike, float]
@ -20,10 +22,40 @@ DotInput = Tuple[numpy.typing.ArrayLike, float]
_logger = logging.getLogger(__name__) _logger = logging.getLogger(__name__)
def get_a_result( def get_a_result(input) -> int:
discretisation, dots, index model, dot_inputs, lows, highs, monte_carlo_count, max_frequency, seed = input
) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
return (index, discretisation.solve_for_index(dots, index)) rng = numpy.random.default_rng(seed)
sample_dipoles = model.get_monte_carlo_dipole_inputs(
monte_carlo_count, max_frequency, rng_to_use=rng
)
vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
def get_a_result_using_pairs(input) -> int:
(
model,
dot_inputs,
pair_inputs,
local_lows,
local_highs,
nonlocal_lows,
nonlocal_highs,
monte_carlo_count,
max_frequency,
) = input
sample_dipoles = model.get_n_single_dipoles(monte_carlo_count, max_frequency)
local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
pair_inputs, sample_dipoles
)
nonlocal_matches = pdme.util.fast_v_calc.between(
nonlocal_vals, nonlocal_lows, nonlocal_highs
)
combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
return numpy.count_nonzero(combined_matches)
class BayesRun: class BayesRun:
@ -35,11 +67,11 @@ class BayesRun:
dot_inputs : Sequence[DotInput] dot_inputs : Sequence[DotInput]
The dot inputs for this bayes run. The dot inputs for this bayes run.
discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)] models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
The models to evaluate. The models to evaluate.
actual_model_discretisation : pdme.model.Discretisation actual_model : pdme.model.DipoleModel
The discretisation for the model which is actually correct. The model which is actually correct.
filename_slug : str filename_slug : str
The filename slug to include. The filename slug to include.
@ -50,29 +82,66 @@ class BayesRun:
def __init__( def __init__(
self, self,
dot_inputs: Sequence[DotInput], dot_positions: Sequence[numpy.typing.ArrayLike],
discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], frequency_range: Sequence[float],
actual_model: pdme.model.Model, models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
actual_model: pdme.model.DipoleModel,
filename_slug: str, filename_slug: str,
run_count: int, run_count: int = 100,
max_frequency: float = None, low_error: float = 0.9,
high_error: float = 1.1,
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, end_threshold: float = None,
chunksize: int = CHUNKSIZE,
) -> None: ) -> None:
self.dot_inputs = dot_inputs self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
self.discretisations = [disc for (_, disc) in discretisations_with_names] dot_positions, frequency_range
self.model_names = [name for (name, _) in discretisations_with_names] )
self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
self.dot_inputs
)
self.models = [model for (_, model) in models_with_names]
self.model_names = [name for (name, _) in models_with_names]
self.actual_model = actual_model self.actual_model = actual_model
self.model_count = len(self.discretisations)
self.n: int
try:
self.n = self.actual_model.n # type: ignore
except AttributeError:
self.n = 1
self.model_count = len(self.models)
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.run_count = run_count
self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"] self.low_error = low_error
self.high_error = high_error
self.csv_fields = []
for i in range(self.n):
self.csv_fields.extend(
[
f"dipole_moment_{i+1}",
f"dipole_location_{i+1}",
f"dipole_frequency_{i+1}",
]
)
self.compensate_zeros = True self.compensate_zeros = True
self.chunksize = chunksize
for name in self.model_names: for name in self.model_names:
self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"]) self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
self.probabilities = [1 / self.model_count] * self.model_count self.probabilities = [1 / self.model_count] * self.model_count
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
self.filename = f"{timestamp}-{filename_slug}.csv" self.filename = f"{timestamp}-{filename_slug}.bayesrun.csv"
self.max_frequency = max_frequency self.max_frequency = max_frequency
if end_threshold is not None: if end_threshold is not None:
@ -91,52 +160,95 @@ class BayesRun:
writer.writeheader() writer.writeheader()
for run in range(1, self.run_count + 1): for run in range(1, self.run_count + 1):
frequency: float = run
if self.max_frequency is not None and self.max_frequency > 1:
rng = numpy.random.default_rng()
frequency = rng.uniform(1, self.max_frequency)
dipoles = self.actual_model.get_dipoles(frequency)
dots = dipoles.get_dot_measurements(self.dot_inputs) # Generate the actual dipoles
_logger.info(f"Going to work on dipole at {dipoles.dipoles}") actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
dots = actual_dipoles.get_percent_range_dot_measurements(
self.dot_inputs, self.low_error, self.high_error
)
(
lows,
highs,
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
dots
)
_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
# define a new seed sequence for each run
seed_sequence = numpy.random.SeedSequence(run)
results = [] results = []
_logger.debug("Going to iterate over discretisations now") _logger.debug("Going to iterate over models now")
for disc_count, discretisation in enumerate(self.discretisations): for model_count, model in enumerate(self.models):
_logger.debug(f"Doing discretisation #{disc_count}") _logger.debug(f"Doing model #{model_count}")
with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool: core_count = multiprocessing.cpu_count() - 1 or 1
results.append( with multiprocessing.Pool(core_count) as pool:
pool.starmap( cycle_count = 0
get_a_result, cycle_success = 0
zip( cycles = 0
itertools.repeat(discretisation), while (cycles < self.max_monte_carlo_cycles_steps) and (
itertools.repeat(dots), cycle_success <= self.target_success
discretisation.all_indices(), ):
), _logger.debug(f"Starting cycle {cycles}")
