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014b3b5919
* Update memory requirement description per #326 * Fix R warning with extra regressor; disallow constant extra regressors. * Fix unit test broken in new pandas * Fix diagnostics unit tests for new pandas * Fix copy with extra seasonalities / regressors Py * Fix copy with extra seasonalities / regressors R * Fix weekly_start and yearly_start in R plot_components * Fix plotting in pandas 0.21 by using pydatetime instead of numpy * Version bump * Update README for new version * Fix missing columns in SHF with extra regressor
105 lines
3.8 KiB
R
105 lines
3.8 KiB
R
library(prophet)
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context("Prophet diagnostics tests")
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## Makes R CMD CHECK happy due to dplyr syntax below
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globalVariables(c("y", "yhat"))
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DATA <- head(read.csv('data.csv'), 100)
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DATA$ds <- as.Date(DATA$ds)
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test_that("simulated_historical_forecasts", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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m <- prophet(DATA)
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k <- 2
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for (p in c(1, 10)) {
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for (h in c(1, 3)) {
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df.shf <- simulated_historical_forecasts(
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m, horizon = h, units = 'days', k = k, period = p)
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# All cutoff dates should be less than ds dates
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expect_true(all(df.shf$cutoff < df.shf$ds))
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# The unique size of output cutoff should be equal to 'k'
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expect_equal(length(unique(df.shf$cutoff)), k)
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expect_equal(max(df.shf$ds - df.shf$cutoff),
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as.difftime(h, units = 'days'))
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dc <- diff(df.shf$cutoff)
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dc <- min(dc[dc > 0])
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expect_true(dc >= as.difftime(p, units = 'days'))
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# Each y in df_shf and DATA with same ds should be equal
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df.merged <- dplyr::left_join(df.shf, m$history, by="ds")
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expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0)
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}
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}
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})
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test_that("simulated_historical_forecasts_logistic", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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df <- DATA
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df$cap <- 40
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m <- prophet(df, growth='logistic')
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df.shf <- simulated_historical_forecasts(
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m, horizon = 3, units = 'days', k = 2, period = 3)
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# All cutoff dates should be less than ds dates
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expect_true(all(df.shf$cutoff < df.shf$ds))
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# The unique size of output cutoff should be equal to 'k'
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expect_equal(length(unique(df.shf$cutoff)), 2)
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# Each y in df_shf and DATA with same ds should be equal
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df.merged <- dplyr::left_join(df.shf, m$history, by="ds")
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expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0)
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})
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test_that("simulated_historical_forecasts_extra_regressors", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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df <- DATA
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df$extra <- seq(0, nrow(df) - 1)
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m <- prophet()
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m <- add_seasonality(m, name = 'monthly', period = 30.5, fourier.order = 5)
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m <- add_regressor(m, 'extra')
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m <- fit.prophet(m, df)
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df.shf <- simulated_historical_forecasts(
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m, horizon = 3, units = 'days', k = 2, period = 3)
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# All cutoff dates should be less than ds dates
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expect_true(all(df.shf$cutoff < df.shf$ds))
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# The unique size of output cutoff should be equal to 'k'
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expect_equal(length(unique(df.shf$cutoff)), 2)
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# Each y in df_shf and DATA with same ds should be equal
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df.merged <- dplyr::left_join(df.shf, m$history, by="ds")
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expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0)
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})
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test_that("simulated_historical_forecasts_default_value_check", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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m <- prophet(DATA)
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df.shf1 <- simulated_historical_forecasts(
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m, horizon = 10, units = 'days', k = 1)
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df.shf2 <- simulated_historical_forecasts(
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m, horizon = 10, units = 'days', k = 1, period = 5)
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expect_equal(sum(dplyr::select(df.shf1 - df.shf2, y, yhat)), 0)
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})
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test_that("cross_validation", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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m <- prophet(DATA)
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# Calculate the number of cutoff points
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te <- max(DATA$ds)
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ts <- min(DATA$ds)
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horizon <- as.difftime(4, units = "days")
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period <- as.difftime(10, units = "days")
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k <- 5
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df.cv <- cross_validation(
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m, horizon = 4, units = "days", period = 10, initial = 90)
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expect_equal(length(unique(df.cv$cutoff)), k)
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expect_equal(max(df.cv$ds - df.cv$cutoff), horizon)
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dc <- diff(df.cv$cutoff)
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dc <- min(dc[dc > 0])
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expect_true(dc >= period)
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})
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test_that("cross_validation_default_value_check", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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m <- prophet(DATA)
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df.cv1 <- cross_validation(
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m, horizon = 32, units = "days", period = 10)
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df.cv2 <- cross_validation(
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m, horizon = 32, units = 'days', period = 10, initial = 96)
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expect_equal(sum(dplyr::select(df.cv1 - df.cv2, y, yhat)), 0)
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})
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