Merge branch 'develop' into feature/AutomatingTesting

README.md

@@ -2,7 +2,7 @@
 We propose a framework underlying decentralized social media called SMART, capable of finding relevant interest communities without violating users’ privacy and anonymity. Its objective is to improve trust and eliminate malicious actors in participatory exchanges and collaborative decision making.
 
 ## Project structure
-The *tools* folder contains supportive scripts used during development and must not be used by any microservice.
+The *tools* folder contains supportive scripts used during development and must not be used by any microservice. 
 
 ### Source code in *src/*
 The five folders *data-hub*, *message-broker*, *rest-gateway*, *transaction-hub-in* and *transaction-hub-out* contain the microservices for the five architecture layers respectively.

bin/build.py

@@ -21,6 +21,7 @@ command_args = [{'path': path,
                 for path
                 in paths]
 
+error:int = 0
 res_str = []
 for command_arg in command_args:
     path = command_arg['path']
@@ -34,11 +35,19 @@ for command_arg in command_args:
     os.remove(os.path.join(ROOT, DOCKER_COMPOSE_NAME))
 
     res_str.append(f"{image_name} built with exit code {exit_val}")
+    if exit_val != 0:
+        error = exit_val
 
-    os.system(f"docker push {image_name}")
+    # push created Docker image
+    exit_val = os.system(f"docker push {image_name}")
     
     res_str.append(f"{image_name} pushed with exit code {exit_val}")
+    if exit_val != 0:
+        error = exit_val
+
 
 print(f"Found {len(command_args)} images")
 for s in res_str:
     print(s)
+
+sys.exit(1 if error > 0 else 0)
\ No newline at end of file

bin/deploy.py

 import os
 import sys
 
-EXTERNAL_IP = "143.205.173.225"
-
 # apply or delete config
 kube_command = 'apply'
 if len(sys.argv) == 2:
@@ -14,14 +12,10 @@ for p, _, f in os.walk('./'):
         if 'deployment.yml' == file:
             paths.append(os.path.normpath(p))
 
+error:int = 0
 for path in paths:
-    os.system(f"kubectl {kube_command} -f {path}")
-    
-    # if kube_command == 'apply':
-    #     # apply external ip
-    #     service_name = path.split(os.path.normpath('/'))[-2] # -1 is deployment folder
-    #     if service_name.split('-')[-1] == 'microservice':
+    exit_val = os.system(f"kubectl {kube_command} -f {path}")
+    if exit_val != 0:
+        error = exit_val
+
+sys.exit(1 if error > 0 else 0)
\ No newline at end of file
-    #         # remove microservice suffix
-    #         service_name = service_name[:len(service_name)-len('-microservice')]
-        
-    #     os.system(f"kubectl patch service {service_name} -p '{{\"spec\":{{\"externalIPs\":[\"{EXTERNAL_IP}\"]}}}}'")
\ No newline at end of file

src/data-hub/community-detection-microservice/Dockerfile

@@ -9,7 +9,9 @@ RUN apt-get update
 EXPOSE 5000
 
 WORKDIR /app
+COPY src/modules/ /app/
 COPY src/data-hub/community-detection-microservice/app/ /app/
+
 RUN pip install -r requirements.txt
 RUN chmod a+x main.py
 

src/data-hub/community-detection-microservice/app/configs/swagger.yml

@@ -78,57 +78,57 @@ paths:
           400:
             description: "Invalid input"
 
-  /clusters:
+  /location-clusters:
     get:
-        operationId: "rest.cluster.get"
+        operationId: "rest.cluster.get_locations"
         tags:
             - "Clusters"
-        summary: "Get user communities per date per hour"
+        summary: "Get user communities clustered by location"
         parameters: []
         responses:
             200:
                 description: "Successful operation"
                 schema:
-                  $ref: "#/definitions/UserClusterCollection"
+                  $ref: "#/definitions/LocationClusterCollection"
 
-  /clusters/cluster.png:
-    get:
-        operationId: "rest.cluster.get_image"
-        tags:
-            - "Clusters"
-        summary: "Get user communities per date per hour as image"
-        parameters: []
-        produces:
-            - "image/png"
-        responses:
-            200:
-                description: "Successful operation"
+  # /clusters/cluster.png:
+  #   get:
+  #       operationId: "rest.cluster.get_image"
+  #       tags:
+  #           - "Clusters"
+  #       summary: "Get user communities per date per hour as image"
+  #       parameters: []
+  #       produces:
+  #           - "image/png"
+  #       responses:
+  #           200:
+  #               description: "Successful operation"
   