cycles += 1
current_success = 0
cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
# generate a seed from the sequence for each core.
# note this needs to be inside the loop for monte carlo cycle steps!
# that way we get more stuff.
seeds = seed_sequence.spawn(self.monte_carlo_cycles)
current_success = sum(
pool.imap_unordered(
get_a_result,
[
(
model,
self.dot_inputs_array,
lows,
highs,
self.monte_carlo_count,
self.max_frequency,
seed,
)
for seed in seeds
],
self.chunksize,
)
) )
)
cycle_success += current_success
_logger.debug(f"current running successes: {cycle_success}")
results.append((cycle_count, cycle_success))
_logger.debug("Done, constructing output now") _logger.debug("Done, constructing output now")
row = { row = {
"dipole_moment": dipoles.dipoles[0].p, "dipole_moment_1": actual_dipoles.dipoles[0].p,
"dipole_location": dipoles.dipoles[0].s, "dipole_location_1": actual_dipoles.dipoles[0].s,
"dipole_frequency": dipoles.dipoles[0].w, "dipole_frequency_1": actual_dipoles.dipoles[0].w,
} }
for i in range(1, self.n):
try:
current_dipoles = actual_dipoles.dipoles[i]
row[f"dipole_moment_{i+1}"] = current_dipoles.p
row[f"dipole_location_{i+1}"] = current_dipoles.s
row[f"dipole_frequency_{i+1}"] = current_dipoles.w
except IndexError:
_logger.info(f"Not writing anymore, saw end after {i}")
break
successes: List[float] = [] successes: List[float] = []
counts: List[int] = [] counts: List[int] = []
for model_index, (name, result) in enumerate( for model_index, (name, (count, result)) in enumerate(
zip(self.model_names, results) zip(self.model_names, results)
): ):
count = 0
success = 0
for idx, val in result:
count += 1
if val.success and val.cost <= COST_THRESHOLD:
success += 1
row[f"{name}_success"] = success row[f"{name}_success"] = result
row[f"{name}_count"] = count row[f"{name}_count"] = count
successes.append(max(success, 0.5)) successes.append(max(result, 0.5))
counts.append(count) counts.append(count)
success_weight = sum( success_weight = sum(

93
deepdog/alt_bayes_run_simulpairs.py → deepdog/bayes_run_simulpairs.py
View File

@ -25,7 +25,7 @@ _logger = logging.getLogger(__name__)
def get_a_simul_result_using_pairs(input) -> numpy.ndarray: def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
( (
discretisation, model,
dot_inputs, dot_inputs,
pair_inputs, pair_inputs,
local_lows, local_lows,
@ -42,16 +42,12 @@ def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
local_total = 0 local_total = 0
combined_total = 0 combined_total = 0
sample_dipoles = discretisation.get_model().get_n_single_dipoles( sample_dipoles = model.get_monte_carlo_dipole_inputs(
monte_carlo_count, max_frequency, rng_to_use=rng monte_carlo_count, max_frequency, rng_to_use=rng
) )
local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles( local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
dot_inputs, sample_dipoles local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
) nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal_dipoleses(
local_matches = pdme.util.fast_v_calc.between(
local_vals, local_lows, local_highs
)
nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
pair_inputs, sample_dipoles pair_inputs, sample_dipoles
) )
nonlocal_matches = pdme.util.fast_v_calc.between( nonlocal_matches = pdme.util.fast_v_calc.between(
@ -64,7 +60,7 @@ def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
return numpy.array([local_total, combined_total]) return numpy.array([local_total, combined_total])
class AltBayesRunSimulPairs: class BayesRunSimulPairs:
""" """
A dual pairs-nonpairs Bayes run for a given set of dots. A dual pairs-nonpairs Bayes run for a given set of dots.
