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Inline function calls in tests to prepare for removing some `library(… #234

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Oct 3, 2023
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Inline function calls in tests to prepare for removing some `library(… #234

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4 changes: 0 additions & 4 deletions tests/testthat.R
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,6 @@ library(testthat)

library(xgboost)
library(randomForest)
library(kernlab)
library(earth)
library(thief)
library(smooth)
library(greybox)
Expand All @@ -25,7 +23,5 @@ library(timetk)
library(modeltime)

library(tidyverse)
library(lubridate)


test_check("modeltime")
58 changes: 29 additions & 29 deletions tests/testthat/test-algo-adam_reg-Adam.R
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Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,10 @@ context("TEST adam_reg: ADAM")
# SETUP ----

# Data
m750 <- m4_monthly %>% filter(id == "M750")
m750 <- timetk::m4_monthly %>% dplyr::filter(id == "M750")

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)
splits <- rsample::initial_time_split(m750, prop = 0.8)



Expand All @@ -28,7 +28,7 @@ test_that("adam_reg: Adam, (No xregs), Test Model Fit Object", {
seasonal_differences = 0,
seasonal_ma = 1
) %>%
set_engine("adam")
parsnip::set_engine("adam")


# PARSNIP ----
Expand All @@ -37,12 +37,12 @@ test_that("adam_reg: Adam, (No xregs), Test Model Fit Object", {

# Fit Spec
model_fit <- model_spec %>%
fit(value ~ date, data = training(splits))
fit(value ~ date, data = rsample::training(splits))

# Predictions
predictions_tbl <- model_fit %>%
modeltime_calibrate(testing(splits)) %>%
modeltime_forecast(new_data = testing(splits))
modeltime_calibrate(rsample::testing(splits)) %>%
modeltime_forecast(new_data = rsample::testing(splits))

expect_s3_class(model_fit$fit, "Adam_fit_impl")

Expand All @@ -61,12 +61,12 @@ test_that("adam_reg: Adam, (No xregs), Test Model Fit Object", {
expect_equal(model_fit$preproc$y_var, "value")

# Structure
expect_identical(nrow(testing(splits)), nrow(predictions_tbl))
expect_identical(testing(splits)$date, predictions_tbl$.index)
expect_identical(nrow(rsample::testing(splits)), nrow(predictions_tbl))
expect_identical(rsample::testing(splits)$date, predictions_tbl$.index)

# Out-of-Sample Accuracy Tests

resid <- testing(splits)$value - predictions_tbl$.value
resid <- rsample::testing(splits)$value - predictions_tbl$.value

# - Max Error less than 1500
expect_lte(max(abs(resid)), 3000)
Expand All @@ -78,20 +78,20 @@ test_that("adam_reg: Adam, (No xregs), Test Model Fit Object", {
# * XREGS ----

# Data
m750 <- m4_monthly %>% filter(id == "M750") %>%
mutate(month = month(date, label = TRUE))
m750 <- timetk::m4_monthly %>% dplyr::filter(id == "M750") %>%
dplyr::mutate(month = lubridate::month(date, label = TRUE))

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)
splits <- rsample::initial_time_split(m750, prop = 0.8)

# Fit Spec
model_fit <- model_spec %>%
fit(value ~ date + month, data = training(splits))
fit(value ~ date + month, data = rsample::training(splits))

# Predictions
predictions_tbl <- model_fit %>%
modeltime_calibrate(testing(splits)) %>%
modeltime_forecast(new_data = testing(splits))
modeltime_calibrate(rsample::testing(splits)) %>%
modeltime_forecast(new_data = rsample::testing(splits))


# Model Fit ----
Expand All @@ -115,12 +115,12 @@ test_that("adam_reg: Adam, (No xregs), Test Model Fit Object", {


# Structure
expect_identical(nrow(testing(splits)), nrow(predictions_tbl))
expect_identical(testing(splits)$date, predictions_tbl$.index)
expect_identical(nrow(rsample::testing(splits)), nrow(predictions_tbl))
expect_identical(rsample::testing(splits)$date, predictions_tbl$.index)

# Out-of-Sample Accuracy Tests

resid <- testing(splits)$value - predictions_tbl$.value
resid <- rsample::testing(splits)$value - predictions_tbl$.value

