Merge branch 'feature/community-prediction' into 'develop'

Feature/community prediction

See merge request !48

documentation/external_access.md

@@ -77,7 +77,7 @@ Contains the clustering results. Clustering is performed on all nodes inside one
 
 ```GET https://articonf1.itec.aau.at:30103/api/use-cases/{use_case}/tables/{table}/layers/{layer_name}/clusters``` returns the identified clusters.
 
-```GET https://articonf1.itec.aau.at:30103/api/use-cases/{use_case}/tables/{table}/layers/{layer_name}/timeslices``` returns the identified clusters partitioned based on their nodes' timestamps.
+```GET https://articonf1.itec.aau.at:30103/api/use-cases/{use_case}/tables/{table}/layers/{layer_name}/timeslices``` returns the identified clusters partitioned based on their nodes' [timestamps](schema_information.md#time-slices).
 
 ## RunId
 When a similarity computation is executed, it has an associated RunId which is used to uniquely identify that execution.
@@ -93,3 +93,14 @@ Returns the computed similarity. Two clusters belonging to the SAME layer will b
 Intermediary data-structure used only by the function which computes the similarity. Clusters are connected only to other clusters belonging to a DIFFERENT layer.
 
 ```GET https://articonf1.itec.aau.at:30103/api/use_cases/{use_case}/tables/{table}/connectedClusters``` returns all connected clusters for the given use-case and table.
+
+# Proactive Community Detection Microservice
+https://articonf1.itec.aau.at:30105/api/ui/
+
+This microservice contains predictions of the cluster sizes from the clusters in [role stage discovery microservice](https://articonf1.itec.aau.at:30103/api/ui/#!/Clusters/routes_clustersets_get_by_name) for the week following the latest data in SMART. The timestamps used for temporal devision are documented [here](schema_information.md#time-slices).
+
+Example: Layer $L$ contains 3 clusters with sizes 3, 0, 7 in the most recent week $t$. SMART predicts the sizes in the following week $t+1$ as 5, 0, 6 based on each cluster's structural changes over the last $N=3$ weeks, i.e. $t,\ t-1,\ t-2$.
+
+
+```GET https://articonf1.itec.aau.at:30105/api/use-cases/{use_case}/tables/{table}/layers/{layer_name}/predictions```
+contains the size predictions for all clusters of a layer derived as described above.
\ No newline at end of file

documentation/schema_information.md

@@ -120,3 +120,40 @@ __Example:__ The node for the initial trace of the pizzashop and the layer ```na
             "firstTopping": "toppings[0]//name"
         }
     }
+
+# Clusters 
+## Time Slices
+Time slices are clusters split into time windows. Currently SMART creates one time slice per week for each cluster.
+Currently, the timestamp information is _not_ integrated into the table schema mapping and _not_ converted into a UNIX timestamp during upload.
+The following fields are considered timestamps during the partitioning:
+
+```yaml
+vialog-enum:
+    video: created
+    change: /
+
+car-sharing-official:
+    car: /
+    hash: /
+    media: /
+    offer: available
+    publication: date
+    travel: startDate
+    travelCancelledBy: moment
+    travelFinishedBy: moment
+    travelStartedBy: moment
+    travelSuggestedEndPlaces: /
+    travelUsers: /
+    user: /
+    offerEndPlaces: /
+
+smart-energy:
+    smart-energy: Timestamp
+
+crowd-journalism-enum:
+    video: creationTimestamp
+    tag: /
+    classification: lastUpdate
+    event: /
+    purchase: timestamp
+```

src/data-hub/proactive-community-detection-microservice/app/.gitignore

 # contains raw data for machine learning
 data/
+# backup data for machine learning debugging
+data_bak/ 
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/db/repository.py

@@ -34,14 +34,13 @@ class Repository(MongoRepositoryBase):
             super().drop_collection(collection_)
     
 
-#region LayerPair
+#region Use Case
     def add_use_case(self, use_case: str):
         super().insert_entry(self._use_case_collection, {'name':use_case})
 
     def get_use_cases(self) -> List[str]:
         entries = super().get_entries(self._use_case_collection)
         return [e['name'] for e in entries]
-
 #endregion 
 
 #region Layers
@@ -53,7 +52,7 @@ class Repository(MongoRepositoryBase):
         entries = super().get_entries(self._layer_collection, projection={'_id': 0})
         return [LayerDao(e) for e in entries]
 
-    def get_layers_for_use_case(self, use_case: str) -> LayerDao:
+    def get_layers_for_use_case(self, use_case: str) -> List[LayerDao]:
         entries = super().get_entries(self._layer_collection, selection={'use_case': use_case})
         return [LayerDao(e) for e in entries]
 
@@ -132,6 +131,13 @@ class Repository(MongoRepositoryBase):
         entries = super().get_entries(self._prediction_result_collection, selection={'use_case': use_case}, projection={'_id': 0})
         return [PredictionResult.create_from_dict(e) for e in entries]
 
+    def get_prediction_results_for_layer(self, use_case: str, use_case_table: str, layer_name: str) -> List[PredictionResult]:
+        entries = super().get_entries(self._prediction_result_collection, selection={'use_case': use_case, 'table': use_case_table, 'layer': layer_name}, projection={'_id': 0})
+        return [PredictionResult.create_from_dict(e) for e in entries]
+
     def delete_all_prediction_results(self):
         super().drop_collection(self._prediction_result_collection)
+
+    def delete_prediction_results(self, use_case: str):
+        super().delete_many(self._prediction_result_collection, selection={'use_case': use_case})
 #endregion

src/data-hub/proactive-community-detection-microservice/app/processing/data_prep/cluster_metrics_calc.py

@@ -7,7 +7,7 @@ import json
 import os
 from entities import TimeWindow, Cluster
 
-def store_metrics_for_clusters(use_case: str, layer_name: str, feature_names: List[str]):
+def store_metrics_for_clusters(use_case: str, table: str, layer_name: str, feature_names: List[str]):
     '''
     :param layer_name: Name of the layer for which multiple time windows exist
     :param feature_names: Features of the layer
@@ -15,7 +15,7 @@ def store_metrics_for_clusters(use_case: str, layer_name: str, feature_names: Li
     print(f"Working on {layer_name} cluster metrics")
 
