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Commit f12f43ff authored by Bogdan's avatar Bogdan
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Fixed Similarity, Work on the dummy data upload, Delete func add in repo

parent 9c4399c4
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Showing with 12432 additions and 55 deletions
src/data-hub/role-stage-discovery-microservice/app/db/entities/connected_cluster.py
View file @ f12f43ff
...@@ -11,10 +11,11 @@ class ClusterC: ...@@ -11,10 +11,11 @@ class ClusterC:
:param cluster_connClustDict: Layer name, cluster label and weight for each 'dest' cluster :param cluster_connClustDict: Layer name, cluster label and weight for each 'dest' cluster
''' '''
def __init__(self,cluster_label,cluster_layer,cluster_runId,cluster_containedNodesDict,cluster_connNodesDict,cluster_connClustDict): def __init__(self,cluster_label,cluster_layer,use_case,cluster_runId,cluster_containedNodesDict,cluster_connNodesDict,cluster_connClustDict):
self.cluster_label = cluster_label self.cluster_label = cluster_label
self.cluster_layer = cluster_layer self.cluster_layer = cluster_layer
self.use_case = use_case
self.cluster_runId = cluster_runId self.cluster_runId = cluster_runId
self.cluster_containedNodesDict = cluster_containedNodesDict #Keys are frozensets(touples) == frozenset(uniqueID, cluster and layer) Value is the Node UniqueId self.cluster_containedNodesDict = cluster_containedNodesDict #Keys are frozensets(touples) == frozenset(uniqueID, cluster and layer) Value is the Node UniqueId
self.cluster_connNodesDict = cluster_connNodesDict #Keys are frozensets(touples) (uniqueID:str,node_cluster:str,node_layer:str) Values are NodeC Objects self.cluster_connNodesDict = cluster_connNodesDict #Keys are frozensets(touples) (uniqueID:str,node_cluster:str,node_layer:str) Values are NodeC Objects
......
src/data-hub/role-stage-discovery-microservice/app/db/entities/connected_node.py
View file @ f12f43ff
...@@ -9,7 +9,8 @@ class NodeC: ...@@ -9,7 +9,8 @@ class NodeC:
''' '''
def __init__(self, node_cluster, node_layer, uniqueID): def __init__(self, node_cluster, node_layer, uniqueID,use_case):
self.node_cluster = node_cluster # str self.node_cluster = node_cluster # str
self.node_layer = node_layer # str self.node_layer = node_layer # str
self.uniqueID = uniqueID # str self.uniqueID = uniqueID # str
self.use_case = use_case # str
\ No newline at end of file
src/data-hub/role-stage-discovery-microservice/app/db/repository.py
View file @ f12f43ff
...@@ -28,6 +28,15 @@ class Repository(MongoRepositoryBase): ...@@ -28,6 +28,15 @@ class Repository(MongoRepositoryBase):
self._connected_run = 'connected_run' self._connected_run = 'connected_run'
#TODO find a better name
def delete_FIND_A_BETTER_NAME(self, use_case: str):
colList = [self._layer_collection,self._clusters_collection,self._connected_clusters_collection,self._layer_nodes_collection,self._similarity_collection]
criteria = {"use_case": use_case}
for col in colList:
res = super().delete_entry(col,criteria,True)
print ("###MASS DELETE "+ col + " "+ str(res))
#region Layers #region Layers
def add_layer(self, layer: Layer): def add_layer(self, layer: Layer):
super().insert_entry(self._layer_collection, layer.to_serializable_dict()) super().insert_entry(self._layer_collection, layer.to_serializable_dict())
...@@ -66,6 +75,9 @@ class Repository(MongoRepositoryBase): ...@@ -66,6 +75,9 @@ class Repository(MongoRepositoryBase):
res = super().delete_entry(collection_name,criteria,True) res = super().delete_entry(collection_name,criteria,True)
print ("###REPOSITORY: res= "+ str(res)) print ("###REPOSITORY: res= "+ str(res))
#endregion Layers #endregion Layers
#region Layer Nodes #region Layer Nodes