@ -73,11 +69,11 @@ class AltBayesRunSimulPairs:
dot_inputs : Sequence[DotInput] dot_inputs : Sequence[DotInput]
The dot inputs for this bayes run. The dot inputs for this bayes run.
discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)] models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
The models to evaluate. The models to evaluate.
actual_model_discretisation : pdme.model.Discretisation actual_model : pdme.model.DipoleModel
The discretisation for the model which is actually correct. The modoel for the model which is actually correct.
filename_slug : str filename_slug : str
The filename slug to include. The filename slug to include.
@ -90,8 +86,8 @@ class AltBayesRunSimulPairs:
self, self,
dot_positions: Sequence[numpy.typing.ArrayLike], dot_positions: Sequence[numpy.typing.ArrayLike],
frequency_range: Sequence[float], frequency_range: Sequence[float],
discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
actual_model: pdme.model.Model, actual_model: pdme.model.DipoleModel,
filename_slug: str, filename_slug: str,
run_count: int = 100, run_count: int = 100,
low_error: float = 0.9, low_error: float = 0.9,
@ -120,10 +116,17 @@ class AltBayesRunSimulPairs:
pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs) pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
) )
self.discretisations = [disc for (_, disc) in discretisations_with_names] self.models = [mod for (_, mod) in models_with_names]
self.model_names = [name for (name, _) in discretisations_with_names] self.model_names = [name for (name, _) in models_with_names]
self.actual_model = actual_model self.actual_model = actual_model
self.model_count = len(self.discretisations)
self.n: int
try:
self.n = self.actual_model.n # type: ignore
except AttributeError:
self.n = 1
self.model_count = len(self.models)
self.monte_carlo_count = monte_carlo_count self.monte_carlo_count = monte_carlo_count
self.monte_carlo_cycles = monte_carlo_cycles self.monte_carlo_cycles = monte_carlo_cycles
self.target_success = target_success self.target_success = target_success
@ -139,7 +142,16 @@ class AltBayesRunSimulPairs:
self.pairs_high_error = self.high_error self.pairs_high_error = self.high_error
else: else:
self.pairs_high_error = pairs_high_error self.pairs_high_error = pairs_high_error
self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
self.csv_fields = []
for i in range(self.n):
self.csv_fields.extend(
[
f"dipole_moment_{i+1}",
f"dipole_location_{i+1}",
f"dipole_frequency_{i+1}",
]
)
self.compensate_zeros = True self.compensate_zeros = True
self.chunksize = chunksize self.chunksize = chunksize
for name in self.model_names: for name in self.model_names:
@ -174,11 +186,8 @@ class AltBayesRunSimulPairs:
for run in range(1, self.run_count + 1): for run in range(1, self.run_count + 1):
rng = numpy.random.default_rng()
frequency = rng.uniform(1, self.max_frequency)
# Generate the actual dipoles # Generate the actual dipoles
actual_dipoles = self.actual_model.get_dipoles(frequency) actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
dots = actual_dipoles.get_percent_range_dot_measurements( dots = actual_dipoles.get_percent_range_dot_measurements(
self.dot_inputs, self.low_error, self.high_error self.dot_inputs, self.low_error, self.high_error
@ -208,9 +217,9 @@ class AltBayesRunSimulPairs:
results_pairs = [] results_pairs = []
results_no_pairs = [] results_no_pairs = []
_logger.debug("Going to iterate over discretisations now") _logger.debug("Going to iterate over models now")
for disc_count, discretisation in enumerate(self.discretisations): for model_count, model in enumerate(self.models):
_logger.debug(f"Doing discretisation #{disc_count}") _logger.debug(f"Doing model #{model_count}")
core_count = multiprocessing.cpu_count() - 1 or 1 core_count = multiprocessing.cpu_count() - 1 or 1
with multiprocessing.Pool(core_count) as pool: with multiprocessing.Pool(core_count) as pool:
@ -223,7 +232,7 @@ class AltBayesRunSimulPairs:
<= self.target_success <= self.target_success
): ):
_logger.debug(f"Starting cycle {cycles}") _logger.debug(f"Starting cycle {cycles}")
_logger.debug(f"(pair, no_pair) successes are {(cycle_success_pairs, cycle_success_no_pairs)}")
