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main.py
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executable file
·275 lines (258 loc) · 9.76 KB
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#IMPORTS
import csv;
import numpy as np;
from numpy.linalg import inv;
from scipy.spatial import distance
import math;
import random;
import scipy.cluster.vq as vq;
class dataSet():
def __init__(self):
self.training={'x_matrix':[],'target_vector':[]}
self.validation={'x_matrix':[],'target_vector':[]}
self.testing={'x_matrix':[],'target_vector':[]}
#Partitioning Data Set into Training, Validation and Testing Sets
def partition(x_matrix,target_vector):
tem=dataSet()
tem.training['x_matrix']=[]
tem.training['target_vector']=[]
tem.validation['x_matrix']=[]
tem.validation['target_vector']=[]
tem.testing['x_matrix']=[]
tem.testing['target_vector']=[]
temp=[[],[],[]]
for i in range(0,len(x_matrix)):
t=x_matrix[i]
t.append(target_vector[i])
temp[int(target_vector[i])].append(t)
t_set=[[],[],[]]
for i in range(0,3):
m=len(temp[i])
for j in range(0,m):
if(j<0.7999*m):
t_set[0].append(temp[i][j])
elif(j<0.89989*m):
t_set[1].append(temp[i][j])
else:
t_set[2].append(temp[i][j])
for i in range(0,3):
random.shuffle(t_set[i])
m=len(t_set[i])
for j in range(0,m):
if i==0:
tem.training['target_vector'].append(t_set[i][j].pop())
tem.training['x_matrix'].append(t_set[i][j])
elif i==1:
tem.validation['target_vector'].append(t_set[i][j].pop())
tem.validation['x_matrix'].append(t_set[i][j])
elif i==2:
tem.testing['target_vector'].append(t_set[i][j].pop())
tem.testing['x_matrix'].append(t_set[i][j])
return tem
def getInverseVariance(x_matrix, mu_vector,n):
var_array=[]
for x in x_matrix[0]:
var_array.append(0)
for x_vector in x_matrix:
var_array=np.add(var_array,np.power(np.subtract(x_vector,mu_vector),2))
var_array=np.divide(var_array,n*len(x_matrix))
var_matrix=[]
for i in range(0,len(var_array)):
temp=[]
for j in range(0,len(var_array)):
if(i==j):
if(var_array[i]==0):
temp.append(0.0000000000000001)
else:
temp.append(var_array[i])
else:
temp.append(0)
var_matrix.append(temp)
return inv(np.array(var_matrix))
def get_inv_var_matrix(x_matrix, mu_matrix,n):
inv_var_matrix=[]
for mu_vector in mu_matrix:
inv_var_matrix.append(getInverseVariance(x_matrix,mu_vector,n))
return inv_var_matrix
def phi_x(x_vector, mu_matrix, inv_var_matrix_vector):
result=[]
for i in range(0,len(mu_matrix)+1):
if i==0:
result.append(1)
else:
x_transpose=np.transpose(np.subtract(x_vector,mu_matrix[i-1]))
temp=np.dot(inv_var_matrix_vector[i-1],x_transpose)
temp=np.dot(np.subtract(x_vector,mu_matrix[i-1]),temp)
temp=(-0.5)*temp
temp=math.exp(temp)
result.append(temp)
return result
def phi(x_matrix, mu_matrix, inv_var_matrix):
phi_matrix=[]
for x_vector in x_matrix:
phi_matrix.append(phi_x(x_vector,mu_matrix,inv_var_matrix))
return phi_matrix
def delta_w(eta, e_delta):
return np.dot(-eta,e_delta)
def delta_e(e_d, e_w, lamb):
return np.add(e_d, np.dot(lamb,e_w))
def delta_e_d(target, weight_vector, phi_x_n):
phi_x_n_t=np.transpose(phi_x_n)
temp=np.dot(weight_vector,phi_x_n_t)
temp=np.subtract(target,temp)
temp=np.dot(temp,-1)
temp=np.dot(temp,phi_x_n)
return temp
def delta_e_w(weight_vector):
return weight_vector
def find_w_star(phi_matrix, lamb, target_vector):
