Set default module runtime to 0 (unlimited time). Implemented a 'modu…

…le_passed' attr in all modules to track outcome.

CHANGELOG.md

@@ -16,6 +16,8 @@ N/A
 
 - [Breaking change] Parameter settings for `additional_modules/feature_filtering` were changed from a list of ranges to a dictionary with explicit values.
 - Reworked score assignment for qualitative phenotype data: phenotype-associated features now always receive a score of 1.0, and non-associated ones a score of 0.0.
+- For all modules with runtime restriction, the 'maximum_runtime' parameter was set to a default of '0' (unlimited runtime). Therefore, runtime restriction must now be specified explicitly.
+- Added a 'module_passed' parameter to all modules. This allows a more accurate description via the SummaryWriter (e.g. module was activated but timed out, and lack of e.g. annotation is due to premature ending and not because there were no hits).
 
 ## [0.2.2] 27-05-2024
 

fermo_core/data_analysis/annotation_manager/class_annotation_manager.py

@@ -68,13 +68,15 @@ class AnnotationManager(BaseModel):
     features: Repository
     samples: Repository
 
-    def return_attrs(self: Self) -> tuple[Stats, Repository, Repository]:
+    def return_attrs(
+        self: Self,
+    ) -> tuple[Stats, Repository, Repository, ParameterManager]:
         """Returns modified attributes from AnnotationManager to the calling function
 
         Returns:
             Tuple containing Stats, Feature Repository and Sample Repository objects.
         """
-        return self.stats, self.features, self.samples
+        return self.stats, self.features, self.samples, self.params
 
     def run_analysis(self: Self):
         """Organizes calling of data analysis steps."""
@@ -179,6 +181,7 @@ def run_user_lib_mod_cosine_matching(self: Self):
             mod_cosine_annotator.calculate_scores_mod_cosine()
             mod_cosine_annotator.extract_userlib_scores()
             self.features = mod_cosine_annotator.return_features()
+            self.params.SpectralLibMatchingCosineParameters.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -216,6 +219,7 @@ def run_user_lib_ms2deepscore_matching(self: Self):
             ms2deepscore_annotator.calculate_scores_ms2deepscore()
             ms2deepscore_annotator.extract_userlib_scores()
             self.features = ms2deepscore_annotator.return_features()
+            self.params.SpectralLibMatchingDeepscoreParameters.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -242,6 +246,7 @@ def run_feature_adduct_annotation(self: Self):
             )
             adduct_annotator.run_analysis()
             self.features = adduct_annotator.return_features()
+            self.params.AdductAnnotationParameters.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -264,6 +269,7 @@ def run_neutral_loss_annotation(self: Self):
             )
             neutralloss_annotator.run_analysis()
             self.features = neutralloss_annotator.return_features()
+            self.params.NeutralLossParameters.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -287,6 +293,7 @@ def run_fragment_annotation(self: Self):
             )
             fragment_annotator.run_analysis()
             self.features = fragment_annotator.return_features()
+            self.params.FragmentAnnParameters.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -323,6 +330,7 @@ def run_ms2query_results_assignment(self: Self):
                 self.params.MS2QueryResultsParameters.filepath,
             )
             self.features = ms2query_annotator.return_features()
+
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -331,8 +339,7 @@ def run_ms2query_results_assignment(self: Self):
             return
 
         logger.info(
-            "'AnnotationManager': completed annotation from existing MS2Query "
-            "results"
+            "'AnnotationManager': completed annotation from existing MS2Query results."
         )
 
     def run_ms2query_annotation(self: Self):
@@ -359,6 +366,7 @@ def run_ms2query_annotation(self: Self):
             )
             ms2query_annotator.run_ms2query()
             self.features = ms2query_annotator.return_features()
+            self.params.Ms2QueryAnnotationParameters.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -401,7 +409,6 @@ def run_as_kcb_cosine_annotation(self: Self):
             )
             mibig_bgcs = {key for key, value in kcb_results.items()}
             spec_library = UtilityMethodManager().create_mibig_spec_lib(mibig_bgcs)
-
             kcb_annotator = ModCosAnnotator(
                 features=self.features,
                 active_features=self.stats.active_features,
@@ -417,6 +424,7 @@ def run_as_kcb_cosine_annotation(self: Self):
             kcb_annotator.calculate_scores_mod_cosine()
             kcb_annotator.extract_mibig_scores(kcb_results)
             self.features = kcb_annotator.return_features()
+            self.params.AsKcbCosineMatchingParams.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -478,6 +486,7 @@ def run_as_kcb_deepscore_annotation(self: Self):
             kcb_annotator.calculate_scores_ms2deepscore()
             kcb_annotator.extract_mibig_scores(kcb_results)
             self.features = kcb_annotator.return_features()
+            self.params.AsKcbDeepscoreMatchingParams.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(

fermo_core/data_analysis/class_analysis_manager.py

@@ -67,13 +67,15 @@ class AnalysisManager(BaseModel):
     features: Repository
     samples: Repository
 
-    def return_attributes(self: Self) -> tuple[Stats, Repository, Repository]:
+    def return_attributes(
+        self: Self,
+    ) -> tuple[Stats, Repository, Repository, ParameterManager]:
         """Returns modified attributes to the calling function
 
