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ETL.py
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ETL.py
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__author__ = 'Sriganesh'
import scipy.io
import math
from sklearn import svm
from sklearn.svm import LinearSVC
import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from matplotlib import pyplot as plt
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import minimum_spanning_tree
import networkx as nx
import random
from scipy.stats import norm
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_recall_fscore_support
def loadData(filename):
data = scipy.io.loadmat(filename)
labels, index = getLabels(data['i'])
trials = []
k = 0
for y in data['d']:
if k in index:
k += 1
continue
i = 0
while i < 32:
trials.append(y[0][i])
i += 1
k += 1
return trials, labels
def getLabels(info):
labels = []
indexes = []
i = 0
for x in info[0]:
if x[0][0] == 0 or x[0][0] == 1:
indexes.append(i)
i += 1
continue
if x[len(x)-1][0] == 'P':
for k in range(0,16):
labels.append('P')
for k in range(0,16):
labels.append('S')
else:
for k in range(0,16):
labels.append('S')
for k in range(0,16):
labels.append('P')
i += 1
return labels, indexes
def cross_validate(traindata, trainlabels):
n = 16
i = 0
C_val = [0.0000001, 0.0005, 0.01, 2]
C_label = ["linearSVM C = 0.0000001","linearSVM C = 0.0005","linearSVM C = 0.01","linearSVM C = 2" ]
k_val = [1, 3, 7, 9, 15]
k_label = ["1nn", "3nn", "7nn", "9nn", "15nn"]
linearSVM, knn = [],[]
while i < n:
train, tlabel, test, label = CVsplit(traindata,trainlabels, i)
L = []
for k in C_val:
clf = svm.LinearSVC(C=k)
L.append(fitAndPredict(clf,train,tlabel,test,label))
linearSVM.append(L)
L = []
for j in k_val:
if j < len(train[0]):
clf = KNeighborsClassifier(n_neighbors=j)
L.append(fitAndPredict(clf,train,tlabel,test,label))
if len(L) > 0:
knn.append(L)
i += 1
C = C_val[getBestIndex(linearSVM, C_label)]
k = k_val[getBestIndex(knn, k_label)]
CVplot(linearSVM,C_label)
CVplot(knn,k_label)
return C, k
def getBestIndex(data, val):
X = np.asarray(data)
maximum, index = 0, 0
for i in range(len(data[0])):
temp = sum(X[:,i])/len(X[:,i])
print "Average Accuracy Score for:", val[i], "is:", temp
if temp > maximum:
maximum = temp
index = i
return index
def CVplot(data, val):
X = np.asarray(data)
line, leg = [], []
for i in range(len(data[0])):
line1, = plt.plot(X[:,i], label = val[i])
line.append(line1)
leg.append(val[i])
plt.legend(line,leg)
plt.show()
def fitAndPredict(clf, traindata, trainlabels, testdata, testlabels):
clf.fit(traindata, trainlabels)
mylabel =[]
for x in testdata:
mylabel.append(clf.predict(x))
#print "Accuracy:", accuracy_score(testlabels, mylabel)
#print "(Precision, Recall, F1-Score) Label S:", precision_recall_fscore_support(testlabels, mylabel,labels=['S','P'], pos_label='P',average='binary')
#print "(Precision, Recall, F1-Score) Label P:", precision_recall_fscore_support(testlabels, mylabel,labels=['S','P'], pos_label='S',average='binary')
#print "----------------------"
return accuracy_score(testlabels, mylabel)
def shuffle(data, labels):
start = len(data) - (6*len(data)/10)
end = len(data) - (5*len(data)/10)
print start, end
x = [i for i in range(len(data))]
random.shuffle(x)
traindata = [data[i] for i in x[:start]]
traindata.extend([data[i] for i in x[end:]])
trainlabels = [labels[i] for i in x[:start]]
trainlabels.extend([labels[i] for i in x[end:]])
testdata = [data[i] for i in x[start:end]]
testlabels = [labels[i] for i in x[start:end]]
