tests/testthat/test_randomForest_tidiers.R

In YTLogos/exploratory: R package for Exploratory

context("test tidiers for randomForest")

test_that("test randomForest with binary classification", {
  test_data <- structure(
    list(
      CANCELLED = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
      `Carrier Name` = c("Delta Air Lines", "American Eagle", "American Airlines", "Southwest Airlines", "SkyWest Airlines", "Southwest Airlines", "Southwest Airlines", "Delta Air Lines", "Southwest Airlines", "Atlantic Southeast Airlines", "American Airlines", "Southwest Airlines", "US Airways", "US Airways", "Delta Air Lines", "Atlantic Southeast Airlines", NA, "Atlantic Southeast Airlines", "Delta Air Lines", "Delta Air Lines"),
      CARRIER = c("DL", "MQ", "AA", "DL", "MQ", "AA", "DL", "DL", "MQ", "AA", "AA", "WN", "US", "US", "DL", "EV", "9E", "EV", "DL", "DL"),
      DISTANCE = c(1587, 173, 646, 187, 273, 1062, 583, 240, 1123, 851, 852, 862, 361, 507, 1020, 1092, 342, 489, 1184, 545)), row.names = c(NA, -20L),
    class = c("tbl_df", "tbl", "data.frame"), .Names = c("CANCELLED", "Carrier Name", "CARRIER", "DISTANCE"))

  test_data[["IS_AA"]] <- test_data$CARRIER == "AA"
  model_ret <- build_model(test_data,
                           model_func = randomForestMulti,
                           formula = CARRIER ~ DISTANCE,
                           test_rate = 0.3)
  coef_ret <- model_coef(model_ret)
  model_stats <- model_stats(model_ret, pretty.name = TRUE)
  pred_train_ret <- prediction(model_ret, data = "training")
  pred_test_ret <- prediction(model_ret, data = "test")
})

test_that("test randomForest with binary classification", {
  test_data <- structure(
    list(
      CANCELLED = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
      `Carrier Name` = c("Delta Air Lines", "American Eagle", "American Airlines", "Southwest Airlines", "SkyWest Airlines", "Southwest Airlines", "Southwest Airlines", "Delta Air Lines", "Southwest Airlines", "Atlantic Southeast Airlines", "American Airlines", "Southwest Airlines", "US Airways", "US Airways", "Delta Air Lines", "Atlantic Southeast Airlines", NA, "Atlantic Southeast Airlines", "Delta Air Lines", "Delta Air Lines"),
      CARRIER = c("DL", "MQ", "AA", "DL", "MQ", "AA", "DL", "DL", "MQ", "AA", "AA", "WN", "US", "US", "DL", "EV", "9E", "EV", "DL", "DL"),
      DISTANCE = c(1587, 173, 646, 187, 273, 1062, 583, 240, 1123, 851, 852, 862, 361, 507, 1020, 1092, 342, 489, 1184, 545)), row.names = c(NA, -20L),
    class = c("tbl_df", "tbl", "data.frame"), .Names = c("CANCELLED", "Carrier Name", "CARRIER", "DISTANCE"))

  test_data[["IS_AA"]] <- test_data$CARRIER == "AA"
  model_ret <- build_model(test_data,
                           model_func = randomForestBinary,
                           formula = IS_AA ~ DISTANCE,
                           test_rate = 0.3)
  coef_ret <- model_coef(model_ret)
  model_stats <- model_stats(model_ret, pretty.name = TRUE)
  pred_train_ret <- prediction_binary(model_ret, data = "training")
  pred_test_ret <- prediction_binary(model_ret, data = "test")
})

test_that("test randomForest with regression without localImp", {
  test_data <- structure(
    list(
      CANCELLED = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
      `Carrier Name` = c("Delta Air Lines", "American Eagle", "American Airlines", "Southwest Airlines", "SkyWest Airlines", "Southwest Airlines", "Southwest Airlines", "Delta Air Lines", "Southwest Airlines", "Atlantic Southeast Airlines", "American Airlines", "Southwest Airlines", "US Airways", "US Airways", "Delta Air Lines", "Atlantic Southeast Airlines", NA, "Atlantic Southeast Airlines", "Delta Air Lines", "Delta Air Lines"),
      CARRIER = c("DL", "MQ", "AA", "DL", "MQ", "AA", "DL", "DL", "MQ", "AA", "AA", "WN", "US", "US", "DL", "EV", "9E", "EV", "DL", "DL"),
      DISTANCE = c(1587, 173, 646, 187, 273, 1062, 583, 240, 1123, 851, 852, 862, 361, 507, 1020, 1092, 342, 489, 1184, 545)), row.names = c(NA, -20L),
    class = c("tbl_df", "tbl", "data.frame"), .Names = c("CANCELLED", "Carrier Name", "CARRIER", "DISTANCE"))

