user_points.rst

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.

Related Parameters

• init

Example

.. tabs::
   .. code-tab:: r R

                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)))


   .. code-tab:: python

                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))