-  /agi/clusters:
+  /time-clusters:
     get:
-        operationId: "rest.agi_cluster.get"
+        operationId: "rest.cluster.get_times"
         tags:
             - "Clusters"
-        summary: "Get user communities per date per hour from agi data"
+        summary: "Get user communities clustered by time per hour"
         parameters: []
         responses:
             200:
                 description: "Successful operation"
                 schema:
-                  $ref: "#/definitions/UserClusterCollection"
+                  $ref: "#/definitions/TimeClusterCollection"
 
-  /agi/clusters/cluster.png:
-    get:
-        operationId: "rest.agi_cluster.get_image"
-        tags:
-            - "Clusters"
-        summary: "Get user communities per date per hour from agi data as image"
-        parameters: []
-        produces:
-            - "image/png"
-        responses:
-            200:
-                description: "Successful operation"
+  # /agi/clusters/cluster.png:
+  #   get:
+  #       operationId: "rest.agi_cluster.get_image"
+  #       tags:
+  #           - "Clusters"
+  #       summary: "Get user communities per date per hour from agi data as image"
+  #       parameters: []
+  #       produces:
+  #           - "image/png"
+  #       responses:
+  #           200:
+  #               description: "Successful operation"
               
 definitions:
   Location:
@@ -152,8 +152,27 @@ definitions:
     items:
       $ref: "#/definitions/Location"
 
-  UserCluster:
-    type: "object"
+  LocationCluster:
+    type: object
+    properties:
+      id:
+        type: string
+      cluster_label:
+        type: number
+      nodes:
+        type: array
+        items: 
+          $ref: "#/definitions/Location"
+        # example:
+        #     0: [1dc61b1a0602de0eaee9dba7eece9279c2844202, b4b31bbe5e12f55737e3a910827c81595fbca3eb]
+  
+  LocationClusterCollection:
+    type: array
+    items:
+      $ref: "#/definitions/LocationCluster"
+
+  TimeCluster:
+    type: object
     properties:
       id:
         type: string
@@ -161,16 +180,16 @@ definitions:
         type: string
       hour:
         type: number
-      clusters:
-        type: object
-        additionalProperties:
-            type: array
-            items: 
-                type: string
-        example:
-            0: [1dc61b1a0602de0eaee9dba7eece9279c2844202, b4b31bbe5e12f55737e3a910827c81595fbca3eb]
-  
-  UserClusterCollection:
+      cluster_label:
+        type: number
+      nodes:
+        type: array
+        items: 
+          $ref: "#/definitions/Location"
+        # example:
+        #     0: [1dc61b1a0602de0eaee9dba7eece9279c2844202, b4b31bbe5e12f55737e3a910827c81595fbca3eb]
+
+  TimeClusterCollection:
     type: array
     items:
-      $ref: "#/definitions/UserCluster"
+      $ref: "#/definitions/TimeCluster"
\ No newline at end of file

src/data-hub/community-detection-microservice/app/db/entities/__init__.py

 from db.entities.location import Location
 from db.entities.popular_location import PopularLocation
-from db.entities.user_cluster import UserCluster
+from db.entities.cluster import LocationCluster, TimeCluster
\ No newline at end of file

src/data-hub/community-detection-microservice/app/db/entities/cluster.py

+import json
+from typing import List, Dict
+from datetime import date, datetime
+
+
+class Cluster:
+    def __init__(self, cluster_label: int = None, nodes: List = None):
+        self.cluster_label = cluster_label
+        self.nodes = nodes
+
+
+class LocationCluster(Cluster):
+    def __init__(self, cluster_label: int = None, nodes: List = None,
+                 location_dict: Dict = None, from_db=False):
+        super().__init__(cluster_label, nodes)
+        self.id = f'{self.cluster_label}'
+
+        if location_dict is not None:
+            self.from_serializable_dict(location_dict, from_db)
+
+    def to_serializable_dict(self, for_db=False) -> Dict:
+        return {
+            "id": self.id,
+            "cluster_label": self.cluster_label,
+            "nodes": json.dumps(self.nodes) if for_db else self.nodes
+        }
+
+    def from_serializable_dict(self, location_dict: Dict, from_db=False):
+        self.id = location_dict["id"]
+        self.cluster_label = location_dict["cluster_label"]
+        self.nodes = json.loads(location_dict["nodes"]) \
+            if from_db else location_dict["nodes"]
+
+    def __repr__(self):
+        return json.dumps(self.to_serializable_dict())
+
+    def __str__(self):
+        return f"LocationCluster({self.__repr__()})"
+
+
+class TimeCluster(Cluster):
+    def __init__(self, date: date = None, hour: int = None, cluster_label: int = None, nodes: List = None,
+                 time_dict: Dict = None, from_db=False):
+        super().__init__(cluster_label, nodes)
+        self.date = date
+        self.hour = hour
+        self.id = f'{self.date}-{self.hour}-{self.cluster_label}'
+
+        if time_dict is not None:
+            self.from_serializable_dict(time_dict, from_db)
+
+    def to_serializable_dict(self, for_db=False) -> Dict:
+        return {
+            "id": self.id,
+            "date": str(self.date),
+            "hour": self.hour,
+            "cluster_label": self.cluster_label,
+            "nodes": json.dumps(self.nodes) if for_db else self.nodes
+        }
+
+    def from_serializable_dict(self, time_dict: Dict, from_db=False):
+        self.id = time_dict["id"]
+        self.date = datetime.strptime(time_dict["date"], '%Y-%m-%d').date()
+        self.hour = time_dict["hour"]
+        self.cluster_label = time_dict["cluster_label"]
+        self.nodes = json.loads(time_dict["nodes"]) \
+            if from_db else time_dict["nodes"]
+
+    def __repr__(self):
+        return json.dumps(self.to_serializable_dict())
+
+    def __str__(self):
+        return f"TimeCluster({self.__repr__()})"