# - Max Error less than 1500
expect_lte(max(abs(resid)), 2000)
Expand All @@ -147,23 +147,23 @@ test_that("adam_reg: Adam (workflow)", {
seasonal_differences = 0,
seasonal_ma = 1
) %>%
set_engine("adam")
parsnip::set_engine("adam")

# Recipe spec
recipe_spec <- recipe(value ~ date, data = training(splits))
recipe_spec <- recipes::recipe(value ~ date, data = rsample::training(splits))

# Workflow
wflw <- workflow() %>%
add_recipe(recipe_spec) %>%
add_model(model_spec)
wflw <- workflows::workflow() %>%
workflows::add_recipe(recipe_spec) %>%
workflows::add_model(model_spec)

wflw_fit <- wflw %>%
fit(training(splits))
fit(rsample::training(splits))

# Forecast
predictions_tbl <- wflw_fit %>%
modeltime_calibrate(testing(splits)) %>%
modeltime_forecast(new_data = testing(splits), actual_data = training(splits))
modeltime_calibrate(rsample::testing(splits)) %>%
modeltime_forecast(new_data = rsample::testing(splits), actual_data = rsample::training(splits))

expect_s3_class(wflw_fit$fit$fit$fit, "Adam_fit_impl")

Expand All @@ -183,15 +183,15 @@ test_that("adam_reg: Adam (workflow)", {

# * Test Predictions ----

full_data <- bind_rows(training(splits), testing(splits))
full_data <- dplyr::bind_rows(rsample::training(splits), rsample::testing(splits))

# Structure
expect_identical(nrow(full_data), nrow(predictions_tbl))
expect_identical(full_data$date, predictions_tbl$.index)

# Out-of-Sample Accuracy Tests
predictions_tbl <- predictions_tbl %>% filter(.key == "prediction")
resid <- testing(splits)$value - predictions_tbl$.value
predictions_tbl <- predictions_tbl %>% dplyr::filter(.key == "prediction")
resid <- rsample::testing(splits)$value - predictions_tbl$.value

# - Max Error less than 1500
expect_lte(max(abs(resid)), 3000)
Expand Down
62 changes: 31 additions & 31 deletions tests/testthat/test-algo-adam_reg-auto_adam.R
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Original file line number Diff line number Diff line change
Expand Up @@ -14,25 +14,25 @@ test_that("adam_reg: Auto ADAM, (No xregs), Test Model Fit Object", {
# SETUP

# Data
m750 <- m4_monthly %>% filter(id == "M750")
m750 <- timetk::m4_monthly %>% dplyr::filter(id == "M750")

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)
splits <- rsample::initial_time_split(m750, prop = 0.8)

# Model Spec
model_spec <- adam_reg(
seasonal_period = 12
) %>%
set_engine("auto_adam")
parsnip::set_engine("auto_adam")

# Fit Spec
model_fit <- model_spec %>%
fit(value ~ date, data = training(splits))
fit(value ~ date, data = rsample::training(splits))

# Predictions
predictions_tbl <- model_fit %>%
modeltime_calibrate(testing(splits), quiet = FALSE) %>%
modeltime_forecast(new_data = testing(splits))
modeltime_calibrate(rsample::testing(splits), quiet = FALSE) %>%
modeltime_forecast(new_data = rsample::testing(splits))

expect_s3_class(model_fit$fit, "Auto_adam_fit_impl")

Expand All @@ -51,12 +51,12 @@ test_that("adam_reg: Auto ADAM, (No xregs), Test Model Fit Object", {
expect_equal(model_fit$preproc$y_var, "value")

# Structure
expect_identical(nrow(testing(splits)), nrow(predictions_tbl))
expect_identical(testing(splits)$date, predictions_tbl$.index)
expect_identical(nrow(rsample::testing(splits)), nrow(predictions_tbl))
expect_identical(rsample::testing(splits)$date, predictions_tbl$.index)

# Out-of-Sample Accuracy Tests

resid <- testing(splits)$value - predictions_tbl$.value
resid <- rsample::testing(splits)$value - predictions_tbl$.value

# - Max Error less than 1500
expect_lte(max(abs(resid)), 3000)
Expand All @@ -76,25 +76,25 @@ test_that("adam_reg: Auto ADAM, (XREGS)", {