cycles += 1 cycles += 1
current_success_pairs = 0 current_success_pairs = 0
current_success_no_pairs = 0 current_success_no_pairs = 0
@ -241,7 +250,7 @@ class AltBayesRunSimulPairs:
get_a_simul_result_using_pairs, get_a_simul_result_using_pairs,
[ [
( (
discretisation, model,
self.dot_inputs_array, self.dot_inputs_array,
self.dot_pair_inputs_array, self.dot_pair_inputs_array,
lows, lows,
@ -264,20 +273,36 @@ class AltBayesRunSimulPairs:
cycle_success_no_pairs += current_success_no_pairs cycle_success_no_pairs += current_success_no_pairs
cycle_success_pairs += current_success_pairs cycle_success_pairs += current_success_pairs
_logger.debug(
f"(pair, no_pair) successes are {(cycle_success_pairs, cycle_success_no_pairs)}"
)
results_pairs.append((cycle_count, cycle_success_pairs)) results_pairs.append((cycle_count, cycle_success_pairs))
results_no_pairs.append((cycle_count, cycle_success_no_pairs)) results_no_pairs.append((cycle_count, cycle_success_no_pairs))
_logger.debug("Done, constructing output now") _logger.debug("Done, constructing output now")
row_pairs = { row_pairs = {
"dipole_moment": actual_dipoles.dipoles[0].p, "dipole_moment_1": actual_dipoles.dipoles[0].p,
"dipole_location": actual_dipoles.dipoles[0].s, "dipole_location_1": actual_dipoles.dipoles[0].s,
"dipole_frequency": actual_dipoles.dipoles[0].w, "dipole_frequency_1": actual_dipoles.dipoles[0].w,
} }
row_no_pairs = { row_no_pairs = {
"dipole_moment": actual_dipoles.dipoles[0].p, "dipole_moment_1": actual_dipoles.dipoles[0].p,
"dipole_location": actual_dipoles.dipoles[0].s, "dipole_location_1": actual_dipoles.dipoles[0].s,
"dipole_frequency": actual_dipoles.dipoles[0].w, "dipole_frequency_1": actual_dipoles.dipoles[0].w,
} }
for i in range(1, self.n):
try:
current_dipoles = actual_dipoles.dipoles[i]
row_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
row_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
row_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
row_no_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
row_no_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
row_no_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
except IndexError:
_logger.info(f"Not writing anymore, saw end after {i}")
break
successes_pairs: List[float] = [] successes_pairs: List[float] = []
successes_no_pairs: List[float] = [] successes_no_pairs: List[float] = []
counts: List[int] = [] counts: List[int] = []

160
deepdog/diagnostic.py
View File

@ -1,160 +0,0 @@
from pdme.measurement import OscillatingDipole, OscillatingDipoleArrangement
import pdme
from deepdog.bayes_run import DotInput
import datetime
import numpy
from dataclasses import dataclass
import logging
from typing import Sequence, Tuple
import csv
import itertools
import multiprocessing
_logger = logging.getLogger(__name__)
def get_a_result(discretisation, dots, index):
return (index, discretisation.solve_for_index(dots, index))
@dataclass
class SingleDipoleDiagnostic:
model: str
index: Tuple
bounds: Tuple
actual_dipole: OscillatingDipole
result_dipole: OscillatingDipole
success: bool
def __post_init__(self) -> None:
self.p_actual_x = self.actual_dipole.p[0]
self.p_actual_y = self.actual_dipole.p[1]
self.p_actual_z = self.actual_dipole.p[2]
self.s_actual_x = self.actual_dipole.s[0]
self.s_actual_y = self.actual_dipole.s[1]
self.s_actual_z = self.actual_dipole.s[2]
self.p_result_x = self.result_dipole.p[0]
self.p_result_y = self.result_dipole.p[1]
self.p_result_z = self.result_dipole.p[2]
self.s_result_x = self.result_dipole.s[0]
self.s_result_y = self.result_dipole.s[1]
self.s_result_z = self.result_dipole.s[2]
self.w_actual = self.actual_dipole.w
self.w_result = self.result_dipole.w
class Diagnostic:
"""
Represents a diagnostic for a single dipole moment given a set of discretisations.
Parameters
----------
dot_inputs : Sequence[DotInput]
The dot inputs for this diagnostic.
discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
The models to evaluate.
actual_model_discretisation : pdme.model.Discretisation
The discretisation for the model which is actually correct.
filename_slug : str
The filename slug to include.
run_count: int
The number of runs to do.
