def setUp(self):
self.temp_file = TempSMILESFile()
self.fh = self.temp_file.open()
# See `test_data.py` for data set test cases.
self.dataset = SMILESDataset(self.fh.name)
self.vocab = SMILESVocabulary(self.dataset, need_corpus=True)
def setUp(self):
temp_file = TempSMILESFile(
tempfile_kwargs={'prefix': 'softmax_sampler'})
self.fh = temp_file.open()
dataset = SMILESDataset(self.fh.name)
self.vocabulary = SMILESVocabulary(dataset, need_corpus=True)
self.model = SMILESRNN(len(self.vocabulary))
self.predictor = SoftmaxSearch(self.model, self.vocabulary)
def setUp(self):
self.temp_file = TempSMILESFile(
tempfile_kwargs={'prefix': 'dataloader'})
self.fh = self.temp_file.open()
dataset = SMILESDataset(self.fh.name)
vocabulary = SMILESVocabulary(dataset=dataset, need_corpus=True)
self.dataloader = SMILESBatchColumnSampler(
corpus=vocabulary.corpus,
batch_size=2,
n_steps=4,
shuffle=True,
)
def setUp(self):
temp_file = TempSMILESFile(tempfile_kwargs={'prefix': 'model'})
self.fh = temp_file.open()
dataset = SMILESDataset(self.fh.name)
self.vocabulary = SMILESVocabulary(dataset, need_corpus=True)
self.batch_sampler = SMILESBatchColumnSampler(
corpus=self.vocabulary.corpus,
batch_size=3,
n_steps=8,
)
self.n_rnn_layers = 1 # Used in output/state shape testing.
self.n_rnn_units = 32 # Used in output/state shape testing.
self.model = SMILESRNN(len(self.vocabulary),
use_one_hot=False,
embedding_dim=4,
embedding_dropout=0.25,
embedding_dropout_axes=0,
embedding_init=mx.init.Uniform(),
embedding_prefix='embedding_',
rnn='lstm',
rnn_n_layers=self.n_rnn_layers,
rnn_n_units=self.n_rnn_units,
rnn_i2h_init='xavier_normal',
rnn_h2h_init='orthogonal_normal',
rnn_reinit_state=True,
rnn_detach_state=False,
rnn_state_init=mx.nd.random.uniform,
rnn_dropout=0.0,
rnn_prefix='encoder_',
dense_n_layers=2,
dense_n_units=32,
dense_activation='relu',
dense_dropout=0.5,
dense_init=mx.init.Xavier(),
dense_prefix='decoder_',
dtype='float32',
prefix='model_')
def setUp(self):
self.smiles_string = 'CCc1c[n+]2ccc3c4ccccc4[nH]c3c2cc1'
with TempSMILESFile(smiles_strings=self.smiles_string) as temp_fh:
dataset = SMILESDataset(temp_fh.file_handler.name)
self.vocabulary = SMILESVocabulary(dataset, need_corpus=True)