def test_random_select_errors(self):
obs = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host',
'random-select-errors')
with self.assertRaisesRegex(ValueError, 'uniformly subsampled'):
obs.alpha_table()
with self.assertRaisesRegex(ValueError, 'uniformly subsampled'):
obs.beta_table()
with self.assertRaisesRegex(ValueError, 'uniformly subsampled'):
obs.microbes_over_time()
def test_alpha(self):
skl = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host',
'test-alpha')
np.random.seed(0)
skl.randomly_select(5)
obs = skl.alpha_table(['faith_pd', 'observed_otus'])
self.assertTrue(skl._alpha_diversity_values is not None)
columns = [
'faith_pd_absolute_sum_of_diff', 'faith_pd_abs_mean_diff',
'faith_pd_variance_larger_than_standard_deviation',
'faith_pd_abs_energy', 'observed_otus_absolute_sum_of_diff',
'observed_otus_abs_mean_diff',
'observed_otus_variance_larger_than_standard_deviation',
'observed_otus_abs_energy'
]
data = [[
2.1999999999999993, 0.5499999999999998, 0.0, 23.919999999999995, 2,
0.5, False, 32
],
[
2.200000000000001, 0.5500000000000003, 0.0,
6.760000000000001, 3, 0.75, False, 22
]]
exp = pd.DataFrame(data=data,
index=pd.Index(['A', 'B'], name='Host'),
columns=columns)
pd.util.testing.assert_frame_equal(obs, exp)
def test_alpha_errors(self):
skl = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host',
'random-select-errors')
skl.randomly_select(5)
with self.assertRaisesRegex(ValueError, 'find one or more metrics'):
skl.alpha_table(metrics=['does_not_exist'])