def __init__(self,
exp_file='exp.json',
encoder_file=None,
decoder_file=None,
directory=None):
# files
if directory is not None:
curdir = os.getcwd()
os.chdir(os.path.join(curdir, directory))
# exp_file = os.path.join(directory, exp_file)
# load parameters
self.params = hyperparameters.load_params(exp_file, False)
if encoder_file is not None:
self.params["encoder_weights_file"] = encoder_file
if decoder_file is not None:
self.params["decoder_weights_file"] = decoder_file
# char stuff
chars = yaml.safe_load(open(self.params['char_file']))
self.chars = chars
self.params['NCHARS'] = len(chars)
self.char_indices = dict((c, i) for i, c in enumerate(chars))
self.indices_char = dict((i, c) for i, c in enumerate(chars))
# encoder, decoder
self.enc = load_encoder(self.params)
self.dec = load_decoder(self.params)
self.encode, self.decode = self.enc_dec_functions()
self.data = None
if self.params['do_prop_pred']:
self.property_predictor = load_property_predictor(self.params)
# Load data without normalization as dataframe
df = pd.read_csv(self.params['data_file'])
df.iloc[:, 0] = df.iloc[:, 0].str.strip()
df = df[df.iloc[:, 0].str.len() <= self.params['MAX_LEN']]
self.smiles = df.iloc[:, 0].tolist()
if df.shape[1] > 1:
self.data = df.iloc[:, 1:]
self.estimate_estandarization()
if directory is not None:
os.chdir(curdir)
return
import yaml
import time
import os
from data_input import vectorize_data
from hyperparameters import load_params
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-e',
'--exp_file',
help='experiment file',
default='exp.json')
parser.add_argument('-d', '--directory', help='exp directory', default=None)
args = vars(parser.parse_args())
if args['directory'] is not None:
args['exp_file'] = os.path.join(args['directory'], args['exp_file'])
params = load_params(args['exp_file'])
print("All params:", params)
# load data
X_train, X_test = vectorize_data(params)
print(X_train[0])
import matplotlib.pyplot as plt
plt.imshow(X_train[0].reshape(-1, 35))
plt.show()
epochs=params['epochs'],
initial_epoch=params['prev_epochs'],
callbacks=callbacks,
verbose=keras_verbose,
validation_data=[ X_test, model_test_targets]
)
encoder.save(params['encoder_weights_file'])
decoder.save(params['decoder_weights_file'])
print('time of run : ', time.time() - start_time)
print('**FINISHED**')
return
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--exp_file',
help='experiment file', default='./exp.json')
parser.add_argument('-d', '--directory',
help='exp directory', default=None)
args = vars(parser.parse_args())
if args['directory'] is not None:
args['exp_file'] = os.path.join(args['directory'], args['exp_file'])
params = hyperparameters.load_params(args['exp_file'])
print("All params:--------------------------------\n", params)
print("----------------------------------------")
main_no_prop(params)