src/data-hub/community-detection-microservice/app/db/entities/user_cluster.py

-import json
-
-class UserCluster:
-    def __init__(self, date, hour, clusters):
-        super().__init__()
-        self.date = date
-        self.hour = hour
-        self.clusters = clusters
-        self.id = f'{self.date}-{self.hour}'
-
-    def to_serializable_dict(self, for_db=False):
-        return {
-            "id": self.id,
-            "date": str(self.date),
-            "hour": self.hour,
-            "clusters": json.dumps(self.clusters) if for_db else self.clusters
-        }
-
-    def __repr__(self):
-        return json.dumps(self.to_serializable_dict())
-
-    def __str__(self):
-        return f"UserCluster({self.__repr__()})"

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

-from __future__ import annotations
-
-class LocationDatastore:
-    '''This Singelton simulates a location database'''
-    _instance = None
-
-    @staticmethod 
-    def get_instance() -> LocationDatastore:
-        if LocationDatastore._instance == None:
-            LocationDatastore._instance = LocationDatastore()
-        return LocationDatastore._instance
-
-    def __init__(self):
-        if LocationDatastore._instance != None:
-            raise Exception("This class is a singleton!")
-
-        self.locations = []
-    
-    def add(self, location):
-        self.locations.append(location)
-
-    
-    def get(self):
-        return self.locations
\ No newline at end of file

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

@@ -5,18 +5,21 @@ import json
 
 from db.agi.agi_repository import AgiRepository
 
-from db.entities import Location, UserCluster, PopularLocation
+from db.entities import Location, TimeCluster, PopularLocation, LocationCluster
 from typing import List
 
 
 class Repository(MongoRepositoryBase):
+    '''This repository stores and loads locations and clusters with MongoDb.'''
 
-    def __init__(self, agi_data=False):
-        super().__init__(netconst.COMMUNITY_DETECTION_DB_HOSTNAME, 
-            netconst.COMMUNITY_DETECTION_DB_PORT, 'communityDetectionDb')
+    def __init__(self):
+        super().__init__(netconst.COMMUNITY_DETECTION_DB_HOSTNAME,
+                         netconst.COMMUNITY_DETECTION_DB_PORT,
+                         'communityDetectionDb')
 
-        self._location_collection = 'location_agi' if agi_data else 'location'
-        self._cluster_collection = 'cluster_agi' if agi_data else 'cluster'
+        self._location_collection = 'location'
+        self._location_cluster_collection = 'location_cluster'
+        self._time_cluster_collection = 'time_cluster'
 
         self.agi_repo = AgiRepository()
 
@@ -31,12 +34,18 @@ class Repository(MongoRepositoryBase):
         agi_locations = self.agi_repo.getLocations()
         return [Location(agi_loc) for agi_loc in agi_locations]
 
-    def add_user_cluster(self, cluster: UserCluster):
-        super().insert_entry(self._cluster_collection, cluster.to_serializable_dict(for_db=True))
+    def add_location_cluster(self, cluster: LocationCluster):
+        super().insert_entry(self._location_cluster_collection,
+                             cluster.to_serializable_dict(for_db=True))
 
-    def get_user_clusters(self) -> List[UserCluster]:
-        clusters = super().get_entries(self._cluster_collection)
-        return [UserCluster(c['date'], int(c['hour']), json.loads(c['clusters'])) for c in clusters]
+    def get_location_clusters(self) -> List[LocationCluster]:
+        clusters = super().get_entries(self._location_cluster_collection)
+        return [LocationCluster(location_dict=c, from_db=True) for c in clusters]
 
