def load_run_table(file):
runtable=utils.loadState(file,'runtable1',raiseException=False)
if(runtable is None):
res=utils.loadState(file)
if(len(res)!=3):
#raise Error
logger.warn('File %s can not import'%file)
return
[run_info,datasetdscr,evalres]=res
runtable=getRunTable(run_info,datasetdscr,evalres)
utils.saveState(runtable,file,'runtable1')
return runtable
def load_run_info(file):
runinfo=utils.loadState(file,'info',raiseException=False)
if(runinfo is None):
try:
res=utils.loadState(file)
if(len(res)!=3):
#raise Error
logger.warn('File %s can not import'%file)
return
[run_info,datasetdscr,evalres]=res
runinfo=getRunInfo(run_info,datasetdscr,evalres)
utils.saveState([runinfo],file,'info')
return runinfo
except:
logger.warn('File %s can not import'%file)
return
return runinfo[0]
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)
def get(key, valf):
hkey = hashkey(key)
try:
val = utils.loadState(cachefolder, hkey)
logger.debug(f'cached file found {key} {hkey}')
except Exception as e:
if not (os.path.exists(f'save_data/{hkey}')):
logger.debug(f'cached file not found {key} {hkey}')
else:
logger.error(f'error in cached {e}', exc_info=True)
val = valf()
utils.saveState(val, cachefolder, hkey)
with open(f'save_data/{cachefolder}/{hkey}.txt', 'w') as f:
print(key, file=f)
f.close()
return val
def get_runs_summary2(dataset=''):
list=os.listdir('save_data/')
list.sort(key=lambda f:os.path.getmtime('save_data/'+f),reverse=True)
result=[]
for item in list:
if dataset not in item:
continue
try:
res=utils.loadState(item,'info')
# if(len(res)!=3):
# raise Error
# [run_info,datasetdscr,evalres]=res
# disp_name='dataset:%s date:%s %s'%(run_info['dataset'],run_info['run_date'], evalres[0].shortrunname)
f1=res['folds'][0]['quality']['f1']
disp_name=item+":f1="+str(f1)+"=="+res['folds'][0]['shortrunname']+"===="+str(res['folds'])
result.append((disp_name,item))
except:
logger.warn('File %s can not import'%item)
# raise
return result
def loader(file):
if dataset not in file:
return
try:
res=utils.load_run_info(file)
f1=res['folds'][0]['test']['quality']['f1']
disp_name=file+":f1="+str(f1)+"=="+res['folds'][0]['test']['shortrunname']+"===="+str(res['folds'])
return(disp_name,file)
except:
try:
res=utils.loadState(file)
if(len(res)!=3):
raise Error
[run_info,datasetdscr,evalres]=res
disp_name='dataset:%s date:%s %s'%(run_info['dataset'],run_info['run_date'], evalres[0]['test'].shortrunname)
return(disp_name,file)
except Exception as e:
logger.warn('File %s can not import'%file)
import sys
import traceback
print(e, file=sys.stderr)
traceback.print_exc()
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)
events = pd.DataFrame(events)
if(len(events)>0):
events = events.sort_values(['StartTime'])
events = events.reset_index()
events = events.drop(['index'], axis=1)
return events
if __name__ == '__main__':
import result_analyse.visualisation as vs
import metric.CMbasedMetric as CMbasedMetric
# gt = vs.convert2event(np.array([(65,75), (157,187)]))
# a = vs.convert2event(np.array([(66,73), (78,126)]))
import general.utils as utils
import result_analyse.visualisation as vs
r,p=utils.loadState('ali')
a=utils.loadState('200506_17-08-41-Home1')
# r=a[2][0].real_events
# p=a[2][0].pred_events
vs.plotJoinAct(a[1], r, p)
times=[]
act_data=np.zeros((len(p),12))
for i in range(len(p)):
times.append({'begin':p.iloc[i]['StartTime'],'end':p.iloc[i]['EndTime']})
act_data[i,p.iloc[i]['Activity']]=1
com=EmptyCombiner()
p2=com.combine2(times,act_data)
vs.plotJoinAct(a[1], p, p2)
vs.plotJoinAct(a[1], r, p2)
files=['200515_13-42-24-VanKasteren']
titles='a,b,c'
metric=metric.Metrics.Tatbul()
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
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])
import result_analyse.visualisation as vs
import logging
import numpy as np
from intervaltree.intervaltree import IntervalTree
