Main Update

.env.example

@@ -1,8 +1,12 @@
-TEST_MODE=false
-STABILITY=dev
+TEST_MODE=False 
+DEV_MODE=True 
 
-#DB Config
-sqlHost=""
-sqlPort=
-sqlPassword=""
-sqlSchema=""
+#Database Config 
+DB_HOST=
+DB_PORT=
+DB_PW=
+DB_SCHEMA=
+
+#User Config
+APP_SECRET=
+VERIFICATION_SECRET=

app/database/job_db.py

@@ -1,7 +1,7 @@
 """
 Datenbank-Komponente für Jobs
 """
-from sqlalchemy import Column, String, Integer, DateTime, Enum, Text
+from sqlalchemy import Column, String, Integer, DateTime, Enum, JSON
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import declarative_base, sessionmaker
 
@@ -18,12 +18,12 @@ class DBJob(Base):
     __tablename__ = "jobs"
 
     id = Column(Integer, primary_key=True, index=True)
-    user_id = Column(Integer, nullable=False)
+    user_id = Column(String(255), nullable=False)
     created_at = Column(DateTime, nullable=False)
     job_name = Column(String(255), nullable=True)
     status = Column(Enum(JobStatus), nullable=False)
     job_parameters = Column(String(5000), nullable=False)
-    json_values = Column(Text, nullable=True)
+    json_values = Column(JSON, nullable=True)
 
 
 engine = get_async_engine()

app/models/basic_kmeans_model.py

@@ -93,32 +93,3 @@ class KMeansResult3D(BasicKMeansResult):
     """
     z_label: str
     cluster: List[Cluster3D]
-
-
-class KMeansResultND(BaseModel):
-    """
-    Model representing the result of the KMeans clustering process in n dimensions.
-
-    Attributes:
-    - user_id (int): User ID.
-    - request_id (int): Request ID.
-    - clusters (List[Cluster]): List of resulting clusters.
-    - x_label (str): Label for the X-coordinate.
-    - y_label (str): Label for the Y-coordinate.
-    - iterations (int): Number of iterations the algorithm ran.
-    - used_distance_metric (str): The distance metric used for clustering.
-    - filename (str): Name of the file containing the data points.
-    - k_value (int): Number of clusters used.
-    - important_features (Dict[str, float]): Dictionary of: 
-                            important features with their contributions.
-    """
-    user_id: int
-    request_id: int
-    clusters: List[Cluster]
-    x_label: str
-    y_label: str
-    iterations: int
-    used_distance_metric: str
-    name: str
-    k_value: int
-    important_features: Dict[str, float]

app/models/job_model.py

@@ -23,7 +23,7 @@ class JobResponse(BaseModel):
     Modell für Rückgabe aus Job-Endpunkt
     """
     job_id: int
-    user_id: int
+    user_id: str
     job_name: str
     created_at: str
     status: JobStatus
@@ -36,6 +36,7 @@ class JobResponseFull(JobResponse):
     """
     json_values: str
 
+
 # pylint: disable=too-few-public-methods
 class UserJob:
     """
@@ -53,16 +54,13 @@ class Type(Enum):
     def __init__(self, jobtype, parameters):
         """
         Konstruktur
-        :param jobtype:
-        :param parameters:
         """
         self.jobtype = jobtype
         self.parameters = parameters
 
     def to_json(self):
         """
         Klasse als JSON
-        :return:
         """
         return dumps(self, default=lambda o: o.__dict__,
                      sort_keys=False, indent=None)

app/routers/advanced_kmeans_router.py

@@ -25,7 +25,8 @@ async def advanced_kmeans(
     kmeans_type: str = Query("OptimizedKMeans",
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    normalize: bool = True
 ):
     """
     Endpoint for KMeans clustering with automatic k determination.
@@ -49,7 +50,8 @@ async def advanced_kmeans(
             kmeans_type,
             user_id,
             request_id,
-            selected_columns=[column1, column2]
+            selected_columns=[column1, column2],
+            normalize= normalize
         )
         # Return the KMeansResult object.
         return kmeans_result

