@ -145,6 +145,7 @@ def calculateGrid():
global dataProc
dataProc , length , labels = preprocess ( data )
global D_highSpace
D_highSpace = distance . squareform ( distance . pdist ( dataProc ) )
DEFAULT_NO_DIMS = 2
@ -180,6 +181,8 @@ def calculateGrid():
SelectedListofParams = [ ]
global SelectedProjectionsReturn
SelectedProjectionsReturn = [ ]
global clusterIndex
clusterIndex = procrustesFun ( projectionsAll )
metricNeigh = [ ]
@ -187,6 +190,8 @@ def calculateGrid():
metricCont = [ ]
metricStress = [ ]
metricShepCorr = [ ]
global convertLabels
convertLabels = [ ]
for index , label in enumerate ( labels ) :
if ( label == 0 ) :
@ -206,13 +211,21 @@ def calculateGrid():
else :
pass
global D_lowSpaceList
D_lowSpaceList = [ ]
global KeepKs
KeepKs = [ ]
for index in clusterIndex :
SelectedProjectionsReturn . append ( projectionsAll [ index ] . tolist ( ) )
SelectedListofParams . append ( listofParamsAll [ index ] )
D_lowSpace = distance . squareform ( distance . pdist ( projectionsAll [ index ] ) )
D_lowSpaceList . append ( D_lowSpace )
k = listofParamsAll [ index ] [ 0 ] # k = perplexity
KeepKs . append ( k )
#resultNeigh = neighborhood_hit(np.array(projectionsAll[index]), convertLabels, k)
resultNeigh = trustworthiness ( D_highSpace , D_lowSpace , k )
@ -283,5 +296,87 @@ def background_process():
pass
return jsonify ( { ' projections ' : SelectedProjectionsReturn , ' parameters ' : SelectedListofParams , ' metrics ' : metricsMatrix , ' metricsEntire ' : metricsMatrixEntire } )
@app . route ( ' /receiverOptimizer ' , methods = [ ' POST ' ] )
def OptimizeSelection ( ) :
dataReceived = request . get_data ( ) . decode ( ' utf8 ' ) . replace ( " ' " , ' " ' )
dataReceived = json . loads ( dataReceived )
dataSelected = [ ]
for data in dataReceived :
if data != None :
dataSelected . append ( data )
metricNeigh = [ ]
metricTrust = [ ]
metricCont = [ ]
metricStress = [ ]
metricShepCorr = [ ]
for index , loop in enumerate ( clusterIndex ) :
#resultNeigh = neighborhood_hit(np.array(projectionsAll[index]), convertLabels, k)
resultNeigh = trustworthiness ( D_highSpace [ dataSelected , : ] , D_lowSpaceList [ index ] [ dataSelected , : ] , KeepKs [ index ] )
resultTrust = trustworthiness ( D_highSpace [ dataSelected , : ] , D_lowSpaceList [ index ] [ dataSelected , : ] , KeepKs [ index ] )
resultContinuity = continuity ( D_highSpace [ dataSelected , : ] , D_lowSpaceList [ index ] [ dataSelected , : ] , KeepKs [ index ] )
resultStress = normalized_stress ( D_highSpace [ dataSelected , : ] , D_lowSpaceList [ index ] [ dataSelected , : ] )
resultShep = normalized_stress ( D_highSpace [ dataSelected , : ] , D_lowSpaceList [ index ] [ dataSelected , : ] )
#resultShep = shepard_diagram_correlation(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :])
metricNeigh . append ( resultNeigh )
metricTrust . append ( resultTrust )
metricCont . append ( resultContinuity )
metricStress . append ( resultStress )
metricShepCorr . append ( resultShep )
max_value_neigh = max ( metricNeigh )
min_value_neigh = min ( metricNeigh )
max_value_trust = max ( metricTrust )
min_value_trust = min ( metricTrust )
max_value_cont = max ( metricCont )
min_value_cont = min ( metricCont )
max_value_stress = max ( metricStress )
min_value_stress = min ( metricStress )
max_value_shep = max ( metricShepCorr )
min_value_shep = min ( metricShepCorr )
global metricsMatrixEntireSel
metricsMatrixEntireSel = [ ]
for index , data in enumerate ( metricTrust ) :
#valueNeigh = (metricNeigh[index] - min_value_neigh) / (max_value_neigh - min_value_neigh)
valueNeigh = ( metricTrust [ index ] - min_value_trust ) / ( max_value_trust - min_value_trust )
valueTrust = ( metricTrust [ index ] - min_value_trust ) / ( max_value_trust - min_value_trust )
valueCont = ( metricCont [ index ] - min_value_cont ) / ( max_value_cont - min_value_cont )
valueStress = ( metricStress [ index ] - min_value_stress ) / ( max_value_stress - min_value_stress )
valueShep = ( metricShepCorr [ index ] - min_value_shep ) / ( max_value_shep - min_value_shep )
metricsMatrixEntireSel . append ( [ valueNeigh , valueTrust , valueCont , valueStress , valueShep ] )
sortNeigh = sorted ( range ( len ( metricNeigh ) ) , key = lambda k : metricNeigh [ k ] , reverse = True )
sortTrust = sorted ( range ( len ( metricTrust ) ) , key = lambda k : metricTrust [ k ] , reverse = True )
sortCont = sorted ( range ( len ( metricCont ) ) , key = lambda k : metricCont [ k ] , reverse = True )
sortStress = sorted ( range ( len ( metricStress ) ) , key = lambda k : metricStress [ k ] , reverse = True )
sortShepCorr = sorted ( range ( len ( metricShepCorr ) ) , key = lambda k : metricShepCorr [ k ] , reverse = True )
global metricsMatrixSel
metricsMatrixSel = [ ]
metricsMatrixSel . append ( sortNeigh )
metricsMatrixSel . append ( sortTrust )
metricsMatrixSel . append ( sortCont )
metricsMatrixSel . append ( sortStress )
metricsMatrixSel . append ( sortShepCorr )
return ' OK '
@app . route ( ' /senderOptimizer ' )
def SendOptimizedProjections ( ) :
global metricsMatrixSel
global metricsMatrixEntireSel
return jsonify ( { ' metrics ' : metricsMatrixSel , ' metricsEntire ' : metricsMatrixEntireSel } )
if __name__ == ' __main__ ' :
app . run ( " 0.0.0.0 " , " 5000 " )
