def test_valid_init(self):
eda.SampleFeatureMatrix(
self.sfm5x10_arr, list(range(5)), list(range(10)))
eda.SampleFeatureMatrix(self.sfm5x10_arr, None, list(range(10)))
eda.SampleFeatureMatrix(self.sfm5x10_arr, list(range(5)), None)
eda.SampleFeatureMatrix(np.arange(10).reshape(-1, 1))
eda.SampleFeatureMatrix(np.arange(10).reshape(1, -1))
def test_init_wrong_sid_len(self):
# wrong sid size
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm5x10_lst, list(range(10)), list(range(5)))
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm5x10_lst, list(range(10)))
def test_remove_constant_features():
tsfm = eda.SampleFeatureMatrix([[0, 1, 2, 5], [0, 1, 0, 2], [0, 1, 0, 5],
[0, 1, 0, 5], [0, 1, 0, 5]])
f_tsfm = qc.remove_constant_features(tsfm)
assert f_tsfm.x == [[2, 5], [0, 2], [0, 5], [0, 5], [0, 5]]
tsfm2 = eda.SampleFeatureMatrix([[0, 1, 2, 5]])
f_tsfm2 = qc.remove_constant_features(tsfm2)
assert f_tsfm2._x.shape == (1, 0)
assert f_tsfm2._sids.shape == (1, )
assert f_tsfm2._fids.shape == (0, )
def test_init_non1d_sfids(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm3x3_arr, np.array([[0], [1], [2]]),
np.array([[0], [1], [1]]))
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm3x3_arr, np.array([[0], [1], [2]]),
np.array([0, 1, 2]))
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm3x3_arr, np.array([0, 1, 2]),
np.array([[0], [1], [2]]))
def test_init_dup_sfids(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm5x10_lst, [0, 0, 1, 2, 3])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm5x10_lst, ['0', '0', '1', '2', '3'])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm5x10_lst, None, [0, 0, 1, 2, 3])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm5x10_lst, None,
['0', '0', '1', '2', '3'])
def test_id_x(self):
sids = list("abcdef")
fids = list(range(10, 20))
sfm = eda.SampleFeatureMatrix(
np.random.ranf(60).reshape(6, -1), sids=sids, fids=fids)
# select sf
ss_sfm = sfm.id_x(['a', 'f'], list(range(10, 15)))
assert ss_sfm._x.shape == (2, 5)
assert ss_sfm.sids == ['a', 'f']
assert ss_sfm.fids == list(range(10, 15))
# select with Default
ss_sfm = sfm.id_x()
assert ss_sfm._x.shape == (6, 10)
assert ss_sfm.sids == list("abcdef")
assert ss_sfm.fids == list(range(10, 20))
# select with None
ss_sfm = sfm.id_x(None, None)
assert ss_sfm._x.shape == (6, 10)
assert ss_sfm.sids == list("abcdef")
assert ss_sfm.fids == list(range(10, 20))
# select non-existent inds
# id lookup raises ValueError
with pytest.raises(ValueError) as excinfo:
sfm.id_x([6])
with pytest.raises(ValueError) as excinfo:
sfm.id_x(None, ['a'])
def test_init_bad_fid_type(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [0, 1, 2], [False, True, 2])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [0, 1, 2], [[0], [0, 1], 2])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [0, 1, 2], [(0), (0, 1), 2])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [0, 1, 2], np.array([0, 1, 2]))
def test_init_empty_x_sfids(self):
sfm1 = eda.SampleFeatureMatrix(np.array([[], []]), None, [])
assert sfm1._x.shape == (2, 0)
assert sfm1._sids.shape == (2,)
assert sfm1._fids.shape == (0,)
np.testing.assert_equal(sfm1.s_sum(), [])
np.testing.assert_equal(sfm1.f_sum(), [0, 0])
np.testing.assert_equal(sfm1.s_cv(), [])
np.testing.assert_equal(np.isnan(sfm1.f_cv()), [True, True])
sfm2 = eda.SampleFeatureMatrix(np.empty((0, 0)))
assert sfm2._x.shape == (0, 0)
assert sfm2._sids.shape == (0,)
assert sfm2._fids.shape == (0,)
