Description Usage Arguments Details Value References See Also Examples
View source: R/feature_extraction.R
seq2feature_seq2seq
extract features from response processes by autoencoder.
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seqs 
an object of class 
ae_type 
a string specifies the type of autoencoder. The autoencoder can be an action sequence autoencoder ("action"), a time sequence autoencoder ("time"), or an actiontime sequence autoencoder ("both"). 
K 
the number of features to be extracted. 
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 
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. 
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. 
samples_train, samples_valid, 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 wraps aseq2feature_seq2seq
,
tseq2feature_seq2seq
, and atseq2feature_seq2seq
.
seq2feature_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 
Tang, X., Wang, Z., Liu, J., and Ying, Z. (2020) An exploratory analysis of the latent structure of process data via action sequence autoencoders. British Journal of Mathematical and Statistical Psychology. 74(1), 133.
chooseK_seq2seq
for choosing K
through crossvalidation.
Other feature extraction methods: aseq2feature_seq2seq
,
atseq2feature_seq2seq
,
seq2feature_mds_large
,
seq2feature_mds
,
seq2feature_ngram
,
tseq2feature_seq2seq
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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)
seqs < sub_seqs(cc_data$seqs, samples)
# action sequence autoencoder
K_res < chooseK_seq2seq(seqs=seqs, ae_type="action", K_cand=c(5, 10),
n_epoch=5, n_fold=2, valid_prop=0.2)
seq2seq_res < seq2feature_seq2seq(seqs=seqs, ae_type="action", K=K_res$K,
n_epoch=5, samples_train=1:40, samples_valid=41:50)
theta < seq2seq_res$theta
# time sequence autoencoder
K_res < chooseK_seq2seq(seqs=seqs, ae_type="time", K_cand=c(5, 10),
n_epoch=5, n_fold=2, valid_prop=0.2)
seq2seq_res < seq2feature_seq2seq(seqs=seqs, ae_type="time", K=K_res$K,
n_epoch=5, samples_train=1:40, samples_valid=41:50)
theta < seq2seq_res$theta
# action and time sequence autoencoder
K_res < chooseK_seq2seq(seqs=seqs, ae_type="both", K_cand=c(5, 10),
n_epoch=5, n_fold=2, valid_prop=0.2)
seq2seq_res < seq2feature_seq2seq(seqs=seqs, ae_type="both", K=K_res$K,
n_epoch=5, samples_train=1:40, samples_valid=41:50)
theta < seq2seq_res$theta
plot(seq2seq_res$train_loss, col="blue", type="l")
lines(seq2seq_res$valid_loss, col="red")
}

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