def setUp(self):
res = random_multinomial_model(
num_samples=50,
num_features=5,
reps=1,
low=-1,
high=1,
beta_mean=0,
beta_scale=1,
mu=1000, # sequencing depth
sigma=0.5,
seed=0)
self.table, self.md, self.beta = res
tf.set_random_seed(0)
md = self.md
md.name = 'sampleid'
md = qiime2.Metadata(md)
self.ref_beta, self.ref_stats = multinomial(table=self.table,
metadata=md,
summary_interval=1,
formula="X",
epoch=50000)
self.base_beta, self.base_stats = multinomial(table=self.table,
metadata=md,
summary_interval=1,
formula="1",
epoch=50000)
self.results = "results"
if not os.path.exists(self.results):
os.mkdir(self.results)
def test_fit_consistency(self):
md = self.md
md.name = 'sampleid'
md = qiime2.Metadata(md)
res_beta1, res_stats1, res_biplot1 = multinomial(table=self.table,
metadata=md,
min_sample_count=0,
min_feature_count=0,
formula="X",
epochs=1000)
res_beta2, res_stats2, res_biplot2 = multinomial(table=self.table,
metadata=md,
min_sample_count=0,
min_feature_count=0,
formula="X",
epochs=1000)
npt.assert_array_equal(res_beta1, res_beta2)
end_res_stats1 = res_stats1.to_dataframe().iloc[-1]
end_res_stats2 = res_stats2.to_dataframe().iloc[-1]
npt.assert_array_equal(end_res_stats1, end_res_stats2)
npt.assert_array_equal(res_biplot1.eigvals, res_biplot2.eigvals)
npt.assert_array_equal(res_biplot1.samples, res_biplot2.samples)
npt.assert_array_equal(res_biplot1.features, res_biplot2.features)
def test_fit(self):
tf.set_random_seed(0)
md = self.md
md.name = 'sampleid'
md = qiime2.Metadata(md)
exp_beta = clr(clr_inv(np.hstack((np.zeros((2, 1)), self.beta.T))))
res_beta = multinomial(table=self.table,
metadata=md,
formula="X",
epoch=50000)
npt.assert_allclose(exp_beta, res_beta.T, atol=0.5, rtol=0.5)
def _silent_helper(self, silent):
tf.set_random_seed(0)
md = self.md
md.name = 'sampleid'
md = qiime2.Metadata(md)
f = io.StringIO()
with contextlib.redirect_stderr(f):
res_beta, res_stats, res_biplot = multinomial(table=self.table,
metadata=md,
min_sample_count=0,
min_feature_count=0,
formula="X",
epochs=1000,
silent=silent)
return f
def test_fit(self):
tf.set_random_seed(0)
md = self.md
md.name = 'sampleid'
md = qiime2.Metadata(md)
exp_beta = clr(clr_inv(np.hstack((np.zeros((2, 1)), self.beta.T))))
res_beta, res_stats, res_biplot = multinomial(table=self.table,
metadata=md,
min_sample_count=0,
min_feature_count=0,
formula="X",
epochs=1000)
# test biplot
self.assertIsInstance(res_biplot, OrdinationResults)
u = res_biplot.samples.values
v = res_biplot.features.values.T
npt.assert_allclose(u @ v, res_beta.values, atol=0.5, rtol=0.5)
npt.assert_allclose(exp_beta, res_beta.T, atol=0.6, rtol=0.6)
self.assertGreater(len(res_stats.to_dataframe().index), 1)