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Author SHA1 Message Date
semantic-release
6dfc26104a 0.3.0
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2022-02-14 09:57:42 -06:00
3a6be738b1 feat: Actually uses probabilities to update bayes
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2022-02-14 09:51:02 -06:00
bd240900b4 fix: Actually logs end threshold 2022-02-14 09:50:08 -06:00
0e1fbec043 fix: Fixes bug with end_threshold and better error logging 2022-02-14 09:44:04 -06:00
3d3b1a83f6 feat: Adds end threshold for early abort 2022-02-14 09:27:40 -06:00
63cecba824 Created version 0.2.4
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2022-02-06 19:50:07 -06:00
344998835d fix: Fixes linting 2022-02-06 19:49:02 -06:00
838aeb0cf3 Created version 0.2.3
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2022-02-06 19:46:10 -06:00
e715d329fd fix: diagnostic adds frequencies 2022-02-06 19:46:05 -06:00
521b49f14c Created version 0.2.2
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2022-02-06 19:37:15 -06:00
6d65e8dec5 fix: ignores extra fields in dictwriter 2022-02-06 19:36:59 -06:00
36354c2f2c Adds patch
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2022-02-06 19:29:17 -06:00
3534593557 fix: Take first element of solution list 2022-02-06 19:28:54 -06:00
6ada52f82c Minor version bump
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2022-02-06 19:03:24 -06:00
34e124b18f feat: Adds better diagnostic of full dipole info 2022-02-06 19:03:04 -06:00
4 changed files with 105 additions and 74 deletions

View File

@@ -41,7 +41,7 @@ class BayesRun():
run_count: int run_count: int
The number of runs to do. The number of runs to do.
''' '''
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: 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:
self.dot_inputs = dot_inputs self.dot_inputs = dot_inputs
self.discretisations = [disc for (_, disc) in discretisations_with_names] self.discretisations = [disc for (_, disc) in discretisations_with_names]
self.model_names = [name for (name, _) in discretisations_with_names] self.model_names = [name for (name, _) in discretisations_with_names]
@@ -50,7 +50,6 @@ class BayesRun():
self.run_count = run_count self.run_count = run_count
self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"] self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
self.compensate_zeros = True self.compensate_zeros = True
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"])
@@ -60,6 +59,14 @@ class BayesRun():
self.filename = f"{timestamp}-{filename_slug}.csv" self.filename = f"{timestamp}-{filename_slug}.csv"
self.max_frequency = max_frequency 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: def go(self) -> None:
with open(self.filename, "a", newline="") as outfile: with open(self.filename, "a", newline="") as outfile:
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix") writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
@@ -88,7 +95,8 @@ class BayesRun():
"dipole_location": dipoles.dipoles[0].s, "dipole_location": dipoles.dipoles[0].s,
"dipole_frequency": dipoles.dipoles[0].w "dipole_frequency": dipoles.dipoles[0].w
} }
successes: List[int] = [] successes: List[float] = []
counts: List[int] = []
for model_index, (name, result) in enumerate(zip(self.model_names, results)): for model_index, (name, result) in enumerate(zip(self.model_names, results)):
count = 0 count = 0
success = 0 success = 0
@@ -99,10 +107,11 @@ class BayesRun():
row[f"{name}_success"] = success row[f"{name}_success"] = success
row[f"{name}_count"] = count row[f"{name}_count"] = count
successes.append(max(success, 1)) successes.append(max(success, 0.5))
counts.append(count)
success_weight = sum([succ * prob for succ, prob in zip(successes, self.probabilities)]) success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
new_probabilities = [succ * old_prob / success_weight for succ, old_prob in zip(successes, 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 self.probabilities = new_probabilities
for name, probability in zip(self.model_names, self.probabilities): for name, probability in zip(self.model_names, self.probabilities):
row[f"{name}_prob"] = probability row[f"{name}_prob"] = probability
@@ -111,3 +120,9 @@ class BayesRun():
with open(self.filename, "a", newline="") as outfile: with open(self.filename, "a", newline="") as outfile:
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix") writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
writer.writerow(row) 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

