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# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE.txt, distributed with this software.
# ----------------------------------------------------------------------------
import io
from unittest import TestCase, main
import numpy as np
import pandas as pd
import numpy.testing as npt
from skbio import TreeNode
from skbio.table import example_table
from skbio.diversity._util import (_validate_counts_vector,
_validate_counts_matrix,
_validate_taxa_and_tree,
vectorize_counts_and_tree,
_quantitative_to_qualitative_counts,
_check_taxa_alias,
_table_to_numpy,
_validate_table)
from skbio.tree import DuplicateNodeError, MissingNodeError
class ValidationTests(TestCase):
def test_validate_counts_vector(self):
# python list
obs = _validate_counts_vector([0, 2, 1, 3])
npt.assert_array_equal(obs, np.array([0, 2, 1, 3]))
self.assertEqual(obs.dtype, int)
# numpy array (no copy made)
data = np.array([0, 2, 1, 3])
obs = _validate_counts_vector(data)
npt.assert_array_equal(obs, data)
self.assertEqual(obs.dtype, int)
self.assertTrue(obs is data)
# single element
obs = _validate_counts_vector([42])
npt.assert_array_equal(obs, np.array([42]))
self.assertEqual(obs.dtype, int)
self.assertEqual(obs.shape, (1,))
# keep float
obs = _validate_counts_vector([42.2, 42.7, 0])
npt.assert_array_equal(obs, np.array([42.2, 42.7, 0]))
self.assertEqual(obs.dtype, float)
# cast into int
obs = _validate_counts_vector([42.2, 42.7, 0], cast_int=True)
npt.assert_array_equal(obs, np.array([42, 42, 0]))
self.assertEqual(obs.dtype, int)
# all zeros
obs = _validate_counts_vector([0, 0, 0])
npt.assert_array_equal(obs, np.array([0, 0, 0]))
self.assertEqual(obs.dtype, int)
# all zeros (single value)
obs = _validate_counts_vector([0])
npt.assert_array_equal(obs, np.array([0]))
self.assertEqual(obs.dtype, int)
def test_validate_counts_vector_invalid_input(self):
# wrong data type (strings)
with self.assertRaises(ValueError):
_validate_counts_vector(['a', 'b', 'c'])
# wrong data type (complex numbers)
with self.assertRaises(ValueError):
_validate_counts_vector([1 + 2j, 3 + 4j])
# wrong number of dimensions (2-D)
with self.assertRaises(ValueError):
_validate_counts_vector([[0, 2, 1, 3], [4, 5, 6, 7]])
# wrong number of dimensions (scalar)
with self.assertRaises(ValueError):
_validate_counts_vector(1)
# negative values
with self.assertRaises(ValueError):
_validate_counts_vector([0, 0, 2, -1, 3])
# strings
with self.assertRaises(ValueError):
_validate_counts_vector([0, 0, 'a', -1, 3])
def test_validate_counts_matrix(self):
# basic valid input (n=2)
obs = _validate_counts_matrix([[0, 1, 1, 0, 2],
[0, 0, 2, 1, 3]])
npt.assert_array_equal(obs[0], np.array([0, 1, 1, 0, 2]))
npt.assert_array_equal(obs[1], np.array([0, 0, 2, 1, 3]))
# basic valid input (n=3)
obs = _validate_counts_matrix([[0, 1, 1, 0, 2],
[0, 0, 2, 1, 3],
[1, 1, 1, 1, 1]])
npt.assert_array_equal(obs[0], np.array([0, 1, 1, 0, 2]))
npt.assert_array_equal(obs[1], np.array([0, 0, 2, 1, 3]))
npt.assert_array_equal(obs[2], np.array([1, 1, 1, 1, 1]))
# empty counts vectors
obs = _validate_counts_matrix(np.array([[], []], dtype=int))
npt.assert_array_equal(obs[0], np.array([]))
npt.assert_array_equal(obs[1], np.array([]))
def test_validate_counts_matrix_pandas(self):
obs = _validate_counts_matrix(pd.DataFrame([[0, 1, 1, 0, 2],
[0, 0, 2, 1, 3],
[1, 1, 1, 1, 1]]))
npt.assert_array_equal(obs[0], np.array([0, 1, 1, 0, 2]))
