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rename X_approx_distribution to X_approximate_distribution (#2337)

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This commit is contained in:
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/common/config/__init__.py
+3 -3
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@@ -103,7 +103,7 @@ class ConfigTests(unittest.TestCase):
environment=None,
aws_secrets_manager_region=None,
aws_secrets_manager_secrets=[],
X_approx_distribution="auto",
X_approximate_distribution="auto",
config_file_name="app_config.yml",
):
random_num = random.randrange(999999)
@@ -151,7 +151,7 @@ class ConfigTests(unittest.TestCase):
enable_difexp=enable_difexp,
lfc_cutoff=lfc_cutoff,
top_n=top_n,
X_approx_distribution=X_approx_distribution,
X_approximate_distribution=X_approximate_distribution,
config_file_name=f"temp_dataset_config_{random_num}.yml",
)
external_config = self.custom_external_config(
@@ -187,7 +187,7 @@ class ConfigTests(unittest.TestCase):
enable_difexp="true",
lfc_cutoff=0.01,
top_n=10,
X_approx_distribution="auto",
X_approximate_distribution="auto",
config_file_name="dataset_config.yml",
):
configfile = os.path.join(self.tmp_fixtures_directory, config_file_name)
backend/test/test_server/unit/compute/test_est_dist.py
+8 -8
View File
@@ -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)
backend/test/test_server/unit/data_anndata/test_anndata_adaptor.py
+4 -2
View File
@@ -22,7 +22,7 @@ Test the anndata adaptor using the pbmc3k data set.
@parameterized_class(
("data_locator", "backed", "X_approx_distribution"),
("data_locator", "backed", "X_approximate_distribution"),
[
(f"{PROJECT_ROOT}/example-dataset/pbmc3k.h5ad", False, "auto"),
(f"{FIXTURES_ROOT}/pbmc3k-CSC-gz.h5ad", False, "auto"),
@@ -41,7 +41,9 @@ Test the anndata adaptor using the pbmc3k data set.
class AdaptorTest(unittest.TestCase):
def setUp(self):
config = app_config(
self.data_locator, self.backed, extra_dataset_config=dict(X_approx_distribution=self.X_approx_distribution)
self.data_locator,
self.backed,
extra_dataset_config=dict(X_approximate_distribution=self.X_approximate_distribution),
)
self.data = AnndataAdaptor(DataLocator(self.data_locator), config)