removing refs to MDA, updating method names

howso/analysis_weights.amlg

@@ -133,7 +133,7 @@
 		;mda for each context feature as a ratio of error_with_feature_removed / baseline_error (where baseline uses all the context features)
 		(declare (assoc
 			context_features_mda_map
-				(call !DecreaseInAccuracy (assoc
+				(call !CalculateFeatureAccuracyContributions (assoc
 					context_features context_features
 					action_features (list action_feature)
 					output_ratio (true)

howso/contributions.amlg

@@ -1,7 +1,7 @@
 ;Contains methods for computing and outputting feature contributions.
 (null
 
-	;Calculate and cache feature contributions - average delta between predicted value for action_feature with and without a context feature
+	;Calculate feature prediction contributions - average delta between predicted value for action_feature with and without a context feature
 	; for all specified context_features.
 	;
 	;parameters:
@@ -21,7 +21,7 @@
 	; run_on_local_model: optional, if true will use all the provided cases and not store results to model
 	; context_condition_filter_query: a list of queries that can be used to filter the possible set of cases that can be used for predictions
 	; use_derivation_logic: flag, if true action feature will be derived and the features needed to derive will be predicted
-	#!CalculateFeatureContributions
+	#!CalculateFeaturePredictionContributions
 	(declare
 		(assoc
 			context_features (list)

howso/details_influences.amlg

@@ -47,15 +47,15 @@
 			output
 				(if robust
 					(assoc "feature_robust_accuracy_contributions"
-						(call !DecreaseInAccuracy (assoc
+						(call !CalculateFeatureAccuracyContributions (assoc
 							context_features context_features
 							action_features action_features
 							case_ids local_model_case_ids
 							robust (true)
 						))
 					)
 					(assoc "feature_full_accuracy_contributions"
-						(call !DecreaseInAccuracy (assoc
+						(call !CalculateFeatureAccuracyContributions (assoc
 							context_features context_features
 							action_features action_features
 							case_ids local_model_case_ids
@@ -119,15 +119,15 @@
 			output
 				(if robust
 					(assoc "feature_robust_accuracy_contributions_ex_post"
-						(call !DecreaseInAccuracy (assoc
+						(call !CalculateFeatureAccuracyContributions (assoc
 							context_features context_features
 							action_features action_features
 							case_ids local_model_case_ids
 							robust (true)
 						))
 					)
 					(assoc "feature_full_accuracy_contributions_ex_post"
-						(call !DecreaseInAccuracy (assoc
+						(call !CalculateFeatureAccuracyContributions (assoc
 							context_features context_features
 							action_features action_features
 							case_ids local_model_case_ids
@@ -653,7 +653,7 @@
 
 		(declare (assoc
 			feature_contributions_pair
-				(call !CalculateFeatureContributions (assoc
+				(call !CalculateFeaturePredictionContributions (assoc
 					context_features context_features
 					action_feature action_feature
 					robust robust

howso/influences.amlg

@@ -1,6 +1,6 @@
 ;Contains methods for computing and outputting feature MDA.
 (null
-	;calculate the decrease in accuracy for the specified list of context and action features
+	;calculate the accuracy contribution for each feature as the decrease in accuracy for the specified list of context and action features
 	;outputs an assoc of context features -> decrease in accuracy Mean Absolute Error value
 	; if output_ratio, the ratio is: feature_mae / baseline_mae
 	; else:  feature_mae - baseline_mae
@@ -22,7 +22,7 @@
 	; weight_feature: optional, default '.case_weight'.  name of feature whose values to use as case weights
 	; context_condition_filter_query: a list of queries that can be used to filter the possible set of cases that can be used for predictions
 	; use_derivation_logic: flag, if true action feature will be derived and the features needed to derive will be predicted
-	#!DecreaseInAccuracy
+	#!CalculateFeatureAccuracyContributions
 	(declare
 		(assoc
 			context_features (list)
@@ -95,75 +95,69 @@
 				))
 		))
 
-		(declare (assoc
-			output
-				;knock out each feature one by one and calculate the decrease in accuracy for each feature
-				(zip
-					(or details_context_features context_features)
-					(map
-						(lambda (let
-							(assoc
-								mae_context_features context_features
-							)
+		(zip
+			(or details_context_features context_features)
+			(map
+				(lambda (let
+					(assoc
+						mae_context_features context_features
+					)
 
