is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
| test_diagnostics.R (prophet-0.7) | : | test_diagnostics.R (prophet-1.0) |
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| | |
| skipping to change at line 59 | | skipping to change at line 59 |
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| df$cap <- 40 | | df$cap <- 40 |
| m <- prophet(df, growth = 'logistic') | | m <- prophet(df, growth = 'logistic') |
| df.cv <- cross_validation( | | df.cv <- cross_validation( |
| m, horizon = 1, units = "days", period = 1, initial = 140) | | m, horizon = 1, units = "days", period = 1, initial = 140) |
| expect_equal(length(unique(df.cv$cutoff)), 2) | | expect_equal(length(unique(df.cv$cutoff)), 2) |
| expect_true(all(df.cv$cutoff < df.cv$ds)) | | expect_true(all(df.cv$cutoff < df.cv$ds)) |
| df.merged <- dplyr::left_join(df.cv, m$history, by="ds") | | df.merged <- dplyr::left_join(df.cv, m$history, by="ds") |
| expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0) | | expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0) |
| }) | | }) |
| | |
|
| | test_that("cross_validation_flat", { |
| | skip_if_not(Sys.getenv('R_ARCH') != '/i386') |
| | df <- DATA |
| | m <- prophet(df, growth = 'flat') |
| | df.cv <- cross_validation( |
| | m, horizon = 1, units = "days", period = 1, initial = 140) |
| | expect_equal(length(unique(df.cv$cutoff)), 2) |
| | expect_true(all(df.cv$cutoff < df.cv$ds)) |
| | df.merged <- dplyr::left_join(df.cv, m$history, by="ds") |
| | expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0) |
| | }) |
| | |
| test_that("cross_validation_extra_regressors", { | | test_that("cross_validation_extra_regressors", { |
| skip_if_not(Sys.getenv('R_ARCH') != '/i386') | | skip_if_not(Sys.getenv('R_ARCH') != '/i386') |
| df <- DATA | | df <- DATA |
| df$extra <- seq(0, nrow(df) - 1) | | df$extra <- seq(0, nrow(df) - 1) |
| df$is_conditional_week <- seq(0, nrow(df) - 1) %/% 7 %% 2 | | df$is_conditional_week <- seq(0, nrow(df) - 1) %/% 7 %% 2 |
| m <- prophet() | | m <- prophet() |
| m <- add_seasonality(m, name = 'monthly', period = 30.5, fourier.order = 5) | | m <- add_seasonality(m, name = 'monthly', period = 30.5, fourier.order = 5) |
| m <- add_seasonality(m, name = 'conditional_weekly', period = 7, | | m <- add_seasonality(m, name = 'conditional_weekly', period = 7, |
| fourier.order = 3, prior.scale = 2., | | fourier.order = 3, prior.scale = 2., |
| condition.name = 'is_conditional_week') | | condition.name = 'is_conditional_week') |
| | |
| skipping to change at line 131 | | skipping to change at line 143 |
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| | |
| test_that("performance_metrics", { | | test_that("performance_metrics", { |
| skip_if_not(Sys.getenv('R_ARCH') != '/i386') | | skip_if_not(Sys.getenv('R_ARCH') != '/i386') |
| m <- prophet(DATA) | | m <- prophet(DATA) |
| df_cv <- cross_validation( | | df_cv <- cross_validation( |
| m, horizon = 4, units = "days", period = 10, initial = 90) | | m, horizon = 4, units = "days", period = 10, initial = 90) |
| # Aggregation level none | | # Aggregation level none |
| df_none <- performance_metrics(df_cv, rolling_window = -1) | | df_none <- performance_metrics(df_cv, rolling_window = -1) |
| expect_true(all( | | expect_true(all( |
| sort(colnames(df_none)) | | sort(colnames(df_none)) |
|
| == sort(c('horizon', 'coverage', 'mae', 'mape', 'mse', 'rmse')) | | == sort(c('horizon', 'mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'covera
ge')) |
| )) | | )) |
| expect_equal(nrow(df_none), 16) | | expect_equal(nrow(df_none), 16) |
| # Aggregation level 0 | | # Aggregation level 0 |
| df_0 <- performance_metrics(df_cv, rolling_window = 0) | | df_0 <- performance_metrics(df_cv, rolling_window = 0) |
| expect_equal(nrow(df_0), 4) | | expect_equal(nrow(df_0), 4) |
