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Add output file option and allow pred to be run from anywhere. #1

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Add output file option and allow pred to be run from anywhere. #1

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18 changes: 16 additions & 2 deletions deepscm-master/pred.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,8 @@
#!/usr/bin/env python3

# Import libraries
import os.path

import numpy as np
import argparse

Expand All @@ -9,19 +13,24 @@
# Import custom functions
from utils import one_hot_encoder, load_pred_data


parser = argparse.ArgumentParser(prog='pred.py')
parser.add_argument("--infile", help="DeepSCM input file", type=str, default='DeepSCM_input.txt')
parser.add_argument('--outfile', help="DeepSCM output file", type=str, default='DeepSCM_output.txt')

args = parser.parse_args()

name_list, seq_list = load_pred_data(args.infile)

json_file = open('Conv1D_regression.json', 'r')
script_dir = os.path.dirname(__file__)

json_file = open(os.path.join(script_dir, 'Conv1D_regression.json'), 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)

# load weights into model
loaded_model.load_weights("Conv1D_regression.h5")
loaded_model.load_weights(os.path.join(script_dir, "Conv1D_regression.h5"))

X = [one_hot_encoder(s=x) for x in seq_list]
X = np.transpose(np.asarray(X), (0, 2, 1))
Expand All @@ -30,6 +39,11 @@
loaded_model.compile(optimizer='adam', loss='mae', metrics=[None])
y_pred = loaded_model.predict(X)

out = open(args.outfile, 'w')
out.write('name,SCM_score\n')
for i in range(len(y_pred)):
print("%s %.2f" % (name_list[i], y_pred[i]))

out.write(f'{name_list[i]},{float(y_pred[i])}\n')
out.close()