def test_split_tables_bad_column(self):
with self.assertRaises(Exception):
split_tables(self.otu_table,
self.metabolite_table,
metadata=self.metadata,
training_column='bad',
num_test=10,
min_samples=0)
def test_split_tables_random(self):
res = split_tables(self.otu_table, self.metabolite_table,
num_test=2, min_samples=0)
(train_microbes, test_microbes,
train_metabolites, test_metabolites) = res
npt.assert_allclose(train_microbes.shape, np.array([3, 7]))
npt.assert_allclose(test_microbes.shape, np.array([2, 7]))
npt.assert_allclose(train_metabolites.shape, np.array([3, 9]))
npt.assert_allclose(test_metabolites.shape, np.array([2, 9]))
def test_split_tables_train_column(self):
res = split_tables(self.otu_table, self.metabolite_table,
metadata=self.metadata, training_column='testing',
num_test=10, min_samples=0)
(train_microbes, test_microbes,
train_metabolites, test_metabolites) = res
npt.assert_allclose(train_microbes.shape, np.array([3, 7]))
npt.assert_allclose(test_microbes.shape, np.array([2, 7]))
npt.assert_allclose(train_metabolites.shape, np.array([3, 9]))
npt.assert_allclose(test_metabolites.shape, np.array([2, 9]))
def paired_omics(microbes: biom.Table,
metabolites: biom.Table,
metadata: Metadata = None,
training_column: str = None,
num_testing_examples: int = 5,
min_feature_count: int = 10,
epochs: int = 100,
batch_size: int = 50,
latent_dim: int = 3,
input_prior: float = 1,
output_prior: float = 1,
learning_rate: float = 1e-3,
equalize_biplot: float = False,
arm_the_gpu: bool = False,
summary_interval: int = 60) -> (
pd.DataFrame, OrdinationResults, qiime2.Metadata
):
if metadata is not None:
metadata = metadata.to_dataframe()
if arm_the_gpu:
# pick out the first GPU
device_name = '/device:GPU:0'
else:
device_name = '/cpu:0'
# Note: there are a couple of biom -> pandas conversions taking
# place here. This is currently done on purpose, since we
# haven't figured out how to handle sparse matrix multiplication
# in the context of this algorithm. That is a future consideration.
res = split_tables(
microbes, metabolites,
metadata=metadata, training_column=training_column,
num_test=num_testing_examples,
min_samples=min_feature_count)
(train_microbes_df, test_microbes_df,
train_metabolites_df, test_metabolites_df) = res
train_microbes_coo = coo_matrix(train_microbes_df.values)
test_microbes_coo = coo_matrix(test_microbes_df.values)
with tf.Graph().as_default(), tf.Session() as session:
model = MMvec(
latent_dim=latent_dim,
u_scale=input_prior, v_scale=output_prior,
batch_size=batch_size,
device_name=device_name,
learning_rate=learning_rate)
model(session,
train_microbes_coo, train_metabolites_df.values,
test_microbes_coo, test_metabolites_df.values)
loss, cv = model.fit(epoch=epochs, summary_interval=summary_interval)
ranks = pd.DataFrame(model.ranks(), index=train_microbes_df.columns,
columns=train_metabolites_df.columns)
if latent_dim > 0:
u, s, v = svds(ranks - ranks.mean(axis=0), k=latent_dim)
else:
# fake it until you make it
u, s, v = svds(ranks - ranks.mean(axis=0), k=1)
ranks = ranks.T
ranks.index.name = 'featureid'
s = s[::-1]
u = u[:, ::-1]
v = v[::-1, :]
if equalize_biplot:
microbe_embed = u @ np.sqrt(np.diag(s))
metabolite_embed = v.T @ np.sqrt(np.diag(s))
else:
microbe_embed = u @ np.diag(s)
metabolite_embed = v.T
pc_ids = ['PC%d' % i for i in range(microbe_embed.shape[1])]
features = pd.DataFrame(
microbe_embed, columns=pc_ids,
index=train_microbes_df.columns)
samples = pd.DataFrame(
metabolite_embed, columns=pc_ids,
