ml_clustering_evaluator

0th

Percentile

Spark ML - Clustering Evaluator

Evaluator for clustering results. The metric computes the Silhouette measure using the squared Euclidean distance. The Silhouette is a measure for the validation of the consistency within clusters. It ranges between 1 and -1, where a value close to 1 means that the points in a cluster are close to the other points in the same cluster and far from the points of the other clusters.

Usage
ml_clustering_evaluator(x, features_col = "features",
  prediction_col = "prediction", metric_name = "silhouette",
  uid = random_string("clustering_evaluator_"), ...)
Arguments
x

A spark_connection object or a tbl_spark containing label and prediction columns. The latter should be the output of sdf_predict.

features_col

Name of features column.

prediction_col

Name of the prediction column.

metric_name

The performance metric. Currently supports "silhouette".

uid

A character string used to uniquely identify the ML estimator.

...

Optional arguments; currently unused.

Value

The calculated performance metric

Aliases
  • ml_clustering_evaluator
Examples
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

partitions <- iris_tbl %>%
  sdf_partition(training = 0.7, test = 0.3, seed = 1111)

iris_training <- partitions$training
iris_test <- partitions$test

formula <- Species ~ .

# Train the models
kmeans_model <- ml_kmeans(iris_training, formula = formula)
b_kmeans_model <- ml_bisecting_kmeans(iris_training, formula = formula)
gmm_model <- ml_gaussian_mixture(iris_training, formula = formula)

# Predict
pred_kmeans <- sdf_predict(iris_test, kmeans_model)
pred_b_kmeans <- sdf_predict(iris_test, b_kmeans_model)
pred_gmm <- sdf_predict(iris_test, gmm_model)

# Evaluate
ml_clustering_evaluator(pred_kmeans)
ml_clustering_evaluator(pred_b_kmeans)
ml_clustering_evaluator(pred_gmm)
# }
Documentation reproduced from package sparklyr, version 0.9.2, License: Apache License 2.0 | file LICENSE

Community examples

Looks like there are no examples yet.