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| # R tSNE with 3D plots | ||
| # https://www.appsilon.com/post/r-tsne | ||
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| library(dplyr) | ||
| library(Rtsne) | ||
| library(ggplot2) | ||
| library(plotly) | ||
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| # https://github.com/pjreddie/mnist-csv-png?tab=readme-ov-file | ||
| digits <- read.csv("mnist_train.csv", header = FALSE) | ||
| colnames(digits) <- c("digit", paste0("pixel", 1:784)) | ||
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| # print first row as a matrix | ||
| first_digit <- matrix(digits[1, ] |> | ||
| select(-digit) |> unlist(), nrow = 28, ncol = 28, byrow = TRUE) | ||
| first_digit | ||
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| # visualize the digit | ||
| rotate <- function(x) t(apply(x, 2, rev)) | ||
| image(rotate(first_digit), col = (gray(255:0 / 255))) | ||
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| # Or, if you want to get a glimpse into a larger portion of the dataset, run the following snippet: | ||
| par(mfrow = c(5, 5)) | ||
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| for (i in sample(1:nrow(digits), 25)) { | ||
| digit_matrix <- matrix(digits[i, ] |> | ||
| select(-digit) |> | ||
| unlist(), nrow = 28, ncol = 28, byrow = TRUE) | ||
| image(rotate(digit_matrix), col = gray(255:0 / 255), axes = FALSE, xlab = "", ylab = "") | ||
| } | ||
| par(mfrow = c(1, 1)) | ||
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| # That’s too much to include in a single chart, so we’ll reduce the per-class sample size to 100: | ||
| data_sample <- digits |> | ||
| group_by(digit) |> | ||
| sample_n(100) |> | ||
| ungroup() | ||
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| data_sample |> | ||
| group_by(digit) |> | ||
| count() | ||
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| # let’s also make a feature/target split: | ||
| X <- data_sample |> select(-digit) | ||
| y <- data_sample |> select(digit) | ||
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| # Run tSNE | ||
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| tsne_results <- Rtsne(X, dims = 2, perplexity = 25, verbose = TRUE, max_iter = 1500) | ||
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| tsne_df <- data.frame( | ||
| X = tsne_results$Y[, 1], | ||
| Y = tsne_results$Y[, 2], | ||
| digit = y | ||
| ) | ||
| colors <- c("#E6194B", "#3CB44B", "#FFE119", "#4363D8", "#F58231", "#911EB4", "#46F0F0", "#F032E6", "#BCF60C", "#FABEBE") | ||
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| ggplot(tsne_df, aes(x = X, y = Y, color = factor(digit))) + | ||
| geom_point(size = 1.5) + | ||
| scale_color_manual(values = colors) + | ||
| labs( | ||
| title = "t-SNE 2-Dimensional Digit Visualization", | ||
| x = "t-SNE Dimension 1", | ||
| y = "t-SNE Dimension 2" | ||
| ) + | ||
| theme_minimal() + | ||
| theme( | ||
| plot.title = element_text(size = 20) | ||
| ) | ||
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| # Plotting t-SNE Results in 3 Dimensions | ||
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| tsne_results <- Rtsne(features, dims = 3, perplexity = 30, verbose = TRUE, max_iter = 1500) | ||
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| tsne_df <- data.frame( | ||
| X = tsne_results$Y[, 1], | ||
| Y = tsne_results$Y[, 2], | ||
| Z = tsne_results$Y[, 3], | ||
| digit = factor(labels) | ||
| ) | ||
| head(tsne_df) | ||
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| colors <- c("#E6194B", "#3CB44B", "#FFE119", "#4363D8", "#F58231", "#911EB4", "#46F0F0", "#F032E6", "#BCF60C", "#FABEBE") | ||
| hover_text <- paste( | ||
| "Digit:", tsne_df$digit, "", | ||
| "Dimension 1:", round(tsne_df$X, 3), | ||
| "Dimension 2:", round(tsne_df$Y, 3), | ||
| "Dimension 3:", round(tsne_df$Z, 3) | ||
| ) | ||
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| plot_ly( | ||
| data = tsne_df, | ||
| x = ~X, | ||
| y = ~Y, | ||
| z = ~Z, | ||
| type = "scatter3d", | ||
| mode = "markers", | ||
| marker = list(size = 6), | ||
| text = hover_text, | ||
| hoverinfo = "text", | ||
| color = ~digit, | ||
| colors = colors | ||
| ) |> | ||
| layout( | ||
| title = "t-SNE 3-Dimensional Digit Visualization", | ||
| scene = list( | ||
| xaxis = list(title = "t-SNE Dimension 1"), | ||
| yaxis = list(title = "t-SNE Dimension 2"), | ||
| zaxis = list(title = "t-SNE Dimension 3") | ||
| ) | ||
| ) | ||
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| # Perplexity Tuning | ||
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| # Unlike PCA, the results of t-SNE will often vary (sometimes significantly) because of the tweakable parameters and the nature of gradient descent. | ||
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| # This section demonstrates how to tweak the most important parameter - perplexity - and shows you just how different the results are. | ||
| # The values for this parameter typically range from 5 to 50, so we’ll go over this entire range with the step size of 5. | ||
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| library(gganimate) | ||
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| perplexity_values <- c(5, 10, 15, 20, 25, 30, 35, 40, 45, 50) | ||
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| tsne_results_list <- lapply(perplexity_values, function(perp) { | ||
| tsne <- Rtsne(X, dims = 2, perplexity = perp, verbose = TRUE, max_iter = 1500) | ||
| data.frame( | ||
| X = tsne$Y[, 1], | ||
| Y = tsne$Y[, 2], | ||
| digit = y, | ||
| perplexity = perp | ||
| ) | ||
| }) | ||
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| tsne_df <- do.call(rbind, tsne_results_list) | ||
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| plot <- ggplot(tsne_df, aes(x = X, y = Y, color = factor(digit))) + | ||
| geom_point(size = 1.5) + | ||
| scale_color_manual(values = colors) + | ||
| labs( | ||
| title = "t-SNE 2-Dimensional Digit Visualization", | ||
| subtitle = "Perplexity: {closest_state}", # This will display the perplexity value | ||
| x = "t-SNE Dimension 1", | ||
| y = "t-SNE Dimension 2" | ||
| ) + | ||
| theme_minimal() + | ||
| theme( | ||
| plot.title = element_text(size = 20), | ||
| plot.subtitle = element_text(size = 16) | ||
| ) + | ||
| transition_states(perplexity, transition_length = 2, state_length = 1) + | ||
| ease_aes("linear") | ||
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| animate( | ||
| plot, | ||
| width = 800, | ||
| height = 600, | ||
| res = 100, | ||
| nframes = 300, | ||
| fps = 30, | ||
| renderer = gifski_renderer(file = "tsne-2d-animated.gif") | ||
| ) | ||
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