-    def add_popular_location(self, popular_location: PopularLocation):
-        pass
+    def add_time_cluster(self, cluster: TimeCluster):
+        super().insert_entry(self._time_cluster_collection,
+                             cluster.to_serializable_dict(for_db=True))
+
+    def get_time_clusters(self) -> List[TimeCluster]:
+        clusters = super().get_entries(self._time_cluster_collection)
+        return [TimeCluster(time_dict=c, from_db=True) for c in clusters]

src/data-hub/community-detection-microservice/app/insert_agi_locations.py

+import sys
+import os
+modules_path = '../../../modules/'
+if os.path.exists(modules_path):
+    sys.path.insert(1, modules_path)
+
+from db.repository import Repository
+
+if __name__ == "__main__":
+    repo = Repository()
+
+    locs = repo.get_agi_locations()
+    for l in locs:
+        repo.add_location(l)

src/data-hub/community-detection-microservice/app/processing/clusterer.py

@@ -21,7 +21,7 @@ class Clusterer:
             partition_info = labels
         )
 
-    def _draw_locations(self, locations:np.ndarray=None, centroids:np.ndarray=None, partition_info=None) -> plt.Figure:
+    def _draw_locations(self, locations:np.ndarray=None, centroids:np.ndarray=None, partition_info:List=None) -> plt.Figure:
         fig = plt.Figure()
         axis = fig.add_subplot(1, 1, 1)
 
@@ -43,41 +43,57 @@ class Clusterer:
             
         return fig
     
-    def create_labels(self, locations:List) -> List:
-        if locations is None or len(locations) == 0:
-            return locations # trash in trash out
-
-        locations = self.extract_location_data(locations)
+    def create_labels(self, features:np.ndarray) -> List:
+        if features is None or len(features) == 0:
+            return features # trash in trash out
     
         dbsc = DBSCAN(eps = self.epsilon, min_samples = self.min_points)
-        dbsc = dbsc.fit(locations)
+        dbsc = dbsc.fit(features)
         labels = dbsc.labels_
 
         return labels.tolist()
 
-    def label_locations(self, locations:List[Dict], labels:List) -> List:
-        if locations is None or labels is None:
+    def extract_location_features(self, locations: List[dict]) -> np.ndarray:
+        return np.asarray([(float(l['latitude']), float(l['longitude'])) for l in locations])
+
+    def extract_time_features(self, times: List[Dict]) -> np.ndarray:
+        return np.asarray([((t['timestamp']), 0) for t in times])
+
+    def label_dataset(self, dataset:List[Dict], labels:List) -> List:
+        if dataset is None or labels is None:
             return
 
-        if len(locations) != len(labels):
-            raise ValueError("locations and labels has to have same length")
+        if len(dataset) != len(labels):
+            raise ValueError("dataset and labels has to have same length")
 
-        for i in range(len(locations)):
-            locations[i]['cluster_label'] = labels[i]
+        for i in range(len(dataset)):
+            dataset[i]['cluster_label'] = labels[i]
+
+    def group_by_clusters(self, dataset:List[Dict], labels:List) -> Dict[int, List[Dict]]:
+        clusters = {}
+        for label in labels:
+            clusters[label] = [ds for ds in dataset if ds['cluster_label'] == label]
+        
+        return clusters
 
-    def run(self, locations:List[Dict]) -> Dict[int, List[Dict]]:
+    def cluster_locations(self, locations:List[Dict]) -> Dict[int, List[Dict]]:
+        '''Returns a dictionary with identified clusters and their locations copied from the input'''
         if locations is None or len(locations) == 0:
             # raise Exception("locations has to contain something")
             return {}
 
-        labels = self.create_labels(locations)
-        self.label_locations(locations, labels)
+        features = self.extract_location_features(locations)
+        
+        labels = self.create_labels(features)
+        self.label_dataset(locations, labels)
 
-        clusters = {}
-        for label in labels:
-            clusters[label] = [l for l in locations if l['cluster_label'] == label]
+        return self.group_by_clusters(locations, labels)
         
-        return clusters
+    def cluster_times(self, times:List[Dict]) -> Dict[int, List[Dict]]:
+        '''Returns a dictionary with identified clusters and their times copied from the input'''
+        features = self.extract_time_features(times)
+    
+        labels = self.create_labels(features)
+        self.label_dataset(times, labels)
 
-    def extract_location_data(self, locations: List[dict]) -> np.ndarray:
-        return np.asarray([(float(l['latitude']), float(l['longitude'])) for l in locations])
+        return self.group_by_clusters(times, labels)
\ No newline at end of file
\ No newline at end of file