     # load global cluster centers
-    path_in = f'data/{use_case}/raw/clusters/{layer_name}.json'
+    path_in = f'data/{use_case}/{table}/raw/clusters/{layer_name}.json'
     with open(path_in, 'r') as file:
         clusters = json.loads(file.read())
         cluster_centers: Dict[str, Tuple[float]] = { 
@@ -24,9 +24,9 @@ def store_metrics_for_clusters(use_case: str, layer_name: str, feature_names: Li
             if cluster['label'] != 'noise' 
             }
 
-    path_in = f'data/{use_case}/raw/timeslices/{layer_name}'
-    Path(f'data/{use_case}/cluster_metrics/').mkdir(parents=True, exist_ok=True)
-    path_out = f'data/{use_case}/cluster_metrics/{layer_name}.json'
+    path_in = f'data/{use_case}/{table}/raw/timeslices/{layer_name}'
+    Path(f'data/{use_case}/{table}/cluster_metrics/').mkdir(parents=True, exist_ok=True)
+    path_out = f'data/{use_case}/{table}/cluster_metrics/{layer_name}.json'
 
     complete_clusters: List[Cluster] = []
 
@@ -54,7 +54,7 @@ import collections
 import numpy as np
 from typing import Iterable, Tuple
 
-def create_metrics_training_data(use_case: str, layer_name: str, N: int = 3) -> Iterable[list]:
+def create_metrics_training_data(use_case: str, table: str, layer_name: str, N: int = 3) -> Iterable[list]:
     """
     Loads the metrics training data for an individual layer from disk.
     A single metrics training data point should look like this:
@@ -70,7 +70,7 @@ def create_metrics_training_data(use_case: str, layer_name: str, N: int = 3) ->
     :param layer_name: the name of the layer metrics json file
     """
     
-    path_in = f"data/{use_case}/cluster_metrics/{layer_name}.json"
+    path_in = f"data/{use_case}/{table}/cluster_metrics/{layer_name}.json"
     with open(path_in, 'r') as file:
         data = [Cluster.create_from_dict(cl_d) for cl_d in json.loads(file.read())]
 
@@ -94,7 +94,7 @@ def create_metrics_training_data(use_case: str, layer_name: str, N: int = 3) ->
         tuples.append(cur_metrics)
 
         if len(tuples) == N:
-            label = get_evolution_label(cur_cluster.size, data[i+1].size)
+            label = data[i+1].size # get_evolution_label(cur_cluster.size, data[i+1].size)
             yield list(tuples) + [label]
 ############################
 def flatten_metrics_datapoint(datapoint: list) -> Tuple['X', np.array]:
@@ -122,9 +122,9 @@ def balance_dataset(df: DataFrame) -> DataFrame:
     # nothing happening here, balance only on real training, not during prep
     return df
 
-def store_training_data(use_case: str, layer_name: str):
+def store_training_data(use_case: str, table: str, layer_name: str):
     # load metrics data from disk
-    data: Iterable = create_metrics_training_data(use_case=use_case, layer_name=layer_name)
+    data: Iterable = create_metrics_training_data(use_case=use_case, table=table, layer_name=layer_name)
     
     # flatten and convert to df
     df = convert_metrics_data_to_dataframe(data, columns=COLUMNS, flattening_method=flatten_metrics_datapoint)
@@ -135,8 +135,8 @@ def store_training_data(use_case: str, layer_name: str):
     # shuffle
     df = df.sample(frac=1).reset_index(drop=True)
     
-    Path(f'data/{use_case}/ml_input/single_context/').mkdir(parents=True, exist_ok=True)
-    df.to_csv(f'data/{use_case}/ml_input/single_context/{layer_name}.csv')
+    Path(f'data/{use_case}/{table}/ml_input/single_context/').mkdir(parents=True, exist_ok=True)
+    df.to_csv(f'data/{use_case}/{table}/ml_input/single_context/{layer_name}.csv')
 
 #######################
 
@@ -159,13 +159,13 @@ def run(use_case=None):
     for use_case in use_cases:
         print(f"Executing cluster metrics calc for use case {use_case}")
         
-        layers = [[l.layer_name, l.properties] for l in repo.get_layers_for_use_case(use_case)]
+        layers = repo.get_layers_for_use_case(use_case)
     
         ##################
         for layer in layers:
-            store_metrics_for_clusters(use_case, layer[0], layer[1])
+            store_metrics_for_clusters(layer.use_case, layer.use_case_table, layer.layer_name, layer.properties)
         ###################
-        for name, _ in layers:
-            print(f"Storing training data for {name}")
-            store_training_data(use_case, layer_name=name)
+        for layer in layers:
+            print(f"Storing training data for {layer.layer_name}")
+            store_training_data(layer.use_case, layer.use_case_table, layer.layer_name)
             
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/processing/data_prep/layer_metrics_calc.py

@@ -9,11 +9,11 @@ import os
 from entities import TimeWindow, Layer
 from processing import ClusterMetricsCalculatorFactory
 
-def store_metrics_for_layers(use_case: str, layer_name: str, feature_names: List[str]):
+def store_metrics_for_layers(use_case: str, table: str, layer_name: str, feature_names: List[str]):
     print(f"Working on {layer_name} layer metrics")
 
     # load global cluster centers
-    path_in = f'data/{use_case}/raw/clusters/{layer_name}.json'
+    path_in = f'data/{use_case}/{table}/raw/clusters/{layer_name}.json'
     with open(path_in, 'r') as file:
         clusters = json.loads(file.read())
         cluster_centers: Dict[str, Tuple[float]] = { 
@@ -24,7 +24,7 @@ def store_metrics_for_layers(use_case: str, layer_name: str, feature_names: List
 
     # load time windows 
     all_layers: List[Layer] = []
-    path_in = f'data/{use_case}/raw/timeslices/{layer_name}'
+    path_in = f'data/{use_case}/{table}/raw/timeslices/{layer_name}'
     for root, _, files in os.walk(path_in):
         for f in files:
             with open(os.path.join(root, f), 'r') as file:
@@ -35,8 +35,8 @@ def store_metrics_for_layers(use_case: str, layer_name: str, feature_names: List
                 all_layers.append(layer)
         
     # store the layer metrics
-    Path(f'data/{use_case}/layer_metrics/').mkdir(parents=True, exist_ok=True)
-    path_out = f'data/{use_case}/layer_metrics/{layer_name}.json'
+    Path(f'data/{use_case}/{table}/layer_metrics/').mkdir(parents=True, exist_ok=True)
+    path_out = f'data/{use_case}/{table}/layer_metrics/{layer_name}.json'
     with open(path_out, 'w') as file:
         file.write(json.dumps([l.__dict__ for l in all_layers]))
 #########################
@@ -63,7 +63,7 @@ from typing import Iterable, List, Dict, Any
 import json
 from entities import Layer, Cluster
 
-def create_layer_metrics_training_data(use_case: str, layer_name: str, reference_layer: str, N: int = 2) -> Iterable:
+def create_layer_metrics_training_data(use_case: str, table: str, layer_name: str, reference_layer: str, N: int = 2) -> Iterable:
     """
     Loads the metrics training data for an individual layer from disk.
     