index=train_metabolites_df.columns)
short_method_name = 'mmvec biplot'
long_method_name = 'Multiomics mmvec biplot'
eigvals = pd.Series(s, index=pc_ids)
proportion_explained = pd.Series(s**2 / np.sum(s**2), index=pc_ids)
biplot = OrdinationResults(
short_method_name, long_method_name, eigvals,
samples=samples, features=features,
proportion_explained=proportion_explained)
its = np.arange(len(loss))
convergence_stats = pd.DataFrame(
{
'loss': loss,
'cross-validation': cv,
'iteration': its
}
)
convergence_stats.index.name = 'id'
convergence_stats.index = convergence_stats.index.astype(np.str)
c = convergence_stats['loss'].astype(np.float)
convergence_stats['loss'] = c
c = convergence_stats['cross-validation'].astype(np.float)
convergence_stats['cross-validation'] = c
c = convergence_stats['iteration'].astype(np.int)
convergence_stats['iteration'] = c
return ranks, biplot, qiime2.Metadata(convergence_stats)
def paired_omics(
microbes: biom.Table,
metabolites: biom.Table,
metadata: Metadata = None,
training_column: str = None,
num_testing_examples: int = 5,
min_feature_count: int = 10,
epochs: int = 100,
batch_size: int = 50,
latent_dim: int = 3,
input_prior: float = 1,
output_prior: float = 1,
learning_rate: float = 0.001,
summary_interval: int = 60) -> (pd.DataFrame, OrdinationResults):
if metadata is not None:
metadata = metadata.to_dataframe()
# Note: there are a couple of biom -> pandas conversions taking
# place here. This is currently done on purpose, since we
# haven't figured out how to handle sparse matrix multiplication
# in the context of this algorithm. That is a future consideration.
res = split_tables(microbes,
metabolites,
metadata=metadata,
training_column=training_column,
num_test=num_testing_examples,
min_samples=min_feature_count)
(train_microbes_df, test_microbes_df, train_metabolites_df,
test_metabolites_df) = res
train_microbes_coo = coo_matrix(train_microbes_df.values)
test_microbes_coo = coo_matrix(test_microbes_df.values)
with tf.Graph().as_default(), tf.Session() as session:
model = MMvec(latent_dim=latent_dim,
u_scale=input_prior,
v_scale=output_prior,
learning_rate=learning_rate)
model(session, train_microbes_coo, train_metabolites_df.values,
test_microbes_coo, test_metabolites_df.values)
loss, cv = model.fit(epoch=epochs, summary_interval=summary_interval)
U, V = model.U, model.V
U_ = np.hstack((np.ones(
(model.U.shape[0], 1)), model.Ubias.reshape(-1, 1), U))
V_ = np.vstack(
(model.Vbias.reshape(1, -1), np.ones((1, model.V.shape[1])), V))
ranks = pd.DataFrame(np.hstack((np.zeros(
(model.U.shape[0], 1)), U_ @ V_)),
index=train_microbes_df.columns,
columns=train_metabolites_df.columns)
ranks = ranks - ranks.mean(axis=1).values.reshape(-1, 1)
ranks = ranks - ranks.mean(axis=0)
u, s, v = svds(ranks, k=latent_dim)
s = s[::-1]
u = u[:, ::-1]
v = v[::-1, :]
microbe_embed = u @ np.diag(s)
metabolite_embed = v.T
pc_ids = ['PC%d' % i for i in range(microbe_embed.shape[1])]
features = pd.DataFrame(microbe_embed,
columns=pc_ids,
index=train_microbes_df.columns)
samples = pd.DataFrame(metabolite_embed,
columns=pc_ids,
index=train_metabolites_df.columns)
short_method_name = 'mmvec biplot'
long_method_name = 'Multiomics mmvec biplot'
eigvals = pd.Series(s, index=pc_ids)
proportion_explained = pd.Series(s**2 / np.sum(s**2), index=pc_ids)
biplot = OrdinationResults(short_method_name,
long_method_name,
eigvals,
samples=samples,
features=features,
proportion_explained=proportion_explained)
return ranks, biplot