mirror of
https://github.com/saymrwulf/prophet.git
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Move copy test to test_diagnostics
This commit is contained in:
Ben Letham
parent
55d7d1e62d
commit
8e8e04858a
5 changed files with 142 additions and 199 deletions
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@ -4,8 +4,9 @@ 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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DATA_all <- read.csv('data.csv')
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DATA_all$ds <- as.Date(DATA_all$ds)
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DATA <- head(DATA_all, 100)
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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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@ -132,3 +133,72 @@ test_that("performance_metrics", {
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sort(colnames(df_horizon)) == sort(c('coverage', 'mse', 'horizon'))
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))
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})
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test_that("copy", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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df <- DATA_all
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df$cap <- 200.
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df$binary_feature <- c(rep(0, 255), rep(1, 255))
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inputs <- list(
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growth = c('linear', 'logistic'),
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yearly.seasonality = c(TRUE, FALSE),
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weekly.seasonality = c(TRUE, FALSE),
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daily.seasonality = c(TRUE, FALSE),
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holidays = c('null', 'insert_dataframe')
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)
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products <- expand.grid(inputs)
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for (i in 1:length(products)) {
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if (products$holidays[i] == 'insert_dataframe') {
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holidays <- data.frame(ds=c('2016-12-25'), holiday=c('x'))
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} else {
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holidays <- NULL
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}
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m1 <- prophet(
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growth = as.character(products$growth[i]),
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changepoints = NULL,
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n.changepoints = 3,
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yearly.seasonality = products$yearly.seasonality[i],
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weekly.seasonality = products$weekly.seasonality[i],
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daily.seasonality = products$daily.seasonality[i],
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holidays = holidays,
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seasonality.prior.scale = 1.1,
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holidays.prior.scale = 1.1,
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changepoints.prior.scale = 0.1,
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mcmc.samples = 100,
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interval.width = 0.9,
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uncertainty.samples = 200,
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fit = FALSE
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)
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out <- prophet:::setup_dataframe(m1, df, initialize_scales = TRUE)
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m1 <- out$m
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m1$history <- out$df
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m1 <- prophet:::set_auto_seasonalities(m1)
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m2 <- prophet:::prophet_copy(m1)
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# Values should be copied correctly
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args <- c('growth', 'changepoints', 'n.changepoints', 'holidays',
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'seasonality.prior.scale', 'holidays.prior.scale',
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'changepoints.prior.scale', 'mcmc.samples', 'interval.width',
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'uncertainty.samples')
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for (arg in args) {
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expect_equal(m1[[arg]], m2[[arg]])
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}
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expect_equal(FALSE, m2$yearly.seasonality)
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expect_equal(FALSE, m2$weekly.seasonality)
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expect_equal(FALSE, m2$daily.seasonality)
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expect_equal(m1$yearly.seasonality, 'yearly' %in% names(m2$seasonalities))
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expect_equal(m1$weekly.seasonality, 'weekly' %in% names(m2$seasonalities))
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expect_equal(m1$daily.seasonality, 'daily' %in% names(m2$seasonalities))
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}
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# Check for cutoff and custom seasonality and extra regressors
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changepoints <- seq.Date(as.Date('2012-06-15'), as.Date('2012-09-15'), by='d')
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cutoff <- as.Date('2012-07-25')
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m1 <- prophet(changepoints = changepoints)
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m1 <- add_seasonality(m1, 'custom', 10, 5)
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m1 <- add_regressor(m1, 'binary_feature')
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m1 <- fit.prophet(m1, df)
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m2 <- prophet:::prophet_copy(m1, cutoff)
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changepoints <- changepoints[changepoints <= cutoff]
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expect_equal(prophet:::set_date(changepoints), m2$changepoints)
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expect_true('custom' %in% names(m2$seasonalities))
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expect_true('binary_feature' %in% names(m2$extra_regressors))
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})
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@ -1,60 +0,0 @@
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library(prophet)
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context("Prophet metrics 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("metrics_tests_using_model", {
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# Create dummy model
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m <- prophet(DATA)
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# Create metric data
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forecast <- predict(m, NULL)
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df <- na.omit(dplyr::inner_join(m$history, forecast, by="ds"))
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# Check all metrics wether it is equal to its definition