...@@ -97,6 +109,11 @@ class Repository(MongoRepositoryBase): ...@@ -97,6 +109,11 @@ class Repository(MongoRepositoryBase):
cluster_dicts = [c.to_serializable_dict(for_db=True) for c in clusters] cluster_dicts = [c.to_serializable_dict(for_db=True) for c in clusters]
super().insert_many(self._clusters_collection, cluster_dicts) super().insert_many(self._clusters_collection, cluster_dicts)
def get_clusters_for_layer_of_use_case(self, use_case: str,layer_name: str):
entries = super().get_entries(self._clusters_collection, selection={'use_case': use_case, 'layer_name': layer_name}, projection={'_id': 0})
return [Cluster(cluster_dict=e, from_db=True) for e in entries]
def get_clusters_for_layer(self, use_case: str, use_case_table: str, layer_name: str) -> List[Cluster]: def get_clusters_for_layer(self, use_case: str, use_case_table: str, layer_name: str) -> List[Cluster]:
entries = super().get_entries(self._clusters_collection, selection={'use_case': use_case, 'use_case_table': use_case_table, 'layer_name': layer_name}, projection={'_id': 0}) entries = super().get_entries(self._clusters_collection, selection={'use_case': use_case, 'use_case_table': use_case_table, 'layer_name': layer_name}, projection={'_id': 0})
return [Cluster(cluster_dict=e, from_db=True) for e in entries] return [Cluster(cluster_dict=e, from_db=True) for e in entries]
...@@ -184,7 +201,8 @@ class Repository(MongoRepositoryBase): ...@@ -184,7 +201,8 @@ class Repository(MongoRepositoryBase):
output.append(e) output.append(e)
return output return output
""" """
def delete_all_similarity_data(self):
super().drop_collection(self._similarity_collection)
#endregion #endregion
......
src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/dataInput.py
View file @ f12f43ff
...@@ -67,7 +67,7 @@ def loadJson(url:str) : ...@@ -67,7 +67,7 @@ def loadJson(url:str) :
return jsonData return jsonData
def getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes): def getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes,use_case:str):
''' Calculates the nr of connections/weights between the clusters contained in the "inputLayerDict". Connections are made between clusters from DIFFERENT layers. ''' Calculates the nr of connections/weights between the clusters contained in the "inputLayerDict". Connections are made between clusters from DIFFERENT layers.
:param List[string] layerNameList: Name of the layers to pull from the DB :param List[string] layerNameList: Name of the layers to pull from the DB
...@@ -93,7 +93,7 @@ def getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes): ...@@ -93,7 +93,7 @@ def getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes):
#imports and translates the data from JSON into usefull format #imports and translates the data from JSON into usefull format
#returns layerdiction -> Layer -> clusterDict -> Cluster -> nodesDict -> Nodes #returns layerdiction -> Layer -> clusterDict -> Cluster -> nodesDict -> Nodes
for name in layerNameList: for name in layerNameList:
newData = get_mongoDB_cluster_by_layerName(name)#repo.get_clusters_for_layer(name) newData = repo.get_clusters_for_layer_of_use_case(use_case,name)#repo.get_clusters_for_layer(name)
if newData is not None and len(newData) != 0: if newData is not None and len(newData) != 0:
layerDict = populateWithNewNodesSingleLayer(newData[0:limitNrCluster],layerDict,limitNrNodes) layerDict = populateWithNewNodesSingleLayer(newData[0:limitNrCluster],layerDict,limitNrNodes)
...@@ -117,28 +117,29 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[ ...@@ -117,28 +117,29 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[
print("Entered populateWithNewNodes") print("Entered populateWithNewNodes")
if(layerDict == None): if(layerDict == None):
