Merge branch 'main' into 1983-new_reco_pages

backend/ml/inputs.py

@@ -2,7 +2,7 @@
 from typing import Optional
 
 import pandas as pd
-from django.db.models import Case, F, QuerySet, When
+from django.db.models import Case, F, Q, QuerySet, When
 from django.db.models.expressions import RawSQL
 from solidago.pipeline import TournesolInput
 
@@ -14,6 +14,7 @@
     ContributorScaling,
     Entity,
 )
+from vouch.models import Voucher
 
 
 class MlInputFromDb(TournesolInput):
@@ -189,3 +190,30 @@ def get_individual_scores(
 
         dtf = pd.DataFrame(values)
         return dtf[["user_id", "entity", "criteria", "raw_score"]]
+
+    def get_vouches(self):
+        values = Voucher.objects.filter(
+            by__is_active=True,
+            to__is_active=True,
+        ).values(
+            voucher=F("by__id"),
+            vouchee=F("to__id"),
+            vouch=F("value"),
+        )
+        return pd.DataFrame(values, columns=["voucher", "vouchee", "vouch"])
+
+    def get_users(self):
+        values = (
+            User.objects
+            .filter(is_active=True)
+            .annotate(is_pretrusted=Q(pk__in=User.with_trusted_email()))
+            .values(
+                "is_pretrusted",
+                "trust_score",
+                user_id=F("id"),
+            )
+        )
+        return pd.DataFrame(
+            data=values,
+            columns=["user_id", "is_pretrusted", "trust_score"],
+        ).set_index("user_id")

backend/ml/management/commands/ml_train.py

@@ -1,10 +1,66 @@
+import os
+from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
+
+from django import db
+from django.conf import settings
 from django.core.management.base import BaseCommand
+from solidago.aggregation import EntitywiseQrQuantile
+from solidago.pipeline import Pipeline
+from solidago.post_process.squash import Squash
+from solidago.preference_learning import UniformGBT
+from solidago.scaling import Mehestan, QuantileShift, ScalingCompose, Standardize
+from solidago.trust_propagation import LipschiTrust, NoopTrust
+from solidago.voting_rights import AffineOvertrust
 
 from ml.inputs import MlInputFromDb
-from ml.mehestan.run import MehestanParameters, run_mehestan
+from ml.outputs import TournesolPollOutput, save_tournesol_scores
 from tournesol.models import EntityPollRating, Poll
-from tournesol.models.poll import ALGORITHM_LICCHAVI, ALGORITHM_MEHESTAN
-from vouch.trust_algo import trust_algo
+from tournesol.models.poll import ALGORITHM_MEHESTAN, DEFAULT_POLL_NAME
+
+
+def get_solidago_pipeline(run_trust_propagation: bool = True):
+    if run_trust_propagation:
+        trust_algo = LipschiTrust()
+    else:
+        trust_algo = NoopTrust()
+
+    aggregation_lipshitz = 0.1
+
+    return Pipeline(
+        trust_propagation=trust_algo,
+        voting_rights=AffineOvertrust(),
+        # TODO: use LBFGS (faster) implementation.
+        # Currently requires to install Solidago with "torch" extra.
+        preference_learning=UniformGBT(
+            prior_std_dev=7.0,
+            convergence_error=1e-5,
+            cumulant_generating_function_error=1e-5,
+            high_likelihood_range_threshold=0.25,
+            # max_iter=300,
+        ),
+        scaling=ScalingCompose(
+            Mehestan(),
+            Standardize(
+                dev_quantile=0.9,
+                lipschitz=0.1,
+            ),
+            QuantileShift(
+                quantile=0.1,
+                # target_score is defined to be the recommendability
+                # threshold, i.e the therorical max score that can be
+                # reached by an entity with 2 contributors.
+                target_score=2*aggregation_lipshitz,
+                lipschitz=0.1,
+                error=1e-5,
+            ),
+        ),
+        aggregation=EntitywiseQrQuantile(
+            quantile=0.5,
+            lipschitz=aggregation_lipshitz,
+            error=1e-5,
+        ),
+        post_process=Squash(score_max=100.)
+    )
 