-							;regular Decrease in accuracy is feature knockout instead of scrambling the feature
-							(if (not sensitivity_to_randomization)
-								;remove the feature
-								(assign (assoc
-									mae_context_features (indices (remove context_features_mda (current_value 1)))
-								))
-							)
+					;regular Decrease in accuracy is feature knockout instead of scrambling the feature
+					(if (not sensitivity_to_randomization)
+						;remove the feature
+						(assign (assoc
+							mae_context_features (indices (remove context_features_mda (current_value 1)))
+						))
+					)
 
-							(let
-								(assoc
-									feature_mda
-										(call !CalculateModelMAE (assoc
-											action_features action_features
-											context_features mae_context_features
-											case_ids case_ids
-											ignore_exact_cases (true)
-											classification_precision classification_precision
-											robust_residuals robust
-											custom_hyperparam_map hyperparam_map
-											use_case_weights use_case_weights
-											weight_feature weight_feature
-											context_condition_filter_query context_condition_filter_query
-											use_derivation_logic use_derivation_logic
-
-											;the scramble feature index will be null if doing feature knockout
-											scramble_feature_index
-												(if sensitivity_to_randomization
-													(current_index 2)
-													(null)
-												)
-										))
-								)
+					(let
+						(assoc
+							feature_mda
+								(call !CalculateModelMAE (assoc
+									action_features action_features
+									context_features mae_context_features
+									case_ids case_ids
+									ignore_exact_cases (true)
+									classification_precision classification_precision
+									robust_residuals robust
+									custom_hyperparam_map hyperparam_map
+									use_case_weights use_case_weights
+									weight_feature weight_feature
+									context_condition_filter_query context_condition_filter_query
+									use_derivation_logic use_derivation_logic
+
+									;the scramble feature index will be null if doing feature knockout
+									scramble_feature_index
+										(if sensitivity_to_randomization
+											(current_index 2)
+											(null)
+										)
+								))
+						)
 
-								;output the decrease in accuracy as a ratio of the feature / baseline MAE
-								(if output_ratio
-									;prevent output of (null) in case all maes are 0
-									(if (= 0 baseline_error)
-										.infinity
+						;output the decrease in accuracy as a ratio of the feature / baseline MAE
+						(if output_ratio
+							;prevent output of (null) in case all maes are 0
+							(if (= 0 baseline_error)
+								.infinity
 
-										(/ feature_mda baseline_error)
-									)
+								(/ feature_mda baseline_error)
+							)
 
-									;else output the difference in the MAE
-									(if (= .infinity baseline_error feature_mda)
-										0
+							;else output the difference in the MAE
+							(if (= .infinity baseline_error feature_mda)
+								0
 
-										(- feature_mda baseline_error)
-									)
-								)
+								(- feature_mda baseline_error)
 							)
-						))
-
-						(or details_context_features context_features)
+						)
 					)
-				)
-		))
+				))
 
-		output
+				(or details_context_features context_features)
+			)
+		)
 	)
 
 