@@ -83,12 +83,12 @@ def create_layer_metrics_training_data(use_case: str, layer_name: str, reference
     if N != 2:
         raise NotImplementedError("N is not implemented and fixed to 2!")
     
-    with open(f'data/{use_case}/cluster_metrics/{layer_name}.json') as file:
+    with open(f'data/{use_case}/{table}/cluster_metrics/{layer_name}.json') as file:
         cluster_metrics: List[Cluster] = [Cluster.create_from_dict(e) for e in json.loads(file.read())]
         cluster_ids = {c.cluster_id for c in cluster_metrics}
         cluster_metrics: Dict[Any, Cluster] = {(c.time_window_id, c.cluster_id): c for c in cluster_metrics}
         
-    with open(f'data/{use_case}/layer_metrics/{reference_layer}.json') as file:
+    with open(f'data/{use_case}/{table}/layer_metrics/{reference_layer}.json') as file:
         layer_metrics: List[Layer] = [Layer.create_from_dict(e) for e in json.loads(file.read())]
         layer_metrics: Dict[Any, Layer] = {l.time_window_id: l for l in layer_metrics}
 
@@ -98,7 +98,7 @@ def create_layer_metrics_training_data(use_case: str, layer_name: str, reference
     
     # go through all time windows once...
     prev_time_key = ordered_time_keys[0]
-    for current_time_key in ordered_time_keys[1:]:
+    for idx, current_time_key in enumerate(ordered_time_keys[1:-1]):
         # ...and load the current and previous layer metrics in the reference_layer
         current_layer_metric = layer_metrics[current_time_key]
         prev_layer_metric = layer_metrics[prev_time_key]
@@ -110,7 +110,8 @@ def create_layer_metrics_training_data(use_case: str, layer_name: str, reference
         for cluster_id in cluster_ids:
             current_cluster_metric = cluster_metrics[(current_time_key, cluster_id)]
             prev_cluster_metric = cluster_metrics[(prev_time_key, cluster_id)]
-            evolution_label = get_evolution_label(prev_cluster_metric.size, current_cluster_metric.size)
+            next_time_key = ordered_time_keys[idx+2]
+            evolution_label = cluster_metrics[(next_time_key, cluster_id)].size # get_evolution_label(prev_cluster_metric.size, current_cluster_metric.size)
 
             # yield each combination of reference layer metrics to clusters
             yield [prev_layer_metric_tuple, current_layer_metric_tuple, int(cluster_id), evolution_label]
@@ -149,9 +150,9 @@ def balance_dataset(df: DataFrame) -> DataFrame:
     # nothing happening here, balance only on real training, not during prep
     return df
 
-def store_training_data(use_case: str, layer_name: str, reference_layer_name: str):
+def store_training_data(use_case: str, table: str, layer_name: str, reference_layer_name: str):
     # load metrics data from disk
-    data: Iterable = create_layer_metrics_training_data(use_case=use_case, layer_name=layer_name, reference_layer=reference_layer_name)
+    data: Iterable = create_layer_metrics_training_data(use_case=use_case, table=table, layer_name=layer_name, reference_layer=reference_layer_name)
     
     # convert to X and Y
     df = convert_metrics_data_to_dataframe(data, columns=get_columns(N=2), flattening_method=flatten_layer_metrics_datapoint)
@@ -162,8 +163,8 @@ def store_training_data(use_case: str, layer_name: str, reference_layer_name: st
     # shuffle
     df = df.sample(frac=1).reset_index(drop=True)
 
-    Path(f'data/{use_case}/ml_input/cross_context/').mkdir(parents=True, exist_ok=True)
-    df.to_csv(f'data/{use_case}/ml_input/cross_context/{layer_name}_{reference_layer_name}.csv')
+    Path(f'data/{use_case}/{table}/ml_input/cross_context/').mkdir(parents=True, exist_ok=True)
+    df.to_csv(f'data/{use_case}/{table}/ml_input/cross_context/{layer_name}_{reference_layer_name}.csv')
 
 #########################
 
@@ -186,16 +187,16 @@ def run(use_case=None):
     for use_case in use_cases:
         print(f"Executing layer metrics calc for use case {use_case}")
 
-        layers = [[l.layer_name, l.properties] for l in repo.get_layers_for_use_case(use_case)]
+        layers = repo.get_layers_for_use_case(use_case)
         layer_pairs = repo.get_layer_pairs(use_case)
 
         ################
         for layer in layers:
             try:
-                store_metrics_for_layers(use_case, layer[0], layer[1])
+                store_metrics_for_layers(layer.use_case, layer.use_case_table, layer.layer_name, layer.properties)
             except FileNotFoundError:
                 pass
         ###############
         for ld in layer_pairs:
             print(f"Storing training data for {ld.layer} with L_R={ld.reference_layer}")
-            store_training_data(use_case, layer_name=ld.layer, reference_layer_name=ld.reference_layer)
\ No newline at end of file
+            store_training_data(ld.use_case, table=ld.table, layer_name=ld.layer, reference_layer_name=ld.reference_layer)
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/processing/data_prep/main.py

