k

• Available in: K-Means, PCA, GLRM

• 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, k specifies the rank of matrix approximation. This can be a value from 1 to 9 and defaults to 1.

Related Parameters

K-Means

• estimate_k

Example

R

Python

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 = 0.8, 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
sorted_grid <- h2o.getGrid("seeds_grid", sort_by  = "tot_withinss", decreasing = F)
sorted_grid

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)