layerDict = dict() layerDict = dict()
print(" Layer: "+inputData[0].get("layer_name")) print(" Layer: "+inputData[0].layer_name)
curLayerName = None curLayerName = None
#newClusterDict #newClusterDict
#clusterDict = layerDict.get(curCluster.get("layer_name"),dict())
for curCluster in inputData: for curCluster in inputData:
if(curCluster.get("layer_name")!= curLayerName): if(curCluster.layer_name!= curLayerName):
clusterDict = layerDict.get(curCluster.get("layer_name"),dict()) clusterDict = layerDict.get(curCluster.layer_name,dict())
curLayerName = curCluster.get("layer_name") curLayerName = curCluster.layer_name
oldCluster = clusterDict.get(curCluster.get("cluster_label"),None) oldCluster = clusterDict.get(curCluster.cluster_label,None)
if oldCluster is None: #means this is a new cluster if oldCluster is None: #means this is a new cluster
cluster_containedNodesDict = dict() cluster_containedNodesDict = dict()
else: #means this is an already existing cluster else: #means this is an already existing cluster
cluster_containedNodesDict = oldCluster.cluster_containedNodesDict cluster_containedNodesDict = oldCluster.cluster_containedNodesDict
for curNode in curCluster.get("nodes"): for curNode in curCluster.nodes:
#totalNodesCount+=1 #totalNodesCount+=1
newNode = NodeC( newNode = NodeC(
curCluster.get("cluster_label"), curCluster.cluster_label,
curLayerName, curLayerName,
curNode.get("UniqueID")) curNode.get("UniqueID"),
curCluster.use_case
)
if(newNode != None): if(newNode != None):
if(newNode.uniqueID!= None and newNode.node_cluster!= None and newNode.node_layer!= None): if(newNode.uniqueID!= None and newNode.node_cluster!= None and newNode.node_layer!= None):
if( limitNrNodes>0): if( limitNrNodes>0):
...@@ -151,10 +152,11 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[ ...@@ -151,10 +152,11 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[
#finished node #finished node
if oldCluster is None: if oldCluster is None:
# def __init__(self,cluster_label,cluster_layer,cluster_containedNodesDict,cluster_connNodesDict, cluster_connectionsNr,cluster_connClustDict): # def __init__(self,cluster_label,cluster_layer,use_case,cluster_containedNodesDict,cluster_connNodesDict, cluster_connectionsNr,cluster_connClustDict):
newClusterC = ClusterC( newClusterC = ClusterC(
curCluster.get("cluster_label"), curCluster.cluster_label,
curLayerName, curLayerName,
curCluster.use_case,
None, None,
cluster_containedNodesDict, cluster_containedNodesDict,
dict(), dict(),
...@@ -163,7 +165,7 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[ ...@@ -163,7 +165,7 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[
clusterDict[newClusterC.cluster_label] = newClusterC clusterDict[newClusterC.cluster_label] = newClusterC
else: else:
oldCluster.cluster_containedNodesDict = cluster_containedNodesDict oldCluster.cluster_containedNodesDict = cluster_containedNodesDict
clusterDict[curCluster.get("cluster_label")] = oldCluster clusterDict[curCluster.cluster_label] = oldCluster
#finished cluster #finished cluster
newLayer = LayerC(curLayerName,clusterDict) newLayer = LayerC(curLayerName,clusterDict)
layerDict[curLayerName]= newLayer layerDict[curLayerName]= newLayer
......
src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/dataOutput.py
View file @ f12f43ff
...@@ -28,7 +28,7 @@ def outputFileLayerFunction(layerDict,limitNrNodes,limitNrCluster,runId): ...@@ -28,7 +28,7 @@ def outputFileLayerFunction(layerDict,limitNrNodes,limitNrCluster,runId):
def outputFileSimilFunction(similarityDict,limitNrNodes,limitNrCluster,runId): def outputFileSimilFunction(similarityDict,limitNrNodes,limitNrCluster,runId,use_case):
''' Writes the similarityDict data to a JSON file. ''' Writes the similarityDict data to a JSON file.