phi_transpose=np.transpose(phi_matrix)
temp=np.dot(phi_transpose,phi_matrix)
temp=np.add(np.dot(lamb,np.identity(len(phi_transpose))),temp)
temp=inv(temp)
temp=np.dot(temp,np.dot(phi_transpose,target_vector))
return temp
def closedFormTrain(x_matrix,target_vector,clusterNum,n,lamb):
x=vq.kmeans(np.array(x_matrix),clusterNum-1)
mu_matrix=x[0]
sigma_matrix=get_inv_var_matrix(x_matrix, mu_matrix,n)
phi_matrix=phi(x_matrix,mu_matrix,sigma_matrix)
w_star = find_w_star(phi_matrix,lamb=0.1, target_vector=target_vector)
return [w_star,mu_matrix,sigma_matrix]
def stochasticTrain(x_matrix,target_vector,clusterNum,n,lamb,eta,threshold):
w_temp=[]
temp=[]
x=vq.kmeans(np.array(x_matrix),clusterNum-1)
mu_matrix=x[0]
sigma_matrix=get_inv_var_matrix(x_matrix, mu_matrix,n)
for i in range(0,clusterNum):
w_temp.append(random.random())
prevMax=1
for i in range(0,len(x_matrix)):
phi_vector=phi_x(x_matrix[i],mu_matrix,sigma_matrix)
e_d=delta_e_d(target_vector[i],w_temp,phi_vector)
d_e=delta_e(e_d,w_temp,lamb)
temp.append(d_e)
if(i%10==0):
temp=np.mean(temp,axis=0)
w_d=delta_w(eta,temp)
max_w=w_d[0]
w_temp=np.add(w_temp,w_d)
temp=[]
for each in w_d:
if(max_w<each):
max_w=each
if(math.fabs(max_w)<threshold):
break
return [w_temp,mu_matrix,sigma_matrix]
def calcError(phi_vector,target,w_star,lamb):
err=(target - np.dot(w_star,np.transpose(phi_vector)))**2 + lamb*np.dot(w_star,np.transpose(w_star))
return math.sqrt(err)
def validationError(x_matrix,target_vector,w_star,lamb, mu_matrix, sigma_matrix):
const=lamb*np.dot(w_star,np.transpose(w_star))
err=0
for i in range(0,len(x_matrix)):
err=err+(target_vector[i] - np.dot(w_star,np.transpose(phi_x(x_matrix[i],mu_matrix,sigma_matrix))))**2
return math.sqrt(err/len(x_matrix))
def testError(x_matrix,target_vector,w_star, lamb,mu_matrix, sigma_matrix):
const=lamb*np.dot(w_star,np.transpose(w_star))
err=0
for i in range(0,len(x_matrix)):
err=err+(target_vector[i] - np.dot(w_star,np.transpose(phi_x(x_matrix[i],mu_matrix,sigma_matrix))))**2
return math.sqrt(err/len(x_matrix))
#Loading Data from Input CSV Files(Synthetic Dataset)
x_matrix=[]
target_vector=[]
with open('input.csv', 'rU') as csvfile:
spamreader = csv.reader(csvfile)
for row in spamreader:
temp=[]
for item in row:
temp.append(float(item))
x_matrix.append(temp)
with open('output.csv', 'rU') as csvfile:
spamreader = csv.reader(csvfile)
for row in spamreader:
for item in row:
target_vector.append(float(item))
synthetic_part = partition(x_matrix,target_vector)
#Loading Data from Input CSV Files(Learning to Rank Dataset)
x_matrix=[]
target_vector=[]
with open('Querylevelnorm_X.csv', 'rU') as csvfile:
spamreader = csv.reader(csvfile)
for row in spamreader:
temp=[]
for item in row:
temp.append(float(item))
x_matrix.append(temp)
with open('Querylevelnorm_t.csv', 'rU') as csvfile:
spamreader = csv.reader(csvfile)
for row in spamreader:
for item in row:
target_vector.append(float(item))
part = partition(x_matrix,target_vector)
#Real World Data Set
clusterNum=17
lamb=0
eta=0.1
threshold=0.001
print("-----------LeToR Dataset-----------")
print
#Closed Form Solution
print("---CLOSED FORM SOLUTION----")
print('M : '+str(clusterNum))
print('Lambda : '+str(lamb))
newTrainResult = closedFormTrain(part.training['x_matrix'],part.training['target_vector'], clusterNum=clusterNum,n=0.5,lamb=0)