src/data-hub/community-detection-microservice/app/requirements.txt

-connexion[swagger-ui]
-numpy
-matplotlib
-scikit-learn
+astroid==2.3.3
+attrs==19.3.0
+autopep8==1.5
+certifi==2019.11.28
\ No newline at end of file
+chardet==3.0.4
+Click==7.0
+clickclick==1.2.2
+connexion==2.6.0
+cycler==0.10.0
+Flask==1.1.1
+idna==2.8
+importlib-metadata==1.5.0
+inflection==0.3.1
+isort==4.3.21
+itsdangerous==1.1.0
+Jinja2==2.11.1
+joblib==0.14.1
+jsonschema==3.2.0
+kiwisolver==1.1.0
+lazy-object-proxy==1.4.3
+MarkupSafe==1.1.1
+matplotlib==3.1.3
+mccabe==0.6.1
+numpy==1.18.1
+openapi-spec-validator==0.2.8
+# pkg-resources==0.0.0
+pycodestyle==2.5.0
+pylint==2.4.4
+pymongo==3.10.1
+pyparsing==2.4.6
+pyrsistent==0.15.7
+python-dateutil==2.8.1
+PyYAML==5.3
+requests==2.22.0
+rope==0.16.0
+scikit-learn==0.22.1
+scipy==1.4.1
+six==1.14.0
+swagger-ui-bundle==0.0.6
+typed-ast==1.4.1
+urllib3==1.25.8
+Werkzeug==0.16.1
+wrapt==1.11.2
+zipp==2.1.0

src/data-hub/community-detection-microservice/app/rest/agi_cluster.py

-import io
-from flask import request, Response
-from db.repository import Repository
-from processing.clusterer import Clusterer
-from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-
-repo = Repository(agi_data=True)
-clusterer = Clusterer()
-
-def get():
-    clusters = repo.get_user_clusters()
-    return [c.to_serializable_dict() for c in clusters]
-
-def get_image():
-    return Response(status=501)
-    # todo
-
-    locations = repo.getLocations()
-
-    fig = clusterer.draw_locations(locations)
-
-    output = io.BytesIO()
-    FigureCanvas(fig).print_png(output)
-    return Response(output.getvalue(), mimetype="image/png")
\ No newline at end of file

src/data-hub/community-detection-microservice/app/rest/cluster.py

@@ -7,11 +7,28 @@ from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
 repo = Repository()
 clusterer = Clusterer()
 
-def get():
-    clusters = repo.get_user_clusters()
+def get_locations():
+    clusters = repo.get_location_clusters()
     return [c.to_serializable_dict() for c in clusters]
 
-def get_image():
+def get_times():
+    clusters = repo.get_time_clusters()
+    return [c.to_serializable_dict() for c in clusters]
+
+def get_image_1():
+    return Response(status=501)
+    # todo
+
+    locations = repo.getLocations()
+
+    fig = clusterer.draw_locations(locations)
+
+    output = io.BytesIO()
+    FigureCanvas(fig).print_png(output)
+    return Response(output.getvalue(), mimetype="image/png")
+
+
+def get_image_2():
     return Response(status=501)
     # todo
 

src/data-hub/community-detection-microservice/app/rest/location.py

@@ -6,17 +6,17 @@ repo = Repository()
 
 def post():
     body = request.json
-    insert_location(body)
+    _insert_location(body)
     return Response(status=201)
 
 def post_many():
     body = request.json
     for location in body:
-        insert_location(location)
+        _insert_location(location)
     return Response(status=201)
 
 def get():
     return [l.to_serializable_dict() for l in repo.get_locations()]
 
-def insert_location(location_data: dict):
+def _insert_location(location_data: dict):
     repo.add_location(Location(location_data))

src/data-hub/community-detection-microservice/app/run_clustering.py

@@ -4,31 +4,33 @@ modules_path = '../../../modules/'
 if os.path.exists(modules_path):
     sys.path.insert(1, modules_path)
 
-from processing.clusterer import Clusterer
-from db.repository import Repository
-from datetime import datetime, timedelta
+from db.entities import Location, PopularLocation, LocationCluster, TimeCluster
 from typing import List, Dict, Tuple
-from db.entities import Location, PopularLocation, UserCluster
-import statistics
-from collections import Counter
-import json
+from db.repository import Repository
+from processing.clusterer import Clusterer
+
 
 DEBUG = False
-NR_DECIMAL_FOR_BEST_LOCATIONS = 4
 
-# used to cluster locations of a single user to detect main location per time slice
-main_loc_clusterer = Clusterer()
-# used to cluster the users based on their main location
-user_clusterer = Clusterer()  
+repo = Repository()
 
-time_slices = list(range(24))
+def run_location_clustering():
+    user_clusterer = Clusterer()
 
-repo = Repository(agi_data=True)
+    all_location_traces = repo.get_locations()
 
+    cluster_result = user_clusterer.cluster_locations(
+        [l.to_serializable_dict() for l in all_location_traces])
 
-def run_location_clustering():
-    user_clusters: List[UserCluster] = []
-    popular_locations: List[PopularLocation] = []
+    clusters = [LocationCluster(key, value)
+                for key, value in cluster_result.items()]
+
+    store_clusters('locations', clusters)
+
+
+def run_time_clustering():
+    clusters: List[TimeCluster] = []
+    user_clusterer = Clusterer(epsilon=600)  # clustered within 10 minutes
 
     all_location_traces = repo.get_locations()
 
