fix: dependency conflicts + apply black

django/curator/management/commands/curator_spam_detection.py

@@ -1,81 +1,88 @@
 import logging
 
 from django.core.management.base import BaseCommand
-from curator.spam import SpamDetectionContextFactory, SpamDetectionContext, PresetContextID
+from curator.spam import (
+    SpamDetectionContextFactory,
+    SpamDetectionContext,
+    PresetContextID,
+)
 from curator.spam_processor import UserSpamStatusProcessor
 
 logger = logging.getLogger(__name__)
 
 
 class Command(BaseCommand):
-    help = 'Perform spam detection'
+    help = "Perform spam detection"
+
     def __init__(self) -> None:
-        self.context:SpamDetectionContext = None
+        self.context: SpamDetectionContext = None
 
     def add_arguments(self, parser):
         parser.add_argument(
-            '--context_id',
-            '-id',
+            "--context_id",
+            "-id",
             type=str,
-            default='XGBoost_CountVect_1',
-            help='Classifier options: UserMetadata or Text',
-        )    
+            default="XGBoost_CountVect_1",
+            help="Classifier options: UserMetadata or Text",
+        )
         parser.add_argument(
-            '--predict',
-            '-p',
-            action='store_true',
+            "--predict",
+            "-p",
+            action="store_true",
             default=False,
-            help='Print user_ids of spam users and the metrics of the models used to obtain the predictions.',
+            help="Print user_ids of spam users and the metrics of the models used to obtain the predictions.",
         )
         parser.add_argument(
-            '--fit',
-            '-f',
-            action='store_true',
+            "--fit",
+            "-f",
+            action="store_true",
             default=False,
-            help='Fit all models based on user data labelled by curator.',
+            help="Fit all models based on user data labelled by curator.",
         )
         parser.add_argument(
-            '--get_model_metrics',
-            '-m',
-            action='store_true',
+            "--get_model_metrics",
+            "-m",
+            action="store_true",
             default=False,
-            help='Print the accuracy, precision, recall and f1 scores of the models used to obtain the predictions.',
+            help="Print the accuracy, precision, recall and f1 scores of the models used to obtain the predictions.",
         )
         parser.add_argument(
-            '--load_labels',
-            '-l',
-            action='store_true',
+            "--load_labels",
+            "-l",
+            action="store_true",
             default=False,
-            help='Store bootstrap spam labels to the DB.',
+            help="Store bootstrap spam labels to the DB.",
         )
 
     def handle_predict(self):
         self.context.predict()
-    
+
     def handle_fit(self):
         self.context.train()
 
     def handle_get_model_metrics(self):
         socre_dict = self.context.get_model_metrics()
-        logger.info(socre_dict.pop('test_user_ids'))
-         #TODO tentative
-        logger.info('The list of user_ids can be found in the metrics json file, which was used to calculate the scores.')
+        logger.info(socre_dict.pop("test_user_ids"))
+        # TODO tentative
+        logger.info(
+            "The list of user_ids can be found in the metrics json file, which was used to calculate the scores."
+        )
 
     def handle_load_labels(self):
         self.processor = UserSpamStatusProcessor()
         self.processor.load_labels_from_csv()
 
     def handle(self, *args, **options):
-        context_id_string = options['context_id']
-        if options['predict']:
-            action = 'predict'
-        elif options['fit']:
-            action = 'fit'
-        elif options['get_model_metrics']:
-            action = 'get_model_metrics'
-        elif options['load_labels']:
-            action = 'load_labels'
+        context_id_string = options["context_id"]
+        if options["predict"]:
+            action = "predict"
+        elif options["fit"]:
+            action = "fit"
+        elif options["get_model_metrics"]:
+            action = "get_model_metrics"
+        elif options["load_labels"]:
+            action = "load_labels"
 
         context_id = PresetContextID[context_id_string]
         self.context = SpamDetectionContextFactory.create(context_id)
-        getattr(self, f'handle_{action}')()
+        getattr(self, f"handle_{action}")()

django/curator/migrations/0005_initialize_spam_status.py

@@ -26,4 +26,5 @@ class Migration(migrations.Migration):
 
     operations = [migrations.RunPython(create_user_spam_status, clear_user_spam_status)]
 
-# Generated by Django 3.2.19 on 2023-06-28 22:48
+
+# Generated by Django 3.2.19 on 2023-06-28 22:48

django/curator/models.py

@@ -381,7 +381,7 @@ class UserSpamStatus(models.Model):
     # None = not processed yet
     # True = classifier thinks a user is spam
     # False = classifier does not think a user is spam
-    
+
     label = models.BooleanField(default=None, null=True)
     last_updated = models.DateTimeField(auto_now=True)
     is_training_data = models.BooleanField(default=False)
@@ -393,13 +393,16 @@ def get_recommendations_sorted_by_confidence():
         return UserSpamStatus.objects.all().order_by("text_classifier_confidence")
 
     def __str__(self):
-        return "member_profile={}, label={}, last_updated={}, is_training_data={}".format(
-            str(self.member_profile),
-            str(self.label),
-            str(self.last_updated),
-            str(self.is_training_data),
+        return (
+            "member_profile={}, label={}, last_updated={}, is_training_data={}".format(
+                str(self.member_profile),
+                str(self.label),
+                str(self.last_updated),
+                str(self.is_training_data),
+            )
         )
 
