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This vignette shows a complete lightweight workflow using only datasets that ship with the package:
elcf4r_iflex_example for preprocessing and model
fitting examples.elcf4r_iflex_benchmark_results for inspecting saved
benchmark results.dim(elcf4r_iflex_example)
#> [1] 1008 16
length(unique(elcf4r_iflex_example$entity_id))
#> [1] 3
range(elcf4r_iflex_example$timestamp)
#> [1] "2020-01-06 00:00:00 UTC" "2020-01-19 23:00:00 UTC"
head(elcf4r_iflex_example[, c("entity_id", "timestamp", "y", "temp")])
#> entity_id timestamp y temp
#> 1 Exp_1 2020-01-06 00:00:00 2.043 7.6
#> 2 Exp_1 2020-01-06 01:00:00 2.358 7.5
#> 3 Exp_1 2020-01-06 02:00:00 2.496 7.5
#> 4 Exp_1 2020-01-06 03:00:00 2.076 7.5
#> 5 Exp_1 2020-01-06 04:00:00 2.065 7.5
#> 6 Exp_1 2020-01-06 05:00:00 2.059 7.5We work on one participant to keep the example compact.
id1 <- subset(
elcf4r_iflex_example,
entity_id == unique(elcf4r_iflex_example$entity_id)[1]
)
daily <- elcf4r_build_daily_segments(
id1,
carry_cols = c("participation_phase", "price_signal")
)
dim(daily$segments)
#> [1] 14 24
head(daily$covariates[, c("date", "dow", "temp_mean", "participation_phase")])
#> date dow temp_mean participation_phase
#> Exp_1__2020-01-06 2020-01-06 Monday 7.508333 Phase_1
#> Exp_1__2020-01-07 2020-01-07 Tuesday 7.254167 Phase_1
#> Exp_1__2020-01-08 2020-01-08 Wednesday 6.312500 Phase_1
#> Exp_1__2020-01-09 2020-01-09 Thursday 3.420833 Phase_1
#> Exp_1__2020-01-10 2020-01-10 Friday 1.445833 Phase_1
#> Exp_1__2020-01-11 2020-01-11 Saturday 6.754167 Phase_1We train on the first 10 days and predict the 11th day.
train_days <- daily$covariates$date[1:10]
test_day <- daily$covariates$date[11]
train_long <- subset(id1, date %in% train_days)
test_long <- subset(id1, date == test_day)
fit_gam <- elcf4r_fit_gam(
train_long[, c("y", "time_index", "dow", "month", "temp")],
use_temperature = TRUE
)
pred_gam <- predict(
fit_gam,
newdata = test_long[, c("y", "time_index", "dow", "month", "temp")]
)
fit_mars <- elcf4r_fit_mars(
train_long[, c("y", "time_index", "dow", "month", "temp")],
use_temperature = TRUE
)
pred_mars <- predict(
fit_mars,
newdata = test_long[, c("y", "time_index", "dow", "month", "temp")]
)
fit_kwf <- elcf4r_fit_kwf(
segments = daily$segments[1:10, ],
covariates = daily$covariates[1:10, , drop = FALSE],
target_covariates = daily$covariates[11, , drop = FALSE]
)
pred_kwf <- predict(fit_kwf)
fit_kwf_clustered <- elcf4r_fit_kwf_clustered(
segments = daily$segments[1:10, ],
covariates = daily$covariates[1:10, , drop = FALSE],
target_covariates = daily$covariates[11, , drop = FALSE]
)
pred_kwf_clustered <- predict(fit_kwf_clustered)
naive_day <- as.numeric(daily$segments[10, ])
rbind(
gam = unlist(elcf4r_metrics(test_long$y, pred_gam, naive_pred = naive_day)),
mars = unlist(elcf4r_metrics(test_long$y, pred_mars, naive_pred = naive_day)),
kwf = unlist(elcf4r_metrics(test_long$y, pred_kwf, naive_pred = naive_day)),
kwf_clustered = unlist(elcf4r_metrics(test_long$y, pred_kwf_clustered, naive_pred = naive_day))
)
#> nmae nrmse smape mase
#> gam 0.1961463 0.2355394 0.1796715 0.9274129
#> mars 0.1690549 0.1920249 0.1600477 0.7993201
#> kwf 0.1404839 0.1821267 0.1394477 0.6642319
#> kwf_clustered 0.1640983 0.1907997 0.1529166 0.7758848An LSTM example is available when the keras3 and
tensorflow packages are installed and Python has been
selected explicitly for reticulate, for example with
elcf4r_use_tensorflow_env() or
reticulate::use_virtualenv() before rendering this
vignette:
fit_lstm <- elcf4r_fit_lstm(
segments = daily$segments[1:10, ],
covariates = daily$covariates[1:10, , drop = FALSE],
use_temperature = TRUE,
epochs = 1,
units = 4,
batch_size = 2,
verbose = 0
)
pred_lstm <- predict(fit_lstm)
unlist(elcf4r_metrics(test_long$y, pred_lstm, naive_pred = naive_day))
#> nmae nrmse smape mase
#> 0.2250327 0.2555486 0.2091559 1.0639930The package also exposes a reusable rolling-origin benchmark runner
that works on normalized panels produced by the
elcf4r_read_*() adapters.
