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https://github.com/saymrwulf/prophet.git
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535 lines
18 KiB
R
535 lines
18 KiB
R
library(prophet)
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context("Prophet tests")
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DATA <- read.csv('data.csv')
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N <- nrow(DATA)
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train <- DATA[1:floor(N / 2), ]
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future <- DATA[(ceiling(N/2) + 1):N, ]
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DATA2 <- read.csv('data2.csv')
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test_that("fit_predict", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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m <- prophet(train)
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expect_error(predict(m, future), NA)
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})
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test_that("fit_predict_no_seasons", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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m <- prophet(train, weekly.seasonality = FALSE, yearly.seasonality = FALSE)
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expect_error(predict(m, future), NA)
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})
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test_that("fit_predict_no_changepoints", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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m <- prophet(train, n.changepoints = 0)
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expect_error(predict(m, future), NA)
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})
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test_that("fit_predict_changepoint_not_in_history", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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train_t <- dplyr::mutate(DATA, ds=prophet:::set_date(ds))
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train_t <- dplyr::filter(train_t,
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(ds < prophet:::set_date('2013-01-01')) |
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(ds > prophet:::set_date('2014-01-01')))
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future <- data.frame(ds=DATA$ds)
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m <- prophet(train_t, changepoints=c('2013-06-06'))
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expect_error(predict(m, future), NA)
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})
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test_that("fit_predict_duplicates", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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train2 <- train
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train2$y <- train2$y + 10
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train_t <- rbind(train, train2)
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m <- prophet(train_t)
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expect_error(predict(m, future), NA)
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})
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test_that("fit_predict_constant_history", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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train2 <- train
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train2$y <- 20
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m <- prophet(train2)
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fcst <- predict(m, future)
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expect_equal(tail(fcst$yhat, 1), 20)
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train2$y <- 0
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m <- prophet(train2)
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fcst <- predict(m, future)
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expect_equal(tail(fcst$yhat, 1), 0)
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})
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test_that("setup_dataframe", {
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history <- train
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m <- prophet(history, fit = FALSE)
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out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)
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history <- out$df
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expect_true('t' %in% colnames(history))
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expect_equal(min(history$t), 0)
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expect_equal(max(history$t), 1)
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expect_true('y_scaled' %in% colnames(history))
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expect_equal(max(history$y_scaled), 1)
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})
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test_that("get_changepoints", {
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history <- train
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m <- prophet(history, fit = FALSE)
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out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)
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history <- out$df
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m <- out$m
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m$history <- history
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m <- prophet:::set_changepoints(m)
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cp <- m$changepoints.t
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expect_equal(length(cp), m$n.changepoints)
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expect_true(min(cp) > 0)
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expect_true(max(cp) < N)
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mat <- prophet:::get_changepoint_matrix(m)
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expect_equal(nrow(mat), floor(N / 2))
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expect_equal(ncol(mat), m$n.changepoints)
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})
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test_that("get_zero_changepoints", {
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history <- train
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m <- prophet(history, n.changepoints = 0, fit = FALSE)
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out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)
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m <- out$m
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history <- out$df
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m$history <- history
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m <- prophet:::set_changepoints(m)
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cp <- m$changepoints.t
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expect_equal(length(cp), 1)
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expect_equal(cp[1], 0)
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mat <- prophet:::get_changepoint_matrix(m)
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expect_equal(nrow(mat), floor(N / 2))
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expect_equal(ncol(mat), 1)
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})
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test_that("override_n_changepoints", {
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history <- train[1:20,]
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m <- prophet(history, fit = FALSE)
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out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)
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m <- out$m
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history <- out$df
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m$history <- history
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m <- prophet:::set_changepoints(m)
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expect_equal(m$n.changepoints, 15)
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cp <- m$changepoints.t