...@@ -40,7 +40,7 @@ def outputFileSimilFunction(similarityDict,limitNrNodes,limitNrCluster,runId): ...@@ -40,7 +40,7 @@ def outputFileSimilFunction(similarityDict,limitNrNodes,limitNrCluster,runId):
''' '''
similJSON = convertSimilarityDictToJSON(similarityDict,runId) similJSON = convertSimilarityDictToJSON(similarityDict,runId,use_case)
outputJSON = json.dumps(similJSON, default=lambda o: o.__dict__, indent=4) outputJSON = json.dumps(similJSON, default=lambda o: o.__dict__, indent=4)
try: try:
...@@ -91,7 +91,7 @@ def outputMongoConnClustDict(inputDict,runId): ...@@ -91,7 +91,7 @@ def outputMongoConnClustDict(inputDict,runId):
add_conn_clusters(inputDict,runId) add_conn_clusters(inputDict,runId)
def outputMongoSimilarity(inputDict,runId): def outputMongoSimilarity(inputDict,runId,use_case):
''' Stores cluster_similarity in the database. ''' Stores cluster_similarity in the database.
:param Dict() inputDict: Contains the data to insert :param Dict() inputDict: Contains the data to insert
...@@ -99,7 +99,7 @@ def outputMongoSimilarity(inputDict,runId): ...@@ -99,7 +99,7 @@ def outputMongoSimilarity(inputDict,runId):
:param string runId: Id of the Run :param string runId: Id of the Run
''' '''
add_similarity(inputDict,runId) add_similarity(inputDict,runId,use_case)
def add_connected_run(): def add_connected_run():
...@@ -129,7 +129,7 @@ def add_conn_clusters(inputDict,runId): ...@@ -129,7 +129,7 @@ def add_conn_clusters(inputDict,runId):
for element in outputJSON: for element in outputJSON:
repo.add_connected_cluster(element) repo.add_connected_cluster(element)
def add_similarity(inputDict,runId): def add_similarity(inputDict,runId,use_case):
''' Stores cluster_similarity in the database. ''' Stores cluster_similarity in the database.
:param Dict() inputDict: Contains the data to insert :param Dict() inputDict: Contains the data to insert
...@@ -138,6 +138,6 @@ def add_similarity(inputDict,runId): ...@@ -138,6 +138,6 @@ def add_similarity(inputDict,runId):
''' '''
outputJSON = convertSimilarityDictToJSON(inputDict,runId) outputJSON = convertSimilarityDictToJSON(inputDict,runId,use_case)
for element in outputJSON: for element in outputJSON:
repo.add_single_similarity(element) repo.add_single_similarity(element)
\ No newline at end of file
src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/miscFunctions.py
View file @ f12f43ff
...@@ -109,7 +109,7 @@ def getFrozensetFromConnNodesDict(inputDict): ...@@ -109,7 +109,7 @@ def getFrozensetFromConnNodesDict(inputDict):
return output return output
def convertSimilarityDictToJSON(inputDict,runId): def convertSimilarityDictToJSON(inputDict,runId,use_case):
''' Converts a Similarity Dictionary to JSON format. For outputting to DB ''' Converts a Similarity Dictionary to JSON format. For outputting to DB
:param Dict{} similarityDict: Object which contains Data about the Computed similarities between Clusters :param Dict{} similarityDict: Object which contains Data about the Computed similarities between Clusters
...@@ -125,6 +125,8 @@ def convertSimilarityDictToJSON(inputDict,runId): ...@@ -125,6 +125,8 @@ def convertSimilarityDictToJSON(inputDict,runId):
auxDict["cluster_layer"] = tupleKey[2] auxDict["cluster_layer"] = tupleKey[2]
auxDict["similarityValues"] = inputDict[tupleKey] auxDict["similarityValues"] = inputDict[tupleKey]
auxDict["runId"] = runId auxDict["runId"] = runId
auxDict["use_case"] = use_case
similList.append(auxDict) similList.append(auxDict)
similToJSON = similList similToJSON = similList
#outputJSON = json.dumps(similToJSON, default=lambda o: o.__dict__, indent=4) #outputJSON = json.dumps(similToJSON, default=lambda o: o.__dict__, indent=4)
......