np.testing.assert_equal(sfm2.s_sum(), [])
np.testing.assert_equal(sfm2.f_sum(), [])
np.testing.assert_equal(sfm2.s_cv(), [])
np.testing.assert_equal(sfm2.f_cv(), [])
def test_getters(self):
tsfm = eda.SampleFeatureMatrix(np.arange(10).reshape(5, 2),
['a', 'b', 'c', '1', '2'],
['a', 'z'])
np.testing.assert_equal(tsfm.x, np.array(
np.arange(10).reshape(5, 2), dtype='float64'))
np.testing.assert_equal(tsfm.sids, np.array(['a', 'b', 'c', '1', '2']))
np.testing.assert_equal(tsfm.fids, np.array(['a', 'z']))
assert tsfm.x is not tsfm._x
assert tsfm.sids is not tsfm._sids
assert tsfm.fids is not tsfm._fids
def test_id_x_empty(self):
sids = list("abcdef")
fids = list(range(10, 20))
sfm = eda.SampleFeatureMatrix(
np.random.ranf(60).reshape(6, -1), sids=sids, fids=fids)
empty_s = sfm.id_x([])
assert empty_s._x.shape == (0, 10)
assert empty_s._sids.shape == (0,)
assert empty_s._fids.shape == (10,)
empty_f = sfm.id_x(None, [])
assert empty_f._x.shape == (6, 0)
assert empty_f._sids.shape == (6,)
assert empty_f._fids.shape == (0,)
empty_sf = sfm.id_x([], [])
assert empty_sf._x.shape == (0, 0)
assert empty_sf._sids.shape == (0,)
assert empty_sf._fids.shape == (0,)
def test_cross_labs(self):
rsids = [0, 1, 2, 3, 4]
rlabs = [0, 0, 0, 1, 1]
rscl_samples = eda.SingleLabelClassifiedSamples(
self.sfm5x10_lst, rlabs, rsids)
qsids = [0, 1, 2, 3, 4]
qlabs = [1, 1, 0, 2, 3]
qscl_samples = eda.SingleLabelClassifiedSamples(
self.sfm5x10_lst, qlabs, qsids)
cross_lab_lut = rscl_samples.cross_labs(qscl_samples)
test_lut = {
0: (3, ((0, 1), (1, 2))),
1: (2, ((2, 3), (1, 1)))
}
assert cross_lab_lut == test_lut
qsids2 = [0, 1, 2]
qlabs2 = [1, 1, 0]
qscl_samples2 = eda.SingleLabelClassifiedSamples(
self.sfm3x3_arr, qlabs2, qsids2)
cross_lab_lut2 = rscl_samples.cross_labs(qscl_samples2)
test_lut2 = {
0: (3, ((0, 1), (1, 2)))
}
assert cross_lab_lut2 == test_lut2
with pytest.raises(Exception) as excinfo:
rscl_samples.cross_labs([1, 2, 3])
qsfm = eda.SampleFeatureMatrix(self.sfm5x10_lst)
with pytest.raises(Exception) as excinfo:
rscl_samples.cross_labs(qsfm)
# Contains mismatch to rsids
mm_qsids = [0, 1, 6]
mm_qlabs = [1, 1, 0]
mm_qscl_samples = eda.SingleLabelClassifiedSamples(self.sfm3x3_arr,
mm_qlabs, mm_qsids)
with pytest.raises(Exception) as excinfo:
rscl_samples.cross_labs(mm_qscl_samples)
class TestSampleFeatureMatrix(object):
"""docstring for TestSampleFeatureMatrix"""
sfm5x10_arr = np.random.ranf(50).reshape(5, 10)
sfm3x3_arr = np.random.ranf(9).reshape(3, 3)
sfm5x10_lst = list(map(list, np.random.ranf(50).reshape(5, 10)))
plt_arr = np.arange(60).reshape(6, 10)
plt_sdm = eda.SampleFeatureMatrix(plt_arr,
sids=list("abcdef"),
fids=list(map(lambda i: 'f{}'.format(i),
range(10))))
# array([[ 0, 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]])
ref_plt_f_sum = np.arange(0, 501, 100) + np.arange(10).sum()
ref_plt_s_sum = np.arange(0, 55, 6) + np.arange(0, 51, 10).sum()
ref_plt_f_mean = ref_plt_f_sum / 10
ref_plt_s_mean = ref_plt_s_sum / 6
ref_plt_f_cv = np.arange(10).std(ddof=1) / ref_plt_f_mean
ref_plt_s_cv = np.arange(0, 51, 10).std(ddof=1) / ref_plt_s_mean
ref_plt_f_gc = np.apply_along_axis(eda.stats.gc1d, 1, plt_arr)
ref_plt_s_gc = np.apply_along_axis(eda.stats.gc1d, 0, plt_arr)
ref_plt_f_a15 = np.array([0, 5, 10, 10, 10, 10])
ref_plt_s_a35 = np.array([2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
def test_init_x_none(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(None)