howso/react.amlg

@@ -171,22 +171,22 @@
 			;                       If "all", then returns all including the confusion_matrix. Valid values are: "mae" "confusion_matrix" "r2" "rmse"
 			;						"adjusted_smape" "smape" "spearman_coeff" "precision" "recall" "accuracy" "mcc" "all" "missing_value_accuracy"
 			;
-			;		 "feature_robust_accuracy_contributions" true or false. If true outputs each context feature's robust mean decrease in accuracy of predicting
+			;		 "feature_robust_accuracy_contributions" true or false. If true outputs each context feature's robust Accuracy Contribution in predicting
 			;						the action feature given the context. Uses only the context features of the reacted
 			;						case to determine that area.  Uses robust calculations, which uses uniform sampling from
 			;						the power set of features as the contexts for predictions.
 			;
-			;		 "feature_full_accuracy_contributions" true or false. If true outputs each context feature's full mean decrease in accuracy of predicting
+			;		 "feature_full_accuracy_contributions" true or false. If true outputs each context feature's full Accuracy Contribution in predicting
 			;						the action feature given the context. Uses only the context features of the reacted
 			;						case to determine that area. Uses full calculations, which uses leave-one-out context features for computations.
 			;
-			;		 "feature_robust_accuracy_contributions_ex_post" true or false. If true outputs each context feature's mean decrease in accuracy of
+			;		 "feature_robust_accuracy_contributions_ex_post" true or false. If true outputs each context feature's Accuracy Contribution in
 			;						predicting the action feature as a detail given that the specified prediction was
 			;						already made as specified by the action value. Uses both context and action features of
 			;						the reacted case to determine that area. Uses robust calculations, which uses uniform sampling from
 			;						the power set of features as the contexts for predictions.
 			;
-			;		 "feature_full_accuracy_contributions_ex_post" true or false. If true outputs each context feature's mean decrease in accuracy of
+			;		 "feature_full_accuracy_contributions_ex_post" true or false. If true outputs each context feature's Accuracy Contribution in
 			;						predicting the action feature as a detail given that the specified prediction was
 			;						already made as specified by the action value. Uses both context and action features of
 			;						the reacted case to determine that area. Uses full calculations, which uses leave-one-out for features for computations.
@@ -204,12 +204,12 @@
 			;						both 'feature_contributions' and non-absolute 'directional_feature_contributions'.
 			;						Uses full calculations, which uses leave-one-out context features for computations.
 			;
-			;		 "case_robust_prediction_contributions" true or false. If true outputs each influential case's mean decrease in accuracy of predicting
+			;		 "case_robust_prediction_contributions" true or false. If true outputs each influential case's Accuracy Contribution in predicting
 			;						the action feature in the local model area, as if each individual case were included
 			;						versus not included. Uses only the context features of the reacted case to determine
 			;						that area. Uses robust calculations, which uses uniform sampling from the power set of all combinations of cases.
 			;
-			;		 "case_full_prediction_contributions" true or false. If true outputs each influential case's mean decrease in accuracy of predicting
+			;		 "case_full_prediction_contributions" true or false. If true outputs each influential case's Accuracy Contribution in predicting
 			;						the action feature in the local model area, as if each individual case were included
 			;						versus not included. Uses only the context features of the reacted case to determine
 			;						that area. Uses full calculations, which uses leave-one-out for cases for computations.

howso/react_aggregate.amlg

@@ -1,6 +1,6 @@
-;Contains methods for computing, storing into the trainee, and returning residuals, prediction_stats, feature_mda, and feature_contributions.
+;Contains methods for computing, storing into the trainee, and returning residuals, prediction_stats, accuracy contributions, and prediction contributions.
 (null
-    ;Computes, caches, and returns specified details and feature prediction statistics such as Mean Decrease in Accuracy (MDA), residuals (accuracy, Mean Absolute Error),
+    ;Computes, caches, and returns specified details and feature prediction statistics such as Accuracy Contributions, residuals (accuracy, Mean Absolute Error),
     ; precision, recall, etc. Returns details and feature prediction stats for all features in the format of feature -> assoc stat -> value
 	;{long_running (true) statistically_idempotent (true)}
 	#react_aggregate
@@ -39,7 +39,7 @@
     		;	Applicable only to models > 1000 cases.
 			sub_model_size (null)
 			;{type "string"}
-			;target feature for which to do computations. If "prediction_stats_action_feature" and "feature_influences_action_feature"
+			;target feature for which to do feature influences computations (accuracy contributions or prediction contributions). If "prediction_stats_action_feature" and "feature_influences_action_feature"
 			;	are not provided, they will default to this value.
 			action_feature (null)
 			;{type "string"}
@@ -49,11 +49,11 @@
 			;	If this value is not specified but "action_feature" is provided, this value will default to the value of "action_feature".
 			prediction_stats_action_feature (null)
 			;{type "string"}
-			;When feature influences such as contributions and mda, use this feature as the action feature.
+			;When computing feature influences such as prediction contributions or accuracy contributions, use this feature as the action feature.
 			;	If not provided, will default to the "action_feature" if provided.
 			feature_influences_action_feature (null)
 			;{type "boolean"}
-			;flag, optional. if specified, will attempt to return stats that were computed using hyperpparameters with the
+			;flag, optional. if specified, will attempt to return stats that were computed using hyperparameters with the
     		;	specified robust or non-robust type.
 			robust_hyperparameters (null)
 			;{type "number" min 0}
@@ -91,13 +91,13 @@
 			;				Uses robust computation.
 			;		feature_deviations: optional, none/true/false. if true will compute feature deviations for each feature in action_features. The feature deviation is the mean absolute
 			;				error of predicting the feature using all the context features as well as value of the feature being predicted as the context for each prediction.
-			;		feature_full_accuracy_contributions: optional, none/true/false. if true will compute Mean Decrease in Accuracy (feature_mda) for each context feature at predicting action_feature.
+			;		feature_full_accuracy_contributions: optional, none/true/false. if true will compute Accuracy Contributions for each context feature at predicting action_feature.
 			;				Drop each feature and use the full set of remaining context features for each prediction. Uses full computation.
-			;		feature_full_accuracy_contributions_permutation: optional, none/true/false. Compute feature_full_accuracy_contributions by scrambling each feature and using the full set of remaining context features
+			;		feature_full_accuracy_contributions_permutation: optional, none/true/false. Compute Accuracy Contributions by scrambling each feature and using the full set of remaining context features
 			;				for each prediction.  Uses full computation.
-			;		feature_robust_accuracy_contributions: optional, none/true/false. Compute feature_mda by dropping each feature and using the robust (power set/permutations) set of
+			;		feature_robust_accuracy_contributions: optional, none/true/false. Compute Accuracy Contributions by dropping each feature and using the robust (power set/permutations) set of
 			;				remaining context features for each prediction. Uses robust computation.
-			;		feature_robust_accuracy_contributions_permutation: optional, none/true/false. Compute feature_mda by scrambling each feature and using the robust (power set/permutations)
+			;		feature_robust_accuracy_contributions_permutation: optional, none/true/false. Compute Accuracy Contributions by scrambling each feature and using the robust (power set/permutations)
 			;				set of remaining context features for each prediction.  Uses robust computation.
 			;		selected_prediction_stats": list of strings, optional.  Allowed values are:
 			;						"mae" : Mean absolute error. For continuous features, this is calculated as the mean of absolute values of the difference
@@ -211,7 +211,7 @@
 				)
 			)
 			(conclude
-				(call !Return (assoc errors (list "action_feature must be specified when computing feature_mda_permutation, feature_mda, or feature_contributions.")))
+				(call !Return (assoc errors (list "action_feature must be specified when computing Accuracy Contributions or Prediction Contributions.")))
 			)
 		)
 