src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/similarityMain.py
View file @ f12f43ff
...@@ -39,7 +39,7 @@ from processing.similarityFiles.dataOutput import * ...@@ -39,7 +39,7 @@ from processing.similarityFiles.dataOutput import *
outputToFileFLAG = True outputToFileFLAG = True
def main(layerNameList:List[str] = ["Price_Layer","FinishedTime_Layer","Destination_Layer"]): def main(use_case:str,layerNameList:List[str] ):
''' '''
Executes the similarity calculation by calculating weights between clusters in different layers. Executes the similarity calculation by calculating weights between clusters in different layers.
Then calculating the Euclidean distance between nodes in the same layer based on one other layer each. Then calculating the Euclidean distance between nodes in the same layer based on one other layer each.
...@@ -67,7 +67,7 @@ def main(layerNameList:List[str] = ["Price_Layer","FinishedTime_Layer","Destinat ...@@ -67,7 +67,7 @@ def main(layerNameList:List[str] = ["Price_Layer","FinishedTime_Layer","Destinat
limitNrNodes = -1 #per Layer limitNrNodes = -1 #per Layer
layerDict = getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes) layerDict = getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes,use_case)
if layerDict is None or len(layerDict) == 0: if layerDict is None or len(layerDict) == 0:
LOGGER.error(f"No data for any of the following layers existed: {str(layerNameList)}. Similarity calculation was not performed.") LOGGER.error(f"No data for any of the following layers existed: {str(layerNameList)}. Similarity calculation was not performed.")
return return
...@@ -99,12 +99,12 @@ def main(layerNameList:List[str] = ["Price_Layer","FinishedTime_Layer","Destinat ...@@ -99,12 +99,12 @@ def main(layerNameList:List[str] = ["Price_Layer","FinishedTime_Layer","Destinat
if (outputToFileFLAG == True): if (outputToFileFLAG == True):
print("Outputing data") print("Outputing data")
outputFileLayerFunction(layerDict,totalNodes,totalClusters,runId) outputFileLayerFunction(layerDict,totalNodes,totalClusters,runId)
outputFileSimilFunction(similarityDict,totalNodes,totalClusters,runId) outputFileSimilFunction(similarityDict,totalNodes,totalClusters,runId,use_case)
outputFileTimeFunction(timelist,totalNodes,totalClusters,runId) outputFileTimeFunction(timelist,totalNodes,totalClusters,runId)
#Output to DB #Output to DB
outputMongoConnClustDict(layerDict,runId) outputMongoConnClustDict(layerDict,runId)
outputMongoSimilarity(similarityDict,runId) outputMongoSimilarity(similarityDict,runId,use_case)
#Currently not used in the calculation of connections/similarity, developed for possible future uses #Currently not used in the calculation of connections/similarity, developed for possible future uses
......
src/data-hub/role-stage-discovery-microservice/app/resultLayerDictN300C32.json 0 → 100644
View file @ f12f43ff
This source diff could not be displayed because it is too large. You can view the blob instead.