def test_init_x_bad_type(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix([[0, 1], ['a', 2]])
def test_init_x_1d(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix([1, 2, 3])
def test_init_dup_sfids(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm5x10_lst, [0, 0, 1, 2, 3])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm5x10_lst, ['0', '0', '1', '2', '3'])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm5x10_lst, None, [0, 0, 1, 2, 3])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm5x10_lst, None,
['0', '0', '1', '2', '3'])
def test_init_empty_x_sfids(self):
sfm1 = eda.SampleFeatureMatrix(np.array([[], []]), None, [])
assert sfm1._x.shape == (2, 0)
assert sfm1._sids.shape == (2,)
assert sfm1._fids.shape == (0,)
np.testing.assert_equal(sfm1.s_sum(), [])
np.testing.assert_equal(sfm1.f_sum(), [0, 0])
np.testing.assert_equal(sfm1.s_cv(), [])
with pytest.warns(RuntimeWarning):
np.testing.assert_equal(np.isnan(sfm1.f_cv()), [True, True])
sfm2 = eda.SampleFeatureMatrix(np.empty((0, 0)))
assert sfm2._x.shape == (0, 0)
assert sfm2._sids.shape == (0,)
assert sfm2._fids.shape == (0,)
np.testing.assert_equal(sfm2.s_sum(), [])
np.testing.assert_equal(sfm2.f_sum(), [])
with pytest.warns(RuntimeWarning):
np.testing.assert_equal(sfm2.s_cv(), [])
with pytest.warns(RuntimeWarning):
np.testing.assert_equal(sfm2.f_cv(), [])
def test_init_wrong_sid_len(self):
# wrong sid size
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm5x10_lst, list(range(10)), list(range(5)))
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm5x10_lst, list(range(10)))
def test_init_wrong_fid_len(self):
# wrong fid size
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm5x10_lst, list(range(5)), list(range(2)))
def test_init_wrong_sfid_len(self):
# wrong sid and fid sizes
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm5x10_lst, list(range(10)), list(range(10)))
def test_init_non1d_sfids(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm3x3_arr, np.array([[0], [1], [2]]),
np.array([[0], [1], [1]]))
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm3x3_arr, np.array([[0], [1], [2]]),
np.array([0, 1, 2]))
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(self.sfm3x3_arr, np.array([0, 1, 2]),
np.array([[0], [1], [2]]))
def test_init_bad_sid_type(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [False, True, 2], [0, 1, 1])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [[0], [0, 1], 2], [0, 1, 1])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, np.array([0, 1, 2]), [0, 1, 1])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [(0), (0, 1), 2], [0, 1, 1])
def test_init_bad_fid_type(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [0, 1, 2], [False, True, 2])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [0, 1, 2], [[0], [0, 1], 2])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [0, 1, 2], [(0), (0, 1), 2])
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(
self.sfm3x3_arr, [0, 1, 2], np.array([0, 1, 2]))
def test_valid_init(self):
eda.SampleFeatureMatrix(
self.sfm5x10_arr, list(range(5)), list(range(10)))
eda.SampleFeatureMatrix(self.sfm5x10_arr, None, list(range(10)))
eda.SampleFeatureMatrix(self.sfm5x10_arr, list(range(5)), None)
eda.SampleFeatureMatrix(np.arange(10).reshape(-1, 1))
eda.SampleFeatureMatrix(np.arange(10).reshape(1, -1))
def test_ind_x(self):
sids = list("abcdef")
fids = list(range(10, 20))
sfm = eda.SampleFeatureMatrix(
np.random.ranf(60).reshape(6, -1), sids=sids, fids=fids)
