R/sdts-train.R
sdts_train.RdThis function trains a model that uses a dictionary to predict state changes. Differently from
fluss(), it doesn't look for semantic changes (that may be several), but for binary states like
"on" or "off". Think for example that a human annotator is pressing a switch any time he thinks
that the recorded data is relevant, and releases the switch when he thinks the data is noise. This
algorithm will learn the switching points (even better) and try to predict using new data.
sdts_train(
data,
label,
window_size,
beta = 1,
pat_max = Inf,
parallel = FALSE,
verbose = getOption("tsmp.verbose", 2)
)a vector of numeric. Time series.
a vector of logical. Annotations.
an int or a vector of int. Sliding window sizes.
a numeric. See details. (default is 1).
an int. Max number of shape features captured. (default is Inf).
a logical. Use parallel computation inside (default is FALSE).
an int. See details. (Default is 2).
Returns a list with the learned dictionary score (estimated score), score_hist
(history of scores), pattern (shape features), thold (threshold values).
beta is used to balance F-score towards recall (>1) or precision (<1). verbose changes
how much information is printed by this function; 0 means nothing, 1 means text, 2 adds the
progress bar, 3 adds the finish sound.
Yeh C-CM, Kavantzas N, Keogh E. Matrix profile IV: Using Weakly Labeled Time Series to Predict Outcomes. Proc VLDB Endow. 2017 Aug 1;10(12):1802-12.
Other Scalable Dictionaries:
sdts_predict(),
sdts_score()
# This is a fast toy example and results are useless. For a complete result, run the code inside
#' Not run' section below.
w <- c(110, 220)
subs <- 11000:20000
tr_data <- mp_test_data$train$data[subs]
tr_label <- mp_test_data$train$label[subs]
te_data <- mp_test_data$test$data[subs]
te_label <- mp_test_data$test$label[subs]
model <- sdts_train(tr_data, tr_label, w, verbose = 0)
predict <- sdts_predict(model, te_data, round(mean(w)))
sdts_score(predict, te_label, 1)
# \donttest{
windows <- c(110, 220, 330)
model <- sdts_train(mp_test_data$train$data, mp_test_data$train$label, windows)
#> Stage 1 of 3, compute matrix profile...
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#> Finished in 37.80 secs
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#> STOMP [-------------------------------] 2% at 579 it/s, elapsed: 1s, eta: 31s
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#> Finished in 37.36 secs
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#> STOMP [===============================] 100% at 509 it/s, elapsed: 35s, eta: 0s
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#> Finished in 35.40 secs
#> Stage 2 of 3, evaluate individual candidates...
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#> SDTS-Train [=======================>--] 92% at 3.2 it/s, elapsed: 17s, eta: 2s
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#> SDTS-Train [==========================] 100% at 3.2 it/s, elapsed: 19s, eta: 0s
#>
#> Finished in 18.82 secs
#> Stage 3 of 3, evaluate combination of candidates...
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#> SDTS-Train [=========================] 100% at 0.94 it/s, elapsed: 11m, eta: 0s
#>
#> Finished in 10.63 mins
predict <- sdts_predict(model, mp_test_data$test$data, round(mean(windows)))
sdts_score(predict, mp_test_data$test$label, 1)
# }