 
 class Command(BaseCommand):
@@ -17,37 +73,87 @@ def add_arguments(self, parser):
             help="Disable trust scores computation and preserve existing trust_score values",
         )
         parser.add_argument("--main-criterion-only", action="store_true")
-        parser.add_argument("--alpha", type=float, default=None)
-        parser.add_argument("-W", type=float, default=None)
-        parser.add_argument("--score-shift-quantile", type=float, default=None)
-        parser.add_argument("--score-deviation-quantile", type=float, default=None)
 
     def handle(self, *args, **options):
-        if not options["no_trust_algo"]:
-            # Update "trust_score" for all users
-            trust_algo()
-
-        # Update scores for all polls
         for poll in Poll.objects.filter(active=True):
-            ml_input = MlInputFromDb(poll_name=poll.name)
-
-            if poll.algorithm == ALGORITHM_MEHESTAN:
-                kwargs = {
-                    param: options[param]
-                    for param in ["alpha", "W", "score_shift_quantile", "score_deviation_quantile"]
-                    if options[param] is not None
-                }
-                parameters = MehestanParameters(**kwargs)
-                run_mehestan(
-                    ml_input=ml_input,
-                    poll=poll,
-                    parameters=parameters,
-                    main_criterion_only=options["main_criterion_only"],
+            if poll.algorithm != ALGORITHM_MEHESTAN:
+                raise ValueError(f"Unknown algorithm {poll.algorithm!r}")
+
+            is_default_poll = (poll.name == DEFAULT_POLL_NAME)
+            self.run_poll_pipeline(
+                poll=poll,
+                update_trust_scores=(not options["no_trust_algo"] and is_default_poll),
+                main_criterion_only=options["main_criterion_only"],
+            )
+
+    def run_poll_pipeline(
+        self,
+        poll: Poll,
+        update_trust_scores: bool,
+        main_criterion_only: bool,
+    ):
+        pipeline = get_solidago_pipeline(
+            run_trust_propagation=update_trust_scores
+        )
+        criteria_list = poll.criterias_list
+        criteria_to_run = [poll.main_criteria]
+        if not main_criterion_only:
+            criteria_to_run.extend(
+                c for c in criteria_list if c != poll.main_criteria
+            )
+
+        if settings.MEHESTAN_MULTIPROCESSING:
+            # compute each criterion in parallel
+            cpu_count = os.cpu_count() or 1
+            cpu_count -= settings.MEHESTAN_KEEP_N_FREE_CPU
+            os.register_at_fork(before=db.connections.close_all)
+            executor = ProcessPoolExecutor(max_workers=max(1, cpu_count))
+        else:
+            # In tests, we might prefer to use a single thread to reduce overhead
+            # of multiple processes, db connections, and redundant numba compilation
+            executor = ThreadPoolExecutor(max_workers=1)
+
+        with executor:
+            futures = []
+            for crit in criteria_to_run:
+                pipeline_input = MlInputFromDb(poll_name=poll.name)
+                pipeline_output = TournesolPollOutput(
+                    poll_name=poll.name,
+                    criterion=crit,
+                    save_trust_scores_enabled=(update_trust_scores and crit == poll.main_criteria)
                 )
-            elif poll.algorithm == ALGORITHM_LICCHAVI:
-                raise NotImplementedError("Licchavi is no longer supported")
-            else:
-                raise ValueError(f"unknown algorithm {repr(poll.algorithm)}'")
-            self.stdout.write(f"Starting bulk update of sum_trust_score for poll {poll.name}")
-            EntityPollRating.bulk_update_sum_trust_scores(poll)
-            self.stdout.write(f"Finished bulk update of sum_trust_score for poll {poll.name}")
+
+                futures.append(
+                    executor.submit(
+                        self.run_pipeline_and_close_db,
+                        pipeline=pipeline,
+                        pipeline_input=pipeline_input,
+                        pipeline_output=pipeline_output,
+                        criterion=crit,
+                    )
+                )
+
+            for fut in as_completed(futures):
+                # reraise potential exception
+                fut.result()
+
+        save_tournesol_scores(poll)
+        EntityPollRating.bulk_update_sum_trust_scores(poll)
+
+        self.stdout.write(f"Pipeline for poll {poll.name}: Done")
+
+    @staticmethod
+    def run_pipeline_and_close_db(
+        pipeline: Pipeline,
+        pipeline_input: MlInputFromDb,
+        pipeline_output: TournesolPollOutput,
+        criterion: str
+    ):
+        pipeline.run(
+            input=pipeline_input,
+            criterion=criterion,
+            output=pipeline_output,
+        )
+        # Closing the connection fixes a warning in tests
+        # about open connections to the database.
+        db.connection.close()