# select sf
ss_sfm = sfm.ind_x([0, 5], list(range(9)))
assert ss_sfm._x.shape == (2, 9)
assert ss_sfm.sids == ['a', 'f']
assert ss_sfm.fids == list(range(10, 19))
# select with Default
ss_sfm = sfm.ind_x()
assert ss_sfm._x.shape == (6, 10)
assert ss_sfm.sids == list("abcdef")
assert ss_sfm.fids == list(range(10, 20))
# select with None
ss_sfm = sfm.ind_x(None, None)
assert ss_sfm._x.shape == (6, 10)
assert ss_sfm.sids == list("abcdef")
assert ss_sfm.fids == list(range(10, 20))
# select non-existent inds
with pytest.raises(IndexError) as excinfo:
sfm.ind_x([6])
with pytest.raises(IndexError) as excinfo:
sfm.ind_x(None, ['a'])
def test_ind_x_empty(self):
sids = list("abcdef")
fids = list(range(10, 20))
sfm = eda.SampleFeatureMatrix(
np.random.ranf(60).reshape(6, -1), sids=sids, fids=fids)
empty_s = sfm.ind_x([])
assert empty_s._x.shape == (0, 10)
assert empty_s._sids.shape == (0,)
assert empty_s._fids.shape == (10,)
empty_f = sfm.ind_x(None, [])
assert empty_f._x.shape == (6, 0)
assert empty_f._sids.shape == (6,)
assert empty_f._fids.shape == (0,)
empty_sf = sfm.ind_x([], [])
assert empty_sf._x.shape == (0, 0)
assert empty_sf._sids.shape == (0,)
assert empty_sf._fids.shape == (0,)
def test_id_x(self):
sids = list("abcdef")
fids = list(range(10, 20))
sfm = eda.SampleFeatureMatrix(
np.random.ranf(60).reshape(6, -1), sids=sids, fids=fids)
# select sf
ss_sfm = sfm.id_x(['a', 'f'], list(range(10, 15)))
assert ss_sfm._x.shape == (2, 5)
assert ss_sfm.sids == ['a', 'f']
assert ss_sfm.fids == list(range(10, 15))
# select with Default
ss_sfm = sfm.id_x()
assert ss_sfm._x.shape == (6, 10)
assert ss_sfm.sids == list("abcdef")
assert ss_sfm.fids == list(range(10, 20))
# select with None
ss_sfm = sfm.id_x(None, None)
assert ss_sfm._x.shape == (6, 10)
assert ss_sfm.sids == list("abcdef")
assert ss_sfm.fids == list(range(10, 20))
# select non-existent inds
# id lookup raises ValueError
with pytest.raises(ValueError) as excinfo:
sfm.id_x([6])
with pytest.raises(ValueError) as excinfo:
sfm.id_x(None, ['a'])
def test_id_x_empty(self):
sids = list("abcdef")
fids = list(range(10, 20))
sfm = eda.SampleFeatureMatrix(
np.random.ranf(60).reshape(6, -1), sids=sids, fids=fids)
empty_s = sfm.id_x([])
assert empty_s._x.shape == (0, 10)
assert empty_s._sids.shape == (0,)
assert empty_s._fids.shape == (10,)
empty_f = sfm.id_x(None, [])
assert empty_f._x.shape == (6, 0)
assert empty_f._sids.shape == (6,)
assert empty_f._fids.shape == (0,)
empty_sf = sfm.id_x([], [])
assert empty_sf._x.shape == (0, 0)
assert empty_sf._sids.shape == (0,)
assert empty_sf._fids.shape == (0,)
@pytest.mark.mpl_image_compare
def test_s_ind_regression_scatter_ax(self):
fig, axs = plt.subplots(ncols=2)
fig = self.plt_sdm.s_ind_regression_scatter(
0, 1, figsize=(5, 5), ax=axs[0], ci=None)
plt.close()
return fig
@pytest.mark.mpl_image_compare
def test_s_ind_regression_scatter(self):
return self.plt_sdm.s_ind_regression_scatter(
0, 1, figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
def test_s_id_regression_scatter(self):
return self.plt_sdm.s_id_regression_scatter(
"a", "b", feature_filter=[1, 2, 3], figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
def test_s_ind_regression_scatter_custom_labs(self):
return self.plt_sdm.s_ind_regression_scatter(
0, 1, xlab='X', ylab='Y', figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
def test_s_ind_regression_scatter_custom_bool_ff(self):
return self.plt_sdm.s_ind_regression_scatter(