npt.assert_array_equal(obs[1], np.array([0, 0, 2, 1, 3]))
npt.assert_array_equal(obs[2], np.array([1, 1, 1, 1, 1]))
def test_validate_counts_matrix_cast_int(self):
obs = _validate_counts_matrix(
[[42.2, 42.1, 0], [42.2, 42.1, 1.0]], cast_int=True)
npt.assert_array_equal(obs[0], np.array([42, 42, 0]))
npt.assert_array_equal(obs[1], np.array([42, 42, 1]))
self.assertEqual(obs[0].dtype, int)
self.assertEqual(obs[1].dtype, int)
def test_validate_counts_matrix_negative_counts(self):
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 1, 1, 0, 2], [0, 0, 2, -1, 3]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0, 2, -1, 3], [0, 1, 1, 0, 2]])
def test_validate_counts_matrix_unmatching_ids(self):
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 1, 1, 0, 2],
[0, 0, 2, 1, 3],
[1, 1, 1, 1, 1]], ids=['a', 'b'])
with self.assertRaises(ValueError):
obs = _validate_counts_matrix(pd.DataFrame(
[[0, 1, 1, 0, 2],
[0, 0, 2, 1, 3],
[1, 1, 1, 1, 1]]), ids=['a', 'b'])
def test_validate_counts_matrix_unequal_lengths(self):
# len of vectors not equal
with self.assertRaises(ValueError):
_validate_counts_matrix([[0], [0, 0], [9, 8]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0], [0, 0, 8], [9, 8]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0, 75], [0, 0, 3], [9, 8, 22, 44]])
def test_validate_counts_matrix_invalid_input(self):
with self.assertRaises(ValueError):
_validate_counts_matrix([['a', 'b', 'c']])
with self.assertRaises(ValueError):
_validate_counts_matrix([[1 + 2j, 3 + 4j]])
def test_validate_taxa_and_tree(self):
# basic valid input
tree = TreeNode.read(
io.StringIO(
'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU5:0.75):1.25):0.0)root;'))
counts = [1, 1, 1]
taxa = ['OTU1', 'OTU2', 'OTU3']
self.assertTrue(_validate_taxa_and_tree(counts, taxa, tree) is None)
# all tips observed
tree = TreeNode.read(
io.StringIO(
'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU5:0.75):1.25):0.0)root;'))
counts = [1, 1, 1, 1, 1]
taxa = ['OTU1', 'OTU2', 'OTU3', 'OTU4', 'OTU5']
self.assertTrue(_validate_taxa_and_tree(counts, taxa, tree) is None)
# no tips observed
tree = TreeNode.read(
io.StringIO(
'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU5:0.75):1.25):0.0)root;'))
counts = []
taxa = []
self.assertTrue(_validate_taxa_and_tree(counts, taxa, tree) is None)
# all counts zero
tree = TreeNode.read(
io.StringIO(
'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU5:0.75):1.25):0.0)root;'))
counts = [0, 0, 0, 0, 0]
taxa = ['OTU1', 'OTU2', 'OTU3', 'OTU4', 'OTU5']
self.assertTrue(_validate_taxa_and_tree(counts, taxa, tree) is None)
def test_validate_taxa_and_tree_invalid_input(self):
# tree has duplicated tip ids
tree = TreeNode.read(
io.StringIO(
'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU2:0.75):1.25):0.0)root;'))
counts = [1, 1, 1]
taxa = ['OTU1', 'OTU2', 'OTU3']
self.assertRaises(DuplicateNodeError, _validate_taxa_and_tree,
counts, taxa, tree)
# unrooted tree as input
tree = TreeNode.read(io.StringIO('((OTU1:0.1, OTU2:0.2):0.3, OTU3:0.5,'
'OTU4:0.7);'))
counts = [1, 2, 3]
taxa = ['OTU1', 'OTU2', 'OTU3']
self.assertRaises(ValueError, _validate_taxa_and_tree, counts, taxa, tree)
# taxa has duplicated ids
tree = TreeNode.read(
io.StringIO(
'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU5:0.75):1.25):0.0)root;'))
counts = [1, 2, 3]
taxa = ['OTU1', 'OTU2', 'OTU2']
self.assertRaises(ValueError, _validate_taxa_and_tree, counts, taxa, tree)
# len of vectors not equal
tree = TreeNode.read(