@@ -4,6 +4,7 @@ from processing.data_prep.layer_metrics_calc import run as lrun
 from pathlib import Path
 import json
 import os
+from typing import List
 
 from db.repository import Repository
 
@@ -12,11 +13,11 @@ repo = Repository()
 
 
 def store_clusters_as_files(use_case):
-    path_ = f'data/{use_case}/raw/clusters/'
-    Path(path_).mkdir(parents=True, exist_ok=True)
-
     layers = repo.get_layers_for_use_case(use_case)
     for l in layers:
+        path_ = f'data/{l.use_case}/{l.use_case_table}/raw/clusters/'
+        Path(path_).mkdir(parents=True, exist_ok=True)
+
         clusters = repo.get_clusters_for_layer(use_case, l.use_case_table, l.layer_name)
 
         with open(os.path.join(path_, f'{l.layer_name}.json'), 'w') as file_:
@@ -24,30 +25,25 @@ def store_clusters_as_files(use_case):
             
 
 def store_time_slices_as_files(use_case):
-    path_ = f'data/{use_case}/raw/timeslices/'
-
     layers = repo.get_layers_for_use_case(use_case)
     for l in layers:
-        Path(os.path.join(path_, l.layer_name)).mkdir(parents=True, exist_ok=True)
+        path_ = f'data/{l.use_case}/{l.use_case_table}/raw/timeslices/{l.layer_name}/'
+        Path(path_).mkdir(parents=True, exist_ok=True)
 
         time_slices = repo.get_time_slices_for_layer(use_case, l.use_case_table, l.layer_name)
         for ts in time_slices:
-            with open(os.path.join(path_, l.layer_name, f'{ts.time}.json'), 'w') as file_:
+            with open(os.path.join(path_, f'{ts.time}.json'), 'w') as file_:
                 file_.write(json.dumps(ts.to_serializable_dict()))   
 
 
-def run(use_case=None):
+def run(use_cases: List[str] = None):
     '''Prepares training data for single and cross-context using the file system (data/)'''
-    if use_case is not None:
-        use_cases = [use_case]
-    else:
+    if use_cases is None:
         use_cases = repo.get_use_cases()
     
-
     for use_case in use_cases:
         store_clusters_as_files(use_case)
         store_time_slices_as_files(use_case)
 
         crun(use_case)
         lrun(use_case)
-

src/data-hub/proactive-community-detection-microservice/app/processing/ml/predict_base.py

@@ -11,8 +11,8 @@ def increase_time_window(time_window_id: str) -> str:
 from typing import Tuple
 import pickle
 
-def load_ml_models(use_case, method, layer_name, reference_layer_name=None) -> Tuple['scaler', 'clf']:
-    path_ = f'data/{use_case}/ml_output/{method}/{layer_name}'
+def load_ml_models(use_case, table, method, layer_name, reference_layer_name=None) -> Tuple['scaler', 'clf']:
+    path_ = f'data/{use_case}/{table}/ml_output/{method}/{layer_name}'
 
     if method == 'single_context':
         with open(f'{path_}.model', 'rb') as file:

src/data-hub/proactive-community-detection-microservice/app/processing/ml/predict_cross_context.py

@@ -44,18 +44,19 @@ repo = Repository()
 
 def run_prediction(use_case: str):
     for layerpair in repo.get_layer_pairs(use_case):
+        table = layerpair.table
         layer_name = layerpair.layer
         reference_layer_name = layerpair.reference_layer
 
-        print(f"Predicting {method} for {use_case}//{layer_name} based on {reference_layer_name}")
+        print(f"Predicting {method} for {use_case}//{table}//{layer_name} based on {reference_layer_name}")
         
         ##########################
-        with open(f'data/{use_case}/cluster_metrics/{layer_name}.json') as file:
+        with open(f'data/{use_case}/{table}/cluster_metrics/{layer_name}.json') as file:
             cluster_metrics: List[Cluster] = [Cluster.create_from_dict(e) for e in json.loads(file.read())]
             cluster_ids = {c.cluster_id for c in cluster_metrics}
             cluster_metrics: Dict[Any, Cluster] = {(c.time_window_id, c.cluster_id): c for c in cluster_metrics}
                 
-        with open(f'data/{use_case}/layer_metrics/{reference_layer_name}.json') as file:
+        with open(f'data/{use_case}/{table}/layer_metrics/{reference_layer_name}.json') as file:
             layer_metrics: List[Layer] = [Layer.create_from_dict(e) for e in json.loads(file.read())]
             layer_metrics: Dict[Any, Layer] = {l.time_window_id: l for l in layer_metrics}
         ######################
@@ -77,7 +78,7 @@ def run_prediction(use_case: str):
             # yield each combination of reference layer metrics to clusters
             prediction_metrics_raw.append([prev_layer_metric_tuple, current_layer_metric_tuple, int(cluster_id)])
         #######################
-        scaler, svc = load_ml_models(use_case, method, layer_name, reference_layer_name)
+        scaler, svc = load_ml_models(use_case, table, method, layer_name, reference_layer_name)
         ################
         prediction_cluster_ids = []
         prediction_time_window = increase_time_window(ordered_time_keys[1])
@@ -91,8 +92,9 @@ def run_prediction(use_case: str):
             prediction_metrics.append(flat_)
     
         prediction_results = svc.predict(scaler.transform(np.array(prediction_metrics)))
-        print(np.unique(prediction_results, return_counts=True))
+        # print(np.unique(prediction_results, return_counts=True))
+        prediction_results = np.rint(prediction_results) # round to full numbers
 
         for i in range(len(prediction_cluster_ids)):
-            res = PredictionResult(use_case, use_case, method, layer_name, reference_layer_name, prediction_cluster_ids[i], prediction_time_window, prediction_results[i])
+            res = PredictionResult(use_case, table, method, layer_name, reference_layer_name, prediction_cluster_ids[i], prediction_time_window, prediction_results[i])
             repo.add_prediction_result(res)

src/data-hub/proactive-community-detection-microservice/app/processing/ml/predict_single_context.py

@@ -37,14 +37,19 @@ repo = Repository()
 
 def run_prediction(use_case: str):
     for layer in repo.get_layers_for_use_case(use_case):
+        table = layer.use_case_table
         layer_name = layer.layer_name
-        print(f"Predicting {method} for {use_case}//{layer_name}")
+        print(f"Predicting {method} for {use_case}//{table}//{layer_name}")
 
         #################
-        path_in = f"data/{use_case}/cluster_metrics/{layer_name}.json"
+        path_in = f"data/{use_case}/{table}/cluster_metrics/{layer_name}.json"
         with open(path_in, 'r') as file:
             data = [Cluster.create_from_dict(cl_d) for cl_d in json.loads(file.read())]
 
+        if len(data) == 0:
+            print(f"No data for predicting {use_case}//{table}//{layer_name}.")
+            continue
+
         data.sort(key=lambda cl: (eval(cl.cluster_id), eval(cl.time_window_id)))
         #####################
         cluster_map: Dict['cluster_id', 'time_windows'] = {}
@@ -57,7 +62,7 @@ def run_prediction(use_case: str):
 
             cluster_map[id_].append(cluster)
         ####################       
-        scaler, svc = load_ml_models(use_case, method, layer_name)
+        scaler, svc = load_ml_models(use_case, table, method, layer_name)
         #####################
         # store id, future time window, and flattened metrics to combine the latter during prediction
         prediction_cluster_ids = []
@@ -74,8 +79,9 @@ def run_prediction(use_case: str):
             
