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_beta(self):
skl = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host',
'unittest-test-beta')
path = 'roc-curves/%s/cached-matrices/' % skl.name
# avoid any unwanted accidents
self.to_delete.append('roc-curves/%s/' % skl.name)
np.random.seed(0)
skl.randomly_select(5)
obs = skl.beta_table(['unweighted_unifrac', 'jaccard'])
data = [[
0.3927777777777778, 0.4126532637086283, 0.9375, 0.12499999999999999
], [0.6557886557886559, 0.1365522219610505, 1.0, 0.0]]
columns = [
'unweighted_unifrac_mean', 'unweighted_unifrac_std',
'jaccard_mean', 'jaccard_std'
]
exp = pd.DataFrame(data=data,
columns=columns,
index=pd.Index(['A', 'B'], name='Host'))
pd.util.testing.assert_frame_equal(obs, exp)
self.assertTrue(os.path.exists(path))
self.assertTrue(
os.path.exists(os.path.join(path, 'unweighted_unifrac.full.'
'txt')))
self.assertTrue(os.path.exists(os.path.join(path, 'jaccard.full.txt')))
def test_random_select(self):
np.random.seed(0)
obs = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host',
'random-select')
self.assertTrue(obs.mapping_file is None)
self.assertTrue(obs.biom_table is None)
obs.randomly_select(3)
# if we randomly select three samples there should be 6 in total
self.assertTrue(len(obs.mapping_file) == 6)
self.assertEqual(obs.biom_table.shape, (7, 6))
def test_constructor_errors(self):
with self.assertRaisesRegex(ValueError, 'The gradient category'):
_ = Sculptor(self.bt, self.mf, self.tree, 'XXX', 'Host')
with self.assertRaisesRegex(ValueError, 'The trajectory category'):
_ = Sculptor(self.bt, self.mf, self.tree, 'Day', 'XXX')
with self.assertRaisesRegex(ValueError, 'numeric dtype'):
_ = Sculptor(self.bt, self.mf, self.tree, 'Treatment', 'Host')
# create fake metadata
self.bt.update_ids({i: i + 'xx' for i in self.bt.ids()}, inplace=True)
with self.assertRaisesRegex(ValueError, 'without metadata'):
_ = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host')
def test_microbes_over_time(self):
skl = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host',
'microbes-over-time')
np.random.seed(0)
skl.randomly_select(5)
obs = skl.microbes_over_time()
metrics = ['mean', 'abs_energy', 'non_zero_samples', 'abs_mean_diff']
columns = ['%s_%s' % (a, b) for a, b in product(range(1, 8), metrics)]
index = ['A', 'B']
self.assertEqual(obs.columns.tolist(), columns)
self.assertEqual(obs.index.tolist(), index)
self.assertEqual(obs.values.shape, (2, 28))
def test_constructor(self):
obs = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host', 'test-name')
self.assertTrue(obs.mapping_file is None)
self.assertTrue(obs.biom_table is None)
self.assertEqual(obs.name, 'test-name')
self.assertTrue(obs._alpha_diversity_values is None)
self.assertTrue(obs._beta_diversity_matrices is None)
pd.util.testing.assert_frame_equal(self.mf, obs._original_mf)
np.testing.assert_equal(obs._original_bt.ids(), self.bt.ids())
np.testing.assert_equal(obs._original_bt.ids('observation'),
self.bt.ids('observation'))
a = [self.bt.data(i) for i in self.bt.ids()]
b = [obs._original_bt.data(i) for i in obs._original_bt.ids()]
np.testing.assert_allclose(a, b)
# needed to allow for phylogenetic metrics
for node in obs.tree.postorder():
self.assertTrue(node.length is not None)
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_beta_errors(self):
skl = Sculptor(self.bt, self.mf, self.tree, 'Day', 'Host',
'unittest-beta-errors')
self.to_delete.append('roc-curves/%s' % skl.name)
skl.randomly_select(5)
with self.assertRaisesRegex(ValueError, 'find one or more metrics'):
skl.beta_table(metrics=['does_not_exist'])
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'])