0.3.0

Automatically generated by python-semantic-release

deepdog/bayes_run.py

@@ -41,7 +41,7 @@ class BayesRun():
 	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, 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.discretisations = [disc for (_, disc) 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.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"])
 
@@ -60,6 +59,14 @@ class BayesRun():
 		self.filename = f"{timestamp}-{filename_slug}.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")
@@ -88,7 +95,8 @@ class BayesRun():
 				"dipole_location": dipoles.dipoles[0].s,
 				"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)):
 				count = 0
 				success = 0
@@ -99,10 +107,11 @@ class BayesRun():
 
 				row[f"{name}_success"] = success
 				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)])
-			new_probabilities = [succ * old_prob / success_weight for succ, old_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 / 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
@@ -111,3 +120,9 @@ class BayesRun():
 			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

deepdog/diagnostic.py

@@ -39,6 +39,8 @@ class SingleDipoleDiagnostic():
 		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():
@@ -65,7 +67,7 @@ class Diagnostic():
 		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", "actual_dipole_freq", "success", "p_result_x", "p_result_y", "p_result_z", "s_result_x", "s_result_y", "s_result_z"]
+		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"
@@ -84,14 +86,14 @@ class Diagnostic():
 				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')
+				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), actual_success)
+					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)

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.2.0"
+version = "0.3.0"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
@@ -32,3 +32,6 @@ module = [
 	"scipy.optimize"
 ]
 ignore_missing_imports = true
+
+[tool.semantic_release]
+version_toml = "pyproject.toml:tool.poetry.version"