         # predict all at once for speedup
         prediction_results = svc.predict(scaler.transform(np.array(prediction_metrics)))
-        print(np.unique(prediction_results, return_counts=True))
+        # print(np.unique(prediction_results, return_counts=True))
+        prediction_results = np.rint(prediction_results) # round to full numbers
         
         for i in range(len(prediction_cluster_ids)):
-            res = PredictionResult(use_case, use_case, method, layer_name, None, prediction_cluster_ids[i], prediction_time_windows[i], prediction_results[i])
+            res = PredictionResult(use_case, table, method, layer_name, None, prediction_cluster_ids[i], prediction_time_windows[i], prediction_results[i])
             repo.add_prediction_result(res)

src/data-hub/proactive-community-detection-microservice/app/processing/ml/train_base.py

@@ -29,15 +29,29 @@ def remove_empty_community_class(df):
 ########################
 import sklearn.metrics
 
-def print_report(clfs: list, test_Xs: list, test_Y: 'y', titles: list):
+def print_classification_report(clf, test_X, test_Y, title):
     """
-    Prints all reports.
-    :param clfs: list of classifiers to evaluate
-    :param test_Xs: list of test_X for the corresponding classifier at idx
-    :param test_Y: true classes
-    :param titles: list of titles for the classifiers at idx
+    Prints a classification report.
+    :param clf: classifier to evaluate
+    :param test_X: input features X
+    :param test_Y: true classes Y
+    :param title: title for the report
     """
-    for clf, test_X, title in zip(clfs, test_Xs, titles):
     pred_Y = clf.predict(test_X)        
     print(f"### {title} ###\n", sklearn.metrics.classification_report(y_true=test_Y, y_pred=pred_Y))
+
+def print_regression_report(clf, test_X, test_Y, title):
+    """
+    Prints a regression report.
+    :param clf: regressor to evaluate
+    :param test_X: input features X
+    :param test_Y: true prediction values 
+    :param title: title for the report
+    """
+    pred_Y = clf.predict(test_X)
+    pred_Y = np.rint(pred_Y) # round to full numbers
+
+    print(f"### {title} ###\nR2-score={sklearn.metrics.r2_score(y_true=test_Y, y_pred=pred_Y)}, " \
+          f"MSE={sklearn.metrics.mean_squared_error(y_true=test_Y, y_pred=pred_Y)}, " \
+          f"sanity={sklearn.metrics.mean_squared_error(y_true=test_Y, y_pred=[0]*len(pred_Y))}")
 ########################
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/processing/ml/train_cross_context.py

 import pandas as pd
 from pandas import DataFrame
-from processing.ml.train_base import split_data, remove_empty_community_class, print_report
+from processing.ml.train_base import split_data, remove_empty_community_class, print_regression_report
 
 
 approach = 'cross_context'
+max_sampling_size = 20000
 #######################
 import pickle
 from pathlib import Path
 
-def export_model(model, use_case, layer_name, reference_layer_name, scaler=False):
-    fpath = f'data/{use_case}/ml_output/{approach}'
+def export_model(model, use_case, table, layer_name, reference_layer_name, scaler=False):
+    fpath = f'data/{use_case}/{table}/ml_output/{approach}'
     Path(fpath).mkdir(parents=True, exist_ok=True)
     with open(f'{fpath}/{layer_name}_{reference_layer_name}{"_scaler" if scaler else ""}.model', 'wb') as f:
         pickle.dump(model, f)
 ###################
-from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestRegressor
 n_estimators = 50
-criterion = 'gini'
+criterion = 'mse'
 max_depth = None
 min_samples_leaf = 2
 min_impurity_decrease= 1E-5
 bootstrap=True
+####################
+from sklearn.svm import LinearSVR
+tol = 1E-4
+c = 1
+loss = 'squared_epsilon_insensitive'
+dual = False
 
 
 ###############
@@ -32,16 +39,19 @@ repo = Repository()
 
 def run_training(use_case):
     for layerpair in repo.get_layer_pairs(use_case):
+        table = layerpair.table
         layer_name = layerpair.layer
         reference_layer_name = layerpair.reference_layer
 
+        df: DataFrame = pd.read_csv(f'data/{use_case}/{table}/ml_input/cross_context/{layer_name}_{reference_layer_name}.csv', index_col=0)
         
-        df: DataFrame = pd.read_csv(f'data/{use_case}/ml_input/cross_context/{layer_name}_{reference_layer_name}.csv', index_col=0)
+        if df.empty:
+            print(f"No data for training {use_case}//{table}//{layer_name} on {reference_layer_name}.")
+            continue            
         #######################
         training, testing = split_data(df, shuffle=False)
         #####################
-        training = remove_empty_community_class(training)
-        testing = remove_empty_community_class(testing)
+        training.sample(frac=min(1, max_sampling_size/len(training))).reset_index(drop=True)
         #####################
         from sklearn.preprocessing import StandardScaler
         scaler = StandardScaler()
@@ -52,22 +62,17 @@ def run_training(use_case):
         test_X = scaler.transform(testing[testing.columns[:-1]]) # all except y
         test_Y = testing[testing.columns[-1]]
 
-        export_model(scaler, use_case, layer_name, reference_layer_name, scaler=True)
+        export_model(scaler, use_case, table, layer_name, reference_layer_name, scaler=True)
         ########################
-        from processing import DataSampler
-
-        sampler = DataSampler()
-        try:
-            train_X, train_Y = sampler.sample_median_size(train_X, train_Y, max_size=100000)
-        except ValueError as e: # not enough points for oversampling
-            print(f"Could not sample training data, using original distribution: {e}")
-        ####################
-        rfc = RandomForestClassifier(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth,
+        # RF is a lot better than SVM, but I did not tune hyperparameters for regression
+        rfc = RandomForestRegressor(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth,
                                     min_samples_leaf=min_samples_leaf, min_impurity_decrease=min_impurity_decrease, 
                                     bootstrap=bootstrap)
-        rfc.fit(train_X, train_Y)
-        
-        print_report([rfc], [test_X], test_Y, ["X"])
+        # rfc = LinearSVR(loss=loss, C=c, dual=dual, tol=tol)
 