| expect_equal(length(unique(df_0$h)), 4) | | expect_equal(length(unique(df_0$h)), 4) |
| # Aggregation level 0.2 | | # Aggregation level 0.2 |
| df_horizon <- performance_metrics(df_cv, rolling_window = 0.2) | | df_horizon <- performance_metrics(df_cv, rolling_window = 0.2) |
| expect_equal(nrow(df_horizon), 4) | | expect_equal(nrow(df_horizon), 4) |
| expect_equal(length(unique(df_horizon$horizon)), 4) | | expect_equal(length(unique(df_horizon$horizon)), 4) |
| | |
| skipping to change at line 164 | | skipping to change at line 176 |
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| df_cv$y[1] <- 0. | | df_cv$y[1] <- 0. |
| df_horizon <- performance_metrics(df_cv, metrics = c('coverage', 'mape')) | | df_horizon <- performance_metrics(df_cv, metrics = c('coverage', 'mape')) |
| expect_true(all( | | expect_true(all( |
| sort(colnames(df_horizon)) == sort(c('coverage', 'horizon')) | | sort(colnames(df_horizon)) == sort(c('coverage', 'horizon')) |
| )) | | )) |
| df_horizon <- performance_metrics(df_cv, metrics = c('mape')) | | df_horizon <- performance_metrics(df_cv, metrics = c('mape')) |
| expect_null(df_horizon) | | expect_null(df_horizon) |
| # List of metrics containing non valid metrics | | # List of metrics containing non valid metrics |
| expect_error( | | expect_error( |
| performance_metrics(df_cv, metrics = c('mse', 'error_metric')), | | performance_metrics(df_cv, metrics = c('mse', 'error_metric')), |
|
| 'Valid values for metrics are: mse, rmse, mae, mape, coverage' | | 'Valid values for metrics are: mse, rmse, mae, mape, mdape, smape, coverage
' |
| ) | | ) |
| }) | | }) |
| | |
| test_that("rolling_mean", { | | test_that("rolling_mean", { |
| skip_if_not(Sys.getenv('R_ARCH') != '/i386') | | skip_if_not(Sys.getenv('R_ARCH') != '/i386') |
| x <- 0:9 | | x <- 0:9 |
| h <- 0:9 | | h <- 0:9 |
| df <- prophet:::rolling_mean_by_h(x=x, h=h, w=1, name='x') | | df <- prophet:::rolling_mean_by_h(x=x, h=h, w=1, name='x') |
| expect_equal(x, df$x) | | expect_equal(x, df$x) |
| expect_equal(h, df$horizon) | | expect_equal(h, df$horizon) |
| | |
| skipping to change at line 223 | | skipping to change at line 235 |
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| expect_equal(c(7.), df$horizon) | | expect_equal(c(7.), df$horizon) |
| expect_equal(c(4.5), df$x) | | expect_equal(c(4.5), df$x) |
| }) | | }) |
| | |
| test_that("copy", { | | test_that("copy", { |
| skip_if_not(Sys.getenv('R_ARCH') != '/i386') | | skip_if_not(Sys.getenv('R_ARCH') != '/i386') |
| df <- DATA_all | | df <- DATA_all |
| df$cap <- 200. | | df$cap <- 200. |
| df$binary_feature <- c(rep(0, 255), rep(1, 255)) | | df$binary_feature <- c(rep(0, 255), rep(1, 255)) |
| inputs <- list( | | inputs <- list( |
|
| growth = c('linear', 'logistic'), | | growth = c('linear', 'logistic', 'flat'), |
| yearly.seasonality = c(TRUE, FALSE), | | yearly.seasonality = c(TRUE, FALSE), |
| weekly.seasonality = c(TRUE, FALSE), | | weekly.seasonality = c(TRUE, FALSE), |
| daily.seasonality = c(TRUE, FALSE), | | daily.seasonality = c(TRUE, FALSE), |
| holidays = c('null', 'insert_dataframe'), | | holidays = c('null', 'insert_dataframe'), |
| seasonality.mode = c('additive', 'multiplicative') | | seasonality.mode = c('additive', 'multiplicative') |
| ) | | ) |
| products <- expand.grid(inputs) | | products <- expand.grid(inputs) |
| for (i in 1:length(products)) { | | for (i in 1:length(products)) { |
| if (products$holidays[i] == 'insert_dataframe') { | | if (products$holidays[i] == 'insert_dataframe') { |
| holidays <- data.frame(ds=c('2016-12-25'), holiday=c('x')) | | holidays <- data.frame(ds=c('2016-12-25'), holiday=c('x')) |
| | | |
| End of changes. 4 change blocks. |
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| 3 lines changed or deleted | | 15 lines changed or added |
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