"""
def __init__(
self,
actual_dipole_moment: numpy.ndarray,
actual_dipole_position: numpy.ndarray,
actual_dipole_frequency: float,
dot_inputs: Sequence[DotInput],
discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
filename_slug: str,
) -> None:
self.dipoles = OscillatingDipoleArrangement(
[
OscillatingDipole(
actual_dipole_moment,
actual_dipole_position,
actual_dipole_frequency,
)
]
)
self.dots = self.dipoles.get_dot_measurements(dot_inputs)
self.discretisations_with_names = discretisations_with_names
self.model_count = len(self.discretisations_with_names)
self.csv_fields = [
"model",
"index",
"bounds",
"p_actual_x",
"p_actual_y",
"p_actual_z",
"s_actual_x",
"s_actual_y",
"s_actual_z",
"w_actual",
"success",
"p_result_x",
"p_result_y",
"p_result_z",
"s_result_x",
"s_result_y",
"s_result_z",
"w_result",
]
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
self.filename = f"{timestamp}-{filename_slug}.diag.csv"
def go(self):
with open(self.filename, "a", newline="") as outfile:
# csv fields
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
writer.writeheader()
for (name, discretisation) in self.discretisations_with_names:
_logger.info(f"Working on discretisation {name}")
results = []
with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
results = pool.starmap(
get_a_result,
zip(
itertools.repeat(discretisation),
itertools.repeat(self.dots),
discretisation.all_indices(),
),
)
with open(self.filename, "a", newline="") as outfile:
writer = csv.DictWriter(
outfile,
fieldnames=self.csv_fields,
dialect="unix",
extrasaction="ignore",
)
for idx, result in results:
bounds = discretisation.bounds(idx)
actual_success = result.success and result.cost <= 1e-10
diag_row = SingleDipoleDiagnostic(
name,
idx,
bounds,
self.dipoles.dipoles[0],
discretisation.model.solution_as_dipoles(result.normalised_x)[
0
],
actual_success,
)
row = vars(diag_row)
_logger.debug(f"Writing result {row}")
writer.writerow(row)

8
poetry.lock generated
View File

@ -335,7 +335,7 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
[[package]] [[package]]
name = "pdme" name = "pdme"
version = "0.6.2" version = "0.8.2"
description = "Python dipole model evaluator" description = "Python dipole model evaluator"
category = "main" category = "main"
optional = false optional = false
@ -740,7 +740,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
[metadata] [metadata]
lock-version = "1.1" lock-version = "1.1"
python-versions = "^3.8,<3.10" python-versions = "^3.8,<3.10"
content-hash = "98877f53c0ca996cd6eaa2c3b7391e391d29c7a4d3f1e08159fc999a3e4ad296" content-hash = "c1b27cf9297e4777e78a72caba5f560be87a0f3b27c001b7a05b18adfdca2d1c"
[metadata.files] [metadata.files]
atomicwrites = [ atomicwrites = [
@ -1025,8 +1025,8 @@ pathspec = [
{file = "pathspec-0.9.0.tar.gz", hash = "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"}, {file = "pathspec-0.9.0.tar.gz", hash = "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"},
] ]
pdme = [ pdme = [
{file = "pdme-0.6.2-py3-none-any.whl", hash = "sha256:7e81081be243006f86c31d3590a77a529764204b3831b83a939a87025d463e26"}, {file = "pdme-0.8.2-py3-none-any.whl", hash = "sha256:872aa43f99d357b8e802036c6de2e529942dd24c76a830142abd0b9ff6fd869c"},
{file = "pdme-0.6.2.tar.gz", hash = "sha256:59c2a3249338317f22cf268c55c90d06b563d42a9278e2826753f6d491379f67"}, {file = "pdme-0.8.2.tar.gz", hash = "sha256:36a2bef50ab2c24a82c44bd9ded473991332358ab393f46e9e0bb0b83d538a0f"},
] ]
pkginfo = [ pkginfo = [
{file = "pkginfo-1.8.2-py2.py3-none-any.whl", hash = "sha256:c24c487c6a7f72c66e816ab1796b96ac6c3d14d49338293d2141664330b55ffc"}, {file = "pkginfo-1.8.2-py2.py3-none-any.whl", hash = "sha256:c24c487c6a7f72c66e816ab1796b96ac6c3d14d49338293d2141664330b55ffc"},

2
pyproject.toml
View File

@ -6,7 +6,7 @@ authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = "^3.8,<3.10" python = "^3.8,<3.10"
pdme = "0.6.2" pdme = "0.8.2"
[tool.poetry.dev-dependencies] [tool.poetry.dev-dependencies]
pytest = ">=6" pytest = ">=6"