-        export_model(rfc, use_case, layer_name, reference_layer_name)
+        rfc.fit(train_X, train_Y)
+        ####################
+        print_regression_report(rfc, test_X, test_Y, f"{layer_name} based on {reference_layer_name}")
+        ####################        
+        export_model(rfc, use_case, table, layer_name, reference_layer_name)
         
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/processing/ml/train_single_context.py

 import pandas as pd
 from pandas import DataFrame
-from processing.ml.train_base import split_data, remove_empty_community_class, print_report
+from processing.ml.train_base import split_data, remove_empty_community_class, print_regression_report
 
 
 approach = 'single_context'
+max_sampling_size = 20000
 #######################
 import pickle 
 from pathlib import Path
 
-def export_model(model, use_case, layer_name, scaler=False):
-    fpath = f'data/{use_case}/ml_output/{approach}'
+def export_model(model, use_case, table, layer_name, scaler=False):
+    fpath = f'data/{use_case}/{table}/ml_output/{approach}'
     Path(fpath).mkdir(parents=True, exist_ok=True)
     with open(f'{fpath}/{layer_name}{"_scaler" if scaler else ""}.model', 'wb') as f:
         pickle.dump(model, f)
 #####################
-from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestRegressor
 n_estimators = 100
-criterion = 'gini'
+criterion = 'mse'
 max_depth = None
 min_samples_leaf = 2
 min_impurity_decrease = 1E-5
 bootstrap=True
+####################
+from sklearn.svm import LinearSVR
+tol = 1E-4
+c = 1
+loss = 'squared_epsilon_insensitive'
+dual = False
 
 
 ###############
@@ -32,15 +39,18 @@ repo = Repository()
 
 def run_training(use_case):
     for layer in repo.get_layers_for_use_case(use_case):
+        table = layer.use_case_table
         layer_name = layer.layer_name
 
+        df: DataFrame = pd.read_csv(f'data/{use_case}/{table}/ml_input/single_context/{layer_name}.csv', index_col=0)
         
-        df: DataFrame = pd.read_csv(f'data/{use_case}/ml_input/single_context/{layer_name}.csv', index_col=0)
+        if df.empty:
+            print(f"No data for training {use_case}//{table}//{layer_name}.")
+            continue
         #######################
         training, testing = split_data(df, shuffle=False)
         #####################
-        training = remove_empty_community_class(training)
-        testing = remove_empty_community_class(testing)
+        training.sample(frac=min(1, max_sampling_size/len(training))).reset_index(drop=True)
         #####################
         from sklearn.preprocessing import StandardScaler
         scaler = StandardScaler()
@@ -51,22 +61,17 @@ def run_training(use_case):
         test_X = scaler.transform(testing[testing.columns[:-1]]) # all except y
         test_Y = testing[testing.columns[-1]]
 
-        export_model(scaler, use_case, layer_name, scaler=True)
+        export_model(scaler, use_case, table, layer_name, scaler=True)
         ########################
-        from processing import DataSampler
-
-        sampler = DataSampler()
-        try:
-            train_X, train_Y = sampler.sample_median_size(train_X, train_Y, max_size=100000)
-        except ValueError as e: # not enough points for oversampling
-            print(f"Could not sample training data, using original distribution: {e}")
-        ####################
-        rfc = RandomForestClassifier(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth,
+        # RF is 10-20% better compared to SVM, but I did not tune hyperparameters for regression
+        rfc = RandomForestRegressor(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth,
                                     min_samples_leaf=min_samples_leaf, min_impurity_decrease=min_impurity_decrease, 
                                     bootstrap=bootstrap)
+        # rfc = LinearSVR(loss=loss, C=c, dual=dual, tol=tol)
+
         rfc.fit(train_X, train_Y)
         ####################
-        print_report([rfc], [test_X], test_Y, ["X"])
+        print_regression_report(rfc, test_X, test_Y, layer_name)
         ####################
-        export_model(rfc, use_case, layer_name)
+        export_model(rfc, use_case, table, layer_name)
         
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/routes/predictions.py

@@ -5,7 +5,7 @@ from db.dao import PredictionResult
 repo = Repository()
 
 def get(use_case, table, layer_name):
-    res = repo.get_prediction_results(use_case)
+    res = repo.get_prediction_results_for_layer(use_case, table, layer_name)
     if res is None or len(res) == 0:
         return Response(status=404)
     else:

src/data-hub/proactive-community-detection-microservice/app/run_dataprep.py

-import sys
-import os
-modules_path = '../../../modules/'
-if os.path.exists(modules_path):
-    sys.path.insert(1, modules_path)
-
-
-from processing.data_prep.main import run
-
-if __name__ == '__main__':
-    '''Creates data/raw files'''
-    run(use_case='community-prediction-youtube-n')
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/run_node_fetching.py

@@ -9,8 +9,10 @@ import urllib3
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
 from processing.fetching import fetching
+from db.repository import Repository
 
 if __name__ == "__main__":
     '''Fetches all required data from business-logic and role-stage-discovery.'''
-    
-    fetching.fetch(selected_use_cases=['community-prediction-youtube-n'], selected_use_case_tables=None)
\ No newline at end of file
+    Repository().DROP(confirm=True)
+    use_cases = ['vialog-enum', 'car-sharing-official', 'smart-energy', 'crowd-journalism-enum']+['community-prediction-youtube-n', 'community-prediction-taxi']
+    fetching.fetch(selected_use_cases=use_cases, selected_use_case_tables=None)
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/run_prediction.py

@@ -4,19 +4,52 @@ modules_path = '../../../modules/'
 if os.path.exists(modules_path):
     sys.path.insert(1, modules_path)
 
-
+import shutil
+from typing import List
 from db.repository import Repository
+
+repo = Repository()
+
+
+from processing.data_prep.main import run as run_data_prep
+def _run_data_preparation(use_cases: List[str] = None):
+    '''Creates data/raw, data/cluster_metrics, data/layer_metrics, and data/ml_input files.'''
+    run_data_prep(use_cases)
+
+
+from processing.ml.train_single_context import run_training as run_single_training
+from processing.ml.train_cross_context import run_training as run_cross_training
+def _run_training(use_cases: List[str] = None):
+    '''Executes the training and creates data/ml_output files.'''
+    for use_case in use_cases:
+        run_single_training(use_case)
+        run_cross_training(use_case)
+
+
 from processing.ml.predict_single_context import run_prediction as run_single_prediction
 from processing.ml.predict_cross_context import run_prediction as run_cross_prediction
+def _run_prediction(use_cases: List[str] = None):
+    '''Executes the predictions and stores them in the DB.'''
+    for use_case in use_cases:
+        repo.delete_prediction_results(use_case)
+        run_single_prediction(use_case)
+        # 20210803 dont execute cross-context for use-cases
+        # run_cross_prediction(use_case)
 