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y <- df$y
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yhat <- df$yhat
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expect_equal(me(m), mean(y-yhat))
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expect_equal(mse(m), mean((y-yhat)^2))
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expect_equal(rmse(m), sqrt(mean((y-yhat)^2)))
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expect_equal(mae(m), mean(abs(y-yhat)))
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expect_equal(mpe(m), 100*mean((y-yhat)/y))
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expect_equal(mape(m), 100*mean(abs((y-yhat)/y)))
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answer <- data.frame(
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me=me(m),
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mse=mse(m),
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rmse=rmse(m),
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mae=mae(m),
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mpe=mpe(m),
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mape=mape(m)
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)
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expect_equal(all_metrics(m), answer)
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})
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test_that("metrics_tests_using_simulated_historical_forecast", {
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#skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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# Create dummy model
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m <- prophet(DATA)
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# Run simulated historical forecast
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df <- simulated_historical_forecasts(m, horizon = 3, units = 'days', k = 2, period = 3)
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# Check all metrics wether it is equal to its definition
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y <- df$y
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yhat <- df$yhat
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expect_equal(me(df=df), mean(y-yhat))
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expect_equal(mse(df=df), mean((y-yhat)^2))
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expect_equal(rmse(df=df), sqrt(mean((y-yhat)^2)))
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expect_equal(mae(df=df), mean(abs(y-yhat)))
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expect_equal(mpe(df=df), 100*mean((y-yhat)/y))
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expect_equal(mape(df=df), 100*mean(abs((y-yhat)/y)))
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answer <- data.frame(
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me=me(df=df),
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mse=mse(df=df),
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rmse=rmse(df=df),
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mae=mae(df=df),
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mpe=mpe(df=df),
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mape=mape(df=df)
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)
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expect_equal(all_metrics(df=df), answer)
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})
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@ -509,72 +509,3 @@ test_that("added_regressors", {
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m <- add_regressor(m, 'constant_feature')
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expect_error(fit.prophet(m, df))
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})
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test_that("copy", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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df <- DATA
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df$cap <- 200.
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df$binary_feature <- c(rep(0, 255), rep(1, 255))
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inputs <- list(
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growth = c('linear', 'logistic'),
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yearly.seasonality = c(TRUE, FALSE),
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weekly.seasonality = c(TRUE, FALSE),
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daily.seasonality = c(TRUE, FALSE),
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holidays = c('null', 'insert_dataframe')
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)
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products <- expand.grid(inputs)
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for (i in 1:length(products)) {
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if (products$holidays[i] == 'insert_dataframe') {
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holidays <- data.frame(ds=c('2016-12-25'), holiday=c('x'))
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} else {
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holidays <- NULL
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}
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m1 <- prophet(
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growth = as.character(products$growth[i]),
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changepoints = NULL,
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n.changepoints = 3,
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yearly.seasonality = products$yearly.seasonality[i],
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weekly.seasonality = products$weekly.seasonality[i],
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daily.seasonality = products$daily.seasonality[i],
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holidays = holidays,
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seasonality.prior.scale = 1.1,
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holidays.prior.scale = 1.1,
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changepoints.prior.scale = 0.1,
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mcmc.samples = 100,
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interval.width = 0.9,
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uncertainty.samples = 200,
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fit = FALSE
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)
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out <- prophet:::setup_dataframe(m1, df, initialize_scales = TRUE)
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m1 <- out$m
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m1$history <- out$df
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m1 <- prophet:::set_auto_seasonalities(m1)
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m2 <- prophet:::prophet_copy(m1)
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# Values should be copied correctly
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args <- c('growth', 'changepoints', 'n.changepoints', 'holidays',
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'seasonality.prior.scale', 'holidays.prior.scale',
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'changepoints.prior.scale', 'mcmc.samples', 'interval.width',
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'uncertainty.samples')
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for (arg in args) {
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expect_equal(m1[[arg]], m2[[arg]])
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}
