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## ---- eval = FALSE------------------------------------------------------------
# library(devtools)
# devtools::install_github("h2oai/h2o4gpu", subdir = "src/interface_r")
## ---- eval = FALSE------------------------------------------------------------
# library(reticulate)
# use_virtualenv("/home/username/venv/h2o4gpu") # set this to the path of your venv
## ---- eval = FALSE------------------------------------------------------------
# library(h2o4gpu)
# library(reticulate) # only needed if using a virtual Python environment
# use_virtualenv("/home/username/venv/h2o4gpu") # set this to the path of your venv
#
# # Prepare data
# x <- iris[1:4]
# y <- as.integer(iris$Species) # all columns, including the response, must be numeric
#
# # Initialize and train the classifier
# model <- h2o4gpu.random_forest_classifier() %>% fit(x, y)
#
# # Make predictions
# pred <- model %>% predict(x)
#
# # Compute classification error using the Metrics package (note this is training error)
# library(Metrics)
# ce(actual = y, predicted = pred)
## ---- eval = FALSE------------------------------------------------------------
# # Load a sample dataset for binary classification
# # Source: https://archive.ics.uci.edu/ml/datasets/HIGGS
# train <- read.csv("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
# test <- read.csv("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
#
# # Create train & test sets (column 1 is the response)
# x_train <- train[, -1]
# y_train <- train[, 1]
# x_test <- test[, -1]
# y_test <- test[, 1]
## ---- eval = FALSE------------------------------------------------------------
# # Train three different binary classification models
# model_gbc <- h2o4gpu.gradient_boosting_classifier() %>% fit(x_train, y_train)
# model_rfc <- h2o4gpu.random_forest_classifier() %>% fit(x_train, y_train)
# model_enc <- h2o4gpu.elastic_net_classifier() %>% fit(x_train, y_train)
## ---- eval = FALSE------------------------------------------------------------
# # Generate predictions (type "prob" gives predicted values instead of predicted label)
# pred_gbc <- model_gbc %>% predict(x_test, type = "prob")
# pred_rfc <- model_rfc %>% predict(x_test, type = "prob")
# pred_enc <- model_enc %>% predict(x_test, type = "prob")
## ---- eval = FALSE------------------------------------------------------------
# head(pred_rfc)
## ---- eval = FALSE------------------------------------------------------------
# # Compare test set performance using AUC
# auc(actual = y_test, predicted = pred_gbc[, 2])
# auc(actual = y_test, predicted = pred_rfc[, 2])
# auc(actual = y_test, predicted = pred_enc[, 2])
## ---- eval = FALSE------------------------------------------------------------
# # Load a sample dataset for regression
# # Source: https://archive.ics.uci.edu/ml/datasets/Abalone
# df <- read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data", header = FALSE)
# str(df)
## ---- eval = FALSE------------------------------------------------------------
# df[, 1] <- as.integer(df[, 1]) #label encode the one factor column
## ---- eval = FALSE------------------------------------------------------------
# # Randomly sample 80% of the rows for the training set
# set.seed(1)
# train_idx <- sample(1:nrow(df), 0.8*nrow(df))
#
# # Create train & test sets (column 9 is the response)
# x_train <- df[train_idx, -9]
# y_train <- df[train_idx, 9]
# x_test <- df[-train_idx, -9]
# y_test <- df[-train_idx, 9]
## ---- eval = FALSE------------------------------------------------------------
# # Train two different regression models
# model_gbr <- h2o4gpu.gradient_boosting_regressor() %>% fit(x_train, y_train)
# model_enr <- h2o4gpu.elastic_net_regressor() %>% fit(x_train, y_train)
#
# # Generate predictions
# pred_gbr <- model_gbr %>% predict(x_test)
# pred_enr <- model_enr %>% predict(x_test)
## ---- eval = FALSE------------------------------------------------------------
# head(pred_gbr)
## ---- eval = FALSE------------------------------------------------------------
# # Compare test set performance using MSE
# mse(actual = y_test, predicted = pred_gbr)
# mse(actual = y_test, predicted = pred_enr)
## ---- eval = FALSE------------------------------------------------------------
# # Prepare data
# iris$Species <- as.integer(iris$Species) # convert to numeric data
#
# # Randomly sample 80% of the rows for the training set
# set.seed(1)
# train_idx <- sample(1:nrow(iris), 0.8*nrow(iris))
# train <- iris[train_idx, ]
# test <- iris[-train_idx, ]
## ---- eval = FALSE------------------------------------------------------------
# model_km <- h2o4gpu.kmeans(n_clusters = 3L) %>% fit(train)
## ---- eval = FALSE------------------------------------------------------------
# test_dist <- model_km %>% transform(test)
# head(test_dist)
## ---- eval = FALSE------------------------------------------------------------
# # Load a sample dataset for binary classification
# # Source: https://archive.ics.uci.edu/ml/datasets/HIGGS
# train <- read.csv("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
# test <- read.csv("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
## ---- eval = FALSE------------------------------------------------------------
# model_pca <- h2o4gpu.pca(n_components = 4) %>% fit(train)
# test_transformed <- model_pca %>% transform(test)
## ---- eval = FALSE------------------------------------------------------------
# model_tsvd <- h2o4gpu.truncated_svd(n_components = 4) %>% fit(train)
# test_transformed <- model_tsvd %>% transform(test)
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