src/data-hub/role-stage-discovery-microservice/app/resultSimilarityDictN300C32.json 0 → 100644
View file @ f12f43ff
[
{
"cluster1_label": 0,
"cluster2_label": 2,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 7.0,
"Heating_Consumption_Layer": 7.0,
"Price_Layer": 3.3166247903554,
"Demand_Layer": 3.3166247903554,
"Position_Layer": 10.44030650891055
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": -1,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 5.0,
"Heating_Consumption_Layer": 5.0,
"Price_Layer": 2.6457513110645907,
"Demand_Layer": 2.6457513110645907,
"Position_Layer": 10.63014581273465
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 1,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 4.0,
"Heating_Consumption_Layer": 4.0,
"Price_Layer": 3.1622776601683795,
"Demand_Layer": 3.1622776601683795,
"Position_Layer": 14.628738838327793
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 3,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 2.0,
"Heating_Consumption_Layer": 2.0,
"Price_Layer": 1.4142135623730951,
"Demand_Layer": 1.4142135623730951,
"Position_Layer": 14.2828568570857
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": -1,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 2.0,
"Heating_Consumption_Layer": 2.0,
"Price_Layer": 3.1622776601683795,
"Demand_Layer": 3.1622776601683795,
"Position_Layer": 4.69041575982343
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 1,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 11.0,
"Heating_Consumption_Layer": 11.0,
"Price_Layer": 4.358898943540674,
"Demand_Layer": 4.358898943540674,
"Position_Layer": 11.357816691600547
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 3,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 5.0,
"Heating_Consumption_Layer": 5.0,
"Price_Layer": 2.23606797749979,
"Demand_Layer": 2.23606797749979,
"Position_Layer": 11.180339887498949
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": -1,
"cluster2_label": 1,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 9.0,
"Heating_Consumption_Layer": 9.0,
"Price_Layer": 4.795831523312719,
"Demand_Layer": 4.795831523312719,
"Position_Layer": 7.280109889280518
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": -1,
"cluster2_label": 3,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 3.0,
"Heating_Consumption_Layer": 3.0,
"Price_Layer": 2.23606797749979,
"Demand_Layer": 2.23606797749979,
"Position_Layer": 10.816653826391969
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 3,
"cluster_layer": "Energy_Consumption_Layer",
"similarityValues": {
"Solar_Production_Layer": 6.0,
"Heating_Consumption_Layer": 6.0,
"Price_Layer": 2.8284271247461903,
"Demand_Layer": 2.8284271247461903,
"Position_Layer": 14.628738838327793
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 1,
"cluster_layer": "Price_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Demand_Layer": 7.0710678118654755,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 3,
"cluster_layer": "Price_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 0.0,
"Heating_Consumption_Layer": 0.0,
"Demand_Layer": 7.0710678118654755,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 2,
"cluster_layer": "Price_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.0,
"Heating_Consumption_Layer": 0.0,
"Demand_Layer": 7.0710678118654755,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 5,
"cluster_layer": "Price_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Demand_Layer": 7.0710678118654755,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 7,
"cluster_layer": "Price_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.0,
"Heating_Consumption_Layer": 0.0,
"Demand_Layer": 7.0710678118654755,
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"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 5,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 6,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.449489742783178,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 7,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 8,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 0.0,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 9,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 3,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.0,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 4,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 5,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.0,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 6,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.449489742783178,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 7,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.0,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 8,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 9,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 3,
"cluster2_label": 4,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 3,
"cluster2_label": 5,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 0.0,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 3,
