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Commit 118ddb39 authored by Bogdan's avatar Bogdan
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User_Demand Visualizations Bug Fixes

parent 8155f69d
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Showing with 104 additions and 69 deletions
src/data-hub/role-stage-discovery-microservice/app/db/repository.py
View file @ 118ddb39
......@@ -73,7 +73,7 @@ class Repository(MongoRepositoryBase):
collection_name = self._layer_collection
criteria = {"use_case" : use_case}
res = super().delete_entry(collection_name,criteria,True)
print ("###REPOSITORY: res= "+ str(res))
print (" Deleting UseCase: "+ str(use_case))
......
src/data-hub/role-stage-discovery-microservice/app/visualization/vis_cluster_results.py
View file @ 118ddb39
......@@ -46,9 +46,9 @@ def show_clustering_result(ax, min_pts, clusters: dict):
colors = [distinct_colors[node['cluster_label']] for node in nodes]
ax.set_title(f'Clustering Result with MinPts={min_pts}')
ax.set_xlabel('Total_Demand_MWh')
ax.set_xlabel('User_Demand_kWh')
ax.set_ylabel('Customer')
ax.scatter( [n['Total_Demand_MWh'] for n in nodes],
ax.scatter( [n['User_Demand_kWh'] for n in nodes],
[n['Customer'] for n in nodes],
c=colors)
......@@ -72,14 +72,14 @@ def show_clusteringSingleFeature_result(ax, min_pts, clusters: dict):
colors = [distinct_colors[node['cluster_label']] for node in nodes]
ax.set_title(f'Clustering Result with MinPts={min_pts}')
ax.set_xlabel('Total_Demand_MWh')
ax.set_xlabel('User_Demand_kWh')
ax.grid(True, axis='x')
#ax.set_ylabel('Customer')
ax.scatter( [n['Total_Demand_MWh'] for n in nodes],
ax.scatter( [n['User_Demand_kWh'] for n in nodes],
[0 for n in nodes],
c=colors)
def show_clusteringSingleFeatureDensity_result(ax, min_pts, clusters: dict):
def show_clusteringSingleFeatureDensityBoxPlot_result(ax, min_pts, clusters: dict):
labels = clusters.keys()
# flatten values in dict
......@@ -95,7 +95,7 @@ def show_clusteringSingleFeatureDensity_result(ax, min_pts, clusters: dict):
if not checkKey(mydict,n['cluster_label']):
mydict[n['cluster_label']] = []
mydict[n['cluster_label']].append(n['Total_Demand_MWh'])
mydict[n['cluster_label']].append(n['User_Demand_kWh'])
......@@ -130,10 +130,10 @@ def show_clusteringSingleFeatureDensity_result(ax, min_pts, clusters: dict):
plt.setp(bp['medians'][0], color=distinct_colors[cluster_label])
ax.set_title(f'Clustering Result with MinPts={min_pts}')
ax.set_xlabel('Total_Demand_MWh')
ax.set_xlabel('User_Demand_kWh')
#ax.set_ylabel('Cluster Label')
ax.grid(True, axis='x')
# ax.scatter( [n['Total_Demand_MWh'] for n in nodes],
# ax.scatter( [n['User_Demand_kWh'] for n in nodes],
# [0 for n in nodes],
# c=colors)
......@@ -143,14 +143,14 @@ def run_clustering(min_points, dataset):
clusterer = Clusterer(min_points=min_points)
return clusterer.cluster_dataset(
dataset=dataset,
features=['Total_Demand_MWh','Customer']
features=['User_Demand_kWh','Customer']
)
def run_clustering_SingleFeature(min_points, dataset):
clusterer = Clusterer(min_points=min_points)
return clusterer.cluster_dataset(
dataset=dataset,
features=['Total_Demand_MWh']
features=['User_Demand_kWh']
)
# res: Dict[Any, ClusterResult] = clusterer.cluster_dataset(
......@@ -173,7 +173,7 @@ def createDataset(inputDict):
for entry in inputSimListOfDict:
newDict = {}
try:
newDict["Total_Demand_MWh"] = float(entry["Total_Demand_MWh"])
newDict["User_Demand_kWh"] = float(entry["User_Demand_kWh"])
newDict["Customer"] = float(entry["Customer"])
dataset.append(newDict)
except:
......@@ -204,10 +204,10 @@ if __name__ == '__main__':
#dataset = [{'1':n[0], '2':n[1]} for n in nodes]
JWT_TOKEN = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VybmFtZSI6InJlZ3VsYXJAaXRlYy5hYXUuYXQiLCJjcmVhdGVkX2F0IjoiMjAyMC0xMC0yMCAxNDoyNDoxMi45MzI3OTAiLCJ2YWxpZF91bnRpbCI6IjIwMjAtMTAtMjEgMTQ6MjQ6MTIuOTMyNzkwIn0.qzaDauyEA4pAnw8K8ik6jTtbEOY24q159GDYbvByaJ4"
#r = requests.get('https://articonf1.itec.aau.at:30103/api/paper/paper/layers/Demand_Layer/nodes', timeout=15)
r = requests.get(
url = 'https://articonf1.itec.aau.at:30103/api/paper/paper/layers/Demand_Layer/nodes',
url = 'https://articonf1.itec.aau.at:30103/api/paper/paper/layers/User_Demand_Layer/nodes',