return (traindata,trainlabels,testdata,testlabels)
def trainAndtest(traindata, trainlabels, testdata, testlabels, C_val, k_val):
print "Test accuracies:"
clf = svm.LinearSVC(C=C_val)
print "SVM Scores:"
print fitAndPredict(clf, traindata, trainlabels,testdata, testlabels)
Gauss = GaussianNB()
print "GNB: Scores"
print fitAndPredict(Gauss, traindata, trainlabels,testdata, testlabels)
print "Nearest Neighbour Scores"
neigh = KNeighborsClassifier(n_neighbors=k_val)
print fitAndPredict(neigh, traindata, trainlabels,testdata, testlabels)
def getClassConditionalData(data, labels):
dataP, dataS = [], []
for i in range(len(data)):
if labels[i] == 'P':
dataP.append(data[i])
if labels[i] == 'S':
dataS.append(data[i])
dataP = np.transpose(dataP)
dataS = np.transpose(dataS)
return dataP, dataS
def getClassProbability(labels):
s, p = 0, 0
for x in labels:
if x == 'S':
s += 1
if x == 'P':
p += 1
Pp = float(p)/float(len(labels))
Ps = float(s)/float(len(labels))
return Pp, Ps
def getParam(data):
Params = []
for x in data:
Mu = np.mean(x)
Sigma = np.var(x)
Params.append((Mu, Sigma))
return Params
def CVsplit(traindata, trainlabels, i=5):
n = 16
limit = len(traindata)/n
start = i * limit
end = (i+1) * limit
if start == 0:
train = traindata[end:]
tlabel = trainlabels[end:]
if end == len(traindata):
train = traindata[:start]
tlabel = trainlabels[:start]
if start != 0 and end != len(traindata):
train = traindata[:start]
train.extend(traindata[end:])
tlabel = trainlabels[:start]
tlabel.extend(trainlabels[end:])
test = traindata[start:end]
label = trainlabels[start:end]
return train,tlabel,test,label
def learnStructure(dataP, dataS, Pp, Ps, TAN= True):
tempMatrix = [[0 for i in range(len(dataP))] for j in range(len(dataP))]
for i in range(len(dataP)):
for j in range(i+1, len(dataP)):
temp = 0.0
if np.corrcoef(dataP[i], dataP[j])[0][1] != 1.0:
temp += Pp * math.log(1-((np.corrcoef(dataP[i], dataP[j])[0][1])**2))
if np.corrcoef(dataS[i], dataS[j])[0][1] != 1.0:
temp += Ps * math.log(1-((np.corrcoef(dataS[i], dataS[j])[0][1])**2))
temp *= (0.5)
tempMatrix[i][j] = temp
#tempMatrix[j][i] = temp
MaxG = nx.DiGraph()
if TAN:
G = nx.from_scipy_sparse_matrix(minimum_spanning_tree(csr_matrix(tempMatrix)))
adjList = G.adj
i = 0
notReturnable = {}
MaxG = getDirectedTree(adjList, notReturnable, MaxG, i)
else:
G = nx.Graph(np.asmatrix(tempMatrix))
adjList = sorted([(u,v,d['weight']) for (u,v,d) in G.edges(data=True)], key=lambda x:x[2])
i = 2
MaxG = getDirectedGraph(adjList, MaxG, i)
return MaxG
def getDirectedGraph(adjList, MaxG, k):
finished = {}
for (u,v,d) in adjList:
if v not in finished:
finished[v] = 1
MaxG.add_edge(u,v)
else:
if finished[v] < k:
finished[v] += 1
MaxG.add_edge(u,v)
#if not nx.is_directed_acyclic_graph(MaxG):
# MaxG.remove_edge(u,v)
return MaxG
def getDirectedTree(adjList, notReturnable, MaxG, i):
x = adjList[i]
notReturnable[i] = 1
L = []
for y in x.keys():
if y not in notReturnable:
MaxG.add_edge(i, y)
L.append(y)
for y in L:
MaxG = getDirectedTree(adjList,notReturnable,MaxG, y)
return MaxG
def getVariables(Tree, data, R = 0.0000001):
Param = getParam(data)
# Do topological sort to figure out nodes with least number of dependence
Nodes = nx.topological_sort(Tree)
Variables = {'TOPO': Nodes}
for i in Nodes:
mean, Var = Param[i]
Mean = {'NodeMean': Param[i][0], 'Beta':[], 'ParentMean':[], 'Parent':[]}
L = []
for x in Tree.predecessors(i):
if x != i:
#Parentmean, ParentVar = Param[x]
PCov = np.cov([data[i], data[x]])[0][1]
PBeta = PCov/Param[x][1]
Mean['Beta'].append(PBeta)
Mean['ParentMean'].append(Param[x][0])
