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rename X_approx_distribution to X_approximate_distribution (#2337)

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Bruce Martin
2021-07-27 13:43:04 -07:00
committed by GitHub
parent 1998c0ad63
commit 0b1ab02a60
20 changed files with 79 additions and 77 deletions
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backend/test/test_server/unit/compute/test_est_dist.py
+8 -8
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@@ -2,7 +2,7 @@ import unittest
import numpy as np
from scipy import sparse
from backend.common.compute.estimate_distribution import estimate_approximate_distribution
from backend.common.constants import XApproxDistribution
from backend.common.constants import XApproximateDistribution
from backend.server.data_common.matrix_loader import MatrixDataLoader
from backend.test.test_server.unit import app_config
from backend.test import PROJECT_ROOT
@@ -20,22 +20,22 @@ class EstDistTest(unittest.TestCase):
def test_adaptestimate_approximate_distribution(self):
adaptor = self.load_dataset(f"{PROJECT_ROOT}/example-dataset/pbmc3k.h5ad")
self.assertEqual(adaptor.get_X_approx_distribution(), XApproxDistribution.NORMAL)
self.assertEqual(adaptor.get_X_approximate_distribution(), XApproximateDistribution.NORMAL)
def test_estimate_approximate_distribution(self):
raw = np.random.exponential(scale=1000, size=(100, 40))
# ndarray
self.assertEqual(estimate_approximate_distribution(raw), XApproxDistribution.COUNT)
self.assertEqual(estimate_approximate_distribution(np.log1p(raw)), XApproxDistribution.NORMAL)
self.assertEqual(estimate_approximate_distribution(raw), XApproximateDistribution.COUNT)
self.assertEqual(estimate_approximate_distribution(np.log1p(raw)), XApproximateDistribution.NORMAL)
# csr_matrix
self.assertEqual(estimate_approximate_distribution(sparse.csr_matrix(raw)), XApproxDistribution.COUNT)
self.assertEqual(estimate_approximate_distribution(sparse.csr_matrix(raw)), XApproximateDistribution.COUNT)
self.assertEqual(
estimate_approximate_distribution(sparse.csr_matrix(np.log1p(raw))), XApproxDistribution.NORMAL
estimate_approximate_distribution(sparse.csr_matrix(np.log1p(raw))), XApproximateDistribution.NORMAL
)
# BIG (ie, trigger MT)
big = np.random.exponential(scale=100, size=(1_000_000, 100))
self.assertEqual(estimate_approximate_distribution(big), XApproxDistribution.COUNT)
self.assertEqual(estimate_approximate_distribution(np.log1p(big)), XApproxDistribution.NORMAL)
self.assertEqual(estimate_approximate_distribution(big), XApproximateDistribution.COUNT)
self.assertEqual(estimate_approximate_distribution(np.log1p(big)), XApproximateDistribution.NORMAL)