chore(release): 0.6.3

Reviewed-on: #14

CHANGELOG.md

@@ -2,6 +2,28 @@
 
 All notable changes to this project will be documented in this file. See [standard-version](https://github.com/conventional-changelog/standard-version) for commit guidelines.
 
+### [0.6.3](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.2...0.6.3) (2022-06-12)
+
+
+### Features
+
+* adds fast filter variant ([2c5c122](https://gitea.deepak.science:2222/physics/deepdog/commit/2c5c1228209e51d17253f07470e2f1e6dc6872d7))
+* adds tester for fast filter real spectrum ([0a1a277](https://gitea.deepak.science:2222/physics/deepdog/commit/0a1a27759b0d4ab01da214b76ab14bf2b1fe00e3))
+
+### [0.6.2](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.1...0.6.2) (2022-05-26)
+
+
+### Features
+
+* adds better import api for real data run ([d7e0f13](https://gitea.deepak.science:2222/physics/deepdog/commit/d7e0f13ca55197b24cb534c80f321ee76b9c4a40))
+
+### [0.6.1](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.0...0.6.1) (2022-05-22)
+
+
+### Features
+
+* adds new runner for real spectra ([bd56f24](https://gitea.deepak.science:2222/physics/deepdog/commit/bd56f247748babb2ee1f2a1182d25aa968bff5a5))
+
 ## [0.6.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.5.0...0.6.0) (2022-05-22)
 
 

deepdog/__init__.py

@@ -2,6 +2,7 @@ import logging
 from deepdog.meta import __version__
 from deepdog.bayes_run import BayesRun
 from deepdog.bayes_run_simulpairs import BayesRunSimulPairs
+from deepdog.real_spectrum_run import RealSpectrumRun
 
 
 def get_version():
@@ -12,6 +13,7 @@ __all__ = [
 	"get_version",
 	"BayesRun",
 	"BayesRunSimulPairs",
+	"RealSpectrumRun",
 ]
 
 

deepdog/real_spectrum_run.py

@@ -0,0 +1,220 @@
+import pdme.inputs
+import pdme.model
+import pdme.measurement
+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, Dict, Union
+import datetime
+import csv
+import multiprocessing
+import logging
+import numpy
+
+
+# TODO: remove hardcode
+CHUNKSIZE = 50
+
+
+_logger = logging.getLogger(__name__)
+
+
+def get_a_result(input) -> int:
+	model, dot_inputs, lows, highs, monte_carlo_count, seed = input
+
+	rng = numpy.random.default_rng(seed)
+	# TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models.
+	sample_dipoles = model.get_monte_carlo_dipole_inputs(
+		monte_carlo_count, None, 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_fast_filter(input) -> int:
+	model, dot_inputs, lows, highs, monte_carlo_count, seed = input
+
+	rng = numpy.random.default_rng(seed)
+	# TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models.
+	sample_dipoles = model.get_monte_carlo_dipole_inputs(
+		monte_carlo_count, None, rng_to_use=rng
+	)
+
+	current_sample = sample_dipoles
+	for di, low, high in zip(dot_inputs, lows, highs):
+
+		if len(current_sample) < 1:
+			break
+		vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(
+			numpy.array([di]), current_sample
+		)
+
+		current_sample = current_sample[numpy.all((vals > low) & (vals < high), axis=1)]
+	return len(current_sample)
+
+
+class RealSpectrumRun:
+	"""
+	A bayes run given some real data.
+
+	Parameters
+	----------
+	measurements : Sequence[pdme.measurement.DotRangeMeasurement]
+	The dot inputs for this bayes run.
+
+	models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
+	The models to evaluate.
+
+	actual_model : pdme.model.DipoleModel
+	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,
+		measurements: Sequence[pdme.measurement.DotRangeMeasurement],
+		models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
+		filename_slug: str,
+		monte_carlo_count: int = 10000,
+		monte_carlo_cycles: int = 10,
+		target_success: int = 100,
+		max_monte_carlo_cycles_steps: int = 10,
+		chunksize: int = CHUNKSIZE,
+		initial_seed: int = 12345,
+		use_fast_filter: bool = True,
+	) -> None:
+		self.measurements = measurements
+		self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
+
+		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.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.csv_fields = []
+
+		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"])
+
+		# for now initialise priors as uniform.
+		self.probabilities = [1 / self.model_count] * self.model_count
+
+		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+		self.use_fast_filter = use_fast_filter
+		ff_string = "no_fast_filter"
+		if self.use_fast_filter:
+			ff_string = "fast_filter"
+		self.filename = f"{timestamp}-{filename_slug}.realdata.{ff_string}.bayesrun.csv"
+		self.initial_seed = initial_seed
+
+	def go(self) -> None:
+		with open(self.filename, "a", newline="") as outfile:
+			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+			writer.writeheader()
+
+		(
+			lows,
+			highs,
+		) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
+			self.measurements
+		)
+
+		# define a new seed sequence for each run
+		seed_sequence = numpy.random.SeedSequence(self.initial_seed)
+
+		results = []
+		_logger.debug("Going to iterate over models now")
+		for model_count, model in enumerate(self.models):
+			_logger.debug(f"Doing model #{model_count}")
+			core_count = multiprocessing.cpu_count() - 1 or 1
+			with multiprocessing.Pool(core_count) 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
+
+					# 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)
+
+					if self.use_fast_filter:
+						result_func = get_a_result_fast_filter
+					else:
+						result_func = get_a_result
+					current_success = sum(
+						pool.imap_unordered(
+							result_func,
+							[
+								(
+									model,
+									self.dot_inputs_array,
+									lows,
+									highs,
+									self.monte_carlo_count,
+									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")
+		row: Dict[str, Union[int, float, str]] = {}
+
+		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)

poetry.lock

@@ -117,14 +117,14 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "coverage"
-version = "6.3.2"
+version = "6.4.1"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-tomli = {version = "*", optional = true, markers = "extra == \"toml\""}
+tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
 
 [package.extras]
 toml = ["tomli"]
@@ -282,7 +282,7 @@ python-versions = "*"
 
 [[package]]
 name = "mypy"
-version = "0.950"
+version = "0.961"
 description = "Optional static typing for Python"
 category = "dev"
 optional = false
@@ -335,7 +335,7 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 
 [[package]]
 name = "pdme"
-version = "0.8.2"
+version = "0.8.5"
 description = "Python dipole model evaluator"
 category = "main"
 optional = false
@@ -740,7 +740,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8,<3.10"
-content-hash = "c1b27cf9297e4777e78a72caba5f560be87a0f3b27c001b7a05b18adfdca2d1c"
+content-hash = "0196604b0dfc7b3dd0bdb19f983358ef9934d47f3c209dbbc7d07712ac5f779d"
 
 [metadata.files]
 atomicwrites = [
@@ -853,47 +853,47 @@ colorama = [
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+    {file = "pdme-0.8.5.tar.gz", hash = "sha256:56f8864caf2c5309ca5d178ed42ee1f375c1eeea516920f7922a0ee9dfd2b969"},
 ]
 pkginfo = [
     {file = "pkginfo-1.8.2-py2.py3-none-any.whl", hash = "sha256:c24c487c6a7f72c66e816ab1796b96ac6c3d14d49338293d2141664330b55ffc"},

pyproject.toml

@@ -1,18 +1,18 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.6.0"
+version = "0.6.3"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
-pdme = "0.8.2"
+pdme = "^0.8.5"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^4.0.1"
 pytest-cov = "^3.0.0"
-mypy = "^0.950"
+mypy = "^0.961"
 python-semantic-release = "^7.24.0"
 black = "^22.3.0"