timeout=15,
headers = {"Authorization": f"Bearer {JWT_TOKEN}"},
verify = False # ignore ssl error)
......@@ -243,66 +243,66 @@ if __name__ == '__main__':
# dataset = createDataset(inputSimListOfDict)
# print("Started TEST Clustering") #500 = 4 clusters
# clusters = run_clustering_SingleFeature(250, dataset)
# show_clusteringSingleFeatureDensity_result(ax1,250,clusters)
# show_clusteringSingleFeatureDensityBoxPlot_result(ax1,250,clusters)
# dataset = createDataset(inputSimListOfDict)
# print("Started TEST Clustering") #500 = 4 clusters
# clusters = run_clustering_SingleFeature(500, dataset)
# show_clusteringSingleFeatureDensity_result(ax2,500,clusters)
# show_clusteringSingleFeatureDensityBoxPlot_result(ax2,500,clusters)
# dataset = createDataset(inputSimListOfDict)
# print("Started TEST Clustering") #500 = 4 clusters
# clusters = run_clustering_SingleFeature(750, dataset)
# show_clusteringSingleFeatureDensity_result(ax3,750,clusters)
# show_clusteringSingleFeatureDensityBoxPlot_result(ax3,750,clusters)
# dataset = createDataset(inputSimListOfDict)
# print("Started TEST Clustering") #500 = 4 clusters
# clusters = run_clustering_SingleFeature(1000, dataset)
# show_clusteringSingleFeatureDensity_result(ax4,1000,clusters)
# show_clusteringSingleFeatureDensityBoxPlot_result(ax4,1000,clusters)
print("Started 1st Clustering")
dataset = createDataset(inputSimListOfDict)
clusters = run_clustering_SingleFeature(50, dataset)
show_clusteringSingleFeature_result(ax1, 50, clusters)
# print("Started 1st Clustering")
# dataset = createDataset(inputSimListOfDict)
# clusters = run_clustering_SingleFeature(15, dataset)
# show_clusteringSingleFeature_result(ax1, 15, clusters)
print("Started 2nd Clustering")
dataset = createDataset(inputSimListOfDict)
clusters = run_clustering_SingleFeature(100, dataset)
show_clusteringSingleFeature_result(ax2, 100, clusters)
# print("Started 2nd Clustering")
# dataset = createDataset(inputSimListOfDict)
# clusters = run_clustering_SingleFeature(25, dataset)
# show_clusteringSingleFeature_result(ax2, 25, clusters)
print("Started 3rd Clustering")
dataset = createDataset(inputSimListOfDict)
clusters = run_clustering_SingleFeature(150, dataset)
show_clusteringSingleFeature_result(ax3, 150, clusters)
# print("Started 3rd Clustering")
# dataset = createDataset(inputSimListOfDict)
# clusters = run_clustering_SingleFeature(50, dataset)
# show_clusteringSingleFeature_result(ax3, 50, clusters)
print("Started 4th Clustering")
dataset = createDataset(inputSimListOfDict)
clusters = run_clustering_SingleFeature(250, dataset)
show_clusteringSingleFeature_result(ax4, 250, clusters)
# print("Started 4th Clustering")
# dataset = createDataset(inputSimListOfDict)
# clusters = run_clustering_SingleFeature(100, dataset)
# show_clusteringSingleFeature_result(ax4, 100, clusters)
# dataset = createDataset(inputSimListOfDict)
# print("Started 1st Clustering")
# clusters = run_clustering(10, dataset)
# show_clustering_result(ax1, 10, clusters)
dataset = createDataset(inputSimListOfDict)
print("Started 1st Clustering")
clusters = run_clustering(10, dataset)
show_clustering_result(ax1, 10, clusters)
# dataset = createDataset(inputSimListOfDict)
# print("Started 2nd Clustering")
# clusters = run_clustering(15, dataset)
# show_clustering_result(ax2, 15, clusters)
dataset = createDataset(inputSimListOfDict)
print("Started 2nd Clustering")
clusters = run_clustering(15, dataset)
show_clustering_result(ax2, 15, clusters)
# dataset = createDataset(inputSimListOfDict)
# print("Started 3rd Clustering")
# clusters = run_clustering(25, dataset)
# show_clustering_result(ax3, 25, clusters)
dataset = createDataset(inputSimListOfDict)
print("Started 3rd Clustering")
clusters = run_clustering(25, dataset)
show_clustering_result(ax3, 25, clusters)
# dataset = createDataset(inputSimListOfDict)
# print("Started 4th Clustering")
# clusters = run_clustering(50, dataset)
# show_clustering_result(ax4, 50, clusters)
dataset = createDataset(inputSimListOfDict)
print("Started 4th Clustering")
clusters = run_clustering(50, dataset)
show_clustering_result(ax4, 50, clusters)
#agePhysics = [ 25, 31, 31, 31, 12,28,29,31,33,34,35,36,34,39,40,41,48 ]
# basic plot
......
