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docs(readme): rename ff -> flrn to match glrn pattern
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@m-muecke
m-muecke committed Jan 5, 2025
1 parent 53a75f9 commit 0796102
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48 changes: 24 additions & 24 deletions README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -54,21 +54,21 @@ library(mlr3learners)
task = tsk("airpassengers")
task$select(setdiff(task$feature_names, "date"))
measure = msr("regr.rmse")
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
newdata = data.frame(passengers = rep(NA_real_, 3L))
prediction = ff$predict_newdata(newdata, task)
prediction = flrn$predict_newdata(newdata, task)
prediction
prediction = ff$predict(task, 142:144)
prediction = flrn$predict(task, 142:144)
prediction
prediction$score(measure)
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(task, ff, resampling)
rr = resample(task, flrn, resampling)
rr$aggregate(measure)
resampling = rsmp("forecast_cv")
rr = resample(task, ff, resampling)
rr = resample(task, flrn, resampling)
rr$aggregate(measure)
```

Expand All @@ -85,29 +85,29 @@ graph = ppl("convert_types", "Date", "POSIXct") %>>%
param_vals = list(is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
)
new_task = graph$train(task)[[1L]]
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(new_task)
prediction = ff$predict(new_task, 142:144)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(new_task)
prediction = flrn$predict(new_task, 142:144)
prediction$score(measure)
row_ids = new_task$nrow - 0:2
ff$predict_newdata(new_task$data(rows = row_ids), new_task)
flrn$predict_newdata(new_task$data(rows = row_ids), new_task)
newdata = new_task$data(rows = row_ids, cols = new_task$feature_names)
ff$predict_newdata(newdata, new_task)
flrn$predict_newdata(newdata, new_task)
resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(new_task, ff, resampling)
rr = resample(new_task, flrn, resampling)
rr$aggregate(measure)
resampling = rsmp("forecast_cv")
rr = resample(new_task, ff, resampling)
rr = resample(new_task, flrn, resampling)
rr$aggregate(measure)
```

### mlr3pipelines integration

```{r}
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% ff)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(measure)
```
Expand Down Expand Up @@ -138,8 +138,8 @@ graph = ppl("convert_types", "Date", "POSIXct") %>>%
year = FALSE, is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE
)
)
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% ff)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% flrn)$train(task)
max_date = task$data()[.N, date]
newdata = data.frame(
Expand Down Expand Up @@ -177,13 +177,13 @@ graph = ppl("convert_types", "Date", "POSIXct") %>>%
)
task = graph$train(task)[[1L]]
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
prediction = ff$predict(task, 4460:4464)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
prediction = flrn$predict(task, 4460:4464)
prediction$score(measure)
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(task, ff, resampling)
rr = resample(task, flrn, resampling)
rr$aggregate(measure)
```

Expand All @@ -210,13 +210,13 @@ task = tsibbledata::aus_livestock |>
setorder(month) |>
as_task_fcst(target = "count", index = "month")
task = graph$train(task)[[1L]]
ff = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
tab = task$backend$data(
rows = task$row_ids, cols = c(task$backend$primary_key, "month.year")
)
setnames(tab, c("row_id", "year"))
row_ids = tab[year >= 2015, row_id]
prediction = ff$predict(task, row_ids)
prediction = flrn$predict(task, row_ids)
prediction$score(measure)
# global forecasting
Expand All @@ -228,12 +228,12 @@ task = tsibbledata::aus_livestock |>
setorder(state, month) |>
as_task_fcst(target = "count", index = "month", key = "state")
task = graph$train(task)[[1L]]
ff = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
tab = task$backend$data(
rows = task$row_ids, cols = c(task$backend$primary_key, "month.year", "state")
)
setnames(tab, c("row_id", "year", "state"))
row_ids = tab[year >= 2015 & state == "Western Australia", row_id]
prediction = ff$predict(task, row_ids)
prediction = flrn$predict(task, row_ids)
prediction$score(measure)
```
106 changes: 53 additions & 53 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -39,38 +39,38 @@ library(mlr3learners)
task = tsk("airpassengers")
task$select(setdiff(task$feature_names, "date"))
measure = msr("regr.rmse")
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
newdata = data.frame(passengers = rep(NA_real_, 3L))
prediction = ff$predict_newdata(newdata, task)
prediction = flrn$predict_newdata(newdata, task)
prediction
#> <PredictionRegr> for 3 observations:
#> row_ids truth response
#> 1 NA 450.4252
#> 2 NA 474.6646
#> 3 NA 475.6742
prediction = ff$predict(task, 142:144)
#> 1 NA 448.7741
#> 2 NA 473.3513
#> 3 NA 478.6528
prediction = flrn$predict(task, 142:144)
prediction
#> <PredictionRegr> for 3 observations:
#> row_ids truth response
#> 1 461 460.5518
#> 2 390 411.4576
#> 3 432 397.5393
#> 1 461 453.1701
#> 2 390 408.2967
#> 3 432 396.3043
prediction$score(measure)
#> regr.rmse
#> 23.43906
#> 23.5956

ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(task, ff, resampling)
rr = resample(task, flrn, resampling)
rr$aggregate(measure)
#> regr.rmse
#> 101.0441
#> 106.5602

resampling = rsmp("forecast_cv")
rr = resample(task, ff, resampling)
rr = resample(task, flrn, resampling)
rr$aggregate(measure)
#> regr.rmse
#> 51.86913
#> 52.03231
```

### Multivariate
Expand All @@ -86,49 +86,49 @@ graph = ppl("convert_types", "Date", "POSIXct") %>>%
param_vals = list(is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
)
new_task = graph$train(task)[[1L]]
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(new_task)
prediction = ff$predict(new_task, 142:144)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(new_task)
prediction = flrn$predict(new_task, 142:144)
prediction$score(measure)
#> regr.rmse
#> 17.72294
#> 18.15987

row_ids = new_task$nrow - 0:2
ff$predict_newdata(new_task$data(rows = row_ids), new_task)
flrn$predict_newdata(new_task$data(rows = row_ids), new_task)
#> <PredictionRegr> for 3 observations:
#> row_ids truth response
#> 1 432 409.5736
#> 2 390 392.3477
#> 3 461 394.1802
#> 1 432 410.0752
#> 2 390 389.3914
#> 3 461 394.7900
newdata = new_task$data(rows = row_ids, cols = new_task$feature_names)
ff$predict_newdata(newdata, new_task)
flrn$predict_newdata(newdata, new_task)
#> <PredictionRegr> for 3 observations:
#> row_ids truth response
#> 1 NA 409.5736
#> 2 NA 392.3477
#> 3 NA 394.1802
#> 1 NA 410.0752
#> 2 NA 389.3914
#> 3 NA 394.7900

resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(new_task, ff, resampling)
rr = resample(new_task, flrn, resampling)
rr$aggregate(measure)
#> regr.rmse
#> 85.07956
#> 81.67667

resampling = rsmp("forecast_cv")
rr = resample(new_task, ff, resampling)
rr = resample(new_task, flrn, resampling)
rr$aggregate(measure)
#> regr.rmse
#> 46.85798
#> 39.40938
```

### mlr3pipelines integration

``` r
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% ff)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(measure)
#> regr.rmse
#> 19.05406
#> 20.05668
```

### Example: Forecasting electricity demand
Expand Down Expand Up @@ -157,8 +157,8 @@ graph = ppl("convert_types", "Date", "POSIXct") %>>%
year = FALSE, is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE
)
)
ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% ff)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% flrn)$train(task)

max_date = task$data()[.N, date]
newdata = data.frame(
Expand All @@ -171,13 +171,13 @@ prediction = glrn$predict_newdata(newdata, task)
prediction
#> <PredictionRegr> for 14 observations:
#> row_ids truth response
#> 1 NA 186.5476
#> 2 NA 191.9933
#> 3 NA 184.2625
#> 1 NA 186.6728
#> 2 NA 190.9780
#> 3 NA 183.3363
#> --- --- ---
#> 12 NA 215.9142
#> 13 NA 219.8321
#> 14 NA 219.5090
#> 12 NA 215.7447
#> 13 NA 220.3997
#> 14 NA 222.3319
```

### Global Forecasting
Expand Down Expand Up @@ -205,18 +205,18 @@ graph = ppl("convert_types", "Date", "POSIXct") %>>%
)
task = graph$train(task)[[1L]]

ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
prediction = ff$predict(task, 4460:4464)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
prediction = flrn$predict(task, 4460:4464)
prediction$score(measure)
#> regr.rmse
#> 24920.84
#> 22658.84

ff = ForecastLearner$new(lrn("regr.ranger"), 1:3)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
resampling = rsmp("forecast_holdout", ratio = 0.8)
rr = resample(task, ff, resampling)
rr = resample(task, flrn, resampling)
rr$aggregate(measure)
#> regr.rmse
#> 82599.95
#> 80742.63
```

### Example: Global vs Local Forecasting
Expand All @@ -242,16 +242,16 @@ task = tsibbledata::aus_livestock |>
setorder(month) |>
as_task_fcst(target = "count", index = "month")
task = graph$train(task)[[1L]]
ff = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
tab = task$backend$data(
rows = task$row_ids, cols = c(task$backend$primary_key, "month.year")
)
setnames(tab, c("row_id", "year"))
row_ids = tab[year >= 2015, row_id]
prediction = ff$predict(task, row_ids)
prediction = flrn$predict(task, row_ids)
prediction$score(measure)
#> regr.rmse
#> 32133.38
#> 32041.71

# global forecasting
task = tsibbledata::aus_livestock |>
Expand All @@ -262,14 +262,14 @@ task = tsibbledata::aus_livestock |>
setorder(state, month) |>
as_task_fcst(target = "count", index = "month", key = "state")
task = graph$train(task)[[1L]]
ff = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
tab = task$backend$data(
rows = task$row_ids, cols = c(task$backend$primary_key, "month.year", "state")
)
setnames(tab, c("row_id", "year", "state"))
row_ids = tab[year >= 2015 & state == "Western Australia", row_id]
prediction = ff$predict(task, row_ids)
prediction = flrn$predict(task, row_ids)
prediction$score(measure)
#> regr.rmse
#> 30955.33
#> 31719.35
```

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