 
-if __name__ == '__main__':
-    '''Executes the predictions.'''
+def _run_cleanup(use_cases: List[str] = None):
+    '''Deletes all files in data/ for the use-cases'''
+    for use_case in use_cases:
+        path_ = f'data/{use_case}/'
+        if os.path.exists(path_):
+            shutil.rmtree(path_)
 
-    use_case='community-prediction-youtube-n'
 
-    repo = Repository()
-    repo.delete_all_prediction_results()
+if __name__ == '__main__':
+    use_cases = ['vialog-enum', 'car-sharing-official', 'smart-energy', 'crowd-journalism-enum']
+    # use_cases = ['community-prediction-youtube-n', 'community-prediction-taxi']
 
-    run_single_prediction(use_case)
-    run_cross_prediction(use_case)
\ No newline at end of file
+    _run_data_preparation(use_cases)
+    _run_training(use_cases)
+    _run_prediction(use_cases)
+    _run_cleanup(use_cases)
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/run_training.py

-import sys
-import os
-modules_path = '../../../modules/'
-if os.path.exists(modules_path):
-    sys.path.insert(1, modules_path)
-
-
-from processing.ml.train_single_context import run_training as run_single_training
-from processing.ml.train_cross_context import run_training as run_cross_training
-
-if __name__ == '__main__':
-    '''Executes the training.'''
-
-    use_case='community-prediction-youtube-n'
-    run_single_training(use_case)
-    run_cross_training(use_case)
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/deployment/deployment.yml

@@ -32,6 +32,15 @@ spec:
         image: alexx882/proactive-community-detection-microservice
         ports:
         - containerPort: 5000
+        imagePullPolicy: Always
+        volumeMounts:
+        - mountPath: /srv/articonf
+          name: articonf
+      volumes:
+      - name: articonf
+        hostPath:
+          path: /srv/articonf
+          type: Directory
 ---
 apiVersion: v1
 kind: Service

src/data-hub/role-stage-discovery-microservice/app/db/repository.py

@@ -111,6 +111,9 @@ class Repository(MongoRepositoryBase):
         entries = super().get_entries(self._time_slice_collection, selection={'use_case': use_case, 'use_case_table': use_case_table, 'layer_name': layer_name})
         return [TimeSlice(time_slice_dict=e, from_db=True) for e in entries]
 
+    def delete_time_slices(self, use_case: str):
+        super().delete_many(self._time_slice_collection, selection={'use_case': use_case})
+
     def remove_all_time_slices(self):
         super().drop_collection(self._time_slice_collection)
 

src/data-hub/role-stage-discovery-microservice/app/run_time_slicing.py

@@ -13,16 +13,61 @@ from typing import Tuple, Dict, Any, List
 TimeSliceKey = Tuple[int, int]
 
 # TODO extract information about time features (maybe from table mapping)
-TIME_PROPERTY_NAMES = ['timestamp']
+TIME_PROPERTY_NAMES = [
+    # vialog-enum
+    'created',
+    # car-sharing-official
+    'available',
+    'date',
+    'startDate',
+    'moment',
+    # smart-energy
+    'Timestamp',
+    # crowd-journalism-enum
+    'creationTimestamp',
+    'lastUpdate',
+    'timestamp',
+    
+    # community-prediction-youtube-n
+    'timestamp',
+    # community-prediction-taxi
+    'timestamp',
+    ]
 
 repo = Repository()
 
+def try_convert_string_to_time(timestamp: str) -> datetime:
+    '''
+    Tries to convert a timestamp string by applying a few common conversion methods, 
+    e.g. unix timestamp, human readable, ISO 8601
+    '''
+    try:
+        return datetime.utcfromtimestamp(float(timestamp))
+    except ValueError:
+        pass
+
+    try:
+        return datetime.strptime(timestamp, '%Y-%m-%d %H:%M:%S')
+    except ValueError:
+        pass
+
+    try:
+        return datetime.strptime(timestamp, '%d-%m-%Y %H:%M:%S')
+    except ValueError:
+        pass
+
+    try:
+        return datetime.strptime(timestamp, "%Y-%m-%dT%H:%M:%SZ")
+    except ValueError:
+        pass
+
+    raise ValueError(f"No conversion available for time data: {timestamp}")
+
 
 def convert_to_time_slice_key(timestamp: str) -> TimeSliceKey:
     '''Returns the tuple (year, week_of_year) from a timestamp. This is used as the key for the slicing.'''
-    # time = datetime.utcfromtimestamp(float(timestamp[0:10]))
-    # time = datetime.strptime(timestamp, '%Y-%m-%d %H:%M:%S')
-    time = datetime.utcfromtimestamp(float(timestamp))
+    time = try_convert_string_to_time(timestamp)    
+    # print(time)
     (y, w, _) = time.isocalendar()
     return (y, w)
 
@@ -42,7 +87,7 @@ def split_clusterset_by_time(layer: Layer, clusters: List[Cluster]) -> Dict[Time
 
     for cluster in clusters:
         if cluster.cluster_label == -1:
-            print("Noise cluster was ignored.")
+            # print("Noise cluster was ignored.")
             continue
 
         for node in cluster.nodes:
@@ -67,38 +112,21 @@ def get_layers() -> List[Layer]:
     return repo.get_layers()
 
 def get_clusters_for_layer(use_case, use_case_table, layer_name)-> List[Cluster]:
-    # return repo.get_clusters_for_layer(use_case, use_case_table, layer_name)
-    json_path = f'_predictions/clusters/{layer_name}.json'
-
-    if os.path.exists(json_path):
-        with open(json_path, 'r') as file:
-            return [Cluster(cluster_dict=e, from_db=False) for e in json.loads(file.read())]
-
-    return []
+    return repo.get_clusters_for_layer(use_case, use_case_table, layer_name)
 
 def get_layer_nodes(use_case, use_case_table, layer_name)-> List[dict]:
-    # return repo.get_layer_nodes(use_case, use_case_table, layer_name)
-    return []
+    return repo.get_layer_nodes(use_case, use_case_table, layer_name)
 
 def add_time_slice(timeslice):
     try:
         repo.add_time_slice(timeslice)  
-        pass
     except:
         print(f"Error while storing time slice in db for {timeslice.layer_name}")
 
