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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.
# ----------------------------------------------------------------------------
from unittest import TestCase, main
import numpy as np
import numpy.testing as npt
import pandas as pd
from scipy.spatial.distance import euclidean
from skbio.sequence import DNA, Protein
from skbio import DistanceMatrix, OrdinationResults
from skbio.embedding._protein import ProteinVector
from skbio.embedding._embedding import (
Embedding,
SequenceEmbedding,
SequenceVector,
embed_vec_to_numpy,
embed_vec_to_dataframe,
embed_vec_to_distances,
embed_vec_to_ordination
)
class EmbeddingTests(TestCase):
def setUp(self):
self.emb = np.random.randn(62, 10)
self.seq = "IGKEEIQQRLAQFVDHWKELKQLAAARGQRLEESLEYQQFVANVEEEEAWINEKMTLVASED"
def test_id(self):
emb, s = self.emb, self.seq
p_emb = Embedding(emb, list(s))
npt.assert_array_equal(p_emb.ids, np.array(list(s)))
def test_embedding(self):
emb, s = self.emb, self.seq
p_emb = Embedding(emb, s)
self.assertTupleEqual(p_emb.embedding.shape, (62, 10))
def test_str(self):
with self.assertRaises(NotImplementedError):
Embedding(self.emb, self.seq).__str__()
def test_assert_length(self):
msg = "The embedding (62) must have the same length as the ids (63)."
with self.assertRaises(ValueError) as cm:
Embedding(self.emb, self.seq + "A")
self.assertEqual(str(cm.exception), msg)
class SequenceEmbeddingTests(TestCase):
def setUp(self):
self.emb = np.random.randn(62, 10)
self.seq = "IGKEEIQQRLAQFVDHWKELKQLAAARGQRLEESLEYQQFVANVEEEEAWINEKMTLVASED"
def test_repr(self):
emb, s = self.emb, self.seq
p_emb = SequenceEmbedding(emb, s)
rstr = repr(p_emb)
self.assertIn("SequenceEmbedding", rstr)
self.assertIn("62", rstr)
self.assertIn("10", rstr)
self.assertIn("IGKEEIQQRL", rstr)
def test_str(self):
emb, s = self.emb, self.seq
p_emb = SequenceEmbedding(emb, s)
self.assertEqual(p_emb.__str__(), s)
self.assertEqual(p_emb.sequence, s)
self.assertEqual(str(p_emb.ids.tobytes().decode("ascii")), s)
def test_bytes(self):
emb, s = self.emb, self.seq
p_emb = SequenceEmbedding(emb, s)
res = p_emb.bytes()
res_str = str(res.tobytes().decode("ascii"))
self.assertEqual(res_str, s)
def test_init(self):
emb, s = self.emb, self.seq
# sequence as string
p_emb = SequenceEmbedding(emb, s)
self.assertTupleEqual(p_emb.embedding.shape, (62, 10))
# sequence as bytes
p_emb = SequenceEmbedding(emb, s.encode("ascii"))
self.assertTupleEqual(p_emb.embedding.shape, (62, 10))
# sequence as skbio.Sequence
p_emb = SequenceEmbedding(emb, Protein(s))
self.assertTupleEqual(p_emb.embedding.shape, (62, 10))
def test_assert_length(self):
msg = "The embedding (62) must have the same length as the ids (63)."
with self.assertRaises(ValueError) as cm:
SequenceEmbedding(self.emb, self.seq + "A")
self.assertEqual(str(cm.exception), msg)
class SequenceVectorTests(TestCase):
def setUp(self):
# Create some sample SequenceVector objects for testing
self.vector1 = np.array([1, 2, 3])
self.vector2 = np.array([4, 5, 6])
self.vector3 = np.array([7, 8, 9])
self.bad_vector = np.array([7, 8])
self.seq_vectors = [
SequenceVector(self.vector1, "ACGT"),
SequenceVector(self.vector2, "GCTA"),
SequenceVector(self.vector3, "TTAG")
]
def test_init(self):
vec = np.array([1, 2, 3])
seq = "ACGT"
# sequence as string
obs = SequenceVector(vec, seq)
npt.assert_array_equal(obs.vector, vec)
npt.assert_array_equal(obs.embedding, vec.reshape(1, -1))
npt.assert_array_equal(obs.ids, np.array([b"ACGT"]))
# sequence as bytes
obs = SequenceVector(vec, seq.encode("ascii"))
npt.assert_array_equal(obs.vector, vec)
# sequence as skbio.Sequence
obs = SequenceVector(vec, DNA(seq))
npt.assert_array_equal(obs.vector, vec)
# input is a matrix, not a vector
vec2d = np.vstack([vec, vec])
msg = "Only one vector per sequence is allowed."
