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docs(readme): more clean-up of first sections
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@m-muecke
m-muecke committed Jan 19, 2025
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41 changes: 9 additions & 32 deletions README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,7 @@ other to support to support machine learning forecasting, i.e. using regression
learners and applying them to forecasting tasks. The design of the latter is
still in flux and may change.

### Example: native forecast learner
### Example: forecasting with forecast learner

First lets create a helper function to generate new data for forecasting tasks.

Expand Down Expand Up @@ -119,12 +119,13 @@ learner = lrn("fcst.auto_arima",
learner$predict_newdata(newdata, task)
```

### machine learning forecasting
### Example: forecasting with regression learner

```{r, message = FALSE}
library(mlr3learners)
task = tsk("airpassengers")
# we have to remove the date feature for regression learners
task$select(setdiff(task$feature_names, "date"))
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)$train(task)
newdata = data.frame(passengers = rep(NA_real_, 3L))
Expand All @@ -144,43 +145,19 @@ rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
```

### Multivariate
Or with some feature engineering using mlr3pipelines:

```{r}
library(mlr3pipelines)
task = tsk("airpassengers")
# datefeatures currently requires POSIXct
graph = ppl("convert_types", "Date", "POSIXct") %>>%
po("datefeatures",
param_vals = list(is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
)
new_task = graph$train(task)[[1L]]
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)$train(new_task)
prediction = flrn$predict(new_task, 142:144)
prediction$score(msr("regr.rmse"))
row_ids = new_task$nrow - 0:2
flrn$predict_newdata(new_task$data(rows = row_ids), new_task)
newdata = new_task$data(rows = row_ids, cols = new_task$feature_names)
flrn$predict_newdata(newdata, new_task)
resampling = rsmp("forecast_holdout", ratio = 0.9)
rr = resample(new_task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
resampling = rsmp("forecast_cv")
rr = resample(new_task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
```

### mlr3pipelines integration

```{r}
graph = ppl("convert_types", "Date", "POSIXct") %>>%
po("datefeatures",
param_vals = list(is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
param_vals = list(
week_of_year = FALSE, day_of_year = FALSE, day_of_month = FALSE, day_of_week = FALSE,
is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE
)
)
task = tsk("airpassengers")
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
Expand Down
111 changes: 36 additions & 75 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ the other to support to support machine learning forecasting, i.e. using
regression learners and applying them to forecasting tasks. The design
of the latter is still in flux and may change.

### Example: native forecast learner
### Example: forecasting with forecast learner

First lets create a helper function to generate new data for forecasting
tasks.
Expand Down Expand Up @@ -140,106 +140,67 @@ learner$predict_newdata(newdata, task)
#> 12 NA 452.1168 463.7584 512.9829 562.2074 573.8491 512.9829
```

### machine learning forecasting
### Example: forecasting with regression learner

``` r
library(mlr3learners)

task = tsk("airpassengers")
# we have to remove the date feature for regression learners
task$select(setdiff(task$feature_names, "date"))
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)$train(task)
newdata = data.frame(passengers = rep(NA_real_, 3L))
prediction = flrn$predict_newdata(newdata, task)
prediction
#> <PredictionRegr> for 3 observations:
#> row_ids truth response
#> 1 NA 433.7011
#> 2 NA 431.2296
#> 3 NA 452.5381
#> 1 NA 435.6505
#> 2 NA 437.5554
#> 3 NA 456.4960
prediction = flrn$predict(task, 142:144)
prediction
#> <PredictionRegr> for 3 observations:
#> row_ids truth response
#> 1 461 456.7375
#> 2 390 412.8622
#> 3 432 430.5879
#> 1 461 458.2301
#> 2 390 414.4641
#> 3 432 433.7197
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 13.45168
#> 14.24925

flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)
resampling = rsmp("forecast_holdout", ratio = 0.9)
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 47.85272
#> 47.63902

resampling = rsmp("forecast_cv")
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 25.117
#> 25.78916
```

### Multivariate
Or with some feature engineering using mlr3pipelines:

