sparklyr (version 1.8.4)

ml_metrics_binary: Extracts metrics from a fitted table

Description

The function works best when passed a `tbl_spark` created by `ml_predict()`. The output `tbl_spark` will contain the correct variable types and format that the given Spark model "evaluator" expects.

Usage

ml_metrics_binary(
  x,
  truth = label,
  estimate = rawPrediction,
  metrics = c("roc_auc", "pr_auc"),
  ...
)

Arguments

x

A `tbl_spark` containing the estimate (prediction) and the truth (value of what actually happened)

truth

The name of the column from `x` with an integer field containing the binary response (0 or 1). The `ml_predict()` function will create a new field named `label` which contains the expected type and values. `truth` defaults to `label`.

estimate

The name of the column from `x` that contains the prediction. Defaults to `rawPrediction`, since its type and expected values will match `truth`.

metrics

A character vector with the metrics to calculate. For binary models the possible values are: `roc_auc` (Area under the Receiver Operator curve), `pr_auc` (Area under the Precesion Recall curve). Defaults to: `roc_auc`, `pr_auc`

...

Optional arguments; currently unused.

Details

The `ml_metrics` family of functions implement Spark's `evaluate` closer to how the `yardstick` package works. The functions expect a table containing the truth and estimate, and return a `tibble` with the results. The `tibble` has the same format and variable names as the output of the `yardstick` functions.

Examples

if (FALSE) {
sc <- spark_connect("local")
tbl_iris <- copy_to(sc, iris)
prep_iris <- tbl_iris %>%
  mutate(is_setosa = ifelse(Species == "setosa", 1, 0))
iris_split <- sdf_random_split(prep_iris, training = 0.5, test = 0.5)
model <- ml_logistic_regression(iris_split$training, "is_setosa ~ Sepal_Length")
tbl_predictions <- ml_predict(model, iris_split$test)
ml_metrics_binary(tbl_predictions)
}