def dataPlot(layersize=[100, 50, 25],triangleAffineSum=3,lineRotateMag=16):
# "LR",'SVM'
resultDict={}
actFile=scipy.io.loadmat('./trainedWeights/DeepNet_' + '_'.join(map(str, layersize))+'_Act.mat' )
otherActFile=scipy.io.loadmat('./trainedWeights/OtherAna_' + '_'.join(map(str, layersize))+'_Act.mat' )
# labelFile=scipy.io.loadmat('labelDict.mat' )
labelDict=pickle.load( open( "labelDict.p", "rb" ) )
actDict={}
i=0
for imgID in labelDict:
i=labelDict[imgID]['grandIndex']
actDict[imgID]={'codeLayerProbs':actFile['codeLayerProbs'][i,], 'rawData':actFile['input2DOrdered'][i,],'reconstrProbs':actFile['reconstrProbs'][i,], 'pcaAct':otherActFile['PCAReducedData'][i,]}
# visualize
predictorVect=['codeLayerProbs']
#['codeLayerProbs','pcaAct','rawData','reconstrProbs']
plotVect=[
{'X':predictorVect, 'Y':'subType', 'constraints': {'type':['twoLines'],'subType':[['intersecting'],['parallel']]},'probeName':'intersect/parallel'},
{'X':predictorVect, 'Y':'type', 'constraints': {'type':['quadrilateral','triangle','circle']},'probeName':'Object Type'},
{'X':predictorVect, 'Y':'scaleMag', 'constraints': {'type':['line']},'probeName':'lineLength'},
{'X':predictorVect, 'Y':'affineIndex', 'constraints': {'type':['triangle'],'affineIndex':range(0,int(triangleAffineSum/2+1)),'triangleHeight':[0]},'probeName':'triangleSubType'},
{'X':predictorVect, 'Y':'subType', 'constraints': {'type':['twoLines'],'subType':[['intersecting'],['intersecting', 'perpendicular']]},'probeName':'perpendicular'},
{'X':predictorVect, 'Y':'rotateMag', 'constraints': {'type':['line'],'rotateMag':np.linspace(0,90,lineRotateMag/2+1).tolist()},'probeName':'lineClockWise'},
{'X':predictorVect, 'Y':'angleIndex', 'constraints': {'type':['twoLines'],'subType':[['intersecting'],['intersecting', 'perpendicular']]},'probeName':'angleMag'},
{'X':predictorVect, 'Y':'subType', 'constraints': {'type':['lineCircle'],'subType':[['intersecting'],['not-intersecting']]},'probeName':'lineCircle Relations'},
{'X':predictorVect, 'Y':'d', 'constraints': {'type':['twoLines'],'subType':[['parallel']]},'probeName':'dMag'}
]
myPlotCSV = open('./resultsCSVandPlot/plotDict.csv', 'w')
fieldnames = ['probeName', 'predicted','X1','X2', 'Y']
myPlotCSVWriter = csv.DictWriter(myPlotCSV, fieldnames=fieldnames)
myPlotCSVWriter.writeheader()
for item in plotVect:
for pred in item['X']:
X,Y= util.getXandY(actDict, labelDict, predictor=pred, predicted=item['Y'], constraits=item['constraints'])
for imgIndex in range(len(Y)):
myPlotCSVWriter.writerow({'probeName':item['probeName'],'predicted':item['Y'],'X1':X[imgIndex][0],'X2':X[imgIndex][1],'Y':Y[imgIndex]})
myPlotCSV.close()
{'X':predictorVect, 'Y':'affineIndex', 'constraints': {'type':['quadrilateral'],'shapeIndex':[0],'affineIndex':range(0,int(quadriShapeSum/2+1))},'comparisonSum':2, 'oriAna':'perpendicularNN', 'probeName':'perpend2quadriCongru'},
{'X':predictorVect, 'Y':'scaleMag', 'constraints': {'type':['circle']},'comparisonSum':-1,'oriAna':'lineLength','probeName':'lineLength2circleSize'},
# {'X':predictorVect, 'Y':'rotateMag', 'constraints': {'type':['quadrilateral'],'affineIndex':[int(quadriShapeSum/2)], 'rotateMag':np.linspace(0,90,quadriRotateMag/2+1).tolist()},'comparisonSum':-1, 'oriAna':'lineClockWise', 'probeName':'lineClock2rectClock'}, ##'shapeIndex':[0],
{'X':predictorVect, 'Y':'rotateMag', 'constraints': {'type':['twoLines'],'rotateMag':np.linspace(0,90,lineRotateMag/2+1).tolist()},'comparisonSum':-1, 'oriAna':'lineClockWise', 'probeName':'lineClock2twoLinesClock'},
]
for model in modelVect:
# model = ["NB", "LG", "SVM"]"
print "Using model=", model
evaluation = {}
for item in anaVect:
# print item
evaluation[item['probeName']] = {}
for pred in item['X']:
print "#################### Training Phase ####################"
X_train, Y_train = util.getXandY(actDict_train, labelDict_train, predictor=pred, predicted=item['Y'], constraits=item['constraints'])
X_train, Y_train = util.bootStrapLabels(X_train,Y_train,1000)
print item['probeName'], len(Y_train)
train_accu, fitted = util.myClassifier(X_train,Y_train,model,CV=5)
#evaluation[item['probeName']][pred]=accu
print "#################### Testing Phase ####################"
X_test, Y_test = util.getXandY(actDict_test, labelDict_test, predictor=pred, predicted=item['Y'], constraits=item['constraints'])
X_test, Y_test = util.bootStrapLabels(X_test,Y_test,1000)
print item['probeName'], len(Y_test)
test_predictions = fitted.predict(X_test)
test_accu = sum([int(test_predictions[q]==Y_test[q]) for q in range(len(Y_test))])/len(Y_test)
#accu_test, fitted = util.myClassifier(X_test,Y_test,model,CV=5)
myCSVWriter.writerow({'iterIndex':iterIndex, 'probeName':item['probeName'],'model':model,'predicted':item['Y'],'predictor':pred,'self/other':'self','train_accuracy':train_accu,'test_accuracy':test_accu,'single/doubleFigs':'single','comparisonSum':'NA'})
# Double Figure ############################################################
compareDoubleNN={}
