read vouches in TournesolInput

backend/ml/inputs.py

@@ -14,6 +14,7 @@
     ContributorScaling,
     Entity,
 )
+from vouch.models import Voucher
 
 
 class MlInputFromDb(TournesolInput):
@@ -189,3 +190,14 @@ 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="by__id",
+            vouchee="to__id",
+            vouch="value",
+        )
+        return pd.DataFrame(values)

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")
+        )
     )

solidago/src/solidago/pipeline/inputs.py

@@ -57,14 +57,26 @@ def get_individual_scores(
     ) -> Optional[pd.DataFrame]:
         raise NotImplementedError
 
+    @abstractmethod
     def get_vouches(self):
-        # TODO: make abstract and implement in subclasses
-        return pd.DataFrame(columns=["voucher", "vouchee", "vouch"])
+        """Fetch data about vouches shared between users
+
+        Returns:
+        - DataFrame with columns
+            * `voucher`: int, user_id of the user who gives the vouch
+            * `vouchee`: int, user_id of the user who receives the vouch
+            * `vouch`: float, value of this vouch
+        """
+        raise NotImplementedError
+
+    def get_users(self):
+        users = self.ratings_properties.groupby("user_id").first()[["trust_score"]]
+        users["is_pretrusted"] = users["trust_score"] >= 0.8
+        return users
 
     def get_pipeline_kwargs(self, criterion: str):
         ratings_properties = self.ratings_properties
-        users = ratings_properties.groupby("user_id").first()[["trust_score"]]
-        users["is_pretrusted"] = users["trust_score"] >= 0.8
+        users = self.get_users()
         vouches = self.get_vouches()
         comparisons = self.get_comparisons(criteria=criterion)
         entities_ids = set(comparisons["entity_a"].unique()) | set(
@@ -134,26 +146,25 @@ def __init__(self, dataset_zip: Union[str, BinaryIO]):
                 # Fill trust_score on newly created users for which it was not computed yet
                 self.users.trust_score = pd.to_numeric(self.users.trust_score).fillna(0.0)
 
-            username_to_user_id = pd.Series(
+            self.username_to_user_id = pd.Series(
                 data=self.users.index, index=self.users["public_username"]
             )
-            self.comparisons = self.comparisons.join(username_to_user_id, on="public_username")
-            
+            self.comparisons = self.comparisons.join(self.username_to_user_id, on="public_username")
+
             with (zipfile.Path(zip_file) / "vouchers.csv").open(mode="rb") as vouchers_file:
                 # keep_default_na=False is required otherwise some public usernames
                 # such as "NA" are converted to float NaN.
                 self.vouchers = pd.read_csv(vouchers_file, keep_default_na=False)
-            
+
             with (zipfile.Path(zip_file) / "collective_criteria_scores.csv").open(mode="rb") as collective_scores_file:
                 # keep_default_na=False is required otherwise some public usernames
                 # such as "NA" are converted to float NaN.
                 self.collective_scores = pd.read_csv(collective_scores_file, keep_default_na=False)
-            
+
             with (zipfile.Path(zip_file) / "individual_criteria_scores.csv").open(mode="rb") as individual_scores_file:
                 # keep_default_na=False is required otherwise some public usernames
                 # such as "NA" are converted to float NaN.
                 self.individual_scores = pd.read_csv(individual_scores_file, keep_default_na=False)
-
 
     @classmethod
     def download(cls) -> "TournesolInputFromPublicDataset":
@@ -197,3 +208,16 @@ def get_individual_scores(
     ) -> Optional[pd.DataFrame]:
         # TODO: read contributor scores from individual_scores.csv
         return None
+
+    def get_vouches(self):
+        vouchers = self.vouchers[
+            self.vouchers.by_username.isin(self.username_to_user_id.index)
+            & self.vouchers.to_username.isin(self.username_to_user_id.index)
+        ]
+        return pd.DataFrame(
+            {
+                "voucher": vouchers.by_username.map(self.username_to_user_id),
+                "vouchee": vouchers.to_username.map(self.username_to_user_id),
+                "vouch": vouchers.value,
+            }
+        )