``init`` -------- - Available in: GLRM, K-means - Hyperparameter: yes Description ~~~~~~~~~~~ This option specifies the initialization mode used in K-Means. The options are Random, Furthest, PlusPlus, and User. - **Random**: Choose :math:`K` clusters from the set of :math:`N` observations at random so that each observation has an equal chance of being chosen. - **Furthest** (Default): a. Choose one center :math:`m_{1}` at random. b. Calculate the difference between :math:`m_{1}` and each of the remaining :math:`N-1` observations :math:`x_{i}`. :math:`d(x_{i}, m_{1}) = ||(x_{i}-m_{1})||^2` c. Choose :math:`m_{2}` to be the :math:`x_{i}` that maximizes :math:`d(x_{i}, m_{1})`. d. Repeat until :math:`K` centers have been chosen. - **PlusPlus**: a. Choose one center :math:`m_{1}` at random. b. Calculate the difference between :math:`m_{1}` and each of the remaining :math:`N-1` observations :math:`x_{i}`. :math:`d(x_{i}, m_{1}) = \|(x_{i}-m_{1})\|^2` c. Let :math:`P(i)` be the probability of choosing :math:`x_{i}` as :math:`m_{2}`. Weight :math:`P(i)` by :math:`d(x_{i}, m_{1})` so that those :math:`x_{i}` furthest from :math:`m_{2}` have a higher probability of being selected than those :math:`x_{i}` close to :math:`m_{1}`. d. Choose the next center :math:`m_{2}` by drawing at random according to the weighted probability distribution. e. Repeat until :math:`K` centers have been chosen. - **User** initialization allows you to specify a file (using the ``user_points`` parameter) that includes a vector of initial cluster centers. **Notes**: - The user-specified points dataset must have the same number of columns as the training observations. - This option is ignored when ``estimate_k`` is enabled. In this case, the algorithm is deterministic. - If this option is not specified but a user-points file is specified, then this value will default to ``user``. Related Parameters ~~~~~~~~~~~~~~~~~~ - `estimate_k `__ - `user_points `__ Example ~~~~~~~ .. example-code:: .. code-block:: r 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 `init` parameter: # build the model with three clusters seeds_kmeans <- h2o.kmeans(x = predictors, k = 3, init='Furthest', 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 `init` 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 `init` if desired hyper_params <- list( init = c("PlusPlus", "Furthest", "Random") ) # 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, k = 3, 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 .. code-block:: python 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 `init` parameter: # initialize the estimator then train the model seeds_kmeans = H2OKMeansEstimator(k = 3, init='Furthest', 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("sum-of-square error for valid:",seeds_kmeans.tot_withinss(valid = True)) # grid over `init` # import Grid Search from h2o.grid.grid_search import H2OGridSearch # select the values for `init` 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 `init` if desired hyper_params = {'init': ["PlusPlus", "Furthest", "Random"]} # 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(k = 3, 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)