-    # try:
-    #     json_path = f'_predictions/timeslices/{timeslice.layer_name}/{timeslice.time}.json'.replace(', ', '_').replace('(', '').replace(')', '')
-    #     if not os.path.exists(os.path.dirname(json_path)):
-    #         os.makedirs(os.path.dirname(json_path))
-
-    #     with open(json_path, 'w') as file:
-    #         file.write(json.dumps(timeslice.to_serializable_dict(for_db=False)))
-    # except Exception as e:
-    #     print(f"Error while writing json for {timeslice.layer_name}: {e}")
 
 def run_time_slicing(selected_use_cases: List[str] = None, selected_use_case_tables: List[str] = None, selected_layer_names: List[str] = None):
     layers = get_layers()
+
     for layer in layers:
         layer_name = layer.layer_name
         use_case = layer.use_case
@@ -128,5 +156,6 @@ def run_time_slicing(selected_use_cases: List[str] = None, selected_use_case_tab
 
 
 if __name__ == "__main__":
-    # repo.remove_all_time_slices()
-    run_time_slicing(selected_use_cases=['community-prediction-youtube-n'])
\ No newline at end of file
+    use_case = 'community-prediction-youtube-n'
+    repo.delete_time_slices(use_case)
+    run_time_slicing(selected_use_cases=[use_case]) 
\ No newline at end of file

src/modules/database/MongoRepositoryBase.py

@@ -27,6 +27,10 @@ class MongoRepositoryBase:
         collection = self._database[collection_name]
         return collection.find(selection, projection)
 
+    def delete_many(self, collection_name, selection: dict = {'__confirm__': '__false__'}):
+        collection = self._database[collection_name]
+        collection.delete_many(selection)
+
     def close_connection(self):
         self._mongo_client.close()
         self._mongo_client = None

src/modules/network_constants.py

@@ -59,7 +59,7 @@ else:
 if server:
     PROACTIVE_COMMUNITY_DETECTION_HOSTNAME = 'proactive-community-detection'
     PROACTIVE_COMMUNITY_DETECTION_REST_PORT = 80
-    PROACTIVE_COMMUNITY_DETECTION_DB_HOSTNAME = f'{ROLESTAGE_DISCOVERY_HOSTNAME}-db'
+    PROACTIVE_COMMUNITY_DETECTION_DB_HOSTNAME = f'{PROACTIVE_COMMUNITY_DETECTION_HOSTNAME}-db'
     PROACTIVE_COMMUNITY_DETECTION_DB_PORT = 27017
 else:
     PROACTIVE_COMMUNITY_DETECTION_HOSTNAME = 'articonf1.itec.aau.at'

src/modules/security/token_manager.py

@@ -20,13 +20,13 @@ class TokenManager:
     def __init__(self):
         self._token = None
 
-    def getToken(self) -> str:
+    def getToken(self, admin=False) -> str:
         if self._token == None:
             credentials_path = get_resources_path()
             
             print("Looking for credentials at ... "+str(credentials_path))
 
-            with open(f'{credentials_path}/regular_user_credentials.json') as file_handler:
+            with open(f"{credentials_path}/{'admin' if admin else 'regular'}_user_credentials.json") as file_handler:
                 credentials = json.loads(file_handler.read())
             
             url = f'https://{network_constants.REST_GATEWAY_HOSTNAME}:{network_constants.REST_GATEWAY_REST_PORT}/api/tokens'

src/participation-hub/business-logic-microservice/app/_add_use_case_scripts/requestPost.py

@@ -6,7 +6,7 @@ from typing import List
 
 def postTableToSwagger(use_case:str, table:dict ):
 
-    jwt = TokenManager.getInstance().getToken()
+    jwt = TokenManager.getInstance().getToken(admin=True)
 
     url = f"https://articonf1.itec.aau.at:30420/api/use-cases/{use_case}/tables"
 
@@ -23,7 +23,7 @@ def postTableToSwagger(use_case:str, table:dict ):
 
 def postLayersToSwagger(use_case:str, layers: List[dict]):
     
-    jwt = TokenManager.getInstance().getToken()
+    jwt = TokenManager.getInstance().getToken(admin=True)
 
     for layer in layers:
         url = f"https://articonf1.itec.aau.at:30420/api/layers"

src/participation-hub/business-logic-microservice/app/_add_use_case_scripts/vialog/add_vialog_schema.py

-import sys
-import os
-from pathlib import Path
-from typing import Dict, Any
-import requests
-
-modules_paths = ['.', '../../../modules/']
-for modules_path in modules_paths:
-    if os.path.exists(modules_path):
-        sys.path.insert(1, modules_path)
-
-from _add_use_case_scripts.vialog.tables import add_user, add_video, add_change
-
-import network_constants as nc
-from security.token_manager import TokenManager
-
-def add_use_case(use_case: str):
-    #use_case = "vialog"
-
-    jwt = TokenManager.getInstance().getToken()
-    url = f"https://articonf1.itec.aau.at:30420/api/use-cases"
-    response = requests.post(
-            url,
-            verify=False, 
-            proxies = { "http":None, "https":None },
-            headers = { "Authorization": f"Bearer {jwt}"},
-            json = {"name": use_case}
-        )
-    
-    print(url+": "+str(response.content))     
-
-if __name__ == "__main__":
-    use_case = "vialog"
-    
-    # disable ssl warnings :) 
-    requests.packages.urllib3.disable_warnings() 
-
-    add_use_case(use_case)
-    add_user.main(use_case)
-    add_video.main(use_case)
-    add_change.main(use_case)
\ No newline at end of file

src/participation-hub/business-logic-microservice/app/_add_use_case_scripts/vialog/add_vialog_schema_new_enum.py

@@ -17,7 +17,7 @@ from security.token_manager import TokenManager
 def add_use_case(use_case: str):
     #use_case = "vialog"
 
-    jwt = TokenManager.getInstance().getToken()
+    jwt = TokenManager.getInstance().getToken(admin=True)
     url = f"https://articonf1.itec.aau.at:30420/api/use-cases"
     response = requests.post(
             url,
@@ -30,7 +30,7 @@ def add_use_case(use_case: str):
     print(url+": "+str(response.content))     
 
 if __name__ == "__main__":
-    use_case = "vialog-new-enum"
+    use_case = "vialog-enum"
     
     # disable ssl warnings :) 
     requests.packages.urllib3.disable_warnings()