demo_randomSet_v1.py

#------------------------------------------------------------------------------
#
#   Advanced Machine Learning - SVR vs. RVR
#
#   Author: Lukas Huber
#
#
#   Supervisor: Billard Aude
#
#------------------------------------------------------------------------------

# Libraries
import matplotlib.pyplot as plt

import numpy as np # Plots
import csv 

import random # Random number generator
import time # Time measurements

# Basic math function
from math import pi, sin

# Machine Learning Database
from sklearn.svm import SVR
from sklearn.cross_validation import train_test_split

from skbayes.rvm_ard_models import RegressionARD,ClassificationARD,RVR,RVC

# -----

plt.close('all')

# Count line numbers
inFileName = 'Datasets/human-development/gender_development.csv'
with open(inFileName, "rb") as csvfile:
    datareader = csv.reader(csvfile, delimiter = ',')
    rowNumb = -1
    for row in datareader:
        rowNumb += 1

# Define lists
countryCode = []
countryName = []
countryGDI = []
countryHDI_f = []
rowNames = []

with open(inFileName, "rb") as csvfile:
    datareader = csv.reader(csvfile, delimiter = ',')
    rowCount = 0
    for row in datareader:
        if(rowCount==0):
            rowCount += 1
            rowNames.append(row)
        else:
            if(row[2] != '..'):
                #countryGDI[rowCount] = float(row[2]))
                countryGDI.append(float(row[2]))
                countryHDI_f.append(float(row[3]))

                rowCount += 1
                countryName.append(row[1])
countryGDI = np.transpose(np.array([countryGDI]))
print(type(countryGDI))
print(countryGDI.shape)

countryHDI_f = np.transpose(np.array([countryHDI_f]))
print(type(countryHDI_f))
print(countryHDI_f.shape)

# CreateSHOX presonal sample
N_samp = 168; # Sample size
x_min = 0
x_max = 2*pi

# Create random random pseudo-measurement in form of sinus curve with noise          
x_val = np.sort(2*pi*np.random.rand(N_samp,1),axis=0);
y_val = np.sin(x_val).ravel() + np.ravel(np.random.normal(0,0.5,(N_samp,1)))

# Create reference sinus curve with 100 steps         
N_range = 100         
x2_val = [i/N_range*2*pi for i in range(N_range)]

print(type(x_val))
print(x_val.shape)
print(type(y_val))
print(y_val.shape)

# Change value
#x_val = countryGDI
#y_val = countryHDI_f

# Define Regression Function
svr_lin = SVR(kernel='linear', C=1e3)
svr_poly = SVR(kernel='poly', C=1e3, degree=3)
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
rvm = RVR(kernel = 'rbf', gamma=1) ### CHANGE TO RVR

# Proceed regression using Support Vector Regression (SVR)
t1 = time.time()
y_rbf = svr_rbf.fit(x_val, y_val).predict(x_val)
t2 = time.time()
t_svr_rbf = t2-t1
print('Support Vector Regression with RBF kernel takes {} s'.format(t_svr_rbf))

t1 = time.time()
y_lin = svr_lin.fit(x_val, y_val).predict(x_val)
t2 = time.time()
t_svr_lin = t2-t1
print('Support Vector Regression with linear  kernel takes {} s'.format(t_svr_lin))

t1 = time.time()
y_poly = svr_poly.fit(x_val, y_val).predict(x_val)
t2 = time.time()
t_svr_poly = t2-t1
print('Support Vector Regression with polynomial kernel takes {} s'.format(t_svr_poly))

# Proceed reression using Relevance Vector Regression (RVR)
t1 = time.time()
y_rvr = rvm.fit(x_val,y_val).predict(x_val)
t2 = time.time()
t_rvr = t2-t1
print('Relevance Vector Regression takes {} s'.format(t_rvr))

# Plot Data
plt.scatter(x_val,y_val, color='red',label='Datapoints')
plt.hold('on')
plt.xlim([x_min,x_max])
plt.plot(x2_val, [sin(x2_val[i]) for i in range(len(x2_val))], c='k',label='Original function')         
# Regression Plot
lw = 2
plt.plot(x_val, y_rbf, color='navy', lw=lw, label='RBF model')
plt.plot(x_val, y_lin, color='c', lw=lw, label='Linear model')
plt.plot(x_val, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model')

plt.plot(x_val, y_rvr, color='magenta', lw=lw, label='RVR using RBF')


# Plot specification
plt.xlabel('Data')
plt.ylabel('Target')
plt.title('Support Vector Regression')
plt.legend()
plt.show() # display plot in cmd


# Computation Time 


# Computation Cost
   # HOW# HOW

# Precision


# Memory Cost
   # HOW


print('Succesfully finished the demo script!')