Adds bayes run

deepdog/__init__.py

@@ -1,9 +1,13 @@
 import logging
 from deepdog.meta import __version__
+from deepdog.bayes_run import BayesRun
 
 
 def get_version():
 	return __version__
 
 
+__all__ = ["get_version", "BayesRun"]
+
+
 logging.getLogger(__name__).addHandler(logging.NullHandler())

deepdog/bayes_run.py

@@ -0,0 +1,108 @@
+import pdme.model
+from typing import Sequence, Tuple, List
+import datetime
+import itertools
+import csv
+import logging
+import numpy
+import scipy.optimize
+import multiprocessing
+
+
+# TODO: remove hardcode
+COST_THRESHOLD = 1e-10
+
+
+# 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(discretisation, dots, index) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
+	return (index, discretisation.solve_for_index(dots, index))
+
+
+class BayesRun():
+	'''
+	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_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int) -> None:
+		self.dot_inputs = dot_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.run_count = run_count
+		self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
+		self.compensate_zeros = True
+
+		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")
+		self.filename = f"{timestamp}-{filename_slug}.csv"
+
+	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):
+			dipoles = self.actual_model.get_dipoles(run)
+
+			dots = dipoles.get_dot_measurements(self.dot_inputs)
+			_logger.info(f"Going to work on dipole at {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:
+					results.append(pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(dots), discretisation.all_indices())))
+
+			_logger.debug("Done, constructing output now")
+			row = {
+				"dipole_moment": dipoles.dipoles[0].p,
+				"dipole_location": dipoles.dipoles[0].s,
+				"dipole_frequency": dipoles.dipoles[0].w
+			}
+			successes: List[int] = []
+			for model_index, (name, result) in enumerate(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}_count"] = count
+				successes.append(max(success, 1))
+
+			success_weight = sum([succ * prob for succ, prob in zip(successes, self.probabilities)])
+			new_probabilities = [succ * old_prob / success_weight for succ, old_prob in zip(successes, 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)

poetry.lock

@@ -117,7 +117,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
 
 [[package]]
 name = "pdme"
-version = "0.0.1"
+version = "0.4.1"
 description = "Python dipole model evaluator"
 category = "main"
 optional = false
@@ -125,6 +125,7 @@ python-versions = ">=3.8,<3.10"
 
 [package.dependencies]
 numpy = ">=1.21.1,<2.0.0"
+scipy = ">=1.5,<1.6"
 
 [[package]]
 name = "pluggy"
@@ -209,6 +210,17 @@ pytest = ">=4.6"
 [package.extras]
 testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtualenv"]
 
+[[package]]
+name = "scipy"
+version = "1.5.4"
+description = "SciPy: Scientific Library for Python"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+numpy = ">=1.14.5"
+
 [[package]]
 name = "toml"
 version = "0.10.2"
@@ -236,7 +248,7 @@ python-versions = ">=3.6"
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8,<3.10"
-content-hash = "380c692a00857c85c6767daa93b458f8d768a6b05dbd94a2e14fe84cc9d2f3ea"
+content-hash = "d0cc6fcaa5f489054917fd097c7f86bb314b4f7b76c14cddecb9cd9ef1a24d72"
 
