Computes the F-Score of a SDTS prediction.
sdts_score(pred, gtruth, beta = 1)a vector of logical. Predicted annotation from sdts_predict()
a vector of logical. Ground truth annotation.
a numeric. See details. (default is 1).
Returns a list with f_score, precision and recall.
beta is used to balance F-score towards recall (>1) or precision (<1).
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_train()
# 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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#> Stage 2 of 3, evaluate individual candidates...
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#> Stage 3 of 3, evaluate combination of candidates...
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predict <- sdts_predict(model, mp_test_data$test$data, round(mean(windows)))
sdts_score(predict, mp_test_data$test$label, 1)
# }