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expect_equal(length(cp), 15)
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})
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test_that("fourier_series_weekly", {
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mat <- prophet:::fourier_series(DATA$ds, 7, 3)
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true.values <- c(0.9165623, 0.3998920, 0.7330519, -0.6801727, -0.3302791,
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-0.9438833)
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expect_equal(true.values, mat[1, ], tolerance = 1e-6)
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})
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test_that("fourier_series_yearly", {
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mat <- prophet:::fourier_series(DATA$ds, 365.25, 3)
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true.values <- c(0.69702635, -0.71704551, -0.99959923, 0.02830854,
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0.73648994, 0.67644849)
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expect_equal(true.values, mat[1, ], tolerance = 1e-6)
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})
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test_that("growth_init", {
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history <- DATA[1:468, ]
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history$cap <- max(history$y)
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m <- prophet(history, growth = 'logistic', fit = FALSE)
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out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)
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m <- out$m
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history <- out$df
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params <- prophet:::linear_growth_init(history)
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expect_equal(params[1], 0.3055671, tolerance = 1e-6)
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expect_equal(params[2], 0.5307511, tolerance = 1e-6)
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params <- prophet:::logistic_growth_init(history)
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expect_equal(params[1], 1.507925, tolerance = 1e-6)
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expect_equal(params[2], -0.08167497, tolerance = 1e-6)
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})
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test_that("piecewise_linear", {
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t <- seq(0, 10)
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m <- 0
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k <- 1.0
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deltas <- c(0.5)
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changepoint.ts <- c(5)
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y <- prophet:::piecewise_linear(t, deltas, k, m, changepoint.ts)
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y.true <- c(0, 1, 2, 3, 4, 5, 6.5, 8, 9.5, 11, 12.5)
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expect_equal(y, y.true)
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t <- t[8:length(t)]
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y.true <- y.true[8:length(y.true)]
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y <- prophet:::piecewise_linear(t, deltas, k, m, changepoint.ts)
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expect_equal(y, y.true)
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})
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test_that("piecewise_logistic", {
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t <- seq(0, 10)
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cap <- rep(10, 11)
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m <- 0
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k <- 1.0
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deltas <- c(0.5)
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changepoint.ts <- c(5)
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y <- prophet:::piecewise_logistic(t, cap, deltas, k, m, changepoint.ts)
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y.true <- c(5.000000, 7.310586, 8.807971, 9.525741, 9.820138, 9.933071,
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9.984988, 9.996646, 9.999252, 9.999833, 9.999963)
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expect_equal(y, y.true, tolerance = 1e-6)
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t <- t[8:length(t)]
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y.true <- y.true[8:length(y.true)]
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cap <- cap[8:length(cap)]
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y <- prophet:::piecewise_logistic(t, cap, deltas, k, m, changepoint.ts)
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expect_equal(y, y.true, tolerance = 1e-6)
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})
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test_that("holidays", {
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holidays = data.frame(ds = c('2016-12-25'),
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holiday = c('xmas'),
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lower_window = c(-1),
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upper_window = c(0))
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df <- data.frame(
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ds = seq(prophet:::set_date('2016-12-20'),
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prophet:::set_date('2016-12-31'), by='d'))
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m <- prophet(train, holidays = holidays, fit = FALSE)
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out <- prophet:::make_holiday_features(m, df$ds)
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feats <- out$holiday.features
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priors <- out$prior.scales
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expect_equal(nrow(feats), nrow(df))
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expect_equal(ncol(feats), 2)
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expect_equal(sum(colSums(feats) - c(1, 1)), 0)
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expect_true(all(priors == c(10., 10.)))
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holidays = data.frame(ds = c('2016-12-25'),
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holiday = c('xmas'),
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lower_window = c(-1),
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upper_window = c(10))
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m <- prophet(train, holidays = holidays, fit = FALSE)
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out <- prophet:::make_holiday_features(m, df$ds)
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feats <- out$holiday.features
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priors <- out$prior.scales
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expect_equal(nrow(feats), nrow(df))
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expect_equal(ncol(feats), 12)
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expect_true(all(priors == rep(10, 12)))
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# Check prior specifications
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holidays <- data.frame(
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ds = prophet:::set_date(c('2016-12-25', '2017-12-25')),
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holiday = c('xmas', 'xmas'),
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lower_window = c(-1, -1),
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upper_window = c(0, 0),
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prior_scale = c(5., 5.)
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)
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m <- prophet(holidays = holidays, fit = FALSE)
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out <- prophet:::make_holiday_features(m, df$ds)
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priors <- out$prior.scales
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expect_true(all(priors == c(5., 5.)))