 [metadata.files]
 atomicwrites = [
@@ -367,8 +379,8 @@ packaging = [
     {file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"},
 ]
 pdme = [
-    {file = "pdme-0.0.1-py3-none-any.whl", hash = "sha256:f2b8fc969de63e66681f012a1f04eebb2b65e08ba5315dfbb8a19acbadc57858"},
+    {file = "pdme-0.4.1-py3-none-any.whl", hash = "sha256:c8a6b6a9755094a89f55a46b42ba2531424805747b8e23e078928c880721d84f"},
-    {file = "pdme-0.0.1.tar.gz", hash = "sha256:7ae4bbe49de630b1b4c2f30a8d343d532fa88ff7486c47f1d8d287a78d017f8b"},
+    {file = "pdme-0.4.1.tar.gz", hash = "sha256:9c892b23462d87f745735a6cd73346831a1fcb765a1c542dda5909f47b23fc7b"},
 ]
 pluggy = [
     {file = "pluggy-1.0.0-py2.py3-none-any.whl", hash = "sha256:74134bbf457f031a36d68416e1509f34bd5ccc019f0bcc952c7b909d06b37bd3"},
@@ -398,6 +410,33 @@ pytest-cov = [
     {file = "pytest-cov-3.0.0.tar.gz", hash = "sha256:e7f0f5b1617d2210a2cabc266dfe2f4c75a8d32fb89eafb7ad9d06f6d076d470"},
     {file = "pytest_cov-3.0.0-py3-none-any.whl", hash = "sha256:578d5d15ac4a25e5f961c938b85a05b09fdaae9deef3bb6de9a6e766622ca7a6"},
 ]
+scipy = [
+    {file = "scipy-1.5.4-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:4f12d13ffbc16e988fa40809cbbd7a8b45bc05ff6ea0ba8e3e41f6f4db3a9e47"},
+    {file = "scipy-1.5.4-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:a254b98dbcc744c723a838c03b74a8a34c0558c9ac5c86d5561703362231107d"},
+    {file = "scipy-1.5.4-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:368c0f69f93186309e1b4beb8e26d51dd6f5010b79264c0f1e9ca00cd92ea8c9"},
+    {file = "scipy-1.5.4-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:4598cf03136067000855d6b44d7a1f4f46994164bcd450fb2c3d481afc25dd06"},
+    {file = "scipy-1.5.4-cp36-cp36m-win32.whl", hash = "sha256:e98d49a5717369d8241d6cf33ecb0ca72deee392414118198a8e5b4c35c56340"},
+    {file = "scipy-1.5.4-cp36-cp36m-win_amd64.whl", hash = "sha256:65923bc3809524e46fb7eb4d6346552cbb6a1ffc41be748535aa502a2e3d3389"},
+    {file = "scipy-1.5.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9ad4fcddcbf5dc67619379782e6aeef41218a79e17979aaed01ed099876c0e62"},
+    {file = "scipy-1.5.4-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:f87b39f4d69cf7d7529d7b1098cb712033b17ea7714aed831b95628f483fd012"},
+    {file = "scipy-1.5.4-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:25b241034215247481f53355e05f9e25462682b13bd9191359075682adcd9554"},
+    {file = "scipy-1.5.4-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:fa789583fc94a7689b45834453fec095245c7e69c58561dc159b5d5277057e4c"},
+    {file = "scipy-1.5.4-cp37-cp37m-win32.whl", hash = "sha256:d6d25c41a009e3c6b7e757338948d0076ee1dd1770d1c09ec131f11946883c54"},
+    {file = "scipy-1.5.4-cp37-cp37m-win_amd64.whl", hash = "sha256:2c872de0c69ed20fb1a9b9cf6f77298b04a26f0b8720a5457be08be254366c6e"},
+    {file = "scipy-1.5.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:e360cb2299028d0b0d0f65a5c5e51fc16a335f1603aa2357c25766c8dab56938"},
+    {file = "scipy-1.5.4-cp38-cp38-manylinux1_i686.whl", hash = "sha256:3397c129b479846d7eaa18f999369a24322d008fac0782e7828fa567358c36ce"},
+    {file = "scipy-1.5.4-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:168c45c0c32e23f613db7c9e4e780bc61982d71dcd406ead746c7c7c2f2004ce"},
+    {file = "scipy-1.5.4-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:213bc59191da2f479984ad4ec39406bf949a99aba70e9237b916ce7547b6ef42"},
+    {file = "scipy-1.5.4-cp38-cp38-win32.whl", hash = "sha256:634568a3018bc16a83cda28d4f7aed0d803dd5618facb36e977e53b2df868443"},
+    {file = "scipy-1.5.4-cp38-cp38-win_amd64.whl", hash = "sha256:b03c4338d6d3d299e8ca494194c0ae4f611548da59e3c038813f1a43976cb437"},
+    {file = "scipy-1.5.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3d5db5d815370c28d938cf9b0809dade4acf7aba57eaf7ef733bfedc9b2474c4"},
+    {file = "scipy-1.5.4-cp39-cp39-manylinux1_i686.whl", hash = "sha256:6b0ceb23560f46dd236a8ad4378fc40bad1783e997604ba845e131d6c680963e"},
+    {file = "scipy-1.5.4-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:ed572470af2438b526ea574ff8f05e7f39b44ac37f712105e57fc4d53a6fb660"},
+    {file = "scipy-1.5.4-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:8c8d6ca19c8497344b810b0b0344f8375af5f6bb9c98bd42e33f747417ab3f57"},
+    {file = "scipy-1.5.4-cp39-cp39-win32.whl", hash = "sha256:d84cadd7d7998433334c99fa55bcba0d8b4aeff0edb123b2a1dfcface538e474"},
+    {file = "scipy-1.5.4-cp39-cp39-win_amd64.whl", hash = "sha256:cc1f78ebc982cd0602c9a7615d878396bec94908db67d4ecddca864d049112f2"},
+    {file = "scipy-1.5.4.tar.gz", hash = "sha256:4a453d5e5689de62e5d38edf40af3f17560bfd63c9c5bd228c18c1f99afa155b"},
+]
 toml = [
     {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
     {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},

pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
-pdme = "^0.0.1"
+pdme = "^0.4.1"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
@@ -20,5 +20,15 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
-addopts = "--junitxml pytest.xml --cov deepdog --cov-report=xml:coverage.xml --cov-fail-under=90 --cov-report=html"
+addopts = "--junitxml pytest.xml --cov deepdog --cov-report=xml:coverage.xml --cov-report=html"
 junit_family = "xunit1"
+
+[tool.mypy]
+plugins = "numpy.typing.mypy_plugin"
+
+[[tool.mypy.overrides]]
+module = [
+	"scipy",
+	"scipy.optimize"
+]
+ignore_missing_imports = true