def test_proper_initialise(self):
"""jackknife should initialise correctly"""
# Scalar
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
self.assertEqual(test_knife.n, data.shape[1])
self.assertEqual(test_knife._jackknifed_stat, None)
# Vector
mean_stat = stat_maker(mean, data, 1)
test_knife = JackknifeStats(data.shape[1], mean_stat)
self.assertEqual(test_knife.n, data.shape[1])
self.assertEqual(test_knife._jackknifed_stat, None)
def test_jackknife_stats(self):
"""jackknife results should match Sokal & Rolf example"""
# Scalar
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
assert_allclose(test_knife.jackknifed_stat, 1.2905845)
assert_allclose(test_knife.standard_error, 0.2884490)
self.assertTrue(test_knife._jackknifed_stat != None)
# Vector
mean_stat = stat_maker(mean, data, 1)
test_knife = JackknifeStats(data.shape[1], mean_stat)
expected_jk_stat = data.mean(axis=1)
got_jk_stat = test_knife.jackknifed_stat
expected_standard_err = [30.69509346, 1.87179671]
got_standard_err = test_knife.standard_error
for index in [0, 1]:
assert_allclose(got_jk_stat[index], expected_jk_stat[index])
assert_allclose(got_standard_err[index], expected_standard_err[index])
def test_tabular_properties(self):
"""constructs tabular properties"""
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
ss = test_knife.sub_sample_stats
self.assertEqual(ss.shape, (12, 2))
ss = test_knife.sample_stat
pvs = test_knife.pseudovalues
self.assertEqual(pvs.shape, (12, 2))
ss = test_knife.summary_stats
self.assertEqual(ss.shape, (1, 3))
def test_jackknife_stats(self):
"""jackknife results should match Sokal & Rolf example"""
# Scalar
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
self.assertFloatEqual(test_knife.JackknifedStat, 1.2905845)
self.assertFloatEqual(test_knife.Exception, 0.2884490)
self.assertTrue(test_knife._jackknifed_stat is not None)
# Vector
mean_stat = stat_maker(mean, data, 1)
test_knife = JackknifeStats(data.shape[1], mean_stat)
expected_jk_stat = data.mean(axis=1)
got_jk_stat = test_knife.JackknifedStat
expected_standard_err = [30.69509346, 1.87179671]
got_standard_err = test_knife.Exception
for index in [0, 1]:
self.assertFloatEqual(got_jk_stat[index], expected_jk_stat[index])
self.assertFloatEqual(got_standard_err[index],
expected_standard_err[index])
def test_tables(self):
"""jackknife should work for calculators return scalars or vectors"""
# Scalar
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
expected_subsample_stats = [
1.4151,
1.3946,
1.4314,
1.1889,
1.1323,
1.3083,
1.3561,
1.3453,
1.2412,
1.3216,
1.2871,
1.3664,
]
expected_pseudovalues = [
0.1968,
0.4224,
0.0176,
2.6852,
3.3084,
1.3718,
0.8461,
0.9650,
2.1103,
1.2253,
1.6049,
0.7333,
]
test_knife.jackknife()
got_subsample_stats = test_knife._subset_statistics
got_pseudovalues = test_knife._pseudovalues
for index in range(data.shape[1]):
np.testing.assert_almost_equal(
got_subsample_stats[index], expected_subsample_stats[index], 4
)
np.testing.assert_approx_equal(
got_pseudovalues[index], expected_pseudovalues[index], 4
)
# Vector
mean_stat = stat_maker(mean, data, 1)
test_knife = JackknifeStats(data.shape[1], mean_stat)
test_knife.jackknife()
expected_pseudovalues = data.transpose()
expected_subsample_stats = [
[198.9091, 11.8336],
[197.0909, 11.7609],
[204.2727, 12.1155],
[209.2727, 12.9155],
[178.4545, 11.0791],
[192.4545, 11.7882],
[204.2727, 13.0145],
[184.2727, 11.7055],
[206.0909, 12.7618],
[193.3636, 11.7436],
[184.2727, 11.5745],
[194.2727, 12.2773],
]
got_subsample_stats = test_knife._subset_statistics
got_pseudovalues = test_knife._pseudovalues
for index1 in range(data.shape[1]):
for index2 in range(data.shape[0]):
np.testing.assert_almost_equal(
got_subsample_stats[index1][index2],
expected_subsample_stats[index1][index2],
4,
)
np.testing.assert_almost_equal(
got_pseudovalues[index1][index2],
expected_pseudovalues[index1][index2],
4,
)