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# 2 different priors
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holidays2 <- data.frame(
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ds = prophet:::set_date(c('2012-06-06', '2013-06-06')),
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holiday = c('seans-bday', 'seans-bday'),
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lower_window = c(0, 0),
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upper_window = c(1, 1),
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prior_scale = c(8, 8)
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)
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holidays2 <- rbind(holidays, holidays2)
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m <- prophet(holidays = holidays2, fit = FALSE)
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out <- prophet:::make_holiday_features(m, df$ds)
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priors <- out$prior.scales
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expect_true(all(priors == c(8, 8, 5, 5)))
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holidays2 <- data.frame(
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ds = prophet:::set_date(c('2012-06-06', '2013-06-06')),
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holiday = c('seans-bday', 'seans-bday'),
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lower_window = c(0, 0),
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upper_window = c(1, 1)
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)
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holidays2 <- dplyr::bind_rows(holidays, holidays2)
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m <- prophet(holidays = holidays2, fit = FALSE, holidays.prior.scale = 4)
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out <- prophet:::make_holiday_features(m, df$ds)
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priors <- out$prior.scales
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expect_true(all(priors == c(4, 4, 5, 5)))
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# Check incompatible priors
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holidays <- data.frame(
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ds = prophet:::set_date(c('2016-12-25', '2016-12-27')),
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holiday = c('xmasish', 'xmasish'),
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lower_window = c(-1, -1),
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upper_window = c(0, 0),
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prior_scale = c(5., 6.)
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)
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m <- prophet(holidays = holidays, fit = FALSE)
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expect_error(prophet:::make_holiday_features(m, df$ds))
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})
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test_that("fit_with_holidays", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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holidays <- data.frame(ds = c('2012-06-06', '2013-06-06'),
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holiday = c('seans-bday', 'seans-bday'),
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lower_window = c(0, 0),
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upper_window = c(1, 1))
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m <- prophet(DATA, holidays = holidays, uncertainty.samples = 0)
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expect_error(predict(m), NA)
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})
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test_that("make_future_dataframe", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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train.t <- DATA[1:234, ]
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m <- prophet(train.t)
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future <- make_future_dataframe(m, periods = 3, freq = 'day',
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include_history = FALSE)
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correct <- prophet:::set_date(c('2013-04-26', '2013-04-27', '2013-04-28'))
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expect_equal(future$ds, correct)
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future <- make_future_dataframe(m, periods = 3, freq = 'month',
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include_history = FALSE)
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correct <- prophet:::set_date(c('2013-05-25', '2013-06-25', '2013-07-25'))
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expect_equal(future$ds, correct)
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})
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test_that("auto_weekly_seasonality", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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# Should be enabled
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N.w <- 15
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train.w <- DATA[1:N.w, ]
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m <- prophet(train.w, fit = FALSE)
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expect_equal(m$weekly.seasonality, 'auto')
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m <- fit.prophet(m, train.w)
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expect_true('weekly' %in% names(m$seasonalities))
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true <- list(period = 7, fourier.order = 3, prior.scale = 10)
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for (name in names(true)) {
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expect_equal(m$seasonalities$weekly[[name]], true[[name]])
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}
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# Should be disabled due to too short history
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N.w <- 9
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train.w <- DATA[1:N.w, ]
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m <- prophet(train.w)
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expect_false('weekly' %in% names(m$seasonalities))
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m <- prophet(train.w, weekly.seasonality = TRUE)
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expect_true('weekly' %in% names(m$seasonalities))
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# Should be False due to weekly spacing
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train.w <- DATA[seq(1, nrow(DATA), 7), ]
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m <- prophet(train.w)
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expect_false('weekly' %in% names(m$seasonalities))
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m <- prophet(DATA, weekly.seasonality = 2, seasonality.prior.scale = 3)
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true <- list(period = 7, fourier.order = 2, prior.scale = 3)
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for (name in names(true)) {
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expect_equal(m$seasonalities$weekly[[name]], true[[name]])
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}
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})
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test_that("auto_yearly_seasonality", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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# Should be enabled
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m <- prophet(DATA, fit = FALSE)
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expect_equal(m$yearly.seasonality, 'auto')
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m <- fit.prophet(m, DATA)
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expect_true('yearly' %in% names(m$seasonalities))
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true <- list(period = 365.25, fourier.order = 10, prior.scale = 10)
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for (name in names(true)) {
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expect_equal(m$seasonalities$yearly[[name]], true[[name]])
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}
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# Should be disabled due to too short history
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N.w <- 240
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train.y <- DATA[1:N.w, ]