Mean['Parent'].append(x)
L.append(x)
if len(L) > 0:
Depend = [data[i]]
num, dem = 0, 0
for k in L:
Depend = np.vstack((Depend, data[k]))
Parent = Depend[1:]
if len(Parent) > 2:
num = np.linalg.det(np.cov(Depend)) + R
dem = np.linalg.det(np.cov(Parent)) + R
if len(Parent) == 2:
num = np.linalg.det(np.cov(Depend)) + R
dem = np.linalg.det(np.cov(Parent)) + R
if len(Parent) == 1:
num = np.linalg.det(np.cov(Depend)) + R
dem = np.var(Parent) + R
Var = num / dem
Std = math.sqrt(Var)
Variables[i] = (Mean, Std)
return Variables
def infer(Variables, testdata):
Prod = 1.0
for i in Variables['TOPO']:
mean = Variables[i][0]['NodeMean']
for j in range(len(Variables[i][0]['Beta'])):
mean += Variables[i][0]['Beta'][j] * (testdata[Variables[i][0]['Parent'][j]] - Variables[i][0]['ParentMean'][j])
rv = norm(loc=mean, scale=Variables[i][1])
pr = rv.pdf(testdata[i])
if pr > 0.0001:
Prod *= (pr/0.1)
return Prod
def cv_TAN(traindata, trainlabels):
n = 16
i = 0
TAN, KDTAN = [], []
R = [0.01, 1, 10]
R_label = ['regular=0.01', 'regular=1', 'regular=0.10']#, 'regular=4000']
while i < n:
L1, L2 = [], []
train, tlabel, test, label = CVsplit(traindata, trainlabels, i)
dataP, dataS = getClassConditionalData(train, tlabel)
Pp, Ps = getClassProbability(tlabel)
Tree1 = learnStructure(dataP, dataS, Pp, Ps, True)
PVariable1 = [getVariables(Tree1,dataP, r) for r in R]
SVariable1 = [getVariables(Tree1,dataS, r) for r in R]
Tree2 = learnStructure(dataP, dataS, Pp, Ps, False)
PVariable2 = [getVariables(Tree2,dataP, r) for r in R]
SVariable2 = [getVariables(Tree2,dataS, r) for r in R]
for j in range(len(R)):
mylabel1, mylabel2 = [], []
for x in test:
PProd = Pp * infer(PVariable1[j], x)
SProd = Ps * infer(SVariable1[j], x)
temp = PProd + SProd
PProd = PProd/temp
SProd = SProd/temp
if SProd >= PProd:
mylabel1.append('S')
else:
mylabel1.append('P')
PProd = Pp * infer(PVariable2[j], x)
SProd = Ps * infer(SVariable2[j], x)
temp = PProd + SProd
PProd = PProd/temp
SProd = SProd/temp
if SProd >= PProd:
mylabel2.append('S')
else:
mylabel2.append('P')
L1.append(accuracy_score(label, mylabel1))
L2.append(accuracy_score(label, mylabel2))
TAN.append(L1)
KDTAN.append(L2)
i += 1
T = R[getBestIndex(TAN, R_label)]
K = R[getBestIndex(KDTAN, R_label)]
CVplot(TAN,R_label)
CVplot(KDTAN,R_label)
return T,K
if __name__ == "__main__":
data, labels = loadData("avgROI.mat")
TANAcc, GaussAcc = [], []
traindata, trainlabels, testdata, testlabels = shuffle(data, labels)
C, k = cross_validate(traindata,trainlabels)
T, K = cv_TAN(traindata,trainlabels)
trainAndtest(traindata, trainlabels, testdata,testlabels, C, k)
dataP, dataS = getClassConditionalData(traindata, trainlabels)
Pp, Ps = getClassProbability(trainlabels)
Tree = learnStructure(dataP, dataS, Pp, Ps, TAN=True)
PVar = getVariables(Tree, dataP, R = T)
SVar = getVariables(Tree, dataS, R = T)
mylabel = []
for x in testdata:
PProd = Pp * infer(PVar, x)
SProd = Ps * infer(SVar, x)
temp = PProd + SProd
PProd = PProd/temp
SProd = SProd/temp
if SProd >= PProd:
mylabel.append('S')
else:
mylabel.append('P')
print "TAN Accuracy:", accuracy_score(testlabels, mylabel)
Tree = learnStructure(dataP, dataS, Pp, Ps, TAN=False)
PVar = getVariables(Tree, dataP, R = K)
SVar = getVariables(Tree, dataS, R = K)
mylabel = []
for x in testdata:
PProd = Pp * infer(PVar, x)
SProd = Ps * infer(SVar, x)
temp = PProd + SProd
PProd = PProd/temp
SProd = SProd/temp
if SProd >= PProd:
mylabel.append('S')
else:
mylabel.append('P')
print "KDTAN Accuracy:", accuracy_score(testlabels, mylabel)