src/data-hub/role-stage-discovery-microservice/app/visualization/visualizeRawData.py
View file @ 118ddb39
......@@ -15,7 +15,8 @@ def mainViz():
distributionEnergy = dict()
distributionHeating = dict()
distributionPrice = dict()
distributionDemand = dict()
distributionTotalDemand = dict()
distributionUserDemand = dict()
for entry in inputDict:
try:
......@@ -57,10 +58,21 @@ def mainViz():
try:
tdVal=round(float(entry['Total_Demand_MWh']),2)
if(checkKey(distributionDemand,tdVal)):
distributionDemand[tdVal] += 1
if(checkKey(distributionTotalDemand,tdVal)):
distributionTotalDemand[tdVal] += 1
else:
distributionDemand[tdVal] = 1
distributionTotalDemand[tdVal] = 1
except:
pass
try:
eVal=round(float(entry['Energy_Consumption_kWh']),2)
hVal=round(float(entry['Heating_Consumption_kWh']),2)
udVal = round((eVal+hVal),2)
if(checkKey(distributionUserDemand,udVal)):
distributionUserDemand[udVal] += 1
else:
distributionUserDemand[udVal] = 1
except:
pass
......@@ -95,13 +107,13 @@ def mainViz():
#plt.xticks(np.linspace(0,4.5,num=20))
## Heating
list1 = sorted(distributionHeating.items())
x2,y2 = zip(*list1)
plt.bar(x2,y2,color='red',label="Heating", width=0.01)
plt.legend()
plt.title('Heating Consumption')
plt.xlabel('kWh')
plt.show()
# list1 = sorted(distributionHeating.items())
# x2,y2 = zip(*list1)
# plt.bar(x2,y2,color='red',label="Heating", width=0.01)
# plt.legend()
# plt.title('Heating Consumption')
# plt.xlabel('kWh')
# plt.show()
## Price
# #plt.xticks(np.linspace(0,100,num=10))
......@@ -114,13 +126,24 @@ def mainViz():
# plt.show()
### Demand
plt.xticks(np.linspace(0,100,num=10))
list1 = sorted(distributionTotalDemand.items())
x2,y2 = zip(*list1)
plt.bar(x2,y2,color='black',label="Total Demand", width=5)
plt.legend()
plt.title('Total Demand')
plt.xlabel('MWh')
plt.show()
## User Demand
#plt.xticks(np.linspace(0,100,num=10))
# list1 = sorted(distributionDemand.items())
# list1 = sorted(distributionUserDemand.items())
# x2,y2 = zip(*list1)
# plt.bar(x2,y2,color='gray',label="Demand", width=5)
# plt.bar(x2,y2,color='orange',label="User Demand", width=0.01)
# plt.legend()
# plt.title('Total Demand')
# plt.xlabel('MWh')
# plt.title('User Demand')
# plt.xlabel('kWh')
# plt.show()
......
src/data-hub/semantic-linking-microservice/app/dummy_upload.py
View file @ 118ddb39
......@@ -38,7 +38,7 @@ if __name__ == '__main__':
repo.delete_nodes_for_use_case("paper")
print ("###DELETED NODES FROM SEMANTIC LINKING - PAPER USE CASE")
transactionList = []
with open(CSV_FILE, 'r') as file:
reader = csv.reader(file)
......@@ -61,6 +61,11 @@ if __name__ == '__main__':
for idx in range(len(row)):
transaction[titles[idx]] = row[idx]
# also include the user demand, as Total_Demand_WMh is not per user
energy_cons = float(transaction['Energy_Consumption_kWh']) if transaction['Energy_Consumption_kWh'] is not None and transaction['Energy_Consumption_kWh'] != "" else 0
heating_cons = float(transaction['Heating_Consumption_kWh']) if transaction['Heating_Consumption_kWh'] is not None and transaction['Heating_Consumption_kWh'] != "" else 0
transaction['User_Demand_kWh'] = heating_cons + energy_cons
if transaction['Customer'] != old_c:
customerCount +=1
rowCount = 0
......@@ -84,9 +89,16 @@ if __name__ == '__main__':
if(upload_condition):
upload_transaction(transaction)
print(f"uploading for {old_c}")
transactionList.append(transaction)
#print(f"uploading for {old_c}")
rowCount+=1
outputJSON = json.dumps(transactionList, default=lambda o: o.__dict__, indent=4)
try:
with open('EnergyDataSubset.json', 'w') as outfile:
outfile.write(outputJSON)
except ValueError:
print("Error occured when writing the resultSimilarityDict file")
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