GITC-245: Implements the CLR calc in SQL

app/dashboard/management/commands/update_trust_bonus.py

@@ -0,0 +1,46 @@
+'''
+    Copyright (C) 2021 Gitcoin Core
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program. If not, see <http://www.gnu.org/licenses/>.
+
+'''
+
+from django.core.management.base import BaseCommand
+
+from dashboard.models import Profile
+from dashboard.tasks import update_trust_bonus
+
+
+class Command(BaseCommand):
+
+    help = 'Update every users trust_bonus score'
+
+    def add_arguments(self, parser):
+        parser.add_argument(
+            '--call-now',
+            type=int,
+            help="disable execution on celery and call now"
+        )
+
+    def handle(self, *args, **options):
+        profiles = Profile.objects.all()
+        print(profiles.count())
+        for profile in profiles.iterator():
+            if (options['call_now']):
+                params = profile.as_dict
+                params['trust_bonus'] = profile.trust_bonus
+                print("Saving - %s - %s" % (profile.handle, params['trust_bonus']))
+                profile.save()
+            else:
+                update_trust_bonus.delay(profile.pk)

app/dashboard/models.py

@@ -4400,6 +4400,7 @@ def to_dict(self):
             'card_title': f'@{self.handle} | Gitcoin',
             'org_works_with': org_works_with,
             'card_desc': desc,
+            'trust_bonus': self.trust_bonus,
             'avatar_url': self.avatar_url_with_gitcoin_logo,
             'count_bounties_completed': total_fulfilled,
             'works_with_collected': works_with_collected,

app/dashboard/tasks.py

@@ -246,6 +246,21 @@ def profile_dict(self, pk, retry: bool = True) -> None:
             profile.save()
 
 
+@app.shared_task(bind=True, max_retries=3)
+def update_trust_bonus(self, pk):
+    """
+    :param self:
+    :param pk:
+    :return:
+    """
+    profile = Profile.objects.get(pk=pk)
+    params = profile.as_dict
+    if profile.trust_bonus != params.get('trust_bonus', None):
+        params['trust_bonus'] = profile.trust_bonus
+        print("Saving - %s - %s" % (profile.handle, params['trust_bonus']))
+        profile.save()
+
+
 @app.shared_task(bind=True)
 def maybe_market_to_user_slack(self, bounty_pk, event_name, retry: bool = True) -> None:
     """

app/grants/admin.py

@@ -104,7 +104,7 @@ class GrantAdmin(GeneralAdmin):
         'active', 'visible', 'is_clr_eligible',
         'migrated_to', 'region',
         'grant_type', 'categories', 'description', 'description_rich', 'github_project_url', 'reference_url', 'admin_address', 
-        'amount_received', 'amount_received_in_round', 'monthly_amount_subscribed',
+        'amount_received', 'amount_received_in_round', 'monthly_amount_subscribed', 'defer_clr_to', 
         'deploy_tx_id', 'cancel_tx_id', 'admin_profile', 'token_symbol',
         'token_address', 'contract_address', 'contract_version', 'network', 'required_gas_price', 'logo_svg_asset',
         'logo_asset', 'created_on', 'modified_on', 'team_member_list',

app/grants/clr.py

@@ -415,7 +415,12 @@ def predict_clr(save_to_db=False, from_date=None, clr_round=None, network='mainn
         print(f"- starting slim grant calc at {round(time.time(),1)}")
         grants_clr = run_clr_calcs(grant_contributions_curr, v_threshold, uv_threshold, total_pot)
         print(f"- saving slim grant calc at {round(time.time(),1)}")
+        total_count = len(grants_clr.items())
         for grant_calc in grants_clr:
+            counter += 1
+            if counter % 10 == 0 or True:
+                print(f"- {counter}/{total_count} grants iter, pk:{grant_id}, at {round(time.time(),1)}")
+
             pk = grant_calc['id']
             grant = clr_round.grants.using('default').get(pk=pk)
             latest_calc = grant.clr_calculations.using('default').filter(latest=True, grantclr=clr_round).order_by('-pk').first()
@@ -424,10 +429,12 @@ def predict_clr(save_to_db=False, from_date=None, clr_round=None, network='mainn
                 continue
             clr_prediction_curve = copy.deepcopy(latest_calc.clr_prediction_curve)
             clr_prediction_curve[0][1] = grant_calc['clr_amount'] # update only the existing match estimate
+            print(clr_prediction_curve)
             clr_round.record_clr_prediction_curve(grant, clr_prediction_curve)
             grant.save()
         # if we are only calculating slim CLR calculations, return here and save 97% compute power
         print(f"- done calculating at {round(time.time(),1)}")
+        print(f"\nTotal execution time: {(timezone.now() - clr_calc_start_time)}")
         return
 
     print(f"- starting grants iter at {round(time.time(),1)}")
@@ -460,7 +467,6 @@ def predict_clr(save_to_db=False, from_date=None, clr_round=None, network='mainn
         else:
             for amount in potential_donations:
                 # calculate clr with each additional donation and save to grants model
-                # print(f'using {total_pot_close}')
                 predicted_clr, grants_clr, _, _ = calculate_clr_for_donation(
                     grant, amount, grant_contributions_curr, total_pot, v_threshold, uv_threshold
                 )
@@ -480,11 +486,15 @@ def predict_clr(save_to_db=False, from_date=None, clr_round=None, network='mainn
             else:
                 clr_prediction_curve = [[0.0, 0.0, 0.0] for x in range(0, 6)]
 
+            print(clr_prediction_curve)
+
             clr_round.record_clr_prediction_curve(_grant, clr_prediction_curve)
 
             if from_date > (clr_calc_start_time - timezone.timedelta(hours=1)):
                 _grant.save()
 
         debug_output.append({'grant': grant.id, "clr_prediction_curve": (potential_donations, potential_clr), "grants_clr": grants_clr})
 
+    print(f"\nTotal execution time: {(timezone.now() - clr_calc_start_time)}")
+
     return debug_output