Merge branch 'main' into 22357-mda-weight-sdmcap

howso/react_discriminative.amlg

@@ -651,31 +651,25 @@
 
 			;else it is categorical feature, pull the categorical value with the highest weight sum from nearby cases
 			(let
-				;an assoc of categorical action feature values -> sum of their weight
 				(assoc
-					categorical_value_weights_map (assoc)
-					output_value (null)
+					;an assoc of categorical action feature values -> sum of their weight
+					categorical_value_weights_map
+						;iterate over all the nearby cases, to create the mapping between each categorical action value and its summed
+						;weight for this categorical feature.  for each unique categorical value in all the neighbors, sum up its weight if
+						;it's encountered more than once, and select the categorical value with the highest weight below
+						(zip
+							(lambda (+ (current_value 1) (current_value)))
+							candidate_case_values
+							candidate_case_weights
+						)
 				)
-
 				(if output_influence_weights
 					(call !AccumulateInfluenceWeights  (assoc
 						case_ids candidate_case_ids
 						weights candidate_case_weights
 					))
 				)
 
-				;iterate over all the nearby cases, to create the mapping between each categorical action value and its summed
-				;weight for this categorical feature.  for each unique categorical value in all the neighbors, sum up its weight if
-				;it's encountered more than once, and select the categorical value with the highest weight below
-				(assign (assoc
-					categorical_value_weights_map
-						(zip
-							(lambda (+ (current_value 1) (current_value)))
-							candidate_case_values
-							candidate_case_weights
-						)
-				))
-
 				(if output_categorical_action_probabilities
 					(call !AccumulateCategoricalActionProbabilties (assoc
 						action_feature action_feature

howso/train.amlg

@@ -752,7 +752,9 @@
 		;split by batches of cases until next analyze
 		(while (< input_case_index (size cases))
 
-			(if (and thresholds_enabled (not skip_ablation))
+			;Only compute prediction stats for ablation if thresholds are enabled, we're
+			; not skipping ablation, and we have hyperparameters to use for the computation.
+			(if (and thresholds_enabled (not skip_ablation) (size !hyperparameterMetadataMap))
 				(seq
 					(assign (assoc
 						prev_prediction_stats_map new_prediction_stats_map
@@ -836,6 +838,7 @@
 				(and
 					(>= (+ !dataMassChangeSinceLastDataReduction batch_size) !autoAblationMaxNumCases)
 					(not skip_reduce_data)
+					(size !hyperparameterMetadataMap)
 				)
 				(call reduce_data (assoc
 					abs_threshold_map !autoAblationAbsThresholdMap
@@ -879,8 +882,12 @@
 	(let
 		(assoc
 			indices_to_train
-				(if thresholds_satisfied
-					;If one or more thresholds has been satisfied, just train all of the cases in this batch.
+				;If one or more thresholds has been satisfied, or the Trainee has not yet been analyzed,
+				; just train all of the cases in this batch.
+				(if (or
+						thresholds_satisfied
+						(not (size !hyperparameterMetadataMap))
+					)
 					(indices cases)
 					;Otherwise, do the normal ablation filtering.
 					||(filter
@@ -1031,12 +1038,14 @@
 
 				(accum_to_entities (assoc !dataMassChangeSinceLastAnalyze mass_to_accumulate))
 
-				;recompute influence weights entropy
-				(call !ComputeAndStoreInfluenceWeightEntropies (assoc
-					features features
-					weight_feature accumulate_weight_feature
-					use_case_weights (true)
-				))
+				;recompute influence weights entropy only after we've analyzed.
+				(if (size !hyperparameterMetadataMap)
+					(call !ComputeAndStoreInfluenceWeightEntropies (assoc
+						features features
+						weight_feature accumulate_weight_feature
+						use_case_weights (true)
+					))
+				)
 			)
 		)
 

unit_tests/ut_h_basic_ablation.amlg

@@ -2,8 +2,13 @@
 	#unit_test (direct_assign_to_entities (assoc unit_test (load "unit_test.amlg")))
 	(call (load "unit_test_howso.amlg") (assoc name "ut_h_basic_ablation.amlg"))
 
+	;Set a non-default hyperparameter map since ablation checks for non-default
+	; hyperparameters.
 	(call_entity "howso" "set_params" (assoc
-		default_hyperparameter_map (assoc "k" 2 "p" 0.4 "dt" -1)
+		hyperparameter_map
+			{targetless { "A.B.C.D.E." { ".none"
+				{k 2 p 0.4 dt -1}
+			}}}
 	))
 
 	(declare (assoc
@@ -91,7 +96,6 @@
 		obs (call !get_num_cases)
 	))
 
-
 	(print "conviction value of new case: "
 		(call_entity "howso" "react_group" (assoc features (append context_features action_features ) new_cases (list (list (list 1 2 1 3 3)))) ) "\n"
 	)

unit_tests/ut_h_reduce_data.amlg

@@ -158,7 +158,7 @@
             features (append context_labels action_labels)
             cases training_data
             session "iris_session"
-            skip_auto_analyze (true)
+            skip_auto_analyze (false)
         ))
         (call_entity "howso" "analyze" (assoc
             use_case_weights (true)

version.json

@@ -1,6 +1,6 @@
 {
   "version": "0.0.0",
   "dependencies": {
-    "amalgam": "58.1.1"
+    "amalgam": "58.1.2"
   }
 }