library(sparklyr)
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)
#> # A tibble: 2 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc binary 0.988
#> 2 pr_auc binary 0.978Extracts metrics from a fitted table
R/ml_metrics.R
ml_metrics_binary
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
| Arguments | Description |
|---|---|
| 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.