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m <- prophet(train.y)
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expect_false('yearly' %in% names(m$seasonalities))
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m <- prophet(train.y, yearly.seasonality = TRUE)
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expect_true('yearly' %in% names(m$seasonalities))
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m <- prophet(DATA, yearly.seasonality = 7, seasonality.prior.scale = 3)
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true <- list(period = 365.25, fourier.order = 7, prior.scale = 3)
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for (name in names(true)) {
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expect_equal(m$seasonalities$yearly[[name]], true[[name]])
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}
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})
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test_that("auto_daily_seasonality", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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# Should be enabled
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m <- prophet(DATA2, fit = FALSE)
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expect_equal(m$daily.seasonality, 'auto')
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m <- fit.prophet(m, DATA2)
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expect_true('daily' %in% names(m$seasonalities))
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true <- list(period = 1, fourier.order = 4, prior.scale = 10)
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for (name in names(true)) {
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expect_equal(m$seasonalities$daily[[name]], true[[name]])
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}
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# Should be disabled due to too short history
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N.d <- 430
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train.y <- DATA2[1:N.d, ]
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m <- prophet(train.y)
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expect_false('daily' %in% names(m$seasonalities))
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m <- prophet(train.y, daily.seasonality = TRUE)
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expect_true('daily' %in% names(m$seasonalities))
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m <- prophet(DATA2, daily.seasonality = 7, seasonality.prior.scale = 3)
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true <- list(period = 1, fourier.order = 7, prior.scale = 3)
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for (name in names(true)) {
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expect_equal(m$seasonalities$daily[[name]], true[[name]])
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}
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m <- prophet(DATA)
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expect_false('daily' %in% names(m$seasonalities))
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})
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test_that("test_subdaily_holidays", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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holidays <- data.frame(ds = c('2017-01-02'),
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holiday = c('special_day'))
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m <- prophet(DATA2, holidays=holidays)
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fcst <- predict(m)
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expect_equal(sum(fcst$special_day == 0), 575)
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})
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test_that("custom_seasonality", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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holidays <- data.frame(ds = c('2017-01-02'),
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holiday = c('special_day'),
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prior_scale = c(4))
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m <- prophet(holidays=holidays)
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m <- add_seasonality(m, name='monthly', period=30, fourier.order=5)
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true <- list(period = 30, fourier.order = 5, prior.scale = 10)
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for (name in names(true)) {
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expect_equal(m$seasonalities$monthly[[name]], true[[name]])
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}
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expect_error(
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add_seasonality(m, name='special_day', period=30, fourier_order=5)
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)
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expect_error(
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add_seasonality(m, name='trend', period=30, fourier_order=5)
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)
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m <- add_seasonality(m, name='weekly', period=30, fourier.order=5)
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# Test priors
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m <- prophet(holidays = holidays, yearly.seasonality = FALSE)
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m <- add_seasonality(
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m, name='monthly', period=30, fourier.order=5, prior.scale = 2)
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m <- fit.prophet(m, DATA)
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prior.scales <- prophet:::make_all_seasonality_features(
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m, m$history)$prior.scales
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expect_true(all(prior.scales == c(rep(2, 10), rep(10, 6), 4)))
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})
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test_that("added_regressors", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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m <- prophet()
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m <- add_regressor(m, 'binary_feature', prior.scale=0.2)
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m <- add_regressor(m, 'numeric_feature', prior.scale=0.5)
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m <- add_regressor(m, 'binary_feature2', standardize=TRUE)
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df <- DATA
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df$binary_feature <- c(rep(0, 255), rep(1, 255))
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df$numeric_feature <- 0:509
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# Require all regressors in df
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expect_error(
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fit.prophet(m, df)
|
|
)
|
|
df$binary_feature2 <- c(rep(1, 100), rep(0, 410))
|
|
m <- fit.prophet(m, df)
|
|
# Check that standardizations are correctly set
|
|
true <- list(prior.scale = 0.2, mu = 0, std = 1, standardize = 'auto')
|
|
for (name in names(true)) {
|
|
expect_equal(true[[name]], m$extra_regressors$binary_feature[[name]])
|
|
}
|
|
true <- list(prior.scale = 0.5, mu = 254.5, std = 147.368585)
|
|
for (name in names(true)) {
|
|
expect_equal(true[[name]], m$extra_regressors$numeric_feature[[name]],
|
|
tolerance = 1e-5)
|
|
}
|
|
true <- list(prior.scale = 10., mu = 0.1960784, std = 0.3974183)
|
|
for (name in names(true)) {
|
|
expect_equal(true[[name]], m$extra_regressors$binary_feature2[[name]],
|
|
tolerance = 1e-5)
|
|
}
|
|
# Check that standardization is done correctly
|
|
df2 <- prophet:::setup_dataframe(m, df)$df
|
|
expect_equal(df2$binary_feature[1], 0)
|
|
expect_equal(df2$numeric_feature[1], -1.726962, tolerance = 1e-4)
|
|
expect_equal(df2$binary_feature2[1], 2.022859, tolerance = 1e-4)
|
|
# Check that feature matrix and prior scales are correctly constructed
|
|
out <- prophet:::make_all_seasonality_features(m, df2)
|
|
seasonal.features <- out$seasonal.features
|
|
prior.scales <- out$prior.scales
|
|
expect_true('binary_feature' %in% colnames(seasonal.features))
|
|
expect_true('numeric_feature' %in% colnames(seasonal.features))
|
|
expect_true('binary_feature2' %in% colnames(seasonal.features))
|
|
expect_equal(ncol(seasonal.features), 29)
|
|
expect_true(all(sort(prior.scales[27:29]) == c(0.2, 0.5, 10.)))
