def modisco_enrich_patterns(patterns_pkl_file,
modisco_dir,
output_file,
impsf=None):
"""Add stacked_seqlet_imp to pattern `attrs`
Args:
patterns_pkl: patterns.pkl file path
modisco_dir: modisco directory containing
output_file: output file path for patterns.pkl
"""
from basepair.utils import read_pkl, write_pkl
from basepair.cli.imp_score import ImpScoreFile
from basepair.modisco.core import StackedSeqletImp
logger.info("Loading patterns")
modisco_dir = Path(modisco_dir)
patterns = read_pkl(patterns_pkl_file)
mr = ModiscoResult(modisco_dir / 'modisco.h5')
mr.open()
if impsf is None:
imp_file = ImpScoreFile.from_modisco_dir(modisco_dir)
logger.info("Loading ImpScoreFile into memory")
imp_file.cache()
else:
logger.info("Using the provided ImpScoreFile")
imp_file = impsf
logger.info("Extracting profile and importance scores")
extended_patterns = []
for p in tqdm(patterns):
p = p.copy()
profile_width = p.len_profile()
# get the shifted seqlets
seqlets = [
s.pattern_align(**p.attrs['align'])
for s in mr._get_seqlets(p.name)
]
# keep only valid seqlets
valid_seqlets = [
s for s in seqlets if s.valid_resize(profile_width,
imp_file.get_seqlen() + 1)
]
# extract the importance scores
p.attrs['stacked_seqlet_imp'] = imp_file.extract(
valid_seqlets, profile_width=profile_width)
p.attrs['n_seqlets'] = mr.n_seqlets(*p.name.split("/"))
extended_patterns.append(p)
write_pkl(extended_patterns, output_file)
def load_data(model_dir, cache_data=False,
dataspec=None, hparams=None, data=None, preprocessor=None):
if dataspec is not None:
ds = DataSpec.load(dataspec)
else:
ds = DataSpec.load(os.path.join(model_dir, "dataspec.yaml"))
if hparams is not None:
hp = HParams.load(hparams)
else:
hp = HParams.load(os.path.join(model_dir, "hparams.yaml"))
if data is not None:
data_path = data
else:
data_path = os.path.join(model_dir, 'data.pkl')
if os.path.exists(data_path):
train, valid, test = read_pkl(data_path)
else:
train, valid, test = chip_exo_nexus(ds,
peak_width=hp.data.peak_width,
shuffle=hp.data.shuffle,
valid_chr=hp.data.valid_chr,
test_chr=hp.data.test_chr)
# Pre-process the data
logger.info("Pre-processing the data")
if preprocessor is not None:
preproc_path = os.path.join(model_dir, "preprocessor.pkl")
else:
preproc_path = preprocessor
preproc = read_pkl(preproc_path)
train[1] = preproc.transform(train[1])
valid[1] = preproc.transform(valid[1])
try:
test[1] = preproc.transform(test[1])
except Exception:
logger.warn("Test set couldn't be processed")
test = None
return train, valid, test
def from_mdir(cls, model_dir):
"""
Args:
model_dir (str): Path to the model directory
"""
import os
from basepair.cli.schemas import DataSpec
from keras.models import load_model
from basepair.utils import read_pkl
ds = DataSpec.load(os.path.join(model_dir, "dataspec.yaml"))
model = load_model(os.path.join(model_dir, "model.h5"))
preproc_file = os.path.join(model_dir, "preprocessor.pkl")
if os.path.exists(preproc_file):
preproc = read_pkl(preproc_file)
else:
preproc = None
return cls(model=model, fasta_file=ds.fasta_file, tasks=list(ds.task_specs), preproc=preproc)
def load(cls, file_path):
"""Load model from a file
"""
from basepair.utils import read_pkl
# TODO - update to json
return read_pkl(file_path)