"cluster2_label": 6,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 3,
"cluster2_label": 7,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 0.0,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 3,
"cluster2_label": 8,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 3,
"cluster2_label": 9,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.449489742783178,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 4,
"cluster2_label": 5,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 4,
"cluster2_label": 6,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 4,
"cluster2_label": 7,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 4,
"cluster2_label": 8,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 4,
"cluster2_label": 9,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 5,
"cluster2_label": 6,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 5,
"cluster2_label": 7,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 0.0,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 5,
"cluster2_label": 8,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 5,
"cluster2_label": 9,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.449489742783178,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 6,
"cluster2_label": 7,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 6,
"cluster2_label": 8,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.449489742783178,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 6,
"cluster2_label": 9,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.8284271247461903,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 7,
"cluster2_label": 8,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 7,
"cluster2_label": 9,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 2.449489742783178,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 8,
"cluster2_label": 9,
"cluster_layer": "Demand_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 1.4142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 46340.950540531645,
"Position_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 1,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 9.695359714832659,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 2,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 11.575836902790225,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 4,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 8.12403840463596,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 0,
"cluster2_label": 3,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 8.831760866327848,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 2,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 14.142135623730951,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 4,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 12.569805089976535,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 1,
"cluster2_label": 3,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 12.96148139681572,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 4,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 46340.95170580768,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 2,
"cluster2_label": 3,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 46340.951813703614,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
},
{
"cluster1_label": 4,
"cluster2_label": 3,
"cluster_layer": "Position_Layer",
"similarityValues": {
"Solar_Production_Layer": 0.0,
"Energy_Consumption_Layer": 46340.95002263117,
"Heating_Consumption_Layer": 0.0,
"Price_Layer": 0.0,
"Demand_Layer": 0.0
},
"runId": "5f886e7e35b70a1704c728e6",
"use_case": "paper"
}
]
\ No newline at end of file
src/data-hub/role-stage-discovery-microservice/app/resultTimeExecN300C32.txt 0 → 100644
View file @ f12f43ff
StartTime: 2020-10-15 17:44:59.074263
FinishTime: 2020-10-15 17:45:02.969112
PopulateWithNewNodes: 3.82889
CalculateWeights: 0.054965
CalculateSimilarity: 0.010994
TotalTime: 3.894849
RunId: 5f886e7e35b70a1704c728e6
\ No newline at end of file
src/data-hub/role-stage-discovery-microservice/app/run_similarity_calc.py
View file @ f12f43ff
...@@ -33,7 +33,7 @@ def run_similarity_calc_for_Paper_case(): ...@@ -33,7 +33,7 @@ def run_similarity_calc_for_Paper_case():
for key in uc_layers: for key in uc_layers:
layers = uc_layers[key] layers = uc_layers[key]
print(f"Running for use case {key} with layers {str(layers)}.") print(f"Running for use case {key} with layers {str(layers)}.")
SimilarityCalc.main(layerNameList=layers) SimilarityCalc.main(uc,layerNameList=layers)
if __name__ == '__main__': if __name__ == '__main__':
run_similarity_calc_per_use_case() run_similarity_calc_per_use_case()
\ No newline at end of file
src/data-hub/role-stage-discovery-microservice/app/visualisationPaper.py
View file @ f12f43ff
...@@ -21,35 +21,25 @@ import processing.fetching.fetching as fetchy ...@@ -21,35 +21,25 @@ import processing.fetching.fetching as fetchy
import processing.similarityFiles.dataInput as inputData import processing.similarityFiles.dataInput as inputData
################################## from db.repository import *