0, 1, feature_filter=[True]*2 + [False]*8, figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
def test_s_ind_regression_scatter_custom_int_ff(self):
return self.plt_sdm.s_ind_regression_scatter(
0, 1, feature_filter=[0, 1], figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
def test_s_ind_regression_scatter_custom_func_ff(self):
return self.plt_sdm.s_ind_regression_scatter(
0, 1,
feature_filter=lambda x, y: (x in (0, 1, 2)) and (10 < y < 12),
figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
def test_f_ind_regression_scatter_custom_func_sf(self):
# plt_sdm = eda.SampleFeatureMatrix(
# plt_arr,
# sids=list("abcdef"),
# fids=list(map(lambda i: 'f{}'.format(i),
# range(10))))
# array([[ 0, 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]])
return self.plt_sdm.f_ind_regression_scatter(
0, 1,
sample_filter=lambda x, y: (x in (0, 10, 20)) and (10 < y < 30),
figsize=(5, 5), ci=None)
def test_f_id_ind_x_vec(self):
x = self.plt_sdm.f_ind_x_vec(0)
x2 = self.plt_sdm.f_id_x_vec('f0')
np.testing.assert_equal(x, x2)
np.testing.assert_equal(x, [0, 10, 20, 30, 40, 50])
x3 = self.plt_sdm.f_ind_x_vec(6)
x4 = self.plt_sdm.f_id_x_vec('f6')
np.testing.assert_equal(x3, x4)
np.testing.assert_equal(x3, [6, 16, 26, 36, 46, 56])
@pytest.mark.mpl_image_compare
def test_f_ind_regression_scatter_no_ff(self):
return self.plt_sdm.f_ind_regression_scatter(
0, 1, figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
def test_f_ind_regression_scatter_ind_ff(self):
return self.plt_sdm.f_ind_regression_scatter(
0, 1, sample_filter=[0, 2, 5], figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
def test_f_ind_regression_scatter_labs(self):
return self.plt_sdm.f_ind_regression_scatter(
0, 1, sample_filter=[0, 2, 5], figsize=(5, 5), title='testregscat',
xlab='x', ylab='y', ci=None)
@pytest.mark.mpl_image_compare
def test_f_id_regression_scatter(self):
return self.plt_sdm.f_id_regression_scatter(
'f5', 'f6', sample_filter=[0, 2, 5], figsize=(5, 5), ci=None)
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_s_ind_dist_ax(self):
fig, axs = plt.subplots(ncols=2)
fig = self.plt_sdm.s_ind_dist(0, figsize=(5, 5), ax=axs[0])
plt.close()
return fig
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_s_ind_dist(self):
return self.plt_sdm.s_ind_dist(0, figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_s_id_dist(self):
return self.plt_sdm.s_id_dist("a", feature_filter=[1, 2, 3],
figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_s_ind_dist_custom_labs(self):
return self.plt_sdm.s_ind_dist(0, xlab='X', ylab='Y', figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_s_ind_dist_custom_bool_ff(self):
return self.plt_sdm.s_ind_dist(
0, feature_filter=[True]*2 + [False]*8, title='testdist',
figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_s_ind_dist_custom_int_ff(self):
return self.plt_sdm.s_ind_dist(
0, feature_filter=[0, 1], figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_s_ind_dist_custom_func_ff(self):
return self.plt_sdm.s_ind_dist(
0, feature_filter=lambda x: x in (0, 1, 2),
figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_f_ind_dist_custom_func_sf(self):
return self.plt_sdm.f_ind_dist(
0, sample_filter=lambda x: x in (0, 10, 20),
figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_f_ind_dist_no_ff(self):
return self.plt_sdm.f_ind_dist(0, figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_f_ind_dist_ind_ff(self):
return self.plt_sdm.f_ind_dist(0, sample_filter=[0, 2, 5],
figsize=(5, 5))
