from __future__ import print_function
from kipoi.model import BaseModel
import numpy as np
import subprocess
import os
import tempfile
def encode_sequence_into_fasta_file(seq, ofname):
"""writes sequences into fasta file
"""
with open(ofname, "w") as wf:
for i, seq_here in enumerate(seq):
print('>{}'.format(i), file=wf)
print(seq_here, file=wf)
class LsgkmSvm(BaseModel):
def __init__(self, model_file = "model_files/model.txt", threads=1):
self.model_file = model_file
self.threads = threads
#### from https://github.com/kundajelab/dragonn/blob/master/dragonn/models.py#L336
def predict_on_batch(self, inputs):
# write test fasta file
temp_input = tempfile.NamedTemporaryFile(suffix = ".txt")
test_fname = temp_input.name
encode_sequence_into_fasta_file(ofname = test_fname, seq = inputs.tolist())
# test gkmsvm
temp_ofp = tempfile.NamedTemporaryFile(suffix = ".txt")
threads_option = '-T %s' % (str(self.threads))
verbosity_option = '-v 0'
command = ' '.join(['gkmpredict',
test_fname,
self.model_file,
temp_ofp.name,
threads_option,
verbosity_option])
#process = subprocess.Popen(command, shell=True)
#process.wait() # wait for it to finish
exit_code = os.system(command)
temp_input.close()
assert exit_code == 0
# get classification results
temp_ofp.seek(0)
y = np.array([line.split()[-1] for line in temp_ofp], dtype=float)
temp_ofp.close()
return np.expand_dims(y, 1)
