inst/doc/recorder.R

In recorder: Toolkit to Validate New Data for a Predictive Model

## ---- include = FALSE----------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

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set.seed(1)
trn_idx <- sample(seq_len(nrow(iris)), 100)
data_training <- iris[trn_idx, ]
data_new <- iris[-trn_idx, ]

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library(recorder)
tape <- record(data_training)

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str(tape)

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# create sample of row indices.
samples <- lapply(1:3, function(x) {
  set.seed(x) 
  sample(nrow(data_new), 5, replace = FALSE)})

# create numeric values without range, -Inf and Inf.
data_new$Sepal.Width[samples[[1]]] <- -Inf
data_new$Petal.Width[samples[[2]]] <- Inf

# insert NA's in numeric vector.
data_new$Petal.Length[samples[[3]]] <- NA_real_

# insert new column.
data_new$junk <- "junk"

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get_tests_meta_data()

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playback <- play(tape, data_new)

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class(playback)

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playback

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knitr::kable(head(get_failed_tests(playback), 15))

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head(get_failed_tests_string(playback))

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get_clean_rows(playback)

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get_clean_rows(playback, ignore_tests = "new_variable")

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get_clean_rows(playback, 
               ignore_tests = "new_variable",
               ignore_cols = "Petal.Length")

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knitr::kable(head(get_failed_tests(playback, 
                                   ignore_tests = "new_variable",
                                   ignore_cols = "Petal.Length",
                                   ignore_combinations = list(outside_range = "Sepal.Width")),
                  15))