@@ -38,107 +40,39 @@ def run_location_clustering():
         traces_for_cur_date = [
             trace for trace in all_location_traces if trace.timestamp.date() == cur_date]
 
-        location_counter: Dict[str, int] = {}
-
         # for each hour of that day
-        for cur_hour in time_slices:
+        for cur_hour in list(range(24)):
             traces_for_time_slice = [
-                trace for trace in traces_for_cur_date if trace.timestamp.hour - cur_hour == 0]
+                trace for trace in traces_for_cur_date if trace.timestamp.hour == cur_hour]
 
             if len(traces_for_time_slice) == 0:
                 continue
 
-            main_locations = []
-
-            # store the main location for each user
-            users = {trace.user for trace in traces_for_time_slice}
-            for user in users:
-                main_loc = get_main_location_for_user(
-                    traces_for_time_slice, user)
-                main_loc['user'] = user
-                main_locations.append(main_loc)
-
-            # cluster the main locations for all users
-            cluster_result = user_clusterer.run(main_locations)
-
-            clusters = {}
-            for key, vals in cluster_result.items():
-                clusters[key] = [v['user'] for v in vals]
-                # print(f"{cur_date} @ {cur_hour}h-{cur_hour+1}h (Group #{key}): {[v['user'] for v in vals]}")
+            # clustering per hour
+            cluster_result = user_clusterer.cluster_times(
+                [t.to_serializable_dict() for t in traces_for_time_slice])
+            cur_clusters = [TimeCluster(cur_date, cur_hour, key, value)
+                            for key, value in cluster_result.items()]
 
-            # add the clusters for the cur_hour to the global cluster list
-            user_clusters.append(UserCluster(cur_date, cur_hour, clusters))
+            clusters.extend(cur_clusters)
 
-            # add locations for cur_hour to location counter
-            for main_l in main_locations:
-                key = json.dumps({'lat': round(main_l['latitude'], NR_DECIMAL_FOR_BEST_LOCATIONS),
-                                  'long': round(main_l['longitude'], NR_DECIMAL_FOR_BEST_LOCATIONS)})
-                if key not in location_counter:
-                    location_counter[key] = 0
-                location_counter[key] += 1
+    store_clusters('times', clusters)
 
-            # print(f"{cur_date} @ {cur_hour}h-{cur_hour+1}h: {main_locations}")
 
-        # add the top three locations to the global popular location list
-        top_locations = get_top_three_locations(location_counter)
-        top_locations = [json.loads(l[0]) for l in top_locations]
-        popular_locations.append(PopularLocation(cur_date, top_locations))
-
-    store_user_clusters(user_clusters)
-    store_popular_locations(popular_locations)
-
-
-def get_main_location_for_user(location_traces: List[Location], user: str) -> dict:
-    # cluster based on locations
-    locations_for_user = [t for t in location_traces if t.user == user]
-    clusters = main_loc_clusterer.run([l.__dict__
-                                       for l in locations_for_user])
-
-    # largest cluster has most locations
-    max_c = {'id': -1, 'size': 0}
-    for cluster_key, cluster_vals in clusters.items():
-        if len(cluster_vals) > max_c['size']:
-            max_c['id'] = cluster_key
-            max_c['size'] = len(cluster_vals)
-
-    # calculate center of the location from the largest cluster
-    locations_of_largest_cluster = clusters[max_c['id']]
-    center = get_center_of_2d_points(locations_of_largest_cluster)
-    return center
-
-
-def get_center_of_2d_points(points, nr_decimal_places=5) -> dict:
-    center = {}
-    center['latitude'] = round(statistics.mean(
-        [p['latitude'] for p in points]), nr_decimal_places)
-    center['longitude'] = round(statistics.mean(
-        [p['longitude'] for p in points]), nr_decimal_places)
-    return center
-
-
-def get_top_three_locations(location_counts: Dict[str, int]) -> List[Tuple[str, int]]:
-    cnter = Counter(location_counts)
-    max_three = cnter.most_common(3)
-    return max_three
-
-
-def store_user_clusters(user_clusters: List[UserCluster]):
+def store_clusters(type: str, clusters: List):
     if DEBUG:
-        print(user_clusters)
+        print(clusters)
         return
 
-    for c in user_clusters:
-        repo.add_user_cluster(c)
-
-
-def store_popular_locations(popular_locations: List[PopularLocation]):
-    if DEBUG:
-        print(popular_locations)
-        return
+    if type == 'locations':
+        for c in clusters:
+            repo.add_location_cluster(c)
 
