Source code for lcc.tests.test_filter

from matplotlib import pyplot
import unittest

from lcc.db_tier.stars_provider import StarsProvider
from lcc.stars_processing.deciders.supervised_deciders import SVCDec
from lcc.stars_processing.descriptors.curve_shape_descr import CurvesShapeDescr
from lcc.stars_processing.descriptors.hist_shape_descr import HistShapeDescr
from lcc.stars_processing.stars_filter import StarsFilter
import numpy as np


[docs]class Test(unittest.TestCase):
[docs] def setUp(self): days_per_bin = 10 alphabet_size = 15 s_queries = [{"path": "quasars"}] client = StarsProvider().getProvider( obtain_method="FileManager", obtain_params=s_queries) self.s_stars = client.getStarsWithCurves() c_queries = [{"path": "some_stars"}] client = StarsProvider().getProvider( obtain_method="FileManager", obtain_params=c_queries) self.c_stars = client.getStarsWithCurves() self.N = int(np.mean([len(self.s_stars), len(self.c_stars)])) self.lc_shape_descriptor = CurvesShapeDescr(self.s_stars[:self.N / 3], days_per_bin, alphabet_size) self.hist_shape_descriptor = HistShapeDescr( self.s_stars[:self.N / 3], 10, alphabet_size) self.qda_decider = SVCDec()
[docs] def tearDown(self): pass
[docs] def testROC(self): star_filter = StarsFilter( [self.lc_shape_descriptor, self.hist_shape_descriptor], [self.qda_decider]) star_filter.learn( self.s_stars[self.N / 3:2 * self.N / 3], self.c_stars[2 * self.N / 3:]) roc = star_filter.getROCs( self.s_stars, self.c_stars, 10) print roc pyplot.plot(roc[0], roc[1], "b-") pyplot.show()
# self.c_stars[self.N / 2:], 5) if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.testName'] unittest.main()