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expect_equal(FALSE, m2$yearly.seasonality)
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expect_equal(FALSE, m2$weekly.seasonality)
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expect_equal(FALSE, m2$daily.seasonality)
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expect_equal(m1$yearly.seasonality, 'yearly' %in% names(m2$seasonalities))
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expect_equal(m1$weekly.seasonality, 'weekly' %in% names(m2$seasonalities))
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expect_equal(m1$daily.seasonality, 'daily' %in% names(m2$seasonalities))
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}
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# Check for cutoff and custom seasonality and extra regressors
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changepoints <- seq.Date(as.Date('2012-06-15'), as.Date('2012-09-15'), by='d')
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cutoff <- as.Date('2012-07-25')
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m1 <- prophet(changepoints = changepoints)
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m1 <- add_seasonality(m1, 'custom', 10, 5)
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m1 <- add_regressor(m1, 'binary_feature')
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m1 <- fit.prophet(m1, df)
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m2 <- prophet:::prophet_copy(m1, cutoff)
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changepoints <- changepoints[changepoints <= cutoff]
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expect_equal(prophet:::set_date(changepoints), m2$changepoints)
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expect_true('custom' %in% names(m2$seasonalities))
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expect_true('binary_feature' %in% names(m2$extra_regressors))
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})
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@ -10,6 +10,8 @@ from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import itertools
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import numpy as np
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import pandas as pd
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@ -19,9 +21,10 @@ from unittest import TestCase
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from fbprophet import Prophet
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from fbprophet import diagnostics
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DATA = pd.read_csv(
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DATA_all = pd.read_csv(
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os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']
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).head(100)
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)
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DATA = DATA_all.head(100)
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# fb-block 1 end
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# fb-block 2
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@ -165,3 +168,68 @@ class TestDiagnostics(TestCase):
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set(df_horizon.columns),
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{'coverage', 'mse', 'horizon'},
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)
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def test_copy(self):
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df = DATA_all.copy()
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df['cap'] = 200.
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df['binary_feature'] = [0] * 255 + [1] * 255
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# These values are created except for its default values
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holiday = pd.DataFrame(
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{'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
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products = itertools.product(
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['linear', 'logistic'], # growth
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[None, pd.to_datetime(['2016-12-25'])], # changepoints
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[3], # n_changepoints
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[True, False], # yearly_seasonality
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[True, False], # weekly_seasonality
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[True, False], # daily_seasonality
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[None, holiday], # holidays
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[1.1], # seasonality_prior_scale
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[1.1], # holidays_prior_scale
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[0.1], # changepoint_prior_scale
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[100], # mcmc_samples
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[0.9], # interval_width
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[200] # uncertainty_samples
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)
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# Values should be copied correctly
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for product in products:
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m1 = Prophet(*product)
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m1.history = m1.setup_dataframe(
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df.copy(), initialize_scales=True)
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m1.set_auto_seasonalities()
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m2 = diagnostics.prophet_copy(m1)
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self.assertEqual(m1.growth, m2.growth)
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self.assertEqual(m1.n_changepoints, m2.n_changepoints)
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self.assertEqual(m1.changepoints, m2.changepoints)
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self.assertEqual(False, m2.yearly_seasonality)
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self.assertEqual(False, m2.weekly_seasonality)
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self.assertEqual(False, m2.daily_seasonality)
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self.assertEqual(
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m1.yearly_seasonality, 'yearly' in m2.seasonalities)
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self.assertEqual(
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m1.weekly_seasonality, 'weekly' in m2.seasonalities)
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self.assertEqual(
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m1.daily_seasonality, 'daily' in m2.seasonalities)
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if m1.holidays is None:
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self.assertEqual(m1.holidays, m2.holidays)
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else:
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self.assertTrue((m1.holidays == m2.holidays).values.all())
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self.assertEqual(m1.seasonality_prior_scale, m2.seasonality_prior_scale)
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self.assertEqual(m1.changepoint_prior_scale, m2.changepoint_prior_scale)
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self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
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self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
