def setUp(self):
np.random.seed(1)
res = random_multimodal(num_microbes=8,
num_metabolites=8,
num_samples=150,
latent_dim=2,
sigmaQ=2,
microbe_total=1000,
metabolite_total=10000,
seed=1)
(self.microbes, self.metabolites, self.X, self.B, self.U, self.Ubias,
self.V, self.Vbias) = res
n, d1 = self.microbes.shape
n, d2 = self.metabolites.shape
self.microbes = biom.Table(self.microbes.values.T,
self.microbes.columns, self.microbes.index)
self.metabolites = biom.Table(self.metabolites.values.T,
self.metabolites.columns,
self.metabolites.index)
U_ = np.hstack((np.ones((self.U.shape[0], 1)), self.Ubias, self.U))
V_ = np.vstack((self.Vbias, np.ones((1, self.V.shape[1])), self.V))
uv = U_ @ V_
h = np.zeros((d1, 1))
self.exp_ranks = clr_inv(np.hstack((h, uv)))
def setUp(self):
# build small simulation
res = random_multimodal(
num_microbes=100, num_metabolites=1000, num_samples=300,
latent_dim=2, sigmaQ=2,
microbe_total=5000, metabolite_total=10000, seed=1
)
(self.microbes, self.metabolites, self.X, self.B,
self.U, self.Ubias, self.V, self.Vbias) = res
num_train = 10
self.trainX = self.microbes.iloc[:-num_train]
self.testX = self.microbes.iloc[-num_train:]
self.trainY = self.metabolites.iloc[:-num_train]
self.testY = self.metabolites.iloc[-num_train:]