  test_data[["IS_AA"]] <- test_data$CARRIER == "AA"
  model_ret <- build_model(test_data,
                           model_func = randomForestBinary,
                           formula = IS_AA ~ DISTANCE,
                           localImp = FALSE,
                           test_rate = 0.3)
  coef_ret <- model_coef(model_ret, pretty.name = TRUE)
  stats_ret <- model_stats(model_ret)
  pred_train_ret <- prediction(model_ret, data = "training")
  pred_test_ret <- prediction(model_ret, data = "test")
})

test_that("test randomForest with regression", {
  test_data <- structure(
    list(
      CANCELLED = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
      `Carrier Name` = c("Delta Air Lines", "American Eagle", "American Airlines", "Southwest Airlines", "SkyWest Airlines", "Southwest Airlines", "Southwest Airlines", "Delta Air Lines", "Southwest Airlines", "Atlantic Southeast Airlines", "American Airlines", "Southwest Airlines", "US Airways", "US Airways", "Delta Air Lines", "Atlantic Southeast Airlines", NA, "Atlantic Southeast Airlines", "Delta Air Lines", "Delta Air Lines"),
      CARRIER = c("DL", "MQ", "AA", "DL", "MQ", "AA", "DL", "DL", "MQ", "AA", "AA", "WN", "US", "US", "DL", "EV", "9E", "EV", "DL", "DL"),
      DISTANCE = c(1587, 173, 646, 187, 273, 1062, 583, 240, 1123, 851, 852, 862, 361, 507, 1020, 1092, 342, 489, 1184, 545)), row.names = c(NA, -20L),
    class = c("tbl_df", "tbl", "data.frame"), .Names = c("CANCELLED", "Carrier Name", "CARRIER", "DISTANCE"))

  test_data[["IS_AA"]] <- as.integer(test_data$CARRIER == "AA")
  model_ret <- build_model(test_data,
                           model_func = randomForestReg,
                           formula = IS_AA ~ DISTANCE,
                           localImp = TRUE,
                           test_rate = 0.3)
  coef_ret <- model_coef(model_ret, pretty.name = TRUE)
  stats_ret <- model_stats(model_ret)
  pred_train_ret <- prediction(model_ret, data = "training")
  pred_test_ret <- prediction(model_ret, data = "test")
})

test_that("test randomForest with unsupervied", {
  test_data <- structure(
    list(
      CANCELLED = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
      `Carrier Name` = c("Delta Air Lines", "American Eagle", "American Airlines", "Southwest Airlines", "SkyWest Airlines", "Southwest Airlines", "Southwest Airlines", "Delta Air Lines", "Southwest Airlines", "Atlantic Southeast Airlines", "American Airlines", "Southwest Airlines", "US Airways", "US Airways", "Delta Air Lines", "Atlantic Southeast Airlines", NA, "Atlantic Southeast Airlines", "Delta Air Lines", "Delta Air Lines"),
      CARRIER = c("DL", "MQ", "AA", "DL", "MQ", "AA", "DL", "DL", "MQ", "AA", "AA", "WN", "US", "US", "DL", "EV", "9E", "EV", "DL", "DL"),
      DISTANCE = c(1587, 173, 646, 187, 273, 1062, 583, 240, 1123, 851, 852, 862, 361, 507, 1020, 1092, 342, 489, 1184, 545)), row.names = c(NA, -20L),
    class = c("tbl_df", "tbl", "data.frame"), .Names = c("CANCELLED", "Carrier Name", "CARRIER", "DISTANCE"))
  test_data[["IS_AA"]] <- test_data$CARRIER == "AA"
  model_ret <- build_model(test_data,
                           model_func = randomForest::randomForest,
                           formula = ~ DISTANCE,
                           test_rate = 0.3)

  coef_ret <- model_coef(model_ret)
  prediction_ret <- prediction(model_ret)
})