#TODO: get certificates/authorization repo = Repository()
# SSL configuration
# certificate_path = get_resources_path()
# context = (os.path.normpath(f'{certificate_path}/articonf1.crt'), os.path.normpath(f'{certificate_path}/articonf1.key')) # certificate and key files
# if is_running_locally():
# print("Running locally...")
# app.add_api(swagger_util.get_bundled_specs(Path("configs/swagger_local.yml")),
# resolver = connexion.RestyResolver("cms_rest_api"))
# else:
# app.add_api(swagger_util.get_bundled_specs(Path("configs/swagger.yml")),
# resolver = connexion.RestyResolver("cms_rest_api"))
##################################
def deleteWHOOLEData():
print("test")
repo.delete_all_similarity_data()
def mainViz(): def mainViz():
#procesing data #procesing data
#fetchy.fetch_nodes_from_semantic_linking()
#clustering.run_clustering_for_Paper_case()
similCalc.run_similarity_calc_for_Paper_case()
inputData.getClusterDataFromMongo("Paper",None,None) fetchy.fetch_nodes_from_semantic_linking()
clustering.run_clustering_for_Paper_case()
similCalc.run_similarity_calc_for_Paper_case() #TODO FIX
#inputData.getClusterDataFromMongo("Paper",None,None)
#inputData.getSimilarityDataFromMongo(cluster_layer: str= None, batchSize: int=1000, run_id: str=None) #inputData.getSimilarityDataFromMongo(cluster_layer: str= None, batchSize: int=1000, run_id: str=None)
#similarityArrFromMongo = getSimilarityDataFromMongo("Paper") #similarityArrFromMongo = getSimilarityDataFromMongo("Paper")
deleteWHOOLEData()
mainViz() #mainViz()
\ No newline at end of file \ No newline at end of file
src/data-hub/semantic-linking-microservice/app/db/repository.py
View file @ f12f43ff
...@@ -105,10 +105,18 @@ class Repository(MongoRepositoryBase): ...@@ -105,10 +105,18 @@ class Repository(MongoRepositoryBase):
collection = self._database[self._layer_nodes_collection] collection = self._database[self._layer_nodes_collection]
collection.delete_many({"use_case": use_case}) collection.delete_many({"use_case": use_case})
def delete_nodes_for_table(self, use_case, table_name): def delete_nodes_for_table(self, use_case, table_name):
'''Deletes nodes from the given table of the use-case''' '''Deletes nodes from the given table of the use-case'''
collection = self._database[self._layer_nodes_collection] collection = self._database[self._layer_nodes_collection]
collection.delete_many({"use_case": use_case, "table": table_name}) collection.delete_many({"use_case": use_case, "table": table_name})
# def delete_use_case_layers(self, use_case: str):
# collection_name = self._layer_collection
# criteria = {"use_case" : use_case}
# res = super().delete_entry(collection_name,criteria,True)
# print ("###REPOSITORY: res= "+ str(res))
# endregion # endregion
src/data-hub/semantic-linking-microservice/app/dummy_upload.py
View file @ f12f43ff
import csv import csv
import hashlib import hashlib
import datetime
import sys import sys
import os import os
...@@ -13,6 +14,9 @@ from messaging.MessageHandler import MessageHandler ...@@ -13,6 +14,9 @@ from messaging.MessageHandler import MessageHandler
CSV_FILE = r'Energy_Dataset.csv' CSV_FILE = r'Energy_Dataset.csv'
handler = MessageHandler() handler = MessageHandler()
from db.repository import *
repo = Repository()
def upload_transaction(transaction): def upload_transaction(transaction):
'''{"type": "new-trace", '''{"type": "new-trace",
...@@ -31,14 +35,19 @@ def upload_transaction(transaction): ...@@ -31,14 +35,19 @@ def upload_transaction(transaction):
if __name__ == '__main__': if __name__ == '__main__':
repo.delete_nodes_for_use_case("paper")
print ("###DELETED NODES FROM SEMANTIC LINKING - PAPER USE CASE")
with open(CSV_FILE, 'r') as file: with open(CSV_FILE, 'r') as file:
reader = csv.reader(file) reader = csv.reader(file)
titles = next(reader) titles = next(reader)
################# #################
maxCustomers = 5 maxCustomers = 10
maxRows = 10 maxRowsPerMonth = 120 # 24Hr * 5Days
month_memory=[0,0,0,0,0,0,0,0,0,0,0,0]
################## ##################
old_c = None old_c = None
...@@ -55,13 +64,25 @@ if __name__ == '__main__': ...@@ -55,13 +64,25 @@ if __name__ == '__main__':
if transaction['Customer'] != old_c: if transaction['Customer'] != old_c:
customerCount +=1 customerCount +=1
rowCount = 0 rowCount = 0
for i in range(12):
month_memory[i]=maxRowsPerMonth
old_c = transaction['Customer'] old_c = transaction['Customer']
if (customerCount>maxCustomers): if (customerCount>maxCustomers):
print("\nMAX customers reached") print("\nMAX customers reached")
break break
upload_condition = False
datetime_obj= datetime.datetime.strptime(transaction['Timestamp'],'%Y-%m-%d %H:%M:%S')
if(month_memory[datetime_obj.month-1]>0): #MAX XX times per month considered per customer
if(datetime_obj.minute==0): #upload only hourly
month_memory[datetime_obj.month-1]-=1
upload_condition = True
#datetime_obj.month
if(rowCount<maxRows): if(upload_condition):
upload_transaction(transaction) upload_transaction(transaction)
print(f"uploading for {old_c}") print(f"uploading for {old_c}")
rowCount+=1 rowCount+=1
......
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