# coding: utf-8
import numpy as np
from runner import Runner
from mlcin.prototypes.rps import RandomPrototypeSelection
from mlcin.utils.keel import load_dataset
from mlcin.utils.graphics import plot_and_save
if __name__ == '__main__':
# creating prototype generation object
rps = RandomPrototypeSelection()
datasets = ['banana', 'normal', 'normal_multimodal']
for dataset in datasets:
X_orig, y_orig = load_dataset('datasets/artificial/' + dataset + '.data')
y_orig = np.asarray(y_orig, dtype=int)
rps.fit(X_orig, y_orig)
X, y = rps.reduce_data()
print dataset + '\treduction: %.2f' % (1.0 - float(y.shape[0])/len(y_orig))
plot_and_save(X_orig, y_orig, title='ORIGINAL', filename='images/ORIG_' + dataset + '.png')
plot_and_save(X, y, title='RSP', filename='images/RSP_' + dataset + '.png')
X_orig, y_orig = load_dataset('datasets/artificial/' + dataset + '.data')
y_orig = np.asarray(y_orig, dtype=int)
for iteration in iterations:
enpc = ENPC( X_orig, y_orig, 3, iteration )
enpc.run_ENPC()
X, y = enpc.getResult()
path = '../images'
if not os.path.exists( path ):
os.mkdir( path )
path = '../images/' + dataset
if not os.path.exists( path ):
os.mkdir( path )
path = '../images/' + dataset + '/iterations ' + str( iteration )
if not os.path.exists( path ):
os.mkdir( path )
f = open( path + '/reduction.txt', 'w' )
f.write( dataset + '\treduction: %.2f' % ( 1.0 - float(y.shape[0])/len(y_orig) ) )
f.close()
plot_and_save(X_orig, y_orig, title='ORIGINAL', filename=path + '/ORIG_' + dataset + '.png')
plot_and_save(X , y , title='ENPC' , filename=path + '/ENPC_' + dataset + '.png')
from mlcin.prototypes.sgp import SGP
from mlcin.prototypes.tomek_links import TomekLinks
if __name__ == '__main__':
datasets = ['banana', 'normal', 'normal_multimodal']
for dataset in datasets:
X_orig, y_orig = load_dataset('datasets/artificial/' + dataset + '.data')
y_orig = np.asarray(y_orig, dtype=int)
plot_and_save(X_orig, y_orig, title='ORIGINAL', filename='images/ORIG_' + dataset + '.png')
# creating prototype generation object
for mode in range(1,3):
title = ""
if mode == 1:
rps = ReductionBySpacePartitioning1(b=30)
title = "ReductionBySpacePartitioning 1 - b =30"
elif mode == 2:
rps = ReductionBySpacePartitioning2(b=30)
title = "ReductionBySpacePartitioning 2 - b =30"
elif mode == 3:
rps = ReductionBySpacePartitioning3()
title = "ReductionBySpacePartitioning 3"
elif mode == 4:
rps = CNN()
import numpy as np
from runner import Runner
from mlcin.prototypes.rps import RandomPrototypeSelection
from mlcin.utils.keel import load_dataset
from mlcin.utils.graphics import plot_and_save
if __name__ == '__main__':
# creating prototype generation object
rps = RandomPrototypeSelection()
datasets = ['banana', 'normal', 'normal_multimodal']
for dataset in datasets:
X_orig, y_orig = load_dataset('datasets/artificial/' + dataset +
'.data')
y_orig = np.asarray(y_orig, dtype=int)
rps.fit(X_orig, y_orig)
X, y = rps.reduce_data()
print dataset + '\treduction: %.2f' % (1.0 -
float(y.shape[0]) / len(y_orig))
plot_and_save(X_orig,
y_orig,
title='ORIGINAL',
filename='images/ORIG_' + dataset + '.png')
plot_and_save(X,
y,
title='RSP',
filename='images/RSP_' + dataset + '.png')