#To import all shogun classes
from modshogun import *

#Generate some random data
X = 2 * random.randn(20,2)
traindata=r_[X + 5, X + 8].T

print traindata

feats_train=RealFeatures(traindata)

#create array of labels with 1 and -1
trainlab=concatenate((ones(20),-ones(20)))
print trainlab
#convert to shogun format labels
labels=BinaryLabels(trainlab)

# Plot the training data
figure(figsize=(6,4))
gray()
_=scatter(traindata[0, :], traindata[1,:], c=labels, s=50)
title("Training Data")
gray()

#prameters to svm
C=0.9
epsilon=1e-3

svm=LibLinear(C, feats_train, labels)
svm.set_liblinear_solver_type(L2R_L2LOSS_SVC)
svm.set_epsilon(epsilon)

#train
svm.train()

size=100


x1=linspace(0, 14, size)
x2=linspace(0, 14, size)
x, y=meshgrid(x1, x2)
#Generate X-Y grid test data
grid=RealFeatures(array((ravel(x), ravel(y))))

#apply on test grid
predictions = svm.apply(grid)

z=predictions.get_values().reshape((size, size))

#plot
jet()
figure(figsize=(8,6))
title("Classification")
c=pcolor(x, y, z)
_=contour(x, y, z, linewidths=1, colors='black', hold=True)
_=colorbar(c)

gray()
_=scatter(traindata[0, :], traindata[1,:], c=labels, s=50)
gray()

f=SparseRealFeatures()
#Load the file and generate labels.
trainlab=f.load_with_labels(LibSVMFile('../../../data/toy/diabetes_scale.svm'))
labels=BinaryLabels(trainlab)
#Get the feature matrix
mat=f.get_full_feature_matrix()
feats=array(mat[1])
feats=vstack((feats, array(mat[5])))
print feats, feats.shape

#convert to shogun format
feats_train=RealFeatures(feats)
#plot the training data
figure(figsize=(6,5))
_=scatter(feats[0, :], feats[1,:], c=trainlab, s=50)
_=xlabel('Plasma glucose concentration')
_=ylabel('Body mass index')

C=0.9
epsilon=1e-3

svm=LibLinear(C, feats_train, labels)
svm.set_liblinear_solver_type(L2R_L2LOSS_SVC)
svm.set_epsilon(epsilon)

#train
svm.train()


size=100
x1=linspace(-1.2, 1.2, size)
x2=linspace(-1.2, 1.2, size)
x, y=meshgrid(x1, x2)
#Generate X-Y grid test data
grid=RealFeatures(array((ravel(x), ravel(y))))

#apply on test grid
predictions = svm.apply(grid)

z=predictions.get_values().reshape((size, size))

#plot
jet()
figure(figsize=(8,6))
title("Classification")
c=pcolor(x, y, z)
_=contour(x, y, z, linewidths=1, colors='black', hold=True)
_=colorbar(c)

gray()
_=scatter(feats[0, :], feats[1,:], c=trainlab, s=50)
_=xlabel('Plasma glucose concentration')
_=ylabel('Body mass index')

#split features for training and evaluation
feats=array(mat[1])
feats_t=feats[0:700]
feats_e=feats[700:785]
feats=array(mat[5])
feats_t1=feats[0:700]
feats_e1=feats[700:785]
feats_t=vstack((feats_t, feats_t1))
feats_e=vstack((feats_e, feats_e1))

feats_train=RealFeatures(feats_t)
feats_evaluate=RealFeatures(feats_e)

label_t=trainlab[0:700]
labels=BinaryLabels(label_t)
label_e=trainlab[700:785]
labels_true=BinaryLabels(label_e)

svm=LibLinear(C, feats_train, labels)
svm.set_liblinear_solver_type(L2R_L2LOSS_SVC)
svm.set_epsilon(epsilon)

#train and evaluate
svm.train()
output=svm.apply(feats_evaluate)

#use ROCEvaluation to get accuracy
evaluator=ROCEvaluation()
print 'Accuracy(%):'
print evaluator.evaluate(output,labels_true)*100