from general.utils import Data
from general import utils
logger = logging.getLogger(__file__)
from collections import defaultdict
from metric.CMbasedMetric import CMbasedMetric
from metric.event_confusion_matrix import event_confusion_matrix
savedata=utils.loadState('sepg1c')
intree=savedata.intree
segments = defaultdict(dict)
for item in intree.items():
segments[item.begin.value<<64|item.end.value]['begin']=item.begin
segments[item.begin.value<<64|item.end.value]['end']=item.end
segments[item.begin.value<<64|item.end.value][item.data.gindx]=item.data
# segments[str(item.begin)+'-'+str(item.end)][item.data.gindx]=item.data
print('finsihed')
acts=savedata.gacts[-1] #savedata.acts
f=np.zeros((len(segments),len(savedata.gacts)*len(acts)))
label=np.zeros(len(segments))
times=[]
iseg=0
for timeseg in segments:
seg=segments[timeseg]
# b=timeseg>>64
tree.merge_equals(data_reducer=data_reducer)
return tree
def cmTreePlot(tree):
ptree = IntervalTree()
rtree = IntervalTree()
for item in tree:
if not (item.data.R is None):
rtree[item.begin:item.end] = item
if not (item.data.P is None):
ptree[item.begin:item.end] = item
from result_analyse.visualisation import plotJoinTree
plotJoinTree(rtree, ptree)
if __name__ == '__main__':
import result_analyse.visualisation as vs
import metric.CMbasedMetric as CMbasedMetric
# gt = vs.convert2event(np.array([(65,75), (157,187)]))
# a = vs.convert2event(np.array([(66,73), (78,126)]))
import general.utils as utils
r, p = utils.loadState('ali')
cm = event_confusion_matrix(r, p, range(11))
print(cm)
print(CMbasedMetric(cm, average='macro'))
print(CMbasedMetric(cm))
def result_selector(file=result_analyse.resultloader.get_runs()):
if (file == None): return
print('Analysing ', file)
run_info, dataset, evalres = utils.loadState(file)
stime = dataset.activity_events.iloc[0].StartTime
#etime=stime+np.timedelta64(1,'D')
etime = dataset.activity_events.iloc[-1].EndTime
for i in range(len(evalres)):
quality = evalres[i]['test'].quality
print('Evalution quality fold=%d is %s' % (i, quality))
print(len(dataset.sensor_events))
# vs.plot_CM(dataset,evalres)
@interact
def viewFold(fold=range(len(evalres))):
@interact_manual
def view(
start_date=widgets.DatePicker(value=pd.to_datetime(stime)),
end_date=widgets.DatePicker(value=pd.to_datetime(etime)),
debug=widgets.Checkbox(value=False)):
duration = (pd.to_datetime(start_date),
pd.to_datetime(end_date))
duration2 = (pd.to_datetime(start_date),
pd.to_datetime(start_date) +
pd.DateOffset(days=7))
real_events = vs.filterTime(evalres[fold]['test'].real_events,
duration)
pred_events = vs.filterTime(evalres[fold]['test'].pred_events,
duration)
#vs.plotJoinAct(dataset,real_events,pred_events)
acts = [p for p in dataset.activities_map]
labels = [dataset.activities_map[p] for p in acts]
print(acts)
print(labels)
vs.plotJoinAct2(real_events,
pred_events,
acts,
labels,
duration=duration2)
#vs.plot_per_act(dataset,{'test':evalres})
from matplotlib import pyplot as plt
plt.rc_context(rc={'figure.max_open_warning': 0})
import result_analyse.SpiderChart
result_analyse.SpiderChart.radar_factory(5, frame='polygon')
acount = len(dataset.activities_map)
a_fig, a_ax = plt.subplots(
acount - 1,
1,
figsize=(10, acount * .25),
)
# a_fig.tight_layout(pad=3.0)
col = 4
row = int(np.ceil((acount - 1.0) / float(col)))
m_fig, m_ax = plt.subplots(row,
col,
figsize=(col * 3, row * 3),
subplot_kw=dict(projection='radar'))
if type(a_ax) != np.ndarray:
print('dddd', a_ax)
print(type(a_ax))
a_ax = np.array([a_ax])
else:
m_ax = m_ax.flatten()
for i in range(acount - 1, len(m_ax)):
m_ax[i].set_visible(False)
for i in range(1, len(dataset.activities_map)):
# real_events2,pred_events2=vs.remove_gaps(real_events,pred_events,i)
#real_events2,pred_events2=vs.remove_gaps(real_events,pred_events,i,max_events=10)
