add visualization for 10000 subreddits based on author-tf similarities.

[cdsc_reddit.git] / visualization / tsne_vis.py

diff --git a/visualization/tsne_vis.py b/visualization/tsne_vis.py
index a52d812257f1603b9a86c12dfd2a944858923132..c192d215d1d325f38f8963efe7e09c90eb1716ef 100644 (file)
--- a/visualization/tsne_vis.py
+++ b/visualization/tsne_vis.py
@@ -9,16 +9,38 @@ import fire
 import numpy as np
 
 def base_plot(plot_data):
+
+#    base = base.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
+
+    cluster_dropdown = alt.binding_select(options=[str(c) for c in sorted(set(plot_data.cluster))])
+
+    subreddit_dropdown = alt.binding_select(options=sorted(plot_data.subreddit))
+
+    cluster_click_select = alt.selection_single(on='click,',fields=['cluster'], bind=cluster_dropdown, name=' ')
+    # cluster_select = alt.selection_single(fields=['cluster'], bind=cluster_dropdown, name='cluster')
+    # cluster_select_and = cluster_click_select & cluster_select
+    #
+    #    subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
+    
+    color = alt.condition(cluster_click_select ,
+                          alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')),
+                          alt.value("lightgray"))
+  
+    
     base = alt.Chart(plot_data).mark_text().encode(
         alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
         alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
+        color=color,
         text='subreddit')
 
+    base = base.add_selection(cluster_click_select)
+ 
+
     return base
 
 def zoom_plot(plot_data):
     chart = base_plot(plot_data)
-    chart = chart.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
+
     chart = chart.interactive()
     chart = chart.properties(width=1275,height=1000)
 
@@ -35,7 +57,7 @@ def viewport_plot(plot_data):
         alt.X('x',axis=alt.Axis(grid=False)),
         alt.Y('y',axis=alt.Axis(grid=False)),
     )
-
+   
     viewport = viewport.properties(width=600,height=400)
 
     viewport1 = viewport.add_selection(selector1)
@@ -52,7 +74,7 @@ def viewport_plot(plot_data):
                      alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectory2))
     )
 
-    sr = sr.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
+
     sr = sr.properties(width=1275,height=600)
 
 
@@ -71,15 +93,29 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
     distances = np.empty(shape=(centroids.shape[0],centroids.shape[0]))
 
     groups = tsne_data.groupby('cluster')
-    for centroid in centroids.itertuples():
-        c_dists = groups.apply(lambda r: min(np.sqrt(np.square(centroid.x - r.x) + np.square(centroid.y-r.y))))
-        distances[:,centroid.Index] = c_dists
+    
+    points = np.array(tsne_data.loc[:,['x','y']])
+    centers = np.array(centroids.loc[:,['x','y']])
+
+    # point x centroid
+    point_center_distances = np.linalg.norm((points[:,None,:] - centers[None,:,:]),axis=-1)
+    
+    # distances is cluster x point
+    for gid, group in groups:
+        c_dists = point_center_distances[group.index.values,:].min(axis=0)
+        distances[group.cluster.values[0],] = c_dists        
 
     # nbrs = NearestNeighbors(n_neighbors=n_neighbors).fit(centroids) 
     # distances, indices = nbrs.kneighbors()
 
-    nbrs = NearestNeighbors(n_neighbors=n_neighbors,metric='precomputed').fit(distances) 
-    distances, indices = nbrs.kneighbors()
+    nearest = distances.argpartition(n_neighbors,0)
+    indices = nearest[:n_neighbors,:].T
+    # neighbor_distances = np.copy(distances)
+    # neighbor_distances.sort(0)
+    # neighbor_distances = neighbor_distances[0:n_neighbors,:]
+    
+    # nbrs = NearestNeighbors(n_neighbors=n_neighbors,metric='precomputed').fit(distances) 
+    # distances, indices = nbrs.kneighbors()
 
     color_assignments = np.repeat(-1,len(centroids))