# FORECAST PLOTS ----- context("TEST MODELTIME PLOTS") # SETUP ---- # Data m750 <- timetk::m4_monthly %>% dplyr::filter(id == "M750") splits <- rsample::initial_time_split(m750, prop = 0.8) test_that("modeltime plotting", { skip_on_cran() # SETUP # Model Spec model_spec <- arima_reg(seasonal_period = 12) %>% parsnip::set_engine("auto_arima") # PARSNIP INTERFACE ---- model_fit <- model_spec %>% fit(log(value) ~ date, data = rsample::training(splits)) # * Forecast ---- forecast_tbl <- model_fit %>% modeltime_calibrate(new_data = rsample::testing(splits)) %>% modeltime_forecast( actual_data = m750, conf_interval = 0.95) # VISUALIZATIONS WITH CONF INTERVALS ---- # * ggplot2 visualization ---- g <- forecast_tbl %>% dplyr::mutate(dplyr::across(.value:.conf_hi, exp)) %>% plot_modeltime_forecast(.interactive = FALSE) # * plotly visualization ---- suppressWarnings({ # Needed until plotly is resolved: https://github.com/ropensci/plotly/issues/1783 p <- forecast_tbl %>% dplyr::mutate(dplyr::across(.value:.conf_hi, exp)) %>% plot_modeltime_forecast(.interactive = TRUE) }) # "modeltime plot, Test Static ggplot # Structure expect_s3_class(g, "ggplot") expect_s3_class(g$layers[[1]]$geom, "GeomRibbon") # modeltime plot, Test Interactive plotly # Structure expect_s3_class(p, "plotly") # # PLOTS WITHOUT CONF INTERVALS ----- g <- forecast_tbl %>% dplyr::mutate(dplyr::across(.value:.conf_hi, exp)) %>% plot_modeltime_forecast(.interactive = FALSE, .conf_interval_show = FALSE) p <- forecast_tbl %>% dplyr::mutate(dplyr::across(.value:.conf_hi, exp)) %>% plot_modeltime_forecast(.interactive = TRUE, .conf_interval_show = FALSE) # Structure expect_s3_class(g, "ggplot") expect_s3_class(g$layers[[1]]$geom, "GeomLine") # Structure expect_s3_class(p, "plotly") }) # WORKFLOW INTERFACE ---- test_that("modeltime plot - workflow, Test Static ggplot", { skip_on_cran() # SETUP # Model Spec model_spec <- arima_reg(seasonal_period = 12) %>% parsnip::set_engine("auto_arima") # Recipe spec recipe_spec <- recipes::recipe(value ~ date, data = rsample::training(splits)) %>% recipes::step_log(value, skip = FALSE) # Workflow wflw <- workflows::workflow() %>% workflows::add_recipe(recipe_spec) %>% workflows::add_model(model_spec) wflw_fit <- wflw %>% fit(rsample::training(splits)) forecast_tbl <- wflw_fit %>% modeltime_calibrate(rsample::testing(splits)) %>% modeltime_forecast(actual_data = m750, conf_interval = 0.8) # * ggplot2 visualization ---- g <- forecast_tbl %>% dplyr::mutate(dplyr::across(.value:.conf_hi, exp)) %>% plot_modeltime_forecast(.conf_interval_show = TRUE, .interactive = FALSE) # * plotly visualization ---- p <- forecast_tbl %>% dplyr::mutate(dplyr::across(.value:.conf_hi, exp)) %>% plot_modeltime_forecast(.conf_interval_show = TRUE, .interactive = TRUE) # Structure expect_s3_class(g, "ggplot") expect_s3_class(g$layers[[1]]$geom, "GeomRibbon") # Structure expect_s3_class(p, "plotly") })