def computeGem2(gtf,pef ,debug=1):
groundtruth_dataset=datatool.seddata.SED(gtf,'gt',None)
groundtruth_dataset.load()
######
pred=datatool.seddata.SED(pef,'',groundtruth_dataset)
pred.load()
########
evalres={0:{'test':general.utils.Data('SED')}}
evalres[0]['test'].real_events=groundtruth_dataset.activity_events
evalres[0]['test'].pred_events=pred.activity_events
#######
res=an.mergeEvals(groundtruth_dataset,evalres,metric.Metrics.GEM())
import pandas as pd
from IPython.display import display, HTML
compact=pd.DataFrame(columns=['avg'])
actres={}
for k in res:
if(k==0):continue
if(k=='avg'):
a='avg'
actres[k]={e:res['avg'][e] for e in res['avg']}
else:
a=groundtruth_dataset.activities[k]
actres[k]={e:res[k]['avg'][e] for e in res[k]['avg']}
print('act=',a,'==============================')
# print(actres[k])
compact.loc[a]= None
if(len(actres[k])==0):
print('No Eval')
else:
df2=pd.DataFrame(actres[k]).round(2)
for c in df2.columns:
if not(c in compact.columns):compact[c]=None
compact.loc[a][c]=df2.loc['f1'][c]
if(debug):display(HTML(df2.to_html()))
# df2=pd.DataFrame(res['avg']).round(2)
# for c in df2.columns:
# if not(c in compact.columns):compact[c]=None
# compact.loc[a][c]=df2.loc['f1'][c]
compact['avg']=compact.mean(axis=1)
if(debug):
display(HTML(df2.to_html()))
display(HTML(compact.to_html()))
vs.plotJoinMetric({'eval':res},[k for k in res],groundtruth_dataset.activities_map)
return compact
def compare(files=widgets.SelectMultiple(options=result_analyse.
resultloader.get_runs_summary(
dataset),
description='Files',
layout=Layout(
width='100%',
height='180px')),
metric=metrics,
titles="title1,title2"):
run_info = {}
dataset = {}
evalres = {}
res = {}
titles = titles.split(',')
if (len(titles) != len(files)):
print('Titles are not correct. use files names instead')
titles = files
print(files)
for i, file in enumerate(files):
print(i, file)
t = titles[i]
run_info[t], dataset[t], evalres[t] = utils.loadState(file)
# print(evalres[t])
# for i in evalres[t]:
# evalres[t][i]['test'].Sdata=None
dataset[t].sensor_events = None
res[t] = an.mergeEvals(dataset[t], evalres[t], metric)
res = {t: res[t] for t in sorted(res.keys())}
import pandas as pd
from IPython.display import display, HTML
actres = {}
for k in dataset[t].activities_map:
if (k == 0): continue
actres[k] = {(m, e): res[m][k]['avg'][e]
for m in res for e in res[m][k]['avg']}
print('act=', k, '==============================')
# print(actres[k])
if (len(actres[k]) == 0):
print('No Eval')
else:
df2 = pd.DataFrame(actres[k]).round(2)
display(HTML(df2.to_html()))
vs.plotJoinMetric(res, [k for k in res[t]],
dataset[t].activities_map)
if(term in x):
result[terms[term]]=float(x[len(term)+3:])
return result
def eval_my_metric(real,pred,debug=0):
result={}
result['Tatbul(a=0)']=call(real,pred,0,beta=1,alpha=0,gamma='one',delta='flat')
result['Existence(a=1)']=call(real,pred,0,beta=1,alpha=1,gamma='one',delta='udf_delta')
result['Cardinality(γ=reci)']=call(real,pred,0,beta=1,alpha=0,gamma='reciprocal',delta='udf_delta')
result['Positional(δ=flat)']=call(real,pred,0,beta=1,alpha=0,gamma='one',delta='flat')
return result
# print(a)
# # os.system(f'/workspace/TSAD-Evaluator/src/evaluate -v -tn {real} {pred} 1 0 one flat flat')
# print(f'/workspace/TSAD-Evaluator/src/evaluate -v -tn {real} {pred} 1 0 one flat flat');
if __name__ == "__main__":
import result_analyse.resultloader
import result_analyse.kfold_analyse as an
# import metric.MyMetric as mymetric
import metric.TatbulMetric as mymetric
import general.utils as utils
run_info,dataset,evalres=utils.loadState('201104_19-48-50-Home1')
# print(evalres[t])
for i in evalres:
evalres[i]['test'].Sdata=None
dataset.sensor_events=None
res=an.mergeEvals(dataset,evalres,mymetric)
print(res)
evalres={}
res={}
titles=titles.split(',')
if(len(titles)!=len(files)):
print('Titles are not correct. use files names instead')
titles=files
print(files)
for i, file in enumerate(files):
print(i,file)
t=titles[i]
run_info[t],dataset[t],evalres[t]=utils.loadState(file)
# print(evalres[t])
# for i in evalres[t]:
# evalres[t][i]['test'].Sdata=None
dataset[t].sensor_events=None
res[t]=an.mergeEvals(dataset[t],evalres[t],metric)
res={t:res[t] for t in sorted(res.keys())}
import pandas as pd
actres={}
for k in dataset[t].activities_map:
if(k==0):continue
actres[k]={m:res[m][k]['avg'] for m in res}
print('act=',k,'==============================')
print(actres[k])
if(len(actres[k])==0):
print('No Eval')
else:
df2=pd.DataFrame([actres[k]])
print(df2)