def test_plot_hgts(self):
me = Analyze()
me.output = self.tmpdir
me.df = pd.DataFrame(np.array(
[self.dist_gamma, self.dist_lognorm[:800]]).T,
columns=['close', 'distal'])
me.df['hgt'] = (me.df['close'] < 2) & (me.df['distal'] > 2)
me.plot_hgts()
fp = join(self.tmpdir, 'scatter.png')
self.assertTrue(isfile(fp))
remove(fp)
def test_predict_hgt(self):
me = Analyze()
# populate score table
n = 1000
data = {
'sample': ['S1'] * n,
'protein': [f'P{x}' for x in range(n)],
'self':
np.random.choice(self.dist_gamma, n),
'close':
np.concatenate((np.random.choice(self.dist_norm1, int(n / 2)) / 3,
np.random.choice(self.dist_norm2, int(n / 2)))),
'distal':
np.concatenate(
(np.random.choice(self.dist_lognorm, int(n * 3 / 4)),
np.random.choice(self.dist_gamma, int(n / 4)) / 2)),
'match': ['0'] * n
}
me.df = pd.DataFrame(data)
# default setting
me.output = self.tmpdir
me.self_low = False
me.bandwidth = 'auto'
me.bw_steps = 20
me.low_part = 75
me.fixed = 25
me.noise = 50
me.silhouette = 0.5
me.taxdump = {}
me.donor_name = False
me.donor_rank = None
# run prediction
self.assertEqual(me.predict_hgt(), 96)
groups = ['self', 'close', 'distal']
for group in groups[1:]:
fp = join(self.tmpdir, f'{group}.hist.png')
self.assertTrue(isfile(fp))
remove(fp)
fp = join(self.tmpdir, 'scatter.png')
self.assertTrue(isfile(fp))
remove(fp)
fp = join(self.tmpdir, 'hgts')
self.assertTrue(isfile(join(fp, 'S1.txt')))
rmtree(fp)
# constant values
me.df['close'] = 1
me.df.drop('hgt', axis=1, inplace=True)
self.assertEqual(me.predict_hgt(), 0)
self.assertNotIn('hgt', me.df.columns)
remove(join(self.tmpdir, 'close.hist.png'))
def test_write_hgt_list(self):
me = Analyze()
me.output = self.tmpdir
makedirs(join(me.output, 'hgts'), exist_ok=True)
me.donor_name = False
me.donor_rank = None
me.taxdump = taxdump_from_text(taxdump_proteo)
add_children(me.taxdump)
me.df = pd.DataFrame(
[['S1', 'P1', 0.85, '562', True], ['S1', 'P2', 0.95, '622', True],
['S1', 'P3', 1.05, '0', True], ['S2', 'P4', 0.80, '766', True],
['S2', 'P5', 0.20, '0', False]],
columns=['sample', 'protein', 'silh', 'match', 'hgt'])
# default
me.write_hgt_list('S1')
with open(join(me.output, 'hgts', 'S1.txt'), 'r') as f:
obs = f.read()
exp = ('P1\t0.85\t562\n' 'P2\t0.95\t622\n' 'P3\t1.05\t0\n')
self.assertEqual(obs, exp)
# number format and negative result
me.write_hgt_list('S2')
with open(join(me.output, 'hgts', 'S2.txt'), 'r') as f:
self.assertEqual(f.read(), 'P4\t0.8\t766\n')
# raise to family
me.donor_rank = 'family'
me.write_hgt_list('S1')
with open(join(me.output, 'hgts', 'S1.txt'), 'r') as f:
obs = f.read()
exp = ('P1\t0.85\t543\n' 'P2\t0.95\t543\n' 'P3\t1.05\t0\n')
self.assertEqual(obs, exp)
# report taxon name
me.donor_rank = None
me.donor_name = True
me.write_hgt_list('S1')
with open(join(me.output, 'hgts', 'S1.txt'), 'r') as f:
obs = f.read()
exp = ('P1\t0.85\tEscherichia coli\n'
'P2\t0.95\tShigella dysenteriae\n'
'P3\t1.05\tN/A\n')
self.assertEqual(obs, exp)
rmtree(join(me.output, 'hgts'))
def test_make_score_table(self):
me = Analyze()
me.output = self.tmpdir
me.data = {
'S1': [{
'id': 'P1',
'length': 100,
'match': '0',
'self': 1.5,
'close': 0.75,
'distal': 0.0,
'hits': pd.DataFrame([0] * 3)
}, {
'id': 'P2',
'length': 120,
'match': '1224',
'self': 1.625,
'close': 0.225,
'distal': 0.375,
'hits': pd.DataFrame([0] * 5)
}],
'S2': [{
'id': 'P1',
'length': 225,
'match': '620',
'self': 2.35,
'close': 1.05,
'distal': 0.75,
'hits': pd.DataFrame([0] * 6)
}]
}
me.make_score_table()
obs = me.df.values.tolist()
exp = [['S1', 'P1', 100, 3, 1.5, 0.75, 0, '0'],
['S1', 'P2', 120, 5, 1.625, 0.225, 0.375, '1224'],
['S2', 'P1', 225, 6, 2.35, 1.05, 0.75, '620']]
self.assertListEqual(obs, exp)
fp = join(self.tmpdir, 'scores.tsv')
with open(fp, 'r') as f:
obs = [x.split('\t') for x in f.read().splitlines()[1:]]
exp = [[str(y) for y in x] for x in exp]
self.assertListEqual(obs, exp)
remove(fp)
def test_smart_kde(self):
me = Analyze()
# typical case (bimodal distribution)
me.df = pd.Series(np.concatenate([self.dist_norm1, self.dist_norm2]),
name='group').to_frame()
me.bw_steps = 10
me.noise = 50
me.low_part = 75
me.output = self.tmpdir
obs = me.smart_kde('group')
self.assertAlmostEqual(obs, 2.1903958075763343)
file = join(self.tmpdir, 'group.kde.png')
self.assertTrue(isfile(file))
remove(file)
# unable to determine threshold
me.low_part = 0.001
me.df = pd.Series(self.dist_norm1, name='group').to_frame()
self.assertEqual(me.smart_kde('group'), 0)
def test_cluster_kde(self):
me = Analyze()
data = np.concatenate([self.dist_norm1, self.dist_norm2])
me.df = pd.Series(data, name='group').to_frame()
me.bw_steps = 10
me.noise = 50
me.low_part = 75
me.output = self.tmpdir
# grid search
me.bandwidth = 'grid'
obs = me.cluster_kde('group')
self.assertAlmostEqual(obs, 1.855525575742988)
# Silverman's rule-of-thumb
me.bandwidth = 'silverman'
obs = me.cluster_kde('group')
self.assertAlmostEqual(obs, 2.2279977615745703)
# fixed value
me.bandwidth = 0.5
obs = me.cluster_kde('group')
self.assertAlmostEqual(obs, 2.2507008281395433)
# smart KDE
me.bandwidth = 'auto'
obs = me.cluster_kde('group')
self.assertAlmostEqual(obs, 2.1903958075763343)
# clean up
remove(join(self.tmpdir, 'group.kde.png'))
# cannot find threshold (unimodal distribution)
me.df = pd.Series(self.dist_norm1, name='group').to_frame()
me.bandwidth = 'silverman'
obs = me.cluster_kde('group')
self.assertEqual(obs, 0)