+
 # Create a new UserSpamStatus whenever a new MemberProfile is created
 @receiver(post_save, sender=MemberProfile)
 def sync_member_profile_spam_status(sender, instance: MemberProfile, created, **kwargs):
@@ -408,9 +411,10 @@ def sync_member_profile_spam_status(sender, instance: MemberProfile, created, **
             member_profile=instance,
         )
 
+
 class UserSpamPrediction(models.Model):
     spam_status = models.ForeignKey(UserSpamStatus, on_delete=models.CASCADE)
     context_id = models.CharField(max_length=300)
     prediction = models.BooleanField(default=False)
     confidence = models.FloatField(default=0)
-    date_created = models.DateTimeField(auto_now=True)
+    date_created = models.DateTimeField(auto_now=True)

django/curator/spam.py

@@ -9,9 +9,10 @@
     Encoder,
     CategoricalFieldEncoder,
     XGBoostClassifier,
-    CountVectEncoder
+    CountVectEncoder,
 )
 from sklearn.model_selection import train_test_split
+
 logger = logging.getLogger(__name__)
 logging.captureWarnings(True)
 processor = UserSpamStatusProcessor()
@@ -22,17 +23,18 @@ class PresetContextID(Enum):
     Enum for defining preset configurations for spam detection contexts, which include different combinations
     of classifiers and encoders along with specified fields.
     """
-    XGBoost_CountVect_1 = 'XGBoostClassifier CountVectEncoder PresetFields1'
-    XGBoost_CountVect_2 = 'XGBoostClassifier CountVectEncoder PresetFields2'
-    XGBoost_CountVect_3 = 'XGBoostClassifier CountVectEncoder PresetFields3'
-    XGBoost_Bert_1 =      'XGBoostClassifier BertEncoder PresetFields1'
-    NNet_CountVect_1 = 'NNetClassifier CountVectEncoder PresetFields1'
-    NNet_Bert_1 =      'NNetClassifier BertEncoder PresetFields1'
-    NaiveBayes_CountVect_1 = 'NaiveBayesClassifier CountVectEncoder PresetFields1'
-    NaiveBayes_Bert_1 =      'NaiveBayesClassifier BertEncoder PresetFields1'
+
+    XGBoost_CountVect_1 = "XGBoostClassifier CountVectEncoder PresetFields1"
+    XGBoost_CountVect_2 = "XGBoostClassifier CountVectEncoder PresetFields2"
+    XGBoost_CountVect_3 = "XGBoostClassifier CountVectEncoder PresetFields3"
+    XGBoost_Bert_1 = "XGBoostClassifier BertEncoder PresetFields1"
+    NNet_CountVect_1 = "NNetClassifier CountVectEncoder PresetFields1"
+    NNet_Bert_1 = "NNetClassifier BertEncoder PresetFields1"
+    NaiveBayes_CountVect_1 = "NaiveBayesClassifier CountVectEncoder PresetFields1"
+    NaiveBayes_Bert_1 = "NaiveBayesClassifier BertEncoder PresetFields1"
 
     @classmethod
-    def fields(cls, context_id_value:str):
+    def fields(cls, context_id_value: str):
         """
         Determines fields to be included in the dataset based on the context ID value.
 
@@ -42,13 +44,13 @@ def fields(cls, context_id_value:str):
         Returns:
             List[str]: A list of field names included in the specified preset.
         """
-        field_list = ['email', 'affiliations', 'bio']
-        if 'PresetFields2' in context_id_value:
-            field_list.append('is_active')
-        elif 'PresetFields3' in context_id_value:
-            field_list.extend(['personal_url', 'professional_url'])
+        field_list = ["email", "affiliations", "bio"]
+        if "PresetFields2" in context_id_value:
+            field_list.append("is_active")
+        elif "PresetFields3" in context_id_value:
+            field_list.extend(["personal_url", "professional_url"])
         return field_list
-    
+
     @classmethod
     def choices(cls):
         """
@@ -59,27 +61,28 @@ def choices(cls):
         """
         print(tuple((i.value, i.name) for i in cls))
         return tuple((i.value, i.name) for i in cls)
-
 
-class SpamDetectionContext():
+
+class SpamDetectionContext:
     """
     Manages the spam detection process, including setting up classifiers, encoders, and handling data.
     """
-    def __init__(self, contex_id:PresetContextID):
+
+    def __init__(self, contex_id: PresetContextID):
         """
         Initializes a spam detection context with a specified context configuration.
 