with self.assertRaisesRegex(ValueError, msg):
SequenceVector(vec2d, seq)
def test_vector(self):
# Test if the vector attribute is set correctly
for i, vector in enumerate([self.vector1, self.vector2, self.vector3]):
npt.assert_array_equal(self.seq_vectors[i].vector, vector)
def test_sequence(self):
# Test if the sequence attribute is set correctly
for i, sequence in enumerate(["ACGT", "GCTA", "TTAG"]):
self.assertEqual(self.seq_vectors[i].sequence, sequence)
def test_repr(self):
# Test if the __repr__ method returns the correct string
for seq_vector in self.seq_vectors:
self.assertTrue(seq_vector.__repr__().startswith("SequenceVector"))
self.assertIn("vector", seq_vector.__repr__())
# check latent dimension
self.assertIn("4", seq_vector.__repr__())
def test_str(self):
# Test if the __str__ method returns the correct string
for seq_vector in self.seq_vectors:
self.assertEqual(str(seq_vector), seq_vector.sequence)
class EmbedVecUtilityTests(TestCase):
def setUp(self):
self.vector1 = np.array([1, 2, 3])
self.vector2 = np.array([4, 5, 6])
self.vector3 = np.array([7, 8, 9])
self.bad_vector = np.array([7, 8])
self.seq_vectors = [
SequenceVector(self.vector1, "ACGT"),
SequenceVector(self.vector2, "GCTA"),
SequenceVector(self.vector3, "TTAG")
]
def test_embed_vec_to_numpy(self):
# Test if to_numpy returns the correct numpy array
exp = np.array([self.vector1, self.vector2, self.vector3])
obs = embed_vec_to_numpy(self.seq_vectors)
npt.assert_array_equal(obs, exp)
# skip validation
obs = embed_vec_to_numpy(self.seq_vectors, validate=False)
npt.assert_array_equal(obs, exp)
def test_embed_vec_to_numpy_raises(self):
# input contains non-vector
lst = [SequenceVector(self.vector1, "ACGT"),
SequenceEmbedding(np.vstack([self.vector2, self.vector3]), "AT")]
msg = "Input iterable contains objects that do not subclass EmbeddingVector."
with self.assertRaisesRegex(ValueError, msg):
embed_vec_to_numpy(lst)
# mixed sequence types
lst = [SequenceVector(self.vector1, "ACGT"),
ProteinVector(self.vector2, "MKRPL")]
msg = "All objects must be of the same type."
with self.assertRaisesRegex(ValueError, msg):
embed_vec_to_numpy(lst)
# lengths are not equal
lst = [SequenceVector(self.vector1, "ACGT"),
SequenceVector(self.vector2, "GCTA"),
SequenceVector(self.bad_vector, "TTAG")]
msg = "All vectors must have the same length."
with self.assertRaisesRegex(ValueError, msg):
embed_vec_to_numpy(lst)
def test_embed_vec_to_distances(self):
# Test if to_distances returns a DistanceMatrix object
obs = embed_vec_to_distances(self.seq_vectors)
self.assertIsInstance(obs, DistanceMatrix)
self.assertTupleEqual(obs.shape, (3, 3))
self.assertTrue(all(isinstance(d, float) for d in obs.condensed_form()))
d12 = euclidean(self.vector1, self.vector2)
d13 = euclidean(self.vector1, self.vector3)
d23 = euclidean(self.vector2, self.vector3)
exp = DistanceMatrix([[0, d12, d13],
[d12, 0, d23],
[d13, d23, 0]],
ids=["ACGT", "GCTA", "TTAG"])
npt.assert_allclose(obs.data, exp.data)
self.assertEqual(obs.ids, exp.ids)
obs = embed_vec_to_distances(self.seq_vectors, validate=False)
self.assertIsInstance(obs, DistanceMatrix)
def test_embed_vec_to_ordination(self):
# Test if to_ordination returns an OrdinationResults object
obs = embed_vec_to_ordination(self.seq_vectors)
self.assertIsInstance(obs, OrdinationResults)
self.assertEqual(obs.samples.shape, (3, 3))
self.assertEqual(obs.features.shape, (3, 3))
reconstructed = (obs.samples.values @ obs.features.values.T)
npt.assert_allclose(
reconstructed, embed_vec_to_numpy(self.seq_vectors)
)
obs = embed_vec_to_ordination(self.seq_vectors, validate=False)
self.assertIsInstance(obs, OrdinationResults)
def test_embed_vec_to_dataframe(self):
# Test if to_dataframe returns a pandas DataFrame object
obs = embed_vec_to_dataframe(self.seq_vectors)
self.assertIsInstance(obs, pd.DataFrame)
self.assertTupleEqual(obs.shape, (3, 3))
exp = pd.DataFrame([self.vector1, self.vector2, self.vector3],
index=["ACGT", "GCTA", "TTAG"])
pd.testing.assert_frame_equal(obs, exp)
obs = embed_vec_to_dataframe(self.seq_vectors, validate=False)
self.assertIsInstance(obs, pd.DataFrame)
if __name__ == "__main__":
main()
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