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_f_ind_dist_labs(self):
return self.plt_sdm.f_ind_dist(0, sample_filter=[0, 2, 5],
figsize=(5, 5),
xlab='x', ylab='y')
@pytest.mark.mpl_image_compare
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_f_id_dist(self):
return self.plt_sdm.f_id_dist('f5', sample_filter=[0, 2, 5],
figsize=(5, 5))
def test_getters(self):
tsfm = eda.SampleFeatureMatrix(np.arange(10).reshape(5, 2),
['a', 'b', 'c', '1', '2'],
['a', 'z'])
np.testing.assert_equal(tsfm.x, np.array(
np.arange(10).reshape(5, 2), dtype='float64'))
np.testing.assert_equal(tsfm.sids, np.array(['a', 'b', 'c', '1', '2']))
np.testing.assert_equal(tsfm.fids, np.array(['a', 'z']))
assert tsfm.x is not tsfm._x
assert tsfm.sids is not tsfm._sids
assert tsfm.fids is not tsfm._fids
# array([[ 0, 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]])
def test_f_sum(self):
x = self.plt_sdm.f_sum()
assert x.ndim == 1
assert x.shape[0] == 6
np.testing.assert_allclose(x, self.ref_plt_f_sum)
# only need to test that filter has been passed correctly
np.testing.assert_allclose(
self.plt_sdm.f_sum([0, 1, 2]), self.ref_plt_f_sum[:3])
def test_s_sum(self):
x = self.plt_sdm.s_sum()
assert x.ndim == 1
assert x.shape[0] == 10
np.testing.assert_allclose(x, self.ref_plt_s_sum)
np.testing.assert_allclose(
self.plt_sdm.s_sum([0, 1, 2]), self.ref_plt_s_sum[:3])
def test_f_cv(self):
x = self.plt_sdm.f_cv()
assert x.ndim == 1
assert x.shape[0] == 6
np.testing.assert_allclose(self.plt_sdm.f_cv(), self.ref_plt_f_cv)
np.testing.assert_allclose(
self.plt_sdm.f_cv([0, 1, 2]), self.ref_plt_f_cv[:3])
def test_s_cv(self):
x = self.plt_sdm.s_cv()
assert x.ndim == 1
assert x.shape[0] == 10
np.testing.assert_allclose(x, self.ref_plt_s_cv)
np.testing.assert_allclose(self.plt_sdm.s_cv([0, 1, 2]),
self.ref_plt_s_cv[:3])
def test_f_gc(self):
x = self.plt_sdm.f_gc()
assert x.ndim == 1
assert x.shape[0] == 6
np.testing.assert_allclose(x, self.ref_plt_f_gc)
np.testing.assert_allclose(self.plt_sdm.f_gc([0, 1, 2]),
self.ref_plt_f_gc[:3])
def test_s_gc(self):
x = self.plt_sdm.s_gc()
assert x.ndim == 1
assert x.shape[0] == 10
np.testing.assert_allclose(x, self.ref_plt_s_gc)
np.testing.assert_allclose(self.plt_sdm.s_gc([0, 1, 2]),
self.ref_plt_s_gc[:3])
def test_f_ath(self):
x = self.plt_sdm.f_n_above_threshold(15)
assert x.ndim == 1
assert x.shape[0] == 6
np.testing.assert_allclose(x, self.ref_plt_f_a15)
def test_s_ath(self):
x = self.plt_sdm.s_n_above_threshold(35)
assert x.ndim == 1
assert x.shape[0] == 10
np.testing.assert_allclose(x, self.ref_plt_s_a35)
# Because summary dist plot calls hist_dens_plot immediately after
# obtaining the summary statistics vector, the correctness of summary
# statistics vector and hist_dens_plot implies the correctness of the
# plots.
@pytest.mark.filterwarnings("ignore:The 'normed' kwarg is depreca")
def test_summary_stat_dist(self):
self.plt_sdm.f_sum_dist([0, 1, 2])
self.plt_sdm.s_sum_dist([0, 1, 2])
self.plt_sdm.f_cv_dist([0, 1, 2])
self.plt_sdm.s_cv_dist([0, 1, 2])
self.plt_sdm.f_gc_dist([0, 1, 2])
self.plt_sdm.s_gc_dist([0, 1, 2])
self.plt_sdm.f_n_above_threshold_dist(15)
self.plt_sdm.s_n_above_threshold_dist(15)
def test_init_x_1d(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix([1, 2, 3])
def test_init_x_bad_type(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix([[0, 1], ['a', 2]])
def test_init_x_none(self):
with pytest.raises(Exception) as excinfo:
eda.SampleFeatureMatrix(None)