         Params:
             context_id (PresetContextID): The context ID from PresetContextID enum defining the configuration.
         """
         self.contex_id = contex_id
-        self.classifier:SpamClassifier = None
-        self.encoder:Encoder = None
+        self.classifier: SpamClassifier = None
+        self.encoder: Encoder = None
         self.categorical_encoder = CategoricalFieldEncoder()
         self.selected_fields = []
         self.selected_categorical_fields = []
-    
-    def set_classifier(self, classifier:SpamClassifier):
+
+    def set_classifier(self, classifier: SpamClassifier):
         """
         Sets the classifier for the spam detection.
 
@@ -88,7 +91,7 @@ def set_classifier(self, classifier:SpamClassifier):
         """
         self.classifier = classifier
 
-    def set_encoder(self, encoder:Encoder):
+    def set_encoder(self, encoder: Encoder):
         """
         Sets the encoder for processing the features.
 
@@ -97,28 +100,32 @@ def set_encoder(self, encoder:Encoder):
         """
         self.encoder = encoder
 
-    def set_fields(self, fields:List[str]):
+    def set_fields(self, fields: List[str]):
         """
         Sets the fields to be considered for spam detection.
 
         Params:
             fields (List[str]): A list of field names to be processed.
         """
         self.selected_fields = fields
-        self.selected_categorical_fields = [field for field in self.selected_fields if field in processor.field_type['categorical']]
+        self.selected_categorical_fields = [
+            field
+            for field in self.selected_fields
+            if field in processor.field_type["categorical"]
+        ]
 
-    def get_model_metrics(self)->dict:
+    def get_model_metrics(self) -> dict:
         """
         Retrieves the metrics of the trained model.
 
         Returns:
             dict: A dictionary containing the metrics of the model.
         """
         metrics = self.classifier.load_metrics()
-        metrics.pop('test_user_ids')
+        metrics.pop("test_user_ids")
         return metrics
 
-    def train(self, user_ids:List[int]=None):
+    def train(self, user_ids: List[int] = None):
         """
         Trains the model using the specified user data.
 
@@ -130,11 +137,13 @@ def train(self, user_ids:List[int]=None):
         else:
             df = processor.get_selected_users_with_label(user_ids, self.selected_fields)
 
-        self.categorical_encoder.set_categorical_fields(self.selected_categorical_fields)
+        self.categorical_encoder.set_categorical_fields(
+            self.selected_categorical_fields
+        )
         df = self.categorical_encoder.encode(df)
 
         labels = df["label"]
-        feats = df.drop('label', axis=1)
+        feats = df.drop("label", axis=1)
         feats = self.encoder.encode(feats)
 
         (
@@ -145,12 +154,12 @@ def train(self, user_ids:List[int]=None):
         ) = train_test_split(feats, labels, test_size=0.1, random_state=434)
 
         model = self.classifier.train(train_feats, train_labels)
-        processor.update_training_data(train_feats['user_id'])
+        processor.update_training_data(train_feats["user_id"])
         model_metrics = self.classifier.evaluate(model, test_feats, test_labels)
         self.classifier.save(model)
         self.classifier.save_metrics(model_metrics)
 
-    def predict(self, user_ids:List[int]=None):
+    def predict(self, user_ids: List[int] = None):
         """
         Predicts spam status for specified users.
 
@@ -159,25 +168,32 @@ def predict(self, user_ids:List[int]=None):
         """
         if not user_ids:
             df = processor.get_all_users(self.selected_fields)
-        else:    
-            df = processor.get_selected_users(user_ids, self.selected_fields)  #TODO check
+        else:
+            df = processor.get_selected_users(
+                user_ids, self.selected_fields
+            )  # TODO check
 
-        self.categorical_encoder.set_categorical_fields(self.selected_categorical_fields)
+        self.categorical_encoder.set_categorical_fields(
+            self.selected_categorical_fields
+        )
         df = self.categorical_encoder.encode(df)
 
         feats = self.encoder.encode(df)
-        
+
         model = self.classifier.load()
         result_df = self.classifier.predict(model, feats)
         processor.save_predictions(result_df, self.contex_id)
 
-    
-class SpamDetectionContextFactory():
+
+class SpamDetectionContextFactory:
     """
     Factory class to generate SpamDetectionContext instances with predefined configurations.
     """
+
     @classmethod
-    def create(cls, context_id=PresetContextID.XGBoost_CountVect_1)->SpamDetectionContext:
+    def create(
+        cls, context_id=PresetContextID.XGBoost_CountVect_1
+    ) -> SpamDetectionContext:
         """
         Creates a spam detection context based on the specified context ID.
 
@@ -201,4 +217,4 @@ def create(cls, context_id=PresetContextID.XGBoost_CountVect_1)->SpamDetectionCo
         selected_fields = PresetContextID.fields(context_id.value)
         spam_detection_contex.set_fields(selected_fields)
 
-        return spam_detection_contex
+        return spam_detection_contex