|
|
# Check that forecast components are reasonable
|
|
future <- data.frame(
|
|
ds = c('2014-06-01'), binary_feature = c(0), numeric_feature = c(10))
|
|
expect_error(predict(m, future))
|
|
future$binary_feature2 <- 0.
|
|
fcst <- predict(m, future)
|
|
expect_equal(ncol(fcst), 31)
|
|
expect_equal(fcst$binary_feature[1], 0)
|
|
expect_equal(fcst$extra_regressors[1],
|
|
fcst$numeric_feature[1] + fcst$binary_feature2[1])
|
|
expect_equal(fcst$seasonalities[1], fcst$yearly[1] + fcst$weekly[1])
|
|
expect_equal(fcst$seasonal[1],
|
|
fcst$seasonalities[1] + fcst$extra_regressors[1])
|
|
expect_equal(fcst$yhat[1], fcst$trend[1] + fcst$seasonal[1])
|
|
})
|
|
|
|
test_that("copy", {
|
|
inputs <- list(
|
|
growth = c('linear', 'logistic'),
|
|
changepoints = c(NULL, c('2016-12-25')),
|
|
n.changepoints = c(3),
|
|
yearly.seasonality = c(TRUE, FALSE),
|
|
weekly.seasonality = c(TRUE, FALSE),
|
|
daily.seasonality = c(TRUE, FALSE),
|
|
holidays = c(NULL, 'insert_dataframe'),
|
|
seasonality.prior.scale = c(1.1),
|
|
holidays.prior.scale = c(1.1),
|
|
changepoints.prior.scale = c(0.1),
|
|
mcmc.samples = c(100),
|
|
interval.width = c(0.9),
|
|
uncertainty.samples = c(200)
|
|
)
|
|
products <- expand.grid(inputs)
|
|
for (i in 1:length(products)) {
|
|
if (products$holidays[i] == 'insert_dataframe') {
|
|
holidays <- data.frame(ds=c('2016-12-25'), holiday=c('x'))
|
|
} else {
|
|
holidays <- NULL
|
|
}
|
|
m1 <- prophet(
|
|
growth = products$growth[i],
|
|
changepoints = products$changepoints[i],
|
|
n.changepoints = products$n.changepoints[i],
|
|
yearly.seasonality = products$yearly.seasonality[i],
|
|
weekly.seasonality = products$weekly.seasonality[i],
|
|
daily.seasonality = products$daily.seasonality[i],
|
|
holidays = holidays,
|
|
seasonality.prior.scale = products$seasonality.prior.scale[i],
|
|
holidays.prior.scale = products$holidays.prior.scale[i],
|
|
changepoints.prior.scale = products$changepoints.prior.scale[i],
|
|
mcmc.samples = products$mcmc.samples[i],
|
|
interval.width = products$interval.width[i],
|
|
uncertainty.samples = products$uncertainty.samples[i],
|
|
fit = FALSE
|
|
)
|
|
m2 <- prophet:::prophet_copy(m1)
|
|
# Values should be copied correctly
|
|
for (arg in names(inputs)) {
|
|
expect_equal(m1[[arg]], m2[[arg]])
|
|
}
|
|
}
|
|
# Check for cutoff
|
|
changepoints <- seq.Date(as.Date('2012-06-15'), as.Date('2012-09-15'), by='d')
|
|
cutoff <- as.Date('2012-07-25')
|
|
m1 <- prophet(DATA, changepoints = changepoints)
|
|
m2 <- prophet:::prophet_copy(m1, cutoff)
|
|
changepoints <- changepoints[changepoints <= cutoff]
|
|
expect_equal(prophet:::set_date(changepoints), m2$changepoints)
|
|
})
|