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"""Module contains tests for gn3.partial_correlations"""
from unittest import TestCase
import pandas
import pytest
from numpy.testing import assert_allclose
from gn3.computations.partial_correlations import (
fix_samples,
control_samples,
build_data_frame,
dictify_by_samples,
tissue_correlation,
find_identical_traits,
good_dataset_samples_indexes)
sampleslist = ["B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"]
control_traits = (
{
"mysqlid": 36688172,
"data": {
"B6cC3-1": {
"sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
"ndata": None},
"BXD1": {
"sample_name": "BXD1", "value": 7.77141, "variance": None,
"ndata": None},
"BXD12": {
"sample_name": "BXD12", "value": 8.39265, "variance": None,
"ndata": None},
"BXD16": {
"sample_name": "BXD16", "value": 8.17443, "variance": None,
"ndata": None},
"BXD19": {
"sample_name": "BXD19", "value": 8.30401, "variance": None,
"ndata": None},
"BXD2": {
"sample_name": "BXD2", "value": 7.80944, "variance": None,
"ndata": None}}},
{
"mysqlid": 36688172,
"data": {
"B6cC3-21": {
"sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
"ndata": None},
"BXD21": {
"sample_name": "BXD1", "value": 7.77141, "variance": None,
"ndata": None},
"BXD12": {
"sample_name": "BXD12", "value": 8.39265, "variance": None,
"ndata": None},
"BXD16": {
"sample_name": "BXD16", "value": 8.17443, "variance": None,
"ndata": None},
"BXD19": {
"sample_name": "BXD19", "value": 8.30401, "variance": None,
"ndata": None},
"BXD2": {
"sample_name": "BXD2", "value": 7.80944, "variance": None,
"ndata": None}}},
{
"mysqlid": 36688172,
"data": {
"B6cC3-1": {
"sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
"ndata": None},
"BXD1": {
"sample_name": "BXD1", "value": 7.77141, "variance": None,
"ndata": None},
"BXD12": {
"sample_name": "BXD12", "value": None, "variance": None,
"ndata": None},
"BXD16": {
"sample_name": "BXD16", "value": None, "variance": None,
"ndata": None},
"BXD19": {
"sample_name": "BXD19", "value": None, "variance": None,
"ndata": None},
"BXD2": {
"sample_name": "BXD2", "value": 7.80944, "variance": None,
"ndata": None}}})
dictified_control_samples = (
{"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879, "variance": None},
"BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None},
"BXD12": {"sample_name": "BXD12", "value": 8.39265, "variance": None},
"BXD16": {"sample_name": "BXD16", "value": 8.17443, "variance": None},
"BXD19": {"sample_name": "BXD19", "value": 8.30401, "variance": None},
"BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None}},
{"BXD12": {"sample_name": "BXD12", "value": 8.39265, "variance": None},
"BXD16": {"sample_name": "BXD16", "value": 8.17443, "variance": None},
"BXD19": {"sample_name": "BXD19", "value": 8.30401, "variance": None},
"BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None}},
{"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879, "variance": None},
"BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None},
"BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None}})
class TestPartialCorrelations(TestCase):
"""Class for testing partial correlations computation functions"""
@pytest.mark.unit_test
def test_control_samples(self):
"""Test that the control_samples works as expected."""
self.assertEqual(
control_samples(control_traits, sampleslist),
((("B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"),
("BXD12", "BXD16", "BXD19", "BXD2"),
("B6cC3-1", "BXD1", "BXD2")),
((7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944),
(8.39265, 8.17443, 8.30401, 7.80944),
(7.51879, 7.77141, 7.80944)),
((None, None, None, None, None, None), (None, None, None, None),
(None, None, None)),
(6, 4, 3)))
@pytest.mark.unit_test
def test_dictify_by_samples(self):
"""
Test that `dictify_by_samples` generates the appropriate dict
Given:
a sequence of sequences with sample names, values and variances, as
in the output of `gn3.partial_correlations.control_samples` or
the output of `gn3.db.traits.export_informative`
When:
the sequence is passed as an argument into the
`gn3.partial_correlations.dictify_by_sample`
Then:
return a sequence of dicts with keys being the values of the sample
names, and each of who's values being sub-dicts with the keys
'sample_name', 'value' and 'variance' whose values correspond to the
values passed in.
"""
self.assertEqual(
dictify_by_samples(
((("B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"),
("BXD12", "BXD16", "BXD19", "BXD2"),
("B6cC3-1", "BXD1", "BXD2")),
((7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944),
(8.39265, 8.17443, 8.30401, 7.80944),
(7.51879, 7.77141, 7.80944)),
((None, None, None, None, None, None), (None, None, None, None),
(None, None, None)),
(6, 4, 3))),
dictified_control_samples)
@pytest.mark.unit_test
def test_fix_samples(self):
"""
Test that `fix_samples` returns only the common samples
Given:
- A primary trait
- A sequence of control samples
When:
- The two arguments are passed to `fix_samples`
Then:
- Only the names of the samples present in the primary trait that
are also present in ALL the control traits are present in the
return value
- Only the values of the samples present in the primary trait that
are also present in ALL the control traits are present in the
return value
- ALL the values for ALL the control traits are present in the
return value
- Only the variances of the samples present in the primary trait
that are also present in ALL the control traits are present in the
return value
- ALL the variances for ALL the control traits are present in the
return value
- The return value is a tuple of the above items, in the following
order:
((sample_names, ...), (primary_trait_values, ...),
(control_traits_values, ...), (primary_trait_variances, ...)