-    for l in popular_locations:
-        repo.add_popular_location(l)
+    if type == 'times':
+        for c in clusters:
+            repo.add_time_cluster(c)
 
 
 if __name__ == "__main__":
     run_location_clustering()
+    run_time_clustering()

src/data-hub/community-detection-microservice/app/tests/test_cluster.py

+import unittest
+import sys
+sys.path.insert(1, './')
+
+# python -m unittest discover -v tests
+from db.entities.cluster import Cluster
+from db.entities import TimeCluster, LocationCluster
+
+from datetime import date, datetime
+import json
+
+
+class TestCluster(unittest.TestCase):
+    def test_init_Cluster(self):
+        c = Cluster(1, [1, 2, 3])
+
+        self.assertEqual(1, c.cluster_label)
+        self.assertEqual([1, 2, 3], c.nodes)
+
+
+class TestLocationCluster(unittest.TestCase):
+    def setUp(self):
+        self.c = LocationCluster(1, [1, 2, 3])
+
+    def test_init_individualArguments(self):
+        c = LocationCluster(1, [1, 2, 3])
+
+        self.assertEqual('1', c.id)
+        self.assertEqual(1, c.cluster_label)
+        self.assertEqual([1, 2, 3], c.nodes)
+
+    def test_init_dictArgument(self):
+        dict_ = {'id': '123', 'cluster_label': 1, 'nodes': [1, 2, 3]}
+        c = LocationCluster(location_dict=dict_)
+
+        self.assertEqual('123', c.id)
+        self.assertEqual(1, c.cluster_label)
+        self.assertEqual([1, 2, 3], c.nodes)
+
+    def test_init_dictArgument_fromDb(self):
+        dict_ = {'id': '123', 'cluster_label': 1, 'nodes': '[1, 2, 3]'}
+        c = LocationCluster(location_dict=dict_, from_db=True)
+
+        self.assertEqual('123', c.id)
+        self.assertEqual(1, c.cluster_label)
+        self.assertEqual([1, 2, 3], c.nodes)
+
+    def test_to_serializable_dict_noDb(self):
+        c_dict = self.c.to_serializable_dict()
+
+        self.assertEqual(self.c.id, c_dict['id'])
+        self.assertEqual(self.c.cluster_label, c_dict['cluster_label'])
+        self.assertEqual(self.c.nodes, c_dict['nodes'])
+
+    def test_from_serializable_dict_noDb(self):
+        new_c = LocationCluster()
+        new_c.from_serializable_dict(self.c.to_serializable_dict())
+
+        self.assertEqual(self.c.id, new_c.id)
+        self.assertEqual(str(self.c), str(new_c))
+
+    def test_to_serializable_dict_db_jsonNodes(self):
+        c_dict = self.c.to_serializable_dict(for_db=True)
+
+        self.assertEqual(self.c.id, c_dict['id'])
+        self.assertEqual(self.c.cluster_label, c_dict['cluster_label'])
+        self.assertEqual(self.c.nodes, json.loads(c_dict['nodes']))
+
+    def test_from_serializable_dict_fromDb(self):
+        new_c = LocationCluster()
+        new_c.from_serializable_dict(
+            self.c.to_serializable_dict(for_db=True), from_db=True)
+
+        self.assertEqual(self.c.id, new_c.id)
+        self.assertEqual(str(self.c), str(new_c))
+
+
+class TestTimeCluster(unittest.TestCase):
+
+    def setUp(self):
+        self.date_ = date(2020, 1, 1)
+        self.c = TimeCluster(self.date_, 14, 1, [1, 2, 3])
+
+    def test_init_individualArguments(self):
+        c = TimeCluster(self.date_, 14, 1, [1, 2, 3])
+
+        self.assertEqual(f'{self.date_}-14-1', c.id)
+        self.assertEqual(self.date_, c.date)
+        self.assertEqual(14, c.hour)
+        self.assertEqual(1, c.cluster_label)
+        self.assertEqual([1, 2, 3], c.nodes)
+
+    def test_init_dictArgument(self):
+        dict_ = {'id': '123', 'cluster_label': 1, 'nodes': [1, 2, 3],
+                 'date': str(self.date_), 'hour': 14}
+        c = TimeCluster(time_dict=dict_)
+
+        self.assertEqual('123', c.id)
+        self.assertEqual(self.date_, c.date)
+        self.assertEqual(14, c.hour)
+        self.assertEqual(1, c.cluster_label)
+        self.assertEqual([1, 2, 3], c.nodes)
+
+    def test_init_dictArgument_fromDb(self):
+        dict_ = {'id': '123', 'cluster_label': 1, 'nodes': '[1, 2, 3]',
+                 'date': str(self.date_), 'hour': 14}
+        c = TimeCluster(time_dict=dict_, from_db=True)
+
+        self.assertEqual('123', c.id)
+        self.assertEqual(self.date_, c.date)
+        self.assertEqual(14, c.hour)
+        self.assertEqual(1, c.cluster_label)
+        self.assertEqual([1, 2, 3], c.nodes)
+
+    def test_to_serializable_dict_noDb(self):
+        c_dict = self.c.to_serializable_dict()
+
+        self.assertEqual(self.c.id, c_dict['id'])
+        self.assertEqual(self.c.cluster_label, c_dict['cluster_label'])
+        self.assertEqual(self.c.nodes, c_dict['nodes'])
+        self.assertEqual(self.c.date, datetime.strptime(
+            c_dict['date'], '%Y-%m-%d').date())
+        self.assertEqual(self.c.hour, c_dict['hour'])
+
+    def test_from_serializable_dict_noDb(self):
+        new_c = TimeCluster()
+        new_c.from_serializable_dict(self.c.to_serializable_dict())
+
+        self.assertEqual(self.c.id, new_c.id)
+        self.assertEqual(str(self.c), str(new_c))
+
+    def test_to_serializable_dict_fromDb_jsonNodes(self):
+        c_dict = self.c.to_serializable_dict(for_db=True)
+
+        self.assertEqual(self.c.id, c_dict['id'])
+        self.assertEqual(self.c.cluster_label, c_dict['cluster_label'])
+        self.assertEqual(self.c.nodes, json.loads(c_dict['nodes']))
+        self.assertEqual(self.c.date, datetime.strptime(
+            c_dict['date'], '%Y-%m-%d').date())
+        self.assertEqual(self.c.hour, c_dict['hour'])
+
+    def test_from_serializable_dict_fromDb(self):
+        new_c = TimeCluster()
+        new_c.from_serializable_dict(
+            self.c.to_serializable_dict(for_db=True), from_db=True)
+
+        self.assertEqual(self.c.id, new_c.id)
+        self.assertEqual(str(self.c), str(new_c))
+
+
+if __name__ == '__main__':
+    unittest.main()