# Data
m750 <- m4_monthly %>% filter(id == "M750") %>% mutate(month = month(date, label = TRUE))
m750 <- timetk::m4_monthly %>% dplyr::filter(id == "M750") %>% dplyr::mutate(month = lubridate::month(date, label = TRUE))

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)
splits <- rsample::initial_time_split(m750, prop = 0.8)

# Model Spec
model_spec <- adam_reg(
seasonal_period = 12
) %>%
set_engine("auto_adam")
parsnip::set_engine("auto_adam")

# Fit Spec
model_fit <- model_spec %>%
fit(value ~ date + month, data = training(splits))
fit(value ~ date + month, data = rsample::training(splits))

# Predictions
predictions_tbl <- model_fit %>%
modeltime_calibrate(testing(splits)) %>%
modeltime_forecast(new_data = testing(splits))
modeltime_calibrate(rsample::testing(splits)) %>%
modeltime_forecast(new_data = rsample::testing(splits))

expect_s3_class(model_fit$fit, "Auto_adam_fit_impl")

Expand All @@ -115,12 +115,12 @@ test_that("adam_reg: Auto ADAM, (XREGS)", {


# Structure
expect_identical(nrow(testing(splits)), nrow(predictions_tbl))
expect_identical(testing(splits)$date, predictions_tbl$.index)
expect_identical(nrow(rsample::testing(splits)), nrow(predictions_tbl))
expect_identical(rsample::testing(splits)$date, predictions_tbl$.index)

# Out-of-Sample Accuracy Tests

resid <- testing(splits)$value - predictions_tbl$.value
resid <- rsample::testing(splits)$value - predictions_tbl$.value

# - Max Error less than 1500
expect_lte(max(abs(resid)), 3000)
Expand All @@ -139,10 +139,10 @@ test_that("adam_reg: Auto ADAM (workflow), Test Model Fit Object", {
skip_on_cran()

# Data
m750 <- m4_monthly %>% filter(id == "M750") %>% mutate(month = month(date, label = TRUE))
m750 <- timetk::m4_monthly %>% dplyr::filter(id == "M750") %>% dplyr::mutate(month = lubridate::month(date, label = TRUE))

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)
splits <- rsample::initial_time_split(m750, prop = 0.8)

# Model Spec
model_spec <- adam_reg(
Expand All @@ -154,23 +154,23 @@ test_that("adam_reg: Auto ADAM (workflow), Test Model Fit Object", {
seasonal_differences = 0,
seasonal_ma = 1
) %>%
set_engine("auto_adam")
parsnip::set_engine("auto_adam")

# Recipe spec
recipe_spec <- recipe(value ~ date, data = training(splits))
recipe_spec <- recipes::recipe(value ~ date, data = rsample::training(splits))

# Workflow
wflw <- workflow() %>%
add_recipe(recipe_spec) %>%
add_model(model_spec)
wflw <- workflows::workflow() %>%
workflows::add_recipe(recipe_spec) %>%
workflows::add_model(model_spec)

wflw_fit <- wflw %>%
fit(training(splits))
fit(rsample::training(splits))

# Forecast
predictions_tbl <- wflw_fit %>%
modeltime_calibrate(testing(splits)) %>%
modeltime_forecast(new_data = testing(splits), actual_data = training(splits))
modeltime_calibrate(rsample::testing(splits)) %>%
modeltime_forecast(new_data = rsample::testing(splits), actual_data = rsample::training(splits))


expect_s3_class(wflw_fit$fit$fit$fit, "Auto_adam_fit_impl")
Expand All @@ -190,15 +190,15 @@ test_that("adam_reg: Auto ADAM (workflow), Test Model Fit Object", {
expect_equal(names(mld$outcomes), "value")


full_data <- bind_rows(training(splits), testing(splits))
full_data <- dplyr::bind_rows(rsample::training(splits), rsample::testing(splits))

# Structure
expect_identical(nrow(full_data), nrow(predictions_tbl))
expect_identical(full_data$date, predictions_tbl$.index)

# Out-of-Sample Accuracy Tests
predictions_tbl <- predictions_tbl %>% filter(.key == "prediction")
resid <- testing(splits)$value - predictions_tbl$.value
predictions_tbl <- predictions_tbl %>% dplyr::filter(.key == "prediction")
resid <- rsample::testing(splits)$value - predictions_tbl$.value

# - Max Error less than 1500
expect_lte(max(abs(resid)), 3000)
Expand Down
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