(control_traits_variances, ...))
"""
self.assertEqual(
fix_samples(
{"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879,
"variance": None},
"BXD1": {"sample_name": "BXD1", "value": 7.77141,
"variance": None},
"BXD2": {"sample_name": "BXD2", "value": 7.80944,
"variance": None}},
dictified_control_samples),
(("BXD2",), (7.80944,),
(7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944, 8.39265,
8.17443, 8.30401, 7.80944, 7.51879, 7.77141, 7.80944),
(None,),
(None, None, None, None, None, None, None, None, None, None, None,
None, None)))
@pytest.mark.unit_test
def test_find_identical_traits(self):
"""
Test `gn3.partial_correlations.find_identical_traits`.
Given:
- the name of a primary trait
- a sequence of values for the primary trait
- a sequence of names of control traits
- a sequence of values of control traits
When:
- the arguments above are passed to the `find_identical_traits`
function
Then:
- Return ALL trait names that have the same value when up to three
decimal places are considered
"""
for primn, primv, contn, contv, expected in (
("pt", (12.98395,), ("ct0", "ct1", "ct2"),
((0.1234, 2.3456, 3.4567),), tuple()),
("pt", (12.98395, 2.3456, 3.4567), ("ct0", "ct1", "ct2"),
((12.98354, 2.3456, 3.4567), (64.2334, 6.3256, 64.2364),
(4.2374, 67.2345, 7.48234)), ("pt", "ct0")),
("pt", (12.98395, 75.52382), ("ct0", "ct1", "ct2", "ct3"),
((0.1234, 2.3456), (0.3621, 6543.572), (0.1234, 2.3456),
(0.1233, 4.5678)), ("ct0", "ct2"))
):
with self.subTest(
primary_name=primn, primary_value=primv,
control_names=contn, control_values=contv):
self.assertEqual(
find_identical_traits(primn, primv, contn, contv), expected)
@pytest.mark.unit_test
def test_tissue_correlation_error(self):
"""
Test that `tissue_correlation` raises specific exceptions for particular
error conditions.
"""
for primary, target, method, error, error_msg in (
((1, 2, 3), (4, 5, 6, 7), "pearson",
AssertionError,
(
"The lengths of the `primary_trait_values` and "
"`target_trait_values` must be equal")),
((1, 2, 3), (4, 5, 6, 7), "spearman",
AssertionError,
(
"The lengths of the `primary_trait_values` and "
"`target_trait_values` must be equal")),
((1, 2, 3, 4), (5, 6, 7), "pearson",
AssertionError,
(
"The lengths of the `primary_trait_values` and "
"`target_trait_values` must be equal")),
((1, 2, 3, 4), (5, 6, 7), "spearman",
AssertionError,
(
"The lengths of the `primary_trait_values` and "
"`target_trait_values` must be equal")),
((1, 2, 3), (4, 5, 6), "nonexistentmethod",
AssertionError,
(
"Method must be one of: pearson, spearman"))):
with self.subTest(primary=primary, target=target, method=method):
with self.assertRaises(error, msg=error_msg):
tissue_correlation(primary, target, method)
@pytest.mark.unit_test
def test_tissue_correlation(self): # pylint: disable=R0201
"""
Test that the correct correlation values are computed for the given:
- primary trait
- target trait
- method
"""
for primary, target, method, expected in (
((12.34, 18.36, 42.51), (37.25, 46.25, 46.56), "pearson",
(0.6761779252651052, 0.5272701133657985)),
((1, 2, 3, 4, 5), (5, 6, 7, 8, 7), "spearman",
(0.8207826816681233, 0.08858700531354381))):
with self.subTest(primary=primary, target=target, method=method):
assert_allclose(
tissue_correlation(primary, target, method), expected)
@pytest.mark.unit_test
def test_good_dataset_samples_indexes(self):
"""
Test that `good_dataset_samples_indexes` returns correct indices.
"""
self.assertEqual(
good_dataset_samples_indexes(
("a", "e", "i", "k"),
("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l")),
(0, 4, 8, 10))
@pytest.mark.unit_test
def test_build_data_frame(self):
"""
Check that the function builds the correct data frame.
"""
for xdata, ydata, zdata, expected in (
((0.1, 1.1, 2.1), (2.1, 3.1, 4.1), (5.1, 6.1, 7.1),
pandas.DataFrame({
"x": (0.1, 1.1, 2.1), "y": (2.1, 3.1, 4.1),
"z": (5.1, 6.1, 7.1)})),
((0.1, 1.1, 2.1), (2.1, 3.1, 4.1),
((5.1, 6.1, 7.1), (5.2, 6.2, 7.2), (5.3, 6.3, 7.3)),
pandas.DataFrame({
"x": (0.1, 1.1, 2.1), "y": (2.1, 3.1, 4.1),
"z0": (5.1, 6.1, 7.1), "z1": (5.2, 6.2, 7.2),
"z2": (5.3, 6.3, 7.3)}))):
with self.subTest(xdata=xdata, ydata=ydata, zdata=zdata):
self.assertTrue(
build_data_frame(xdata, ydata, zdata).equals(expected))
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