         Returns:
             Tuple containing Stats, Feature Repository and Sample Repository objects.
         """
-        return self.stats, self.features, self.samples
+        return self.stats, self.features, self.samples, self.params
 
     def analyze(self: Self):
         """Organizes calling of data analysis steps."""
@@ -108,6 +110,7 @@ def run_feature_filter(self: Self):
             )
             feature_filter.filter()
             self.stats, self.features, self.samples = feature_filter.return_values()
+            self.params.FeatureFilteringParameters.module_passed = True
         except Exception as e:
             logger.warning(str(e))
             return
@@ -138,6 +141,7 @@ def run_blank_assignment(self: Self):
             )
             blank_assigner.run_analysis()
             self.stats, self.features = blank_assigner.return_attrs()
+            self.params.BlankAssignmentParameters.module_passed = True
         except Exception as e:
             logger.warning(str(e))
             return
@@ -181,6 +185,7 @@ def run_group_factor_assignment(self: Self):
             )
             group_fact_ass.run_analysis()
             self.features = group_fact_ass.return_features()
+            self.params.GroupFactAssignmentParameters.module_passed = True
         except Exception as e:
             logger.warning(str(e))
             return
@@ -211,7 +216,7 @@ def run_phenotype_manager(self: Self):
                 samples=self.samples,
             )
             phenotype_manager.run_analysis()
-            self.stats, self.features = phenotype_manager.return_attrs()
+            self.stats, self.features, self.params = phenotype_manager.return_attrs()
         except Exception as e:
             logger.warning(str(e))
             return
@@ -241,11 +246,9 @@ def run_sim_networks_manager(self: Self):
                 samples=self.samples,
             )
             sim_networks_manager.run_analysis()
-            (
-                self.stats,
-                self.features,
-                self.samples,
-            ) = sim_networks_manager.return_attrs()
+            (self.stats, self.features, self.samples, self.params) = (
+                sim_networks_manager.return_attrs()
+            )
         except Exception as e:
             logger.warning(str(e))
             return
@@ -261,7 +264,9 @@ def run_annotation_manager(self: Self):
                 samples=self.samples,
             )
             annotation_manager.run_analysis()
-            self.stats, self.features, self.samples = annotation_manager.return_attrs()
+            self.stats, self.features, self.samples, self.params = (
+                annotation_manager.return_attrs()
+            )
         except Exception as e:
             logger.warning(str(e))
             return

fermo_core/data_analysis/phenotype_manager/class_phenotype_manager.py

@@ -57,13 +57,13 @@ class PhenotypeManager(BaseModel):
     features: Repository
     samples: Repository
 
-    def return_attrs(self: Self) -> tuple[Stats, Repository]:
+    def return_attrs(self: Self) -> tuple[Stats, Repository, ParameterManager]:
         """Returns modified attributes from PhenotypeManager to the calling function
 
         Returns:
             Tuple containing Stats, Feature Repository objects.
         """
-        return self.stats, self.features
+        return self.stats, self.features, self.params
 
     def run_analysis(self: Self):
         """Organizes calling of phenotype annotation steps."""
@@ -103,6 +103,7 @@ def run_assigner_qualitative(self: Self):
             )
             qual_assigner.run_analysis()
             self.stats, self.features = qual_assigner.return_values()
+            self.params.PhenoQualAssgnParams.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -138,6 +139,7 @@ def run_assigner_quant_percentage(self: Self):
             )
             quant_p_assigner.run_analysis()
             self.stats, self.features = quant_p_assigner.return_values()
+            self.params.PhenoQuantPercentAssgnParams.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(
@@ -174,6 +176,7 @@ def run_assigner_quant_concentration(self: Self):
             )
             quant_conc_assigner.run_analysis()
             self.stats, self.features = quant_conc_assigner.return_values()
+            self.params.PhenoQuantConcAssgnParams.module_passed = True
         except Exception as e:
             logger.error(str(e))
             logger.error(

fermo_core/data_analysis/sim_networks_manager/class_sim_networks_manager.py

@@ -121,13 +121,15 @@ def log_timeout_ms2deepscore(max_time: str):
             f"filter out low-intensity/area peaks with 'feature_filtering' - SKIP"
         )
 
-    def return_attrs(self: Self) -> tuple[Stats, Repository, Repository]:
+    def return_attrs(
+        self: Self,
+    ) -> tuple[Stats, Repository, Repository, ParameterManager]:
         """Returns modified attributes from SimNetworksManager to the calling function
 
         Returns:
             Tuple containing Stats, Feature Repository and Sample Repository objects.
         """
-        return self.stats, self.features, self.samples
+        return self.stats, self.features, self.samples, self.params
 
     def run_analysis(self: Self):
         """Organizes calling of data analysis steps."""
@@ -186,7 +188,7 @@ def run_modified_cosine_alg(self: Self):
         self.store_network_data(
             "modified_cosine", network_data, tuple(filtered_features.get("included"))
         )
-
+        self.params.SpecSimNetworkCosineParameters.module_passed = True
         logger.info("'SimNetworksManager/ModCosineNetworker': completed calculation")
 
     def run_ms2deepscore_alg(self: Self):
@@ -241,7 +243,7 @@ def run_ms2deepscore_alg(self: Self):
         self.store_network_data(
             "ms2deepscore", network_data, tuple(filtered_features.get("included"))
         )
-
+        self.params.SpecSimNetworkDeepscoreParameters.module_passed = True
         logger.info("'SimNetworksManager/Ms2deepscoreNetworker': completed calculation")
 
     def filter_input_spectra(