benchmark_index <- elcf4r_build_benchmark_index(
elcf4r_iflex_example,
carry_cols = c("dataset", "participation_phase", "price_signal")
)
benchmark_small <- elcf4r_benchmark(
panel = elcf4r_iflex_example,
benchmark_index = benchmark_index,
methods = c("gam", "kwf"),
cohort_size = 1,
train_days = 10,
test_days = 2,
include_predictions = FALSE
)
benchmark_small$results
#> benchmark_name dataset entity_id method
#> 1 iflex_hourly_1_ids_10_train_2_test_2_methods iflex Exp_1 gam
#> 2 iflex_hourly_1_ids_10_train_2_test_2_methods iflex Exp_1 kwf
#> 3 iflex_hourly_1_ids_10_train_2_test_2_methods iflex Exp_1 gam
#> 4 iflex_hourly_1_ids_10_train_2_test_2_methods iflex Exp_1 kwf
#> test_date train_start train_end train_days test_points use_temperature
#> 1 2020-01-16 2020-01-06 2020-01-15 10 24 TRUE
#> 2 2020-01-16 2020-01-06 2020-01-15 10 24 TRUE
#> 3 2020-01-17 2020-01-07 2020-01-16 10 24 TRUE
#> 4 2020-01-17 2020-01-07 2020-01-16 10 24 TRUE
#> thermosensitive thermosensitivity_status thermosensitivity_ratio
#> 1 NA insufficient_summer_coverage NA
#> 2 NA insufficient_summer_coverage NA
#> 3 NA insufficient_summer_coverage NA
#> 4 NA insufficient_summer_coverage NA
#> fit_seconds status error_message nmae nrmse smape mase
#> 1 0.016 ok <NA> 0.1961463 0.2355394 0.1796715 0.9274129
#> 2 0.011 ok <NA> 0.1404839 0.1821267 0.1394477 0.6642319
#> 3 0.018 ok <NA> 0.3692809 0.4462295 0.1641611 1.3876600
#> 4 0.013 ok <NA> 0.5393512 0.6402973 0.2242763 2.0267394The package also ships precomputed benchmark results on a fixed iFlex
cohort. In the current build these results cover gam,
mars, kwf, kwf_clustered and
lstm.
head(elcf4r_iflex_benchmark_results)
#> benchmark_name dataset entity_id method
#> 1 iflex_hourly_15_ids_28_train_7_test_5_methods iflex Exp_1 gam
#> 2 iflex_hourly_15_ids_28_train_7_test_5_methods iflex Exp_1 mars
#> 3 iflex_hourly_15_ids_28_train_7_test_5_methods iflex Exp_1 kwf
#> 4 iflex_hourly_15_ids_28_train_7_test_5_methods iflex Exp_1 kwf_clustered
#> 5 iflex_hourly_15_ids_28_train_7_test_5_methods iflex Exp_1 lstm
#> 6 iflex_hourly_15_ids_28_train_7_test_5_methods iflex Exp_1 gam
#> test_date train_start train_end train_days test_points use_temperature
#> 1 2020-02-03 2020-01-06 2020-02-02 28 24 TRUE
#> 2 2020-02-03 2020-01-06 2020-02-02 28 24 TRUE
#> 3 2020-02-03 2020-01-06 2020-02-02 28 24 TRUE
#> 4 2020-02-03 2020-01-06 2020-02-02 28 24 TRUE
#> 5 2020-02-03 2020-01-06 2020-02-02 28 24 TRUE
#> 6 2020-02-04 2020-01-07 2020-02-03 28 24 TRUE
#> thermosensitive thermosensitivity_status thermosensitivity_ratio
#> 1 NA insufficient_summer_coverage NA
#> 2 NA insufficient_summer_coverage NA
#> 3 NA insufficient_summer_coverage NA
#> 4 NA insufficient_summer_coverage NA
#> 5 NA insufficient_summer_coverage NA
#> 6 NA insufficient_summer_coverage NA
#> fit_seconds status error_message nmae nrmse smape mase
#> 1 0.073 ok <NA> 0.2241390 0.3029546 0.11050062 0.7570273
#> 2 0.062 ok <NA> 0.2986949 0.3565707 0.15316209 1.0088391
#> 3 0.067 ok <NA> 0.2622522 0.3346779 0.13014701 0.8857544
#> 4 0.184 ok <NA> 0.2039969 0.2643067 0.09952173 0.6889975
#> 5 1.229 ok <NA> 0.3100784 0.3850647 0.15454029 1.0472868
#> 6 0.024 ok <NA> 0.1502679 0.2054841 0.12878440 0.8595982
aggregate(
cbind(nmae, nrmse, smape, mase, fit_seconds) ~ method,
data = elcf4r_iflex_benchmark_results,
FUN = function(x) round(mean(x, na.rm = TRUE), 4)
)
#> method nmae nrmse smape mase fit_seconds
#> 1 gam 0.2275 0.2908 0.3116 0.8519 0.0238
#> 2 kwf 0.2469 0.3125 0.3303 0.9083 0.0344
#> 3 kwf_clustered 0.2561 0.3289 0.3351 0.9335 0.1276
#> 4 lstm 0.2365 0.2967 0.3373 0.9129 1.2425
#> 5 mars 0.2209 0.2808 0.3031 0.8220 0.0132This object is intended to support reproducible package examples and to provide a stable reference point for future benchmark extensions.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.