@@ -549,7 +549,7 @@
 				(if (get details "feature_full_prediction_contributions")
 					(accum (assoc
 						output
-							(call !CalculateFeatureContributions (assoc
+							(call !CalculateFeaturePredictionContributions (assoc
 								context_features context_features
 								action_feature feature_influences_action_feature
 								robust (false)
@@ -566,7 +566,7 @@
 				(if (get details "feature_robust_prediction_contributions")
 					(accum (assoc
 						output
-							(call !CalculateFeatureContributions (assoc
+							(call !CalculateFeaturePredictionContributions (assoc
 								context_features context_features
 								action_feature feature_influences_action_feature
 								robust (true)
@@ -601,7 +601,7 @@
 						output
 							(assoc
 								"feature_full_accuracy_contributions"
-									(call !DecreaseInAccuracy (assoc
+									(call !CalculateFeatureAccuracyContributions (assoc
 										context_features context_features
 										action_features [feature_influences_action_feature]
 										sensitivity_to_randomization (false)
@@ -622,7 +622,7 @@
 						output
 							(assoc
 								"feature_robust_accuracy_contributions"
-									(call !DecreaseInAccuracy (assoc
+									(call !CalculateFeatureAccuracyContributions (assoc
 										context_features context_features
 										action_features [feature_influences_action_feature]
 										sensitivity_to_randomization (false)
@@ -643,7 +643,7 @@
 						output
 							(assoc
 								"feature_full_accuracy_contributions_permutation"
-									(call !DecreaseInAccuracy (assoc
+									(call !CalculateFeatureAccuracyContributions (assoc
 										context_features context_features
 										action_features [feature_influences_action_feature]
 										sensitivity_to_randomization (true)
@@ -664,7 +664,7 @@
 						output
 							(assoc
 								"feature_robust_accuracy_contributions_permutation"
-									(call !DecreaseInAccuracy (assoc
+									(call !CalculateFeatureAccuracyContributions (assoc
 										context_features context_features
 										action_features [feature_influences_action_feature]
 										sensitivity_to_randomization (true)

howso/react_discriminative.amlg

@@ -246,7 +246,7 @@
 					(get details "feature_robust_prediction_contributions")
 					(get details "case_full_prediction_contributions")
 					(get details "case_robust_prediction_contributions")
-					;feature mda does not need to output C.A.P. because it calls the generic DecreaseInAccuracy method which in turn uses
+					;feature mda does not need to output C.A.P. because it calls the generic CalculateFeatureAccuracyContributions method which in turn uses
 					;ReactDiscriminative method with different feature sets, instead of simply calling GenerateReaction or InterpolateActionValues
 				)