View File

@@ -3,6 +3,7 @@ import pdme
from deepdog.bayes_run import DotInput from deepdog.bayes_run import DotInput
import datetime import datetime
import numpy import numpy
from dataclasses import dataclass
import logging import logging
from typing import Sequence, Tuple from typing import Sequence, Tuple
import csv import csv
@@ -16,6 +17,32 @@ def get_a_result(discretisation, dots, index):
return (index, discretisation.solve_for_index(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(): class Diagnostic():
''' '''
Represents a diagnostic for a single dipole moment given a set of discretisations. Represents a diagnostic for a single dipole moment given a set of discretisations.
@@ -40,10 +67,10 @@ class Diagnostic():
self.discretisations_with_names = discretisations_with_names self.discretisations_with_names = discretisations_with_names
self.model_count = len(self.discretisations_with_names) self.model_count = len(self.discretisations_with_names)
self.csv_fields = ["model", "index", "bounds", "actual_dipole_moment", "actual_dipole_position", "actual_dipole_freq", "success", "result"] 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") timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
self.filename = f"{timestamp}-{filename_slug}.csv" self.filename = f"{timestamp}-{filename_slug}.diag.csv"
def go(self): def go(self):
with open(self.filename, "a", newline="") as outfile: with open(self.filename, "a", newline="") as outfile:
@@ -59,22 +86,14 @@ class Diagnostic():
results = pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(self.dots), discretisation.all_indices())) 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: with open(self.filename, "a", newline='') as outfile:
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix') writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix', extrasaction="ignore")
for idx, result in results: for idx, result in results:
bounds = discretisation.bounds(idx) bounds = discretisation.bounds(idx)
actual_success = result.success and result.cost <= 1e-10 actual_success = result.success and result.cost <= 1e-10
row = { diag_row = SingleDipoleDiagnostic(name, idx, bounds, self.dipoles.dipoles[0], discretisation.model.solution_as_dipoles(result.normalised_x)[0], actual_success)
"model": name, row = vars(diag_row)
"index": idx,
"bounds": bounds,
"actual_dipole_moment": self.dipoles.dipoles[0].p,
"actual_dipole_position": self.dipoles.dipoles[0].s,
"actual_dipole_freq": self.dipoles.dipoles[0].w,
"success": actual_success,
"result": result.normalised_x if actual_success else None,
}
_logger.debug(f"Writing result {row}") _logger.debug(f"Writing result {row}")
writer.writerow(row) writer.writerow(row)

100
poetry.lock generated
View File

@@ -30,11 +30,11 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
[[package]] [[package]]
name = "coverage" name = "coverage"
version = "6.2" version = "6.3.1"
description = "Code coverage measurement for Python" description = "Code coverage measurement for Python"
category = "dev" category = "dev"
optional = false optional = false
python-versions = ">=3.6" python-versions = ">=3.7"
[package.dependencies] [package.dependencies]
tomli = {version = "*", optional = true, markers = "extra == \"toml\""} tomli = {version = "*", optional = true, markers = "extra == \"toml\""}
@@ -117,7 +117,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
[[package]] [[package]]
name = "pdme" name = "pdme"
version = "0.4.1" version = "0.5.0"
description = "Python dipole model evaluator" description = "Python dipole model evaluator"
category = "main" category = "main"
optional = false optional = false
@@ -248,7 +248,7 @@ python-versions = ">=3.6"
[metadata] [metadata]
lock-version = "1.1" lock-version = "1.1"
python-versions = "^3.8,<3.10" python-versions = "^3.8,<3.10"
content-hash = "d0cc6fcaa5f489054917fd097c7f86bb314b4f7b76c14cddecb9cd9ef1a24d72" content-hash = "783e64d96cac128430eb53d6ccee9c5e5693a6be7670c9b403b3322530c9b0cb"
[metadata.files] [metadata.files]
atomicwrites = [ atomicwrites = [
@@ -264,53 +264,47 @@ colorama = [
{file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"}, {file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
] ]
coverage = [ coverage = [
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@@ -379,8 +373,8 @@ packaging = [
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View File

@@ -1,12 +1,12 @@
[tool.poetry] [tool.poetry]
name = "deepdog" name = "deepdog"
version = "0.1.5" version = "0.3.0"
description = "" description = ""
authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"] authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = "^3.8,<3.10" python = "^3.8,<3.10"
pdme = "^0.4.1" pdme = "^0.5.0"
[tool.poetry.dev-dependencies] [tool.poetry.dev-dependencies]
pytest = ">=6" pytest = ">=6"
@@ -32,3 +32,6 @@ module = [
"scipy.optimize" "scipy.optimize"
] ]
ignore_missing_imports = true ignore_missing_imports = true
[tool.semantic_release]
version_toml = "pyproject.toml:tool.poetry.version"