``` r
library(mlr3pipelines)

task = tsk("airpassengers")
# datefeatures currently requires POSIXct
graph = ppl("convert_types", "Date", "POSIXct") %>>%
po("datefeatures",
param_vals = list(is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
)
new_task = graph$train(task)[[1L]]
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)$train(new_task)
prediction = flrn$predict(new_task, 142:144)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 14.46367

row_ids = new_task$nrow - 0:2
flrn$predict_newdata(new_task$data(rows = row_ids), new_task)
#> <PredictionRegr> for 3 observations:
#> row_ids truth response
#> 1 432 437.1584
#> 2 390 435.8279
#> 3 461 461.5812
newdata = new_task$data(rows = row_ids, cols = new_task$feature_names)
flrn$predict_newdata(newdata, new_task)
#> <PredictionRegr> for 3 observations:
#> row_ids truth response
#> 1 NA 437.1584
#> 2 NA 435.8279
#> 3 NA 461.5812

resampling = rsmp("forecast_holdout", ratio = 0.9)
rr = resample(new_task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 49.07941

resampling = rsmp("forecast_cv")
rr = resample(new_task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 26.81219
```

### mlr3pipelines integration

``` r
graph = ppl("convert_types", "Date", "POSIXct") %>>%
po("datefeatures",
param_vals = list(is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
param_vals = list(
week_of_year = FALSE, day_of_year = FALSE, day_of_month = FALSE, day_of_week = FALSE,
is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE
)
)
task = tsk("airpassengers")
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 13.27248
#> 15.60608
```

### Example: Forecasting electricity demand
Expand Down Expand Up @@ -282,13 +243,13 @@ prediction = glrn$predict_newdata(newdata, task)
prediction
#> <PredictionRegr> for 14 observations:
#> row_ids truth response
#> 1 NA 186.6444
#> 2 NA 190.7973
#> 3 NA 184.0170
#> 1 NA 186.2554
#> 2 NA 190.6231
#> 3 NA 184.2325
#> --- --- ---
#> 12 NA 214.6220
#> 13 NA 218.5850
#> 14 NA 220.2067
#> 12 NA 212.5230
#> 13 NA 217.9358
#> 14 NA 219.2381
```

### Global Forecasting
Expand Down Expand Up @@ -321,14 +282,14 @@ flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
prediction = flrn$predict(task, 4460:4464)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 22607.04
#> 23423.99

flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
resampling = rsmp("forecast_holdout", ratio = 0.9)
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 91443.98
#> 90577.72
```

### Example: Global vs Local Forecasting
Expand Down Expand Up @@ -367,7 +328,7 @@ row_ids = tab[year >= 2015, row_id]
prediction = flrn$predict(task, row_ids)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 31118.27
#> 32547.69

# global forecasting
task = tsibbledata::aus_livestock |>
Expand All @@ -388,7 +349,7 @@ row_ids = tab[year >= 2015 & state == "Western Australia", row_id]
prediction = flrn$predict(task, row_ids)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 31580.54
#> 30355.95
```

### Example: Custom PipeOps
Expand Down Expand Up @@ -465,19 +426,19 @@ glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 26.84445
#> 26.33254

newdata = generate_newdata(task, 12L, "month")
glrn$predict_newdata(newdata, task)
#> <PredictionRegr> for 12 observations:
#> row_ids truth response
#> 1 NA 439.1351
#> 2 NA 440.8059
#> 3 NA 459.0752
#> 1 NA 437.3861
#> 2 NA 436.5904
#> 3 NA 456.5291
#> --- --- ---
#> 10 NA 473.3254
#> 11 NA 438.6678
#> 12 NA 441.2223
#> 10 NA 473.6440
#> 11 NA 441.5395
#> 12 NA 440.8554
```

### Example: common target transformations
Expand Down

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