Description Usage Arguments Details Value References See Also Examples
Computes the F-Score of a SDTS prediction.
1 | sdts_score(pred, gtruth, beta = 1)
|
pred |
a |
gtruth |
a |
beta |
a |
beta
is used to balance F-score towards recall (>1
) or precision (<1
).
Returns a list
with f_score
, precision
and recall
.
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.
Website: https://sites.google.com/view/weaklylabeled
Other Scalable Dictionaries:
sdts_predict()
,
sdts_train()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | # 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)
## Not run:
windows <- c(110, 220, 330)
model <- sdts_train(mp_test_data$train$data, mp_test_data$train$label, windows)
predict <- sdts_predict(model, mp_test_data$test$data, round(mean(windows)))
sdts_score(predict, mp_test_data$test$label, 1)
## End(Not run)
|
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