#!/usr/bin/python
"""
This module contains unittests that test splitter nodes
:Author: Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
:Created: 2008/12/18
"""
import unittest
import numpy
if __name__ == '__main__':
import sys
import os
# The root of the code
file_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(file_path[:file_path.rfind('pySPACE')-1])
from pySPACE.resources.data_types.time_series import TimeSeries
from pySPACE.missions.nodes.spatial_filtering.fda import FDAFilterNode
[docs]class FDAFilterTestCase(unittest.TestCase):
"""Test for FDAFilterNode"""
[docs] def setUp(self):
samples = 5000
ranges = [-3.5, 3.5, -3.5, 3.5]
numpy.random.seed(0)
true_data = numpy.zeros((samples, 2))
true_data[:,0] = numpy.random.normal(loc=0.0, scale=1.0, size=(samples,))
true_data[:,1] = numpy.random.normal(loc=0.0, scale=0.5, size=(samples,))
self.classes = [-1 if x < 0 else 1 for x in true_data[:,1]]
mixed_data = numpy.zeros((samples,2))
for i in range(samples):
mixed_data[i,0] = 0.6*true_data[i,0] + 0.4*true_data[i,1] + 1.0
mixed_data[i,1] = 0.4*true_data[i,0] - 0.6*true_data[i,1] + 1.5
self.data = numpy.zeros(mixed_data.shape)
self.data[:,0] = mixed_data[:,0] - numpy.mean(mixed_data[:,1])
self.data[:,1] = mixed_data[:,1] - numpy.mean(mixed_data[:,1])
self.data = [TimeSeries(data, channel_names = [("test_channel_%s" % j) for j in range(2)],
sampling_frequency = 10) for data in self.data]
[docs] def test_fda(self):
"""
Tests that FDA produces the expected transformation matrix on the data
"""
fda_node = FDAFilterNode(retained_channels=2)
for i,data in enumerate(self.data):
fda_node.train(data, self.classes[i])
fda_node.stop_training()
self.assert_(numpy.allclose(fda_node.filters,
numpy.array([[1.56207903, -1.15805762],
[-2.32599494, -0.79980837]])),
"FDA transformation matrix is wrong!")
transformed_data = []
for i,data in enumerate(self.data):
ts = TimeSeries(input_array = data,
channel_names = [("test_channel_%s" % j) for j in range(2)],
sampling_frequency = 10, start_time = 0, end_time = 1)
transformed_data.append(fda_node.execute(ts).view(numpy.ndarray))
self.assert_(numpy.allclose(transformed_data[0:2],
[numpy.array([[ -0.45549484, -1.20700466]]),
numpy.array([[ -1.52271298, 0.16829469]])]),
"FDA-transformed data does not match expectation!")
# self.plot(self.data, transformed_data)
[docs] def plot(self, original_data, transformed_data):
import pylab
pylab.figure(1)
pylab.subplot(1,2,1)
pylab.gcf().subplots_adjust(left=0.04, bottom=0.04, right=0.96, top= 0.96)
pylab.scatter(original_data[:,0], original_data[:,1],
color = ['r' if c==1 else 'b' for c in self.classes],
marker = 'o')
pylab.title("Original")
pylab.subplot(1,2,2)
pylab.scatter(transformed_data[:,0], transformed_data[:,1],
color = ['r' if c==1 else 'b' for c in self.classes], marker = 'o')
pylab.title("Transformed")
pylab.show()
# self.assert_(found, "One data point is never used for testing in cv splitting")
if __name__ == '__main__':
suite = unittest.TestLoader().loadTestsFromName('test_fda')
unittest.TextTestRunner(verbosity=2).run(suite)