src/data-hub/community-detection-microservice/app/tests/test_clusterer.py

@@ -20,13 +20,15 @@ class TestClusterer(unittest.TestCase):
         self.assertEqual([], labels)
 
     def test_create_labels_singleInput_singleCluster(self):
-        labels = self.clusterer.create_labels([self.location(1,2)])
+        features = self.clusterer.extract_location_features([self.location(1,2)])
+        labels = self.clusterer.create_labels(features)
         self.assertEqual(1, len(labels))
 
     def test_create_labels_nearInputs_singleCluster(self):
         locations = [self.location(1,2), self.location(2,2)]
 
-        labels = self.clusterer.create_labels(locations)
+        features = self.clusterer.extract_location_features(locations)
+        labels = self.clusterer.create_labels(features)
 
         self.assertEqual(2, len(labels))
         self.assertEqual(labels[0], labels[1])
@@ -34,36 +36,37 @@ class TestClusterer(unittest.TestCase):
     def test_create_labels_nearInputs_twoClusters(self):
         locations = [self.location(1,2), self.location(2,2), self.location(20,20)]
 
-        labels = self.clusterer.create_labels(locations)
+        features = self.clusterer.extract_location_features(locations)
+        labels = self.clusterer.create_labels(features)
 
         self.assertEqual(3, len(labels))
         self.assertEqual(labels[0], labels[1])
         self.assertNotEqual(labels[0], labels[2])
 
     def test_label_locations_NoneLocations_NoException(self):
-        self.clusterer.label_locations(None, [])
+        self.clusterer.label_dataset(None, [])
 
     def test_label_locations_NoneLabels_NoException(self):
-        self.clusterer.label_locations([], None)
+        self.clusterer.label_dataset([], None)
 
     def test_label_locations_emptyInput_emptyOutput(self):
         locations = []
-        self.clusterer.label_locations(locations, [])
+        self.clusterer.label_dataset(locations, [])
         self.assertEqual(0, len(locations))
 
     def test_label_locations_diffInputLengths_ValueError_1(self):
         with self.assertRaises(ValueError):
-            self.clusterer.label_locations([], [1])
+            self.clusterer.label_dataset([], [1])
 
     def test_label_locations_diffInputLengths_ValueError_2(self):
         with self.assertRaises(ValueError):
-            self.clusterer.label_locations([self.location(1,2)], [])
+            self.clusterer.label_dataset([self.location(1,2)], [])
 
     def test_label_locations_multInput_correctlyLabeled(self):
         locations = [self.location(1,2), self.location(2,2), self.location(20,20)]
         labels = [17,2,20]
 
-        self.clusterer.label_locations(locations, labels)
+        self.clusterer.label_dataset(locations, labels)
 
         self.assertEqual(3, len(locations))
         self.assertHaveLabelsAsNewKey(locations, labels)