new aggregation types

posts/services/common.py

@@ -343,7 +343,7 @@ def compute_sorting_divergence(post: Post) -> dict[int, float]:
             difference = prediction_difference_for_sorting(
                 forecast.get_prediction_values(),
                 cp.get_prediction_values(),
-                question,
+                question.type,
             )
             if (forecast.author_id not in user_divergences) or (
                 abs(user_divergences[forecast.author_id]) < abs(difference)

posts/services/subscriptions.py

@@ -187,7 +187,7 @@ def notify_post_cp_change(post: Post):
             difference = prediction_difference_for_sorting(
                 old_forecast_values,
                 current_forecast_values,
-                question=question,
+                question_type=question.type,
             )
             if max_sorting_diff is None or difference > max_sorting_diff:
                 max_sorting_diff = difference

questions/admin.py

@@ -6,6 +6,7 @@
 from django.utils.html import format_html
 from django_better_admin_arrayfield.admin.mixins import DynamicArrayMixin
 
+from questions.types import AggregationMethod
 from questions.models import (
     AggregateForecast,
     Conditional,
@@ -42,6 +43,7 @@ class QuestionAdmin(CustomTranslationAdmin, DynamicArrayMixin):
         "export_selected_questions_data_anonymized",
         "rebuild_aggregation_history",
         "trigger_scoring",
+        "trigger_scoring_with_all_aggregations",
     ]
     list_filter = [
         "type",
@@ -142,6 +144,24 @@ def trigger_scoring(self, request, queryset: QuerySet[Question]):
 
     trigger_scoring.short_description = "Trigger Scoring (does nothing if not resolved)"
 
+    def trigger_scoring_with_all_aggregations(
+        self, request, queryset: QuerySet[Question]
+    ):
+        from scoring.utils import score_question
+
+        for question in queryset:
+            if question.resolution in ["", None, "ambiguous", "annulled"]:
+                continue
+            score_question(
+                question=question,
+                resolution=question.resolution,
+                aggregation_methods=list(AggregationMethod._member_map_.values()),
+            )
+
+    trigger_scoring_with_all_aggregations.short_description = (
+        "Trigger Scoring (Includes ALL Aggregations) (does nothing if not resolved)"
+    )
+
 
 @admin.register(Conditional)
 class ConditionalAdmin(admin.ModelAdmin):

questions/serializers/common.py

@@ -993,7 +993,7 @@ def serialize_question_movement(
     divergence = prediction_difference_for_sorting(
         f1.forecast_values,
         f2.forecast_values,
-        question,
+        question.type,
     )
 
     if divergence >= threshold:

questions/services.py

@@ -171,7 +171,7 @@ def compute_question_movement(question: Question) -> float | None:
     return prediction_difference_for_sorting(
         cp_now.get_prediction_values(),
         cp_previous.get_prediction_values(),
-        question,
+        question.type,
     )
 
 
@@ -1035,7 +1035,7 @@ def calculate_user_forecast_movement_for_questions(
         divergence = prediction_difference_for_sorting(
             f1.forecast_values,
             f2.forecast_values,
-            question,
+            question.type,
         )
 
         if divergence >= 0.25:

questions/types.py

@@ -6,3 +6,13 @@ class AggregationMethod(models.TextChoices):
     UNWEIGHTED = "unweighted"
     SINGLE_AGGREGATION = "single_aggregation"
     METACULUS_PREDICTION = "metaculus_prediction"
+    MEDALISTS = "medalists"
+    EXPERIENCED_USERS_25_RESOLVED = "experienced_users_25_resolved"
+    IGNORANCE = "ignorance"
+    RECENCY_WEIGHTED_LOG_ODDS = "recency_weighted_log_odds"
+    RECENCY_WEIGHTED_MEAN_NO_OUTLIERS = "recency_weighted_mean_no_outliers"
+    RECENCY_WEIGHTED_MEDALISTS = "recency_weighted_medalists"
+    RECENCY_WEIGHTED_EXPERIENCED_USERS_25_RESOLVED = (
+        "recency_weighted_experienced_users_25_resolved"
+    )
+    RECENCY_WEIGHTED_LOG_ODDS_NO_OUTLIERS = "recency_weighted_log_odds_no_outliers"