k

  • Available in: K-Means, PCA
  • Hyperparameter: yes

Description

K-Means

In K-Means, a “cluster” refers to groups of data in a dataset that are similar to one another. The K-Means algorithm finds clusters and cluster centers in a set of unlabled data.

This option specifies the maximum number of clusters that K-Means will form. If estimate_k is disabled, the model will find \(k\) centroids, otherwise it will find up to \(k\) centroids. This parameter is required and has no default value.

PCA

In PCA,

Example

library(h2o)
h2o.init()

# import the seeds dataset:
# this dataset looks at three different types of wheat varieties
# the original dataset can be found at http://archive.ics.uci.edu/ml/datasets/seeds
seeds <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt")

# set the predictor names
# ignore the 8th column which has the prior known clusters for this dataset
predictors <-colnames(seeds)[-length(seeds)]

# split into train and validation
seeds_splits <- h2o.splitFrame(data = seeds, ratios = .8, seed = 1234)
train <- seeds_splits[[1]]
valid <- seeds_splits[[2]]

# try using the `k` parameter:
# build the model with three clusters
seeds_kmeans <- h2o.kmeans(x = predictors, k = 3, training_frame = train, validation_frame = valid, seed = 1234)

# print the total within cluster sum-of-square error for the validation dataset
print(paste0("Total sum-of-square error for valid dataset: ", h2o.tot_withinss(object = seeds_kmeans, valid = T)))


# select the values for `k` to grid over:
# Note: this dataset is too small to see significant differences between these options
# the purpose of the example is to show how to use grid search with `k` if desired
hyper_params <- list( k = c(2,3,7)  )

# this example uses cartesian grid search because the search space is small
# and we want to see the performance of all models. For a larger search space use
# random grid search instead: list(strategy = "RandomDiscrete")
grid <- h2o.grid(x = predictors, training_frame = train, validation_frame = valid,
                 algorithm = "kmeans", grid_id = "seeds_grid", hyper_params = hyper_params,
                 search_criteria = list(strategy = "Cartesian"), seed = 1234)

## Sort the grid models by TotSS
sortedGrid <- h2o.getGrid("seeds_grid", sort_by  = "tot_withinss", decreasing = F)
sortedGrid

import h2o
from h2o.estimators.kmeans import H2OKMeansEstimator
h2o.init()

# import the seeds dataset:
# this dataset looks at three different types of wheat varieties
# the original dataset can be found at http://archive.ics.uci.edu/ml/datasets/seeds
seeds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt")

# set the predictor names
# ignore the 8th column which has the prior known clusters for this dataset
predictors = seeds.columns[0:7]

# split into train and validation sets
train, valid = seeds.split_frame(ratios = [.8], seed = 1234)

# try using the `k` parameter:
# build the model with three clusters
# initialize the estimator then train the model
seeds_kmeans = H2OKMeansEstimator(k = 3, seed = 1234)
seeds_kmeans.train(x = predictors, training_frame = train, validation_frame=valid)

# print the total within cluster sum-of-square error for the validation dataset
print("Total sum-of-square error for valid dataset:",seeds_kmeans.tot_withinss(valid = True))

# grid over `k`
# import Grid Search
from h2o.grid.grid_search import H2OGridSearch

# select the values for `k` to grid over
# Note: this dataset is too small to see significant differences between these options
# the purpose of the example is to show how to use grid search with `k` if desired
hyper_params = {'k': [2,3,7]}

# this example uses cartesian grid search because the search space is small
# and we want to see the performance of all models. For a larger search space use
# random grid search instead: {'strategy': "RandomDiscrete"}
# initialize the estimator
seeds_kmeans = H2OKMeansEstimator(seed = 1234)

# build grid search with previously made Kmeans and hyperparameters
grid = H2OGridSearch(model = seeds_kmeans, hyper_params = hyper_params,
                     search_criteria = {'strategy': "Cartesian"})

# train using the grid
grid.train(x = predictors, training_frame = train, validation_frame = valid)

# sort the grid models by total within cluster sum-of-square error.
sorted_grid = grid.get_grid(sort_by='tot_withinss', decreasing=False)
print(sorted_grid)