version of weekly_cosine_similarities.py from klone

[cdsc_reddit.git] / visualization / tsne_vis.py

diff --git a/visualization/tsne_vis.py b/visualization/tsne_vis.py
index 1e2aeaef3f810277cbea532659ae5d58f8cfe275..c39a7400e5e5c2ab726eb0a692e4180536d72ce5 100644 (file)
--- a/visualization/tsne_vis.py
+++ b/visualization/tsne_vis.py
@@ -1,37 +1,175 @@
 import pyarrow
 import altair as alt
 alt.data_transformers.disable_max_rows()
 import pyarrow
 import altair as alt
 alt.data_transformers.disable_max_rows()

-alt.data_transformers.enable('data_server')
+alt.data_transformers.enable('default')
+from sklearn.neighbors import NearestNeighbors

 import pandas as pd
 from numpy import random
 import pandas as pd
 from numpy import random

+import fire

 import numpy as np
 import numpy as np

-from sklearn.manifold import TSNE

 
 

-pd.read_feather("tsne_subreddit_fit.feather")
+def base_plot(plot_data):

 
 

-slider = alt.binding_range(min=1,max=100,step=1,name='zoom: ')
-selector = alt.selection_single(name='zoomselect',fields=['zoom'],bind='scales',init={'zoom':1})
+#    base = base.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))

 
 

-xrange = plot_data.x.max()-plot_data.x.min()
-yrange = plot_data.y.max()-plot_data.y.min()
+    cluster_dropdown = alt.binding_select(options=[str(c) for c in sorted(set(plot_data.cluster))])

 
 

-chart = alt.Chart(plot_data).mark_text().encode(
-    alt.X('x',axis=alt.Axis(grid=False)),
-    alt.Y('y',axis=alt.Axis(grid=False)),
-    text='subreddit')
+    #    subreddit_dropdown = alt.binding_select(options=sorted(plot_data.subreddit))

 
 

-#chart = chart.add_selection(selector)
+    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')

 
 

-chart = chart.configure_view(
-    continuousHeight=xrange/20,
-    continuousWidth=yrange/20
-)
+    base = base.add_selection(cluster_click_select)
+ 

 
 

-amount_shown = lambda zoom: {'width':xrange/zoom,'height':yrange/zoom}
+    return base

 
 

-alt.data_transformers.enable('default')
-chart = chart.properties(width=1000,height=1000)
-chart = chart.interactive()
-chart.save("test_tsne_whole.html")
-chart = chart.properties(width=10000,height=10000)
-chart.save("test_tsne_whole.svg")
+def zoom_plot(plot_data):
+    chart = base_plot(plot_data)
+
+    chart = chart.interactive()
+    chart = chart.properties(width=1275,height=800)
+
+    return chart
+
+def viewport_plot(plot_data):
+    selector1 = alt.selection_interval(encodings=['x','y'],init={'x':(-65,65),'y':(-65,65)})
+    selectorx2 = alt.selection_interval(encodings=['x'],init={'x':(30,40)})
+    selectory2 = alt.selection_interval(encodings=['y'],init={'y':(-20,0)})
+
+    base = base_plot(plot_data)
+
+    viewport = base.mark_point(fillOpacity=0.2,opacity=0.2).encode(
+        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)
+
+    viewport2 = viewport.encode(
+        alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1)),
+        alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1))
+    )
+
+    viewport2 = viewport2.add_selection(selectorx2)
+    viewport2 = viewport2.add_selection(selectory2)
+
+    sr = base.encode(alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectorx2)),
+                     alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectory2))
+    )
+
+
+    sr = sr.properties(width=1275,height=600)
+
+
+    chart = (viewport1 | viewport2) & sr
+
+
+    return chart
+
+def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
+    tsne_data = tsne_data.merge(clusters,on='subreddit')
+    
+    centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
+
+    color_ids = np.arange(n_colors)
+
+    distances = np.empty(shape=(centroids.shape[0],centroids.shape[0]))
+
+    groups = tsne_data.groupby('cluster')
+    
+    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()
+
+    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))
+
+    for i in range(len(centroids)):
+        knn = indices[i]
+        knn_colors = color_assignments[knn]
+        available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
+
+        if(len(available_colors) > 0):
+            color_assignments[i] = available_colors[0]
+        else:
+            raise Exception("Can't color this many neighbors with this many colors")
+
+
+    centroids = centroids.reset_index()
+    colors = centroids.loc[:,['cluster']]
+    colors['color'] = color_assignments
+
+    tsne_data = tsne_data.merge(colors,on='cluster')
+    return(tsne_data)
+
+def build_visualization(tsne_data, clusters, output):
+
+    # tsne_data = "/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather"
+    # clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"
+
+    tsne_data = pd.read_feather(tsne_data)
+    clusters = pd.read_feather(clusters)
+
+    tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)
+
+    # sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
+    # sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
+
+    tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')
+
+    term_zoom_plot = zoom_plot(tsne_data)
+
+    term_zoom_plot.save(output)
+
+    term_viewport_plot = viewport_plot(tsne_data)
+
+    term_viewport_plot.save(output.replace(".html","_viewport.html"))
+
+if __name__ == "__main__":
+    fire.Fire(build_visualization)
+
+# commenter_data = pd.read_feather("tsne_author_fit.feather")
+# clusters = pd.read_feather('author_3000_clusters.feather')
+# commenter_data = assign_cluster_colors(commenter_data,clusters,10,8)
+# commenter_zoom_plot = zoom_plot(commenter_data)
+# commenter_viewport_plot = viewport_plot(commenter_data)
+# commenter_zoom_plot.save("subreddit_commenters_tsne_3000.html")
+# commenter_viewport_plot.save("subreddit_commenters_tsne_3000_viewport.html")
+
+# chart = chart.properties(width=10000,height=10000)
+# chart.save("test_tsne_whole.svg")