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self.assertEqual(m1.interval_width, m2.interval_width)
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self.assertEqual(m1.uncertainty_samples, m2.uncertainty_samples)
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# Check for cutoff and custom seasonality and extra regressors
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changepoints = pd.date_range('2012-06-15', '2012-09-15')
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cutoff = pd.Timestamp('2012-07-25')
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m1 = Prophet(changepoints=changepoints)
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m1.add_seasonality('custom', 10, 5)
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m1.add_regressor('binary_feature')
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m1.fit(df)
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m2 = diagnostics.prophet_copy(m1, cutoff=cutoff)
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changepoints = changepoints[changepoints <= cutoff]
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self.assertTrue((changepoints == m2.changepoints).all())
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self.assertTrue('custom' in m2.seasonalities)
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self.assertTrue('binary_feature' in m2.extra_regressors)
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@ -10,7 +10,6 @@ from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import itertools
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import numpy as np
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import pandas as pd
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@ -545,68 +544,3 @@ class TestProphet(TestCase):
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m.add_regressor('constant_feature')
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with self.assertRaises(ValueError):
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m.fit(df.copy())
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def test_copy(self):
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df = DATA.copy()
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df['cap'] = 200.
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df['binary_feature'] = [0] * 255 + [1] * 255
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# These values are created except for its default values
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holiday = pd.DataFrame(
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{'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
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products = itertools.product(
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['linear', 'logistic'], # growth
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[None, pd.to_datetime(['2016-12-25'])], # changepoints
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[3], # n_changepoints
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[True, False], # yearly_seasonality
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[True, False], # weekly_seasonality
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[True, False], # daily_seasonality
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[None, holiday], # holidays
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[1.1], # seasonality_prior_scale
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[1.1], # holidays_prior_scale
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[0.1], # changepoint_prior_scale
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[100], # mcmc_samples
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[0.9], # interval_width
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[200] # uncertainty_samples
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)
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# Values should be copied correctly
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for product in products:
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m1 = Prophet(*product)
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m1.history = m1.setup_dataframe(
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df.copy(), initialize_scales=True)
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m1.set_auto_seasonalities()
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m2 = m1.copy()
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self.assertEqual(m1.growth, m2.growth)
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self.assertEqual(m1.n_changepoints, m2.n_changepoints)
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self.assertEqual(m1.changepoints, m2.changepoints)
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self.assertEqual(False, m2.yearly_seasonality)
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self.assertEqual(False, m2.weekly_seasonality)
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self.assertEqual(False, m2.daily_seasonality)
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self.assertEqual(
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m1.yearly_seasonality, 'yearly' in m2.seasonalities)
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self.assertEqual(
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m1.weekly_seasonality, 'weekly' in m2.seasonalities)
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self.assertEqual(
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m1.daily_seasonality, 'daily' in m2.seasonalities)
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if m1.holidays is None:
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self.assertEqual(m1.holidays, m2.holidays)
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else:
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self.assertTrue((m1.holidays == m2.holidays).values.all())
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self.assertEqual(m1.seasonality_prior_scale, m2.seasonality_prior_scale)
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self.assertEqual(m1.changepoint_prior_scale, m2.changepoint_prior_scale)
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self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
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self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
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self.assertEqual(m1.interval_width, m2.interval_width)
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self.assertEqual(m1.uncertainty_samples, m2.uncertainty_samples)
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# Check for cutoff and custom seasonality and extra regressors
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changepoints = pd.date_range('2012-06-15', '2012-09-15')
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cutoff = pd.Timestamp('2012-07-25')
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m1 = Prophet(changepoints=changepoints)
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m1.add_seasonality('custom', 10, 5)
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m1.add_regressor('binary_feature')
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m1.fit(df)
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m2 = m1.copy(cutoff=cutoff)
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changepoints = changepoints[changepoints <= cutoff]
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self.assertTrue((changepoints == m2.changepoints).all())
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self.assertTrue('custom' in m2.seasonalities)
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self.assertTrue('binary_feature' in m2.extra_regressors)
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