Adds more high-dim viz.

slides/08-viz.qmd

@@ -158,9 +158,14 @@ Provides a way to explore the data or link between different attributes
 :::
 
 :::{.fragment}
-<iframe src="./images/interactive.html" width="100%" height=500 frameborder="0"></iframe>
+<iframe src="./images/interactive.html" width="100%" height=1000 frameborder="0"></iframe>
 :::
 
+# Using interactions
+
+<iframe src="./images/sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_space-RASMM_model-CSD_desc-prob-afq_viz.html" width="100%" height=500 frameborder="0"></iframe>
+
+
 # Dimensionality reduction approaches
 
 ::: {fragment}
@@ -173,5 +178,24 @@ Provides a way to explore the data or link between different attributes
   - UMAP
   - ...
 
+# Demo
+
+# Where does t-sne (and similar) shine?
+
+![](./images/mnist.webp)
+
+# Where does t-sne (and similar) shine?
+![](./images/tsne_nist.png){height=500}
+
+# Where does t-sne (and similar) shine?
+
+![](./images/tsne_fashon_mnist.png){height=500}
+
+
+# Combining dimensionality reduction and animation
+
+![](./images/hypertools.gif){height=400}
+
+[https://hypertools.readthedocs.io/en/latest/](https://hypertools.readthedocs.io/en/latest/)
 
 

slides/code/viz_demo.r

@@ -74,7 +74,7 @@ library(plotly)
 library(stats)
 data(iris)
 
-data <- subset(iris, select = -c(Species))
+X <- subset(iris, select = -c(Species))
 axis <- list(showline = FALSE,
             zeroline = FALSE,
             gridcolor = "#ffff",
@@ -122,14 +122,14 @@ fig <- fig %>%
   )
 fig
 
-pca <- prcomp(data)
+pca <- prcomp(X)
 pca <- data.frame(PC1 = pca$x[, 1],
                   PC2 = pca$x[, 2],
                   Species = iris$Species)
 
 fig <-  plot_ly(data = pca,
-                x = PC1,
-                y = PC2,
+                x = ~PC1,
+                y = ~PC2,
                 type = "scatter",
                 mode = "markers",
                 split = ~iris$Species)
@@ -161,4 +161,27 @@ fig <- fig %>%
     plot_bgcolor = "#e5ecf6"
   )
 
-fig
+fig
+
+
+if (!require(umap)) {
+    install.packages("umap")
+}
+
+library(umap)
+features <- subset(iris, select = -c(Species))
+
+set.seed(0)
+umap <- umap(features, initial_dims = 2)
+pdb <- data.frame(umap$layout, iris$Species)
+
+options(warn = -1)
+fig <-  plot_ly(data = pdb, x =  ~X1, y = ~X2,
+                type = "scatter", mode = "markers", split = ~iris$Species)
+
+fig <- fig %>%
+  layout(
+    plot_bgcolor = "#e5ecf6"
+  )
+
+fig