chore(release): 0.3.4

CHANGELOG.md

@@ -2,6 +2,20 @@
 
 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.
 
+### [0.3.4](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.3...0.3.4) (2022-03-06)
+
+
+### Features
+
+* Changes chunksize for multiprocessing ([0784cd5](https://gitea.deepak.science:2222/physics/deepdog/commit/0784cd53d79e00684506604f094b5d820b3994d4))
+
+### [0.3.3](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.2...0.3.3) (2022-03-06)
+
+
+### Bug Fixes
+
+* Fixes count to use cycles as well ([8617e4d](https://gitea.deepak.science:2222/physics/deepdog/commit/8617e4d2742b112cc824068150682ce3b2cdd879))
+
 ### [0.3.2](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.1...0.3.2) (2022-03-06)
 
 

deepdog/alt_bayes_run.py

@@ -10,8 +10,7 @@ import numpy
 
 
 # TODO: remove hardcode
-COST_THRESHOLD = 1e-10
+CHUNKSIZE = 20
-
 
 # TODO: It's garbage to have this here duplicated from pdme.
 DotInput = Tuple[numpy.typing.ArrayLike, float]
@@ -98,7 +97,7 @@ class AltBayesRun():
 				_logger.debug(f"Doing discretisation #{disc_count}")
 				with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
 					results.append(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)
+						pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, CHUNKSIZE)
 					))
 
 			_logger.debug("Done, constructing output now")
@@ -112,9 +111,9 @@ class AltBayesRun():
 			for model_index, (name, result) in enumerate(zip(self.model_names, results)):
 
 				row[f"{name}_success"] = result
-				row[f"{name}_count"] = self.monte_carlo_count
+				row[f"{name}_count"] = self.monte_carlo_count * self.monte_carlo_cycles
 				successes.append(max(result, 0.5))
-				counts.append(self.monte_carlo_count)
+				counts.append(self.monte_carlo_count * self.monte_carlo_cycles)
 
 			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)]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.3.2"
+version = "0.3.4"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]