io.StringIO(
'(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU5:0.75):1.25):0.0)root;'))
counts = [1, 2]
taxa = ['OTU1', 'OTU2', 'OTU3']
self.assertRaises(ValueError, _validate_taxa_and_tree, counts, taxa, tree)
counts = [1, 2, 3]
taxa = ['OTU1', 'OTU2']
self.assertRaises(ValueError, _validate_taxa_and_tree, counts, taxa, tree)
# tree with no branch lengths
tree = TreeNode.read(io.StringIO('((((OTU1,OTU2),OTU3)),(OTU4,OTU5));'))
counts = [1, 2, 3]
taxa = ['OTU1', 'OTU2', 'OTU3']
self.assertRaises(ValueError, _validate_taxa_and_tree, counts, taxa, tree)
# tree missing some branch lengths
tree = TreeNode.read(
io.StringIO(
'(((((OTU1,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU5:0.75):1.25):0.0)root;'))
counts = [1, 2, 3]
taxa = ['OTU1', 'OTU2', 'OTU3']
self.assertRaises(ValueError, _validate_taxa_and_tree, counts, taxa, tree)
# taxa not present in tree
tree = TreeNode.read(
io.StringIO(
'(((((OTU1:0.25,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,(OTU4:'
'0.75,OTU5:0.75):1.25):0.0)root;'))
counts = [1, 2, 3]
taxa = ['OTU1', 'OTU2', 'OTU32']
self.assertRaises(MissingNodeError, _validate_taxa_and_tree, counts, taxa, tree)
# single node tree
tree = TreeNode.read(io.StringIO('root;'))
counts = []
taxa = []
self.assertRaises(ValueError, _validate_taxa_and_tree, counts, taxa, tree)
def test_vectorize_counts_and_tree(self):
tree = TreeNode.read(io.StringIO("((a:1, b:2)c:3)root;"))
counts = np.array([[0, 1], [1, 5], [10, 1]])
count_array, indexed, branch_lengths = \
vectorize_counts_and_tree(counts, np.array(['a', 'b']), tree)
exp_counts = np.array([[0, 1, 10], [1, 5, 1], [1, 6, 11], [1, 6, 11]])
npt.assert_equal(count_array, exp_counts.T)
def test_quantitative_to_qualitative_counts(self):
counts = np.array([[0, 1], [1, 5], [10, 1]])
exp = np.array([[False, True], [True, True], [True, True]])
obs = _quantitative_to_qualitative_counts(counts)
npt.assert_equal(obs, exp)
counts = np.array([[0, 0, 0], [1, 0, 42]])
exp = np.array([[False, False, False], [True, False, True]])
obs = _quantitative_to_qualitative_counts(counts)
npt.assert_equal(obs, exp)
def test_check_taxa_alias(self):
# for backward compatibility; will be removed in the future
msg = "A list of taxon IDs must be provided."
with self.assertRaises(ValueError) as cm:
_check_taxa_alias(None, None, None)
self.assertEqual(str(cm.exception), msg)
msg = "A phylogenetic tree must be provided."
with self.assertRaises(ValueError) as cm:
_check_taxa_alias([1], None, None)
self.assertEqual(str(cm.exception), msg)
obs = _check_taxa_alias([1], '1', None)
self.assertListEqual(obs, [1])
obs = _check_taxa_alias(None, '1', [1])
self.assertListEqual(obs, [1])
class TableConversionTests(TestCase):
def test_table_to_numpy(self):
exp_data = np.array([[0, 1, 2], [3, 4, 5]]).T
exp_ids = ['S1', 'S2', 'S3']
exp_feat_ids = ['O1', 'O2']
obs_data, obs_ids, obs_feat_ids = _table_to_numpy(example_table)
npt.assert_equal(obs_data, exp_data)
self.assertEqual(obs_ids, exp_ids)
self.assertEqual(obs_feat_ids, exp_feat_ids)
def test_validate_table(self):
self.assertRaises(ValueError, _validate_table, example_table, ['foo', 'bar'], {})
self.assertRaises(ValueError, _validate_table, example_table, None,
{'taxa': 'foo'})
obs_data, obs_ids = _validate_table(example_table, None, {})
exp_data = np.array([[0, 1, 2], [3, 4, 5]]).T
exp_ids = ['S1', 'S2', 'S3']
npt.assert_equal(obs_data, exp_data)
self.assertEqual(obs_ids, exp_ids)
if __name__ == "__main__":
main()
|
