wehub-resource-sync
50 lines
1.7 KiB
R
50 lines
1.7 KiB
R
# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
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# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
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# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
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library(mlflow)
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library(glmnet)
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library(carrier)
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set.seed(40)
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# Read the wine-quality csv file
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data <- read.csv("wine-quality.csv")
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# Split the data into training and test sets. (0.75, 0.25) split.
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sampled <- sample(1:nrow(data), 0.75 * nrow(data))
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train <- data[sampled, ]
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test <- data[-sampled, ]
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# The predicted column is "quality" which is a scalar from [3, 9]
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train_x <- as.matrix(train[, !(names(train) == "quality")])
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test_x <- as.matrix(test[, !(names(train) == "quality")])
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train_y <- train[, "quality"]
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test_y <- test[, "quality"]
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alpha <- mlflow_param("alpha", 0.5, "numeric")
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lambda <- mlflow_param("lambda", 0.5, "numeric")
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with(mlflow_start_run(), {
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model <- glmnet(train_x, train_y, alpha = alpha, lambda = lambda, family= "gaussian", standardize = FALSE)
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predictor <- crate(~ glmnet::predict.glmnet(model, as.matrix(.x)), model = model)
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predicted <- predictor(test_x)
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rmse <- sqrt(mean((predicted - test_y) ^ 2))
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mae <- mean(abs(predicted - test_y))
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r2 <- as.numeric(cor(predicted, test_y) ^ 2)
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message("Elasticnet model (alpha=", alpha, ", lambda=", lambda, "):")
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message(" RMSE: ", rmse)
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message(" MAE: ", mae)
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message(" R2: ", r2)
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mlflow_log_param("alpha", alpha)
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mlflow_log_param("lambda", lambda)
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mlflow_log_metric("rmse", rmse)
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mlflow_log_metric("r2", r2)
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mlflow_log_metric("mae", mae)
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mlflow_log_model(predictor, "model")
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})
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