test_that("test randomForest with unsupervied by 3 classes", {
  test_data <- structure(
    list(
      CANCELLED = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
      `Carrier Name` = c("Delta Air Lines", "American Eagle", "American Airlines", "Southwest Airlines", "SkyWest Airlines", "Southwest Airlines", "Southwest Airlines", "Delta Air Lines", "Southwest Airlines", "Atlantic Southeast Airlines", "American Airlines", "Southwest Airlines", "US Airways", "US Airways", "Delta Air Lines", "Atlantic Southeast Airlines", NA, "Atlantic Southeast Airlines", "Delta Air Lines", "Delta Air Lines"),
      CARRIER = c("DL", "MQ", "AA", "DL", "MQ", "AA", "DL", "DL", "MQ", "AA", "AA", "WN", "US", "US", "DL", "EV", "9E", "EV", "DL", "DL"),
      DISTANCE = c(1587, 173, 646, 187, 273, 1062, 583, 240, 1123, 851, 852, 862, 361, 507, 1020, 1092, 342, 489, 1184, 545)), row.names = c(NA, -20L),
    class = c("tbl_df", "tbl", "data.frame"), .Names = c("CANCELLED", "Carrier Name", "CARRIER", "DISTANCE"))
  test_data[["IS_AA"]] <- test_data$CARRIER == "AA"
  model_ret <- build_model(test_data,
                           model_func = randomForest::randomForest,
                           formula = ~ DISTANCE + CANCELLED,
                           ntree = 3, proximity = TRUE,
                           test_rate = 0.3)

  coef_ret <- model_coef(model_ret)
  prediction_ret <- prediction(model_ret)
})

test_that("test randomForest with classification", {
  test_data <- structure(
    list(
      CANCELLED = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
      `Carrier Name` = c("Delta Air Lines", "American Eagle", "American Airlines", "Southwest Airlines", "SkyWest Airlines", "Southwest Airlines", "Southwest Airlines", "Delta Air Lines", "Southwest Airlines", "Atlantic Southeast Airlines", "American Airlines", "Southwest Airlines", "US Airways", "US Airways", "Delta Air Lines", "Atlantic Southeast Airlines", NA, "Atlantic Southeast Airlines", "Delta Air Lines", "Delta Air Lines"),
      CARRIER = c("DL", "MQ", "AA", "DL", "MQ", "AA", "DL", "DL", "MQ", "AA", "AA", "WN", "US", "US", "DL", "EV", "9E", "EV", "DL", "DL"),
      DISTANCE = c(1587, 173, 646, 187, 273, 1062, 583, 240, 1123, 851, 852, 862, 361, 507, 1020, 1092, 342, 489, 1184, 545)), row.names = c(NA, -20L),
    class = c("tbl_df", "tbl", "data.frame"), .Names = c("CANCELLED", "Carrier Name", "CARRIER", "DISTANCE"))

  test_data[["IS_AA"]] <- as.factor(test_data$CARRIER == "AA")
  model_ret <- build_model(test_data,
                           model_func = randomForestMulti,
                           formula = CARRIER ~ DISTANCE,
                           localImp = TRUE,
                           test_rate = 0.3)
  coef_ret <- model_coef(model_ret)
  model_stats <- model_stats(model_ret, pretty.name = TRUE)
  pred_train_ret <- prediction(model_ret, data = "training")
  pred_test_ret <- prediction(model_ret, data = "test")
})

test_that("test randomForest with classification", {
  test_data <- structure(
    list(
      CANCELLED = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
      `Carrier Name` = c("Delta Air Lines", "American Eagle", "American Airlines", "Southwest Airlines", "SkyWest Airlines", "Southwest Airlines", "Southwest Airlines", "Delta Air Lines", "Southwest Airlines", "Atlantic Southeast Airlines", "American Airlines", "Southwest Airlines", "US Airways", "US Airways", "Delta Air Lines", "Atlantic Southeast Airlines", NA, "Atlantic Southeast Airlines", "Delta Air Lines", "Delta Air Lines"),
      CARRIER = c("DL", "MQ", "AA", "DL", "MQ", "AA", "DL", "DL", "MQ", "AA", "AA", "WN", "US", "US", "DL", "EV", "9E", "EV", "DL", "DL"),
      DISTANCE = c(1587, 173, 646, 187, 273, 1062, 583, 240, 1123, 851, 852, 862, 361, 507, 1020, 1092, 342, 489, 1184, 545)), row.names = c(NA, -20L),
    class = c("tbl_df", "tbl", "data.frame"), .Names = c("CANCELLED", "Carrier Name", "CARRIER", "DISTANCE"))

  test_data[["IS_AA"]] <- test_data$CARRIER == "AA"
  model_ret <- build_model(test_data,
                           model_func = randomForestMulti,
                           formula = CARRIER ~ poly(DISTANCE, 3),
                           localImp = TRUE,
                           test_rate = 0.3)
  coef_ret <- model_coef(model_ret)
  model_stats <- model_stats(model_ret, pretty.name = TRUE)
  pred_train_ret <- prediction(model_ret, data = "training")
  pred_test_ret <- prediction(model_ret, data = "test")
})