newErr=validationError(part.training['x_matrix'],part.training['target_vector'],newTrainResult[0], lamb,newTrainResult[1], newTrainResult[2])
print('Training_Error : '+str(newErr))
newErr=validationError(part.validation['x_matrix'],part.validation['target_vector'],newTrainResult[0],lamb,newTrainResult[1], newTrainResult[2])
print('Validation_Error : '+str(newErr))
newErr=testError(part.testing['x_matrix'],part.testing['target_vector'],newTrainResult[0],lamb,newTrainResult[1], newTrainResult[2])
print('Testing_Error : '+str(newErr))
print
#Stocastic Gradient Descent
print("---SGD SOLUTION----")
print('M : '+str(clusterNum))
print('Lambda : '+str(lamb))
print('Eta : '+str(eta))
newTrainResult = stochasticTrain(part.training['x_matrix'],part.training['target_vector'], clusterNum=clusterNum,n=0.5,lamb=lamb, eta=eta, threshold=threshold)
newErr=validationError(part.training['x_matrix'],part.training['target_vector'],newTrainResult[0], lamb,newTrainResult[1], newTrainResult[2])
print('Training_Error : '+str(newErr))
newErr=validationError(part.validation['x_matrix'],part.validation['target_vector'],newTrainResult[0], lamb,newTrainResult[1], newTrainResult[2])
print('Validation_Error : '+str(newErr))
newErr=testError(part.testing['x_matrix'],part.testing['target_vector'],newTrainResult[0],lamb,newTrainResult[1], newTrainResult[2])
print('Testing_Error : '+str(newErr))
print
#Synthetic Data Set
clusterNum=8
lamb=0.2
eta=1
threshold=0.0001
print("-----------Synthetic Dataset-----------")
print
#Closed Form Solution
print("---CLOSED FORM SOLUTION----")
print('M : '+str(clusterNum))
print('Lambda : '+str(lamb))
newTrainResult = closedFormTrain(synthetic_part.training['x_matrix'],synthetic_part.training['target_vector'], clusterNum=clusterNum,n=0.5,lamb=0)
newErr=validationError(synthetic_part.training['x_matrix'],synthetic_part.training['target_vector'],newTrainResult[0],lamb,newTrainResult[1], newTrainResult[2])
print('Training_Error : '+str(newErr))
newErr=validationError(synthetic_part.validation['x_matrix'],synthetic_part.validation['target_vector'],newTrainResult[0],lamb,newTrainResult[1], newTrainResult[2])
print('Validation_Error : '+str(newErr))
newErr=testError(synthetic_part.testing['x_matrix'],synthetic_part.testing['target_vector'],newTrainResult[0],lamb,newTrainResult[1], newTrainResult[2])
print('Testing_Error : '+str(newErr))
print
#Stocastic Gradient Descent
lamb=0
print("---SGD SOLUTION----")
print('M : '+str(clusterNum))
print('Lambda : '+str(lamb))
print('Eta : '+str(eta))
newTrainResult = stochasticTrain(synthetic_part.training['x_matrix'],synthetic_part.training['target_vector'], clusterNum=clusterNum,n=0.5,lamb=lamb, eta=eta, threshold=threshold)
newErr=validationError(synthetic_part.training['x_matrix'],synthetic_part.training['target_vector'],newTrainResult[0],lamb,newTrainResult[1], newTrainResult[2])
print('Training_Error : '+str(newErr))
newErr=validationError(synthetic_part.validation['x_matrix'],synthetic_part.validation['target_vector'],newTrainResult[0], lamb,newTrainResult[1], newTrainResult[2])
print('Validation_Error : '+str(newErr))
newErr=testError(synthetic_part.testing['x_matrix'],synthetic_part.testing['target_vector'],newTrainResult[0],lamb,newTrainResult[1], newTrainResult[2])
print('Testing_Error : '+str(newErr))