real_events2, pred_events2 = real_events, pred_events
vs.plotJoinAct(dataset,
real_events,
pred_events,
onlyAct=i,
ax=a_ax[i - 1])
try:
# vs.plotJoinAct(dataset,real_events2,pred_events2,onlyAct=i,ax=a_ax[i-1])
vs.plotMyMetric2(dataset,
real_events2,
pred_events2,
onlyAct=i,
ax=m_ax[i - 1],
debug=debug,
calcne=0)
except Exception as e:
import sys
import traceback
print(e, file=sys.stderr)
traceback.print_exc()
# print('error in ',i)
#vs.plotWardMetric(dataset,real_events,pred_events,onlyAct=i)
# vs.plotJoinMyMetric(dataset,real_events2,pred_events2,calcne=0)
# a_fig.show()
m_fig.tight_layout(pad=0, h_pad=-20.0, w_pad=3.0)
def result_selector(file=result_analyse.resultloader.get_runs()):
if (file == None): return
print('Analysing ', file)
run_info, dataset, evalres = utils.loadState(file)
stime = dataset.activity_events.iloc[0].StartTime
#etime=stime+np.timedelta64(1,'D')
etime = dataset.activity_events.iloc[-1].EndTime
for i in range(len(evalres)):
quality = evalres[i]['test'].quality
print('Evalution quality fold=%d is %s' % (i, quality))
print(len(dataset.sensor_events))
# vs.plot_CM(dataset,evalres)
@interact
def viewFold(fold=range(len(evalres))):
@interact_manual
def view(
start_date=widgets.DatePicker(value=pd.to_datetime(stime)),
end_date=widgets.DatePicker(value=pd.to_datetime(etime)),
debug=widgets.Checkbox(value=False)):
duration = (pd.to_datetime(start_date),
pd.to_datetime(end_date))
duration2 = (pd.to_datetime(start_date),
pd.to_datetime(start_date) +
pd.DateOffset(days=7))
real_events = vs.filterTime(evalres[fold]['test'].real_events,
duration)
pred_events = vs.filterTime(evalres[fold]['test'].pred_events,
duration)
#vs.plotJoinAct(dataset,real_events,pred_events)
acts = [p for p in dataset.activities_map]
labels = [dataset.activities_map[p] for p in acts]
print(acts)
print(labels)
vs.plotJoinAct2(real_events,
pred_events,
acts,
labels,
duration=duration2)
#vs.plot_per_act(dataset,{'test':evalres})
from matplotlib import pyplot as plt
plt.rc_context(rc={'figure.max_open_warning': 0})
acount = len(dataset.activities_map)
for i in range(1, len(dataset.activities_map)):
# real_events2,pred_events2=vs.remove_gaps(real_events,pred_events,i)
#real_events2,pred_events2=vs.remove_gaps(real_events,pred_events,i,max_events=10)
real_events2, pred_events2 = real_events, pred_events
# vs.plotJoinAct(dataset,real_events,pred_events,onlyAct=i,ax=a_ax[i-1])
try:
# vs.plotJoinAct(dataset,real_events2,pred_events2,onlyAct=i,ax=a_ax[i-1])
vs.plotWardMetric(dataset,
real_events,
pred_events,
onlyAct=i)
except Exception as e:
import sys
import traceback
print(e, file=sys.stderr)
traceback.print_exc()
import numpy as np
from activity_fetcher.CookActivityFetcher import CookActivityFetcher
def calc_cm_per_s_event(dataset, evalres):
activities = dataset.activities
summycm = np.zeros((len(activities), len(activities)))
for i in range(len(evalres)):
# print(evalres[i]['test'].__dict__)
sdata = evalres[i]['test'].Sdata
cook = CookActivityFetcher()
cook.precompute(sdata)
c = 0
for k in range(0, len(sdata.s_event_list)):
real = cook.getActivity2(sdata.s_event_list, [k])
while (c < len(sdata.set_window)
and k >= max(sdata.set_window[c])):
c = c + 1
if (c >= len(sdata.set_window)):
break
pred = sdata.label[c]
summycm[real][pred] = summycm[real][pred] + 1
# evalres[i]['test'].Sdata.s_event_list[evalres[i]['test'].Sdata.set_window[400][-1]][1]
return summycm
if __name__ == '__main__':
[run_info, datasetdscr, evalres] = utils.loadState('200515_13-22-21-Home2')
calc_cm_per_s_event(datasetdscr, evalres)
def display_result(file):
[run_info,datasetdscr,evalres]=utils.loadState(file)
for i in range(len(evalres)):
evaldata=evalres[i]['test']
quality=evaldata.quality
logger.debug('Evalution quality fold=%d is f1=%.2f acc=%.2f precision=%.2f recall=%.2f' % (i, quality.f1,quality.accuracy,quality.precision,quality.recall))
