import os
import matplotlib
matplotlib.use('AGG')
import numpy as np
import pytest
from keras import backend as K
from keras.layers import Conv2D
from pkg_resources import resource_filename
from janggu import Janggu
from janggu import inputlayer
from janggu import outputconv
from janggu.data import Bioseq
from janggu.data import Cover
from janggu.layers import DnaConv2D
from janggu.layers import LocalAveragePooling2D
@pytest.mark.filterwarnings("ignore:inspect")
@pytest.mark.filterwarnings("ignore:The binary")
def test_create_from_array_whole_genome_true_from_pred(tmpdir):
os.environ['JANGGU_OUTPUT'] = tmpdir.strpath
# load the dataset
# The pseudo genome represents just a concatenation of all sequences
# in sample.fa and sample2.fa. Therefore, the results should be almost
# identically to the models obtained from classify_fasta.py.
REFGENOME = resource_filename('janggu', 'resources/pseudo_genome.fa')
# ROI contains regions spanning positive and negative examples
ROI_FILE = resource_filename('janggu', 'resources/roi_train.bed')
# PEAK_FILE only contains positive examples
PEAK_FILE = resource_filename('janggu', 'resources/scores.bed')
DNA = Bioseq.create_from_refgenome('dna', refgenome=REFGENOME,
roi=ROI_FILE,
binsize=200, stepsize=200,
order=1,
store_whole_genome=True)
LABELS = Cover.create_from_bed('peaks', roi=ROI_FILE,
bedfiles=PEAK_FILE,
binsize=200, stepsize=200,
resolution=200,
store_whole_genome=True)
@inputlayer
@outputconv('sigmoid')
def double_stranded_model_dnaconv(inputs, inp, oup, params):
with inputs.use('dna') as layer:
layer = DnaConv2D(Conv2D(params[0], (params[1], 1),
activation=params[2]))(layer)
output = LocalAveragePooling2D(window_size=layer.shape.as_list()[1],
name='motif')(layer)
return inputs, output
modeltemplate = double_stranded_model_dnaconv
K.clear_session()
# create a new model object
model = Janggu.create(template=modeltemplate,
modelparams=(30, 21, 'relu'),
inputs=DNA,
outputs=LABELS)
model.compile(optimizer='adadelta', loss='binary_crossentropy',
metrics=['acc'])
pred = model.predict(DNA)
cov_out = Cover.create_from_array('BindingProba', pred, LABELS.gindexer,
store_whole_genome=True)
assert pred.shape == cov_out.shape
np.testing.assert_equal(pred, cov_out[:])
assert len(cov_out.gindexer) == len(pred)
assert len(cov_out.garray.handle) == 1
@pytest.mark.filterwarnings("ignore:inspect")
@pytest.mark.filterwarnings("ignore:The binary")
def test_create_from_array_whole_genome_true(tmpdir):
os.environ['JANGGU_OUTPUT'] = tmpdir.strpath
# load the dataset
# The pseudo genome represents just a concatenation of all sequences
# in sample.fa and sample2.fa. Therefore, the results should be almost
# identically to the models obtained from classify_fasta.py.
# ROI contains regions spanning positive and negative examples
ROI_FILE = resource_filename('janggu', 'resources/roi_train.bed')
# PEAK_FILE only contains positive examples
PEAK_FILE = resource_filename('janggu', 'resources/scores.bed')
LABELS = Cover.create_from_bed('peaks', roi=ROI_FILE,
bedfiles=[PEAK_FILE]*5,
binsize=200, stepsize=200,
resolution=200,
store_whole_genome=True)
pred = LABELS[:]
for storage in ['ndarray', 'sparse', 'hdf5']:
print(storage)
cov_out = Cover.create_from_array('BindingProba', pred,
LABELS.gindexer,
cache=True,
storage=storage,
store_whole_genome=True)
np.testing.assert_equal(cov_out[:], LABELS[:])
np.testing.assert_equal(cov_out.shape, LABELS.shape)
@pytest.mark.filterwarnings("ignore:The binary")
def test_create_from_array_whole_genome_false_pred(tmpdir):
os.environ['JANGGU_OUTPUT'] = tmpdir.strpath
# load the dataset
# The pseudo genome represents just a concatenation of all sequences
# in sample.fa and sample2.fa. Therefore, the results should be almost
# identically to the models obtained from classify_fasta.py.
REFGENOME = resource_filename('janggu', 'resources/pseudo_genome.fa')
# ROI contains regions spanning positive and negative examples
ROI_FILE = resource_filename('janggu', 'resources/roi_train.bed')
# PEAK_FILE only contains positive examples
PEAK_FILE = resource_filename('janggu', 'resources/scores.bed')
DNA = Bioseq.create_from_refgenome('dna', refgenome=REFGENOME,
roi=ROI_FILE,
binsize=200, stepsize=200,
order=1,
store_whole_genome=False)
LABELS = Cover.create_from_bed('peaks', roi=ROI_FILE,
bedfiles=PEAK_FILE,
binsize=200, stepsize=200,
resolution=200,
store_whole_genome=False)
@inputlayer
@outputconv('sigmoid')
def double_stranded_model_dnaconv(inputs, inp, oup, params):
with inputs.use('dna') as layer:
layer = DnaConv2D(Conv2D(params[0], (params[1], 1),
activation=params[2]))(layer)
output = LocalAveragePooling2D(window_size=layer.shape.as_list()[1],
name='motif')(layer)
return inputs, output
modeltemplate = double_stranded_model_dnaconv
K.clear_session()
# create a new model object
model = Janggu.create(template=modeltemplate,
modelparams=(30, 21, 'relu'),
inputs=DNA,
outputs=LABELS)
model.compile(optimizer='adadelta', loss='binary_crossentropy',
metrics=['acc'])
pred = model.predict(DNA)
cov_out = Cover.create_from_array('BindingProba', pred, LABELS.gindexer,
store_whole_genome=False)
assert pred.shape == cov_out.shape
np.testing.assert_equal(pred, cov_out[:])
assert len(cov_out.gindexer) == len(pred)
assert len(cov_out.garray.handle['data']) == len(pred)
@pytest.mark.filterwarnings("ignore:inspect")
@pytest.mark.filterwarnings("ignore:The binary")
def test_create_from_array_whole_genome_false(tmpdir):
os.environ['JANGGU_OUTPUT'] = tmpdir.strpath
# load the dataset
# The pseudo genome represents just a concatenation of all sequences
# in sample.fa and sample2.fa. Therefore, the results should be almost
# identically to the models obtained from classify_fasta.py.
# ROI contains regions spanning positive and negative examples
ROI_FILE = resource_filename('janggu', 'resources/roi_train.bed')
# PEAK_FILE only contains positive examples
PEAK_FILE = resource_filename('janggu', 'resources/scores.bed')
LABELS = Cover.create_from_bed('peaks', roi=ROI_FILE,
bedfiles=[PEAK_FILE]*5,
binsize=200, stepsize=200,
resolution=200,
store_whole_genome=False)
pred = LABELS[:]
for storage in ['ndarray', 'sparse', 'hdf5']:
print(storage)
cov_out = Cover.create_from_array('BindingProba', pred,
LABELS.gindexer,
cache=True,
storage=storage,
store_whole_genome=False)
np.testing.assert_equal(cov_out[:], LABELS[:])
np.testing.assert_equal(cov_out.shape, LABELS.shape)
Datasets
Models
