better visualization

.gitignore

@@ -11,3 +11,6 @@
 *.swo
 
 embeddings_visualization.html
+
+# Coredumps
+/core

src/bin/visualize_embeddings.rs

@@ -88,11 +88,19 @@ async fn main() -> Result<()> {
         return Ok(());
     };
 
+    // Get affinity dimension (should be number of items)
+    let affinity_dims = if let Some(first_row) = rows.first() {
+        let affinities: Vec<f64> = first_row.get(3);
+        affinities.len()
+    } else {
+        return Ok(());
+    };
+
     // Convert data to ndarray format
     let mut data = Array2::zeros((n_items, n_dims));
     let mut item_ids = Vec::with_capacity(n_items);
     let mut cluster_ids = Vec::with_capacity(n_items);
-    let mut affinity_data = Array2::zeros((n_items, n_dims)); // Changed from n_items to n_dims for affinity dimension
+    let mut affinity_data = Array2::zeros((n_items, affinity_dims)); // Use full affinity dimension
 
     for (i, row) in rows.iter().enumerate() {
         let item_id: i32 = row.get(0);
@@ -103,15 +111,9 @@ async fn main() -> Result<()> {
         item_ids.push(item_id);
         cluster_ids.push(cluster_id);
         data.row_mut(i).assign(&ArrayView1::from(&embedding));
-
-        // Ensure affinity vector has the right length by truncating or padding if necessary
-        let mut affinity_vec = vec![0.0; n_dims];
-        for (j, &val) in affinities.iter().take(n_dims).enumerate() {
-            affinity_vec[j] = val;
-        }
         affinity_data
             .row_mut(i)
-            .assign(&ArrayView1::from(&affinity_vec));
+            .assign(&ArrayView1::from(&affinities));
     }
 
     // Perform PCA on both embeddings and affinity vectors
@@ -160,15 +162,17 @@ async fn main() -> Result<()> {
             .text_array(text)
             .marker(
                 plotly::common::Marker::new()
-                    .size(8)
-                    .symbol(plotly::common::MarkerSymbol::Circle),
+                    .size(10)
+                    .symbol(plotly::common::MarkerSymbol::Circle)
+                    .opacity(0.9)
+                    .line(plotly::common::Line::new().width(1.0).color("white")),
             )
             .show_legend(true);
 
         plot.add_trace(trace);
     }
 
-    // Plot affinity vectors
+    // Plot affinity vectors with distinct appearance
     for cluster_id in &unique_clusters {
         let indices: Vec<_> = cluster_ids
             .iter()
@@ -191,7 +195,7 @@ async fn main() -> Result<()> {
             .collect();
         let text: Vec<_> = indices
             .iter()
-            .map(|&i| format!("Item {}", item_ids[i]))
+            .map(|&i| format!("Item {} (Affinity)", item_ids[i]))
             .collect();
 
         let trace = Scatter3D::new(x, y, z)
@@ -200,8 +204,10 @@ async fn main() -> Result<()> {
             .text_array(text)
             .marker(
                 plotly::common::Marker::new()
-                    .size(8)
-                    .symbol(plotly::common::MarkerSymbol::Square),
+                    .size(7)
+                    .symbol(plotly::common::MarkerSymbol::Diamond)
+                    .opacity(0.7)
+                    .line(plotly::common::Line::new().width(1.0).color("black")),
             )
             .show_legend(true);
 
@@ -210,9 +216,7 @@ async fn main() -> Result<()> {
 
     plot.set_layout(
         Layout::new()
-            .title(Title::new(
-                "Item Embeddings and Affinities Visualization (PCA)",
-            ))
+            .title(Title::new("Item Embeddings (●) vs Affinity Vectors (◆)"))
             .show_legend(true)
             .legend(Legend::new().x(1.0).y(0.5))
             .margin(Margin::new().left(100).right(100).top(100).bottom(100))

src/main.rs

@@ -33,13 +33,21 @@ struct Args {
     #[arg(long, default_value = "10000")]
     batch_size: i32,
 
+    /// Learning rate
+    #[arg(long, default_value = "0.01")]
+    learning_rate: f32,
+
     /// Number of factors for matrix factorization
     #[arg(long, default_value = "8")]
     factors: i32,
 
+    /// Lambda for regularization
+    #[arg(long, default_value = "0.0")]
+    lambda1: f32,
+
     /// Lambda for regularization
     #[arg(long, default_value = "0.1")]
-    lambda: f32,
+    lambda2: f32,
 
     /// Number of threads for matrix factorization (defaults to number of CPU cores)
     #[arg(long, default_value_t = num_cpus::get() as i32)]
@@ -236,9 +244,11 @@ async fn main() -> Result<()> {
     // Set up training parameters
     let model = Model::params()
         .factors(args.factors as i32)
-        .lambda_p2(args.lambda)
-        .lambda_q2(args.lambda)
-        .learning_rate(0.01)
+        .lambda_p1(args.lambda1)
+        .lambda_q1(args.lambda1)
+        .lambda_p2(args.lambda2)
+        .lambda_q2(args.lambda2)
+        .learning_rate(args.learning_rate)
         .iterations(100)
         .loss(Loss::OneClassL2)
         .c(0.00001)