app/routers/advanced_n_d_kmeans_router.py

@@ -22,7 +22,8 @@ async def advanced_kmeans_nd(
     kmeans_type: str = Query("OptimizedKMeans",
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    use_3d_model:bool = False
 ):
     """
     Endpoint for advanced N-D KMeans clustering 
@@ -44,7 +45,8 @@ async def advanced_kmeans_nd(
             distance_metric=distance_metric,
             kmeans_type=kmeans_type,
             user_id=user_id,
-            request_id=request_id
+            request_id=request_id,
+            use_3d_model=use_3d_model
         )
         # Return the KMeansResultND object.
         return advanced_kmeans_result_nd

app/routers/advanced_three_d_kmeans_router.py

@@ -16,19 +16,20 @@
 # pylint: disable=duplicate-code
 async def advanced_kmeans_3d(
     file: UploadFile = File(...),
-    column1: int = Query(0, alias="Column 1",
+    column1: int = Query(0,
                           description="Index of the first column"),
-    column2: int = Query(1, alias="Column 2",
+    column2: int = Query(1,
                           description="Index of the second column"),
-    column3: int = Query(2, alias="Column 3",
+    column3: int = Query(2,
                           description="Index of the third column"),
     distance_metric: str = Query(
             "EUCLIDEAN",
             description="/".join(BaseOptimizedKMeans.supported_distance_metrics.keys())),
     kmeans_type: str = Query("OptimizedKMeans",
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    normalize: bool = True
 ):
     """
     Endpoint for advanced 3D KMeans clustering with automatic k determination.
@@ -53,7 +54,8 @@ async def advanced_kmeans_3d(
             kmeans_type=kmeans_type,
             user_id=user_id,
             request_id=request_id,
-            selected_columns=[column1, column2, column3]
+            selected_columns=[column1, column2, column3],
+            normalize=normalize
         )
         # Return the KMeansResult3D object.
         return kmeans_result_3d

app/routers/basic_kmeans_router.py

@@ -25,9 +25,10 @@ async def kmeans(
             description="/".join(BaseOptimizedKMeans.supported_distance_metrics.keys())),
     kmeans_type: str = Query("OptimizedKMeans",
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
-    k_clusters: int = Query(2, alias="kCluster", description="Number of clusters"),
+    k_clusters: int = Query(2, description="Number of clusters"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    normalize: bool = True
 ):
     """
     Endpoint for KMeans clustering.
@@ -53,7 +54,8 @@ async def kmeans(
             kmeans_type=kmeans_type,
             user_id=user_id,
             request_id=request_id,
-            selected_columns=[column1, column2]
+            selected_columns=[column1, column2],
+            normalize=normalize
         )
         # Return the KMeansResult object.
         return kmeans_result

app/routers/basic_n_d_kmeans_router.py

@@ -22,7 +22,8 @@ async def kmeans_nd(
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     k_clusters: int = Query(2, description="Number of clusters"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    use_3d_model:bool=False
 ):
     """
     Endpoint for N-D KMeans clustering with dimensionality reduction to 2D.
@@ -45,7 +46,8 @@ async def kmeans_nd(
             distance_metric=distance_metric,
             kmeans_type=kmeans_type,
             user_id=user_id,
-            request_id=request_id
+            request_id=request_id,
+            use_3d_model=use_3d_model
         )
         # Return the KMeansResultND object.
         return kmeans_result_nd

app/routers/basic_three_d_kmeans_router.py

@@ -27,7 +27,8 @@ async def kmeans_3d(
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     k_clusters: int = Query(2, description="Number of clusters"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    normalize: bool = Query(True, description="Normalize the data before clustering")
 ):
     """
     Endpoint for 3D KMeans clustering.
@@ -54,7 +55,8 @@ async def kmeans_3d(
             kmeans_type=kmeans_type,
             user_id=user_id,
             request_id=request_id,
-            selected_columns=[column1, column2, column3]
+            selected_columns=[column1, column2, column3],
+            normalize=normalize
         )
         # Return the KMeansResult3D object.
         return kmeans_result_3d