test: remove AnomalyDetection dependency from time series tests (#36082)

tests/testthat/test_anomaly_detection.R

@@ -1,51 +1,5 @@
 context("test anomaly detection functions")
 
-#TODO write test to check that negative/positive anomaly is actuall negative/positive compared to expected values.
-
-test_that("do_anomary_detection with aggregation", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
-  raw_data <- raw_data %>% rename(`time stamp`=timestamp) # test column name with space
-  first_ten <- raw_data %>% dplyr::slice(1:10)
-  raw_data <- dplyr::bind_rows(raw_data, first_ten)
-  ret <- raw_data %>%
-    do_anomaly_detection(`time stamp`, e_value=TRUE, time_unit = "hour")
-  expect_true(is.data.frame(ret))
-})
-
-test_that("do_anomary_detection", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
-  ret <- raw_data %>%
-    do_anomaly_detection(timestamp, count, e_value=TRUE, time_unit = "hour")
-  expect_equal(ncol(ret), 7)
-})
-
-test_that("do_anomary_detection grouped case", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
-  expect_error({
-    ret <- raw_data %>%
-      dplyr::group_by(timestamp) %>%
-      do_anomaly_detection(timestamp, count, e_value=TRUE)
-  }, "timestamp is grouped. Please ungroup it.")
-
-  expect_error({
-    ret <- raw_data %>%
-      dplyr::group_by(count) %>%
-      do_anomaly_detection(timestamp, count, e_value=TRUE)
-  }, "count is grouped. Please ungroup it.")
-})
-
-test_that("do_anomary_detection without value_col", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
-  raw_data <- dplyr::bind_rows(raw_data, raw_data, raw_data)
-  ret <- raw_data %>%
-    do_anomaly_detection(timestamp, e_value=TRUE, time_unit = "hour")
-  expect_true(is.data.frame(ret))
-})
-
 test_that("do_anomary_detection with Date data", {
   ts <- seq.Date(as.Date("2010-01-01"), as.Date("2010-12-31"), by="day")
   raw_data <- data.frame(timestamp=ts, y=runif(length(ts)))

tests/testthat/test_arima_1.R

@@ -1,8 +1,10 @@
 context("test ARIMA functions")
 test_that("exp_arima with aggregation", {
   Sys.setenv(TZ="UTC") # set time zone for test stability for tests with time unit smaller than day.
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   raw_data <- raw_data %>% rename(`time stamp`=timestamp, `cou nt`=count)
 
   model_df <- raw_data %>%
@@ -45,8 +47,10 @@ test_that("exp_arima with aggregation", {
 
 test_that("exp_arima with minutes", {
   Sys.setenv(TZ="UTC") # set time zone for test stability for tests with time unit smaller than day.
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   raw_data <- raw_data %>% rename(`time stamp`=timestamp, `cou nt`=count)
 
   ret <- raw_data %>% tail(100) %>%
@@ -292,8 +296,10 @@ test_that("exp_arima test mode with extra regressor", {
 })
 
 test_that("exp_arima wrong grouping case", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   expect_error({
     ret <- raw_data %>%
       dplyr::group_by(timestamp) %>%
@@ -308,8 +314,10 @@ test_that("exp_arima wrong grouping case", {
 })
 
 test_that("exp_arima grouped case", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   raw_data1 <- raw_data
   raw_data2 <- raw_data
   raw_data1 <- raw_data1 %>% mutate(group='A')
@@ -326,17 +334,21 @@ test_that("exp_arima grouped case", {
 })
 
 test_that("exp_arima without value_col", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   ret <- raw_data %>%
     exp_arima(timestamp, , 10)
   # verify that the last forecasted_value is not NA to test #9211
   expect_true(!is.na(ret$data[[1]]$forecasted_value[[length(ret$data[[1]]$forecasted_value)]]))
 })
 
 test_that("exp_arima with all-NA value col", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   data <- raw_data %>% mutate(count=NA) # Make the count column all-NA.
   ret <- data %>%
     exp_arima(timestamp, count, 10)

tests/testthat/test_arima_2.R

@@ -1,10 +1,12 @@
 context("test exp_arima")
 test_that("exp_arima with aggregation", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
 
   # Create time gap intentionally.
-  raw_data <- raw_data %>% filter(as.Date(timestamp) != as.Date("1980-09-26"))
+  raw_data <- raw_data %>% filter(as.Date(timestamp) != as.Date("2014-01-02"))
 
   raw_data <- raw_data %>% rename(`time stamp`=timestamp, `cou nt`=count)
   raw_data$pre_col1 <- runif(nrow(raw_data))

tests/testthat/test_prophet_1.R

@@ -1,8 +1,10 @@
 context("test prophet functions")
 test_that("do_prophet with aggregation", {
   Sys.setenv(TZ="UTC") # set time zone for test stability for tests with time unit smaller than day.
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   raw_data <- raw_data %>% rename(`time stamp`=timestamp, `cou nt`=count)
   ret <- raw_data %>%
     do_prophet(`time stamp`, `cou nt`, 10, time_unit = "day")
@@ -359,8 +361,10 @@ test_that("do_prophet test mode with extra regressor", {
 
 
 test_that("do_prophet grouped case", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   expect_error({
     ret <- raw_data %>%
       dplyr::group_by(timestamp) %>%
@@ -375,8 +379,10 @@ test_that("do_prophet grouped case", {
 })
 
 test_that("do_prophet without value_col", {
-  data("raw_data", package = "AnomalyDetection")
-  raw_data$timestamp <- as.POSIXct(raw_data$timestamp)
+  raw_data <- data.frame(
+    timestamp = seq(as.POSIXct("2014-01-01 00:00:00", tz="UTC"), by="1 hour", length.out=1400),
+    count = round(abs(rnorm(1400, mean=1000, sd=200)))
+  )
   ret <- raw_data %>%
     do_prophet(timestamp, NULL, 10)
   expect_true(is.data.frame(ret))