ml_evaluator

Spark ML - Evaluators

Description

A set of functions to calculate performance metrics for prediction models. Also see the Spark ML Documentation https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.evaluation.packagehttps://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.evaluation.package

Usage

ml_binary_classification_evaluator(
  x,
  label_col = "label",
  raw_prediction_col = "rawPrediction",
  metric_name = "areaUnderROC",
  uid = random_string("binary_classification_evaluator_"),
  ...
)

ml_binary_classification_eval(
  x,
  label_col = "label",
  prediction_col = "prediction",
  metric_name = "areaUnderROC"
)

ml_multiclass_classification_evaluator(
  x,
  label_col = "label",
  prediction_col = "prediction",
  metric_name = "f1",
  uid = random_string("multiclass_classification_evaluator_"),
  ...
)

ml_classification_eval(
  x,
  label_col = "label",
  prediction_col = "prediction",
  metric_name = "f1"
)

ml_regression_evaluator(
  x,
  label_col = "label",
  prediction_col = "prediction",
  metric_name = "rmse",
  uid = random_string("regression_evaluator_"),
  ...
)

Arguments

Argument Description
x A spark_connection object or a tbl_spark containing label and prediction columns. The latter should be the output of sdf_predict.
label_col Name of column string specifying which column contains the true labels or values.
raw_prediction_col Raw prediction (a.k.a. confidence) column name.
metric_name The performance metric. See details.
uid A character string used to uniquely identify the ML estimator.
Optional arguments; currently unused.
prediction_col Name of the column that contains the predicted label or value NOT the scored probability. Column should be of type Double.

Details

The following metrics are supported

  • Binary Classification: areaUnderROC (default) or areaUnderPR (not available in Spark 2.X.)

  • Multiclass Classification: f1 (default), precision, recall, weightedPrecision, weightedRecall or accuracy; for Spark 2.X: f1 (default), weightedPrecision, weightedRecall or accuracy.

  • Regression: rmse (root mean squared error, default), mse (mean squared error), r2, or mae (mean absolute error.)

ml_binary_classification_eval() is an alias for ml_binary_classification_evaluator() for backwards compatibility.

ml_classification_eval() is an alias for ml_multiclass_classification_evaluator() for backwards compatibility.

Value

The calculated performance metric

Examples


sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)

partitions <- mtcars_tbl %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

mtcars_training <- partitions$training
mtcars_test <- partitions$test

# for multiclass classification
rf_model <- mtcars_training %>%
  ml_random_forest(cyl ~ ., type = "classification")

pred <- ml_predict(rf_model, mtcars_test)

ml_multiclass_classification_evaluator(pred)

# for regression
rf_model <- mtcars_training %>%
  ml_random_forest(cyl ~ ., type = "regression")

pred <- ml_predict(rf_model, mtcars_test)

ml_regression_evaluator(pred, label_col = "cyl")

# for binary classification
rf_model <- mtcars_training %>%
  ml_random_forest(am ~ gear + carb, type = "classification")

pred <- ml_predict(rf_model, mtcars_test)

ml_binary_classification_evaluator(pred)