`user_points`¶

Available in: K-Means
Hyperparameter: no

Description¶

This option allows you to specify a dataframe, where each row represents an initial cluster center.

Notes:

The user-specified points must have the same number of columns as the training observations.
The number of rows must equal the number of clusters.
init=furthest by default. However, if a user-points file is specified and a value for init is not, then init will automatically change to user.

Example¶

library(h2o)
h2o.init()

# import the iris dataset:
# this dataset is used to classify the type of iris plant
# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Iris
iris <- h2o.importFile("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")

# convert response column to a factor
iris['class'] <- as.factor(iris['class'])

# set the predictor names
predictors <- colnames(iris)[-length(iris)]

# split into train and validation
iris_splits <- h2o.splitFrame(data = iris, ratios = 0.8, seed = 1234)
train <- iris_splits[[1]]
valid <- iris_splits[[2]]

# specify your points
point1 <- c(4.9, 3.0, 1.4, 0.2)
point2 <- c(5.6, 2.5, 3.9, 1.1)
point3 <- c(6.5, 3.0, 5.2, 2.0)

# create an H2OFrame with your points
points <- as.h2o(t(data.frame(point1, point2, point3)))

# take a look at the H2OFrame
print(points)

# try using the `user_points` parameter:
iris_kmeans <- h2o.kmeans(x = predictors, k = 3, user_points =  points, 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 = iris_kmeans, valid = T)))

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

# import the iris dataset:
# this dataset is used to classify the type of iris plant
# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Iris
iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")

# convert response column to a factor
iris['class'] = iris['class'].asfactor()

# set the predictor names and the response column name
predictors = iris.columns[:-1]

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

# specify your points
point1 = [4.9,3.0,1.4,0.2]
point2 = [5.6,2.5,3.9,1.1]
point3 = [6.5,3.0,5.2,2.0]

# create an H2OFrame with your points
points = h2o.H2OFrame([point1, point2, point3])

# take a look at the H2OFrame
print(points)

# try using the `user_points` parameter:
# initialize the estimator then train the model
iris_kmeans = H2OKMeansEstimator(k = 3, user_points = points, seed = 1234)
iris_kmeans.train(x=predictors, training_frame=iris, validation_frame=valid)

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