def multinomial(table: biom.Table,
metadata: Metadata,
formula: str,
training_column: str = None,
num_random_test_examples: int = 10,
epoch: int = 10,
batch_size: int = 5,
beta_prior: float = 1,
learning_rate: float = 0.1,
clipnorm: float = 10,
min_sample_count: int = 10,
min_feature_count: int = 10,
summary_interval: int = 60) -> (pd.DataFrame):
# load metadata and tables
metadata = metadata.to_dataframe()
# match them
table, metadata, design = match_and_filter(table, metadata, formula,
training_column,
num_random_test_examples,
min_sample_count,
min_feature_count)
# convert to dense representation
dense_table = table.to_dataframe().to_dense().T
# split up training and testing
trainX, testX, trainY, testY = split_training(dense_table, metadata,
design, training_column,
num_random_test_examples)
model = MultRegression(learning_rate=learning_rate,
clipnorm=clipnorm,
beta_mean=beta_prior,
batch_size=batch_size,
save_path=None)
with tf.Graph().as_default(), tf.Session() as session:
model(session, trainX, trainY, testX, testY)
model.fit(epoch=epoch,
summary_interval=summary_interval,
checkpoint_interval=None)
md_ids = np.array(design.columns)
obs_ids = table.ids(axis='observation')
beta_ = clr(clr_inv(np.hstack((np.zeros((model.p, 1)), model.B))))
beta_ = pd.DataFrame(
beta_.T,
columns=md_ids,
index=obs_ids,
)
return beta_
def test_fit(self):
tf.set_random_seed(0)
model = MultRegression(batch_size=10, learning_rate=1e-3, beta_scale=1)
Y = np.array(self.table.matrix_data.todense()).T
X = self.md.values
trainX = X[:-5]
trainY = Y[:-5]
testX = X[-5:]
testY = Y[-5:]
with tf.Graph().as_default(), tf.Session() as session:
model(session, trainX, trainY, testX, testY)
loss, cv, _ = model.fit(epoch=int(50000))
self.assertGreater(len(loss), 1)
self.assertGreater(len(cv), 1)
npt.assert_allclose(self.beta, model.B.T, atol=0.1, rtol=0.1)
def multinomial(table: biom.Table,
metadata: Metadata,
formula: str,
training_column: str = DEFAULTS["training-column"],
num_random_test_examples: int = (
DEFAULTS["num-random-test-examples"]
),
epochs: int = DEFAULTS["epochs"],
batch_size: int = DEFAULTS["batch-size"],
differential_prior: float = DEFAULTS["differential-prior"],
learning_rate: float = DEFAULTS["learning-rate"],
clipnorm: float = DEFAULTS["clipnorm"],
min_sample_count: int = DEFAULTS["min-sample-count"],
min_feature_count: int = DEFAULTS["min-feature-count"],
summary_interval: int = DEFAULTS["summary-interval"],
random_seed: int = DEFAULTS["random-seed"],
) -> (
pd.DataFrame, qiime2.Metadata, skbio.OrdinationResults
):
# load metadata and tables
metadata = metadata.to_dataframe()
# match them
table, metadata, design = match_and_filter(
table, metadata,
formula, min_sample_count, min_feature_count
)
# convert to dense representation
dense_table = table.to_dataframe().to_dense().T
# split up training and testing
trainX, testX, trainY, testY = split_training(
dense_table, metadata, design,
training_column, num_random_test_examples,
seed=random_seed,
)
model = MultRegression(learning_rate=learning_rate, clipnorm=clipnorm,
beta_mean=differential_prior,
batch_size=batch_size,
save_path=None)
with tf.Graph().as_default(), tf.Session() as session:
tf.set_random_seed(random_seed)
model(session, trainX, trainY, testX, testY)
loss, cv, its = model.fit(
epochs=epochs,
summary_interval=summary_interval,
checkpoint_interval=None)
md_ids = np.array(design.columns)
obs_ids = table.ids(axis='observation')
beta_ = np.hstack((np.zeros((model.p, 1)), model.B))
beta_ = beta_ - beta_.mean(axis=1).reshape(-1, 1)
differentials = pd.DataFrame(
beta_.T, columns=md_ids, index=obs_ids,
)
differentials.index.name = 'featureid'
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
# regression biplot
if differentials.shape[-1] > 1:
u, s, v = np.linalg.svd(differentials)
pc_ids = ['PC%d' % i for i in range(len(s))]
samples = pd.DataFrame(u[:, :len(s)] @ np.diag(s),
columns=pc_ids, index=differentials.index)
features = pd.DataFrame(v.T[:, :len(s)],
columns=pc_ids, index=differentials.columns)
short_method_name = 'regression_biplot'
long_method_name = 'Multinomial regression biplot'
eigvals = pd.Series(s, index=pc_ids)
proportion_explained = eigvals**2 / (eigvals**2).sum()
biplot = OrdinationResults(
short_method_name, long_method_name, eigvals,
samples=samples, features=features,
proportion_explained=proportion_explained)
else:
# this is to handle the edge case with only intercepts
biplot = OrdinationResults('', '', pd.Series(), pd.DataFrame())
return differentials, qiime2.Metadata(convergence_stats), biplot