File size: 3,419 Bytes
9d901ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | """Tests for DeepPTR posterior extraction."""
import numpy as np
import pytest
import torch
from scptr.deep._model import DeepPTR
from scptr.deep._guide import posterior_gamma, extract_latent
@pytest.fixture
def trained_model_and_adata():
"""A small model with matching AnnData (untrained, just for shape checks)."""
from anndata import AnnData
rng = np.random.RandomState(0)
n, g = 50, 20
s = rng.poisson(5, size=(n, g)).astype(np.float32)
u = rng.poisson(2, size=(n, g)).astype(np.float32)
adata = AnnData(X=s)
adata.layers["spliced"] = s
adata.layers["unspliced"] = u
torch.manual_seed(0)
model = DeepPTR(n_genes=g, d_T=3, d_PT=3, d_hidden=16, n_enc_layers=1)
model.eval()
return model, adata
class TestPosteriorGamma:
def test_shapes(self, trained_model_and_adata):
model, adata = trained_model_and_adata
gamma_mean, gamma_var = posterior_gamma(
model, adata, n_samples=5, batch_size=16, device="cpu"
)
assert gamma_mean.shape == (adata.n_obs, adata.n_vars)
assert gamma_var.shape == (adata.n_obs, adata.n_vars)
def test_positive_values(self, trained_model_and_adata):
model, adata = trained_model_and_adata
gamma_mean, gamma_var = posterior_gamma(
model, adata, n_samples=5, device="cpu"
)
assert (gamma_mean >= 0).all()
assert (gamma_var >= 0).all()
def test_no_nans(self, trained_model_and_adata):
model, adata = trained_model_and_adata
gamma_mean, gamma_var = posterior_gamma(
model, adata, n_samples=5, device="cpu"
)
assert not np.isnan(gamma_mean).any()
assert not np.isnan(gamma_var).any()
def test_more_samples_lower_variance_of_mean(self, trained_model_and_adata):
"""With more MC samples, the posterior mean estimate should be more stable."""
model, adata = trained_model_and_adata
means = []
for _ in range(3):
gm, _ = posterior_gamma(model, adata, n_samples=2, device="cpu")
means.append(gm.mean())
spread_few = np.std(means)
means = []
for _ in range(3):
gm, _ = posterior_gamma(model, adata, n_samples=20, device="cpu")
means.append(gm.mean())
spread_many = np.std(means)
# Not a hard guarantee, but should generally hold
# Use a generous threshold — we just want a sanity check
assert spread_many < spread_few * 5
class TestExtractLatent:
def test_shapes(self, trained_model_and_adata):
model, adata = trained_model_and_adata
z_T, z_PT = extract_latent(model, adata, batch_size=16, device="cpu")
assert z_T.shape == (adata.n_obs, model.d_T)
assert z_PT.shape == (adata.n_obs, model.d_PT)
def test_deterministic(self, trained_model_and_adata):
model, adata = trained_model_and_adata
z_T1, z_PT1 = extract_latent(model, adata, device="cpu")
z_T2, z_PT2 = extract_latent(model, adata, device="cpu")
np.testing.assert_allclose(z_T1, z_T2, atol=1e-6)
np.testing.assert_allclose(z_PT1, z_PT2, atol=1e-6)
def test_no_nans(self, trained_model_and_adata):
model, adata = trained_model_and_adata
z_T, z_PT = extract_latent(model, adata, device="cpu")
assert not np.isnan(z_T).any()
assert not np.isnan(z_PT).any()
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