backend/ml/outputs.py

@@ -176,8 +176,10 @@ def save_entity_scores(
         scores: pd.DataFrame,
         score_mode="default",
     ):
-        scores_iterator = scores[["entity_id", "score", "uncertainty"]].itertuples(index=False)
+        if len(scores) == 0:
+            return
 
+        scores_iterator = scores[["entity_id", "score", "uncertainty"]].itertuples(index=False)
         with transaction.atomic():
             EntityCriteriaScore.objects.filter(
                 poll=self.poll,

backend/settings/settings.py

@@ -364,6 +364,11 @@
 
 TWITTERBOT_CREDENTIALS = server_settings.get("TWITTERBOT_CREDENTIALS", {})
 
+if "DJANGO_LOG_LEVEL" in os.environ:
+    DJANGO_LOG_LEVEL = os.environ["DJANGO_LOG_LEVEL"]
+else:
+    DJANGO_LOG_LEVEL = server_settings.get("DJANGO_LOG_LEVEL", "INFO")
+
 LOGGING = {
     "version": 1,
     "disable_existing_loggers": False,
@@ -374,7 +379,7 @@
     },
     "root": {
         "handlers": ["console"],
-        "level": os.environ.get("DJANGO_LOG_LEVEL", "DEBUG"),
+        "level": DJANGO_LOG_LEVEL,
     },
     "loggers": {
         "factory": {

backend/tests/requirements.txt

@@ -8,10 +8,8 @@ pylint-django==2.5.3
 pylint-json2html==0.4.0
 
 # Unit tests tools
-faker==13.15.1
 pytest==7.1.3
 pytest-html==3.1.1
-pytest-mock==3.8.2
 
 # Pytest for django
 pytest-django==4.5.2

backend/tournesol/lib/public_dataset.py

@@ -291,7 +291,7 @@ def write_comparisons_file(
         "criteria",
         "score",
         "score_max",
-        "week_date"
+        "week_date",
     ]
     writer = csv.DictWriter(write_target, fieldnames=fieldnames)
     writer.writeheader()
@@ -413,7 +413,9 @@ def write_vouchers_file(write_target):
             "to_username": voucher.to.username,
             "value": voucher.value,
         }
-        for voucher in Voucher.objects.filter(is_public=True)
-        .select_related("by", "to")
-        .order_by("by__username", "to__username")
+        for voucher in (
+            Voucher.objects.filter(is_public=True, by__is_active=True, to__is_active=True)
+            .select_related("by", "to")
+            .order_by("by__username", "to__username")
+        )
     )

backend/tournesol/management/commands/load_public_dataset.py

@@ -105,12 +105,12 @@ def handle(self, *args, **options):
                     entity_1=videos[entity_a],
                     entity_2=videos[entity_b],
                 )
-                for _, values in rows.iterrows():
+                for values in rows.itertuples(index=False):
                     ComparisonCriteriaScore.objects.create(
                         comparison=comparison,
-                        criteria=values["criteria"],
-                        score=values["score"],
-                        score_max=values["score_max"],
+                        criteria=values.criteria,
+                        score=values.score,
+                        score_max=values.score_max,
                     )
                 nb_comparisons += 1
             print(f"Created {nb_comparisons} comparisons")