Description Usage Arguments Details Value See Also Examples
View source: R/autoencoder_functions.R
atseq2feature_seq2seq
extract features from action and timestamp sequences by a
sequence autoencoder.
1 2 3 4 5 
atseqs 
a list of two elements, first element is the list of 
K 
the number of features to be extracted. 
weights 
a vector of 2 elements for the weight of the loss of action sequences (categorical_crossentropy) and time sequences (mean squared error), respectively. The total loss is calculated as the weighted sum of the two losses. 
cumulative 
logical. If TRUE, the sequence of cumulative time up to each event is used as input to the neural network. If FALSE, the sequence of interarrival time (gap time between an event and the previous event) will be used as input to the neural network. Default is FALSE. 
log 
logical. If TRUE, for the timestamp sequences, input of the neural net is the base10 log of the original sequence of times plus 1 (i.e., log10(t+1)). If FALSE, the original sequence of times is used. 
rnn_type 
the type of recurrent unit to be used for modeling
response processes. 
n_epoch 
the number of training epochs for the autoencoder. 
method 
the method for computing features from the output of an
recurrent neural network in the encoder. Available options are

step_size 
the learning rate of optimizer. 
optimizer_name 
a character string specifying the optimizer to be used
for training. Availabel options are 
samples_train 
vectors of indices specifying the training, validation and test sets for training autoencoder. 
samples_valid 
vectors of indices specifying the training, validation and test sets for training autoencoder. 
samples_test 
vectors of indices specifying the training, validation and test sets for training autoencoder. 
pca 
logical. If TRUE, the principal components of features are returned. Default is TRUE. 
verbose 
logical. If TRUE, training progress is printed. 
return_theta 
logical. If TRUE, extracted features are returned. 
This function trains a sequencetosequence autoencoder using keras. The encoder of the autoencoder consists of a recurrent neural network. The decoder consists of another recurrent neural network followed by a fully connected layer with softmax activation for actions and another fully connected layer with ReLU activation for times. The outputs of the encoder are the extracted features.
The output of the encoder is a function of the encoder recurrent neural network.
It is the last latent state of the encoder recurrent neural network if method="last"
and the average of the encoder recurrent neural network latent states if method="avg"
.
tseq2feature_seq2seq
returns a list containing
theta 
a matrix containing 
train_loss 
a vector of length 
valid_loss 
a vector of length 
test_loss 
a vector of length 
chooseK_seq2seq
for choosing K
through crossvalidation.
Other feature extraction methods: aseq2feature_seq2seq
,
seq2feature_mds_large
,
seq2feature_mds
,
seq2feature_ngram
,
seq2feature_seq2seq
,
tseq2feature_seq2seq
1 2 3 4 5 6 7 8 9 10 11 12 13  if (!system("python c 'import tensorflow as tf'", ignore.stdout = TRUE, ignore.stderr= TRUE)) {
n < 50
data(cc_data)
samples < sample(1:length(cc_data$seqs$time_seqs), n)
atseqs < sub_seqs(cc_data$seqs, samples)
action_and_time_seq2seq_res < atseq2feature_seq2seq(atseqs, 5, rnn_type="lstm", n_epoch=5,
samples_train=1:40, samples_valid=41:50)
features < action_and_time_seq2seq_res$theta
plot(action_and_time_seq2seq_res$train_loss, col="blue", type="l",
ylim = range(c(action_and_time_seq2seq_res$train_loss,
action_and_time_seq2seq_res$valid_loss)))
lines(action_and_time_seq2seq_res$valid_loss, col="red", type = 'l')
}

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