visualization/tsne_vis.py

   1 import pyarrow
   2 import altair as alt
   3 alt.data_transformers.disable_max_rows()
   4 alt.data_transformers.enable('default')
   5 from sklearn.neighbors import NearestNeighbors
   6 import pandas as pd
   7 from numpy import random
   8 import fire
   9 import numpy as np
  10
  11 def base_plot(plot_data):
  12
  13 #    base = base.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
  14
  15     cluster_dropdown = alt.binding_select(options=[str(c) for c in sorted(set(plot_data.cluster))])
  16
  17     #    subreddit_dropdown = alt.binding_select(options=sorted(plot_data.subreddit))
  18
  19     cluster_click_select = alt.selection_single(on='click',fields=['cluster'], bind=cluster_dropdown, name=' ')
  20     # cluster_select = alt.selection_single(fields=['cluster'], bind=cluster_dropdown, name='cluster')
  21     # cluster_select_and = cluster_click_select & cluster_select
  22     #
  23     #    subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
  24
  25     color = alt.condition(cluster_click_select ,
  26                           alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')),
  27                           alt.value("lightgray"))
  28
  29
  30     base = alt.Chart(plot_data).mark_text().encode(
  31         alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
  32         alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
  33         color=color,
  34         text='subreddit')
  35
  36     base = base.add_selection(cluster_click_select)
  37
  38
  39     return base
  40
  41 def zoom_plot(plot_data):
  42     chart = base_plot(plot_data)
  43
  44     chart = chart.interactive()
  45     chart = chart.properties(width=1275,height=800)
  46
  47     return chart
  48
  49 def viewport_plot(plot_data):
  50     selector1 = alt.selection_interval(encodings=['x','y'],init={'x':(-65,65),'y':(-65,65)})
  51     selectorx2 = alt.selection_interval(encodings=['x'],init={'x':(30,40)})
  52     selectory2 = alt.selection_interval(encodings=['y'],init={'y':(-20,0)})
  53
  54     base = base_plot(plot_data)
  55
  56     viewport = base.mark_point(fillOpacity=0.2,opacity=0.2).encode(
  57         alt.X('x',axis=alt.Axis(grid=False)),
  58         alt.Y('y',axis=alt.Axis(grid=False)),
  59     )
  60
  61     viewport = viewport.properties(width=600,height=400)
  62
  63     viewport1 = viewport.add_selection(selector1)
  64
  65     viewport2 = viewport.encode(
  66         alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1)),
  67         alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1))
  68     )
  69
  70     viewport2 = viewport2.add_selection(selectorx2)
  71     viewport2 = viewport2.add_selection(selectory2)
  72
  73     sr = base.encode(alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectorx2)),
  74                      alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectory2))
  75     )
  76
  77
  78     sr = sr.properties(width=1275,height=600)
  79
  80
  81     chart = (viewport1 | viewport2) & sr
  82
  83
  84     return chart
  85
  86 def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
  87     tsne_data = tsne_data.merge(clusters,on='subreddit')
  88
  89     centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
  90
  91     color_ids = np.arange(n_colors)
  92
  93     distances = np.empty(shape=(centroids.shape[0],centroids.shape[0]))
  94
  95     groups = tsne_data.groupby('cluster')
  96
  97     points = np.array(tsne_data.loc[:,['x','y']])
  98     centers = np.array(centroids.loc[:,['x','y']])
  99
 100     # point x centroid
 101     point_center_distances = np.linalg.norm((points[:,None,:] - centers[None,:,:]),axis=-1)
 102
 103     # distances is cluster x point
 104     for gid, group in groups:
 105         c_dists = point_center_distances[group.index.values,:].min(axis=0)
 106         distances[group.cluster.values[0],] = c_dists
 107
 108     # nbrs = NearestNeighbors(n_neighbors=n_neighbors).fit(centroids)
 109     # distances, indices = nbrs.kneighbors()
 110
 111     nearest = distances.argpartition(n_neighbors,0)
 112     indices = nearest[:n_neighbors,:].T
 113     # neighbor_distances = np.copy(distances)
 114     # neighbor_distances.sort(0)
 115     # neighbor_distances = neighbor_distances[0:n_neighbors,:]
 116
 117     # nbrs = NearestNeighbors(n_neighbors=n_neighbors,metric='precomputed').fit(distances)
 118     # distances, indices = nbrs.kneighbors()
 119
 120     color_assignments = np.repeat(-1,len(centroids))
 121
 122     for i in range(len(centroids)):
 123         knn = indices[i]
 124         knn_colors = color_assignments[knn]
 125         available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
 126
 127         if(len(available_colors) > 0):
 128             color_assignments[i] = available_colors[0]
 129         else:
 130             raise Exception("Can't color this many neighbors with this many colors")
 131
 132
 133     centroids = centroids.reset_index()
 134     colors = centroids.loc[:,['cluster']]
 135     colors['color'] = color_assignments
 136
 137     tsne_data = tsne_data.merge(colors,on='cluster')
 138     return(tsne_data)
 139
 140 def build_visualization(tsne_data, clusters, output):
 141
 142     # tsne_data = "/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather"
 143     # clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"
 144
 145     tsne_data = pd.read_feather(tsne_data)
 146     clusters = pd.read_feather(clusters)
 147
 148     tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)
 149
 150     # sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
 151     # sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
 152
 153     tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')
 154
 155     term_zoom_plot = zoom_plot(tsne_data)
 156
 157     term_zoom_plot.save(output)
 158
 159     term_viewport_plot = viewport_plot(tsne_data)
 160
 161     term_viewport_plot.save(output.replace(".html","_viewport.html"))
 162
 163 if __name__ == "__main__":
 164     fire.Fire(build_visualization)
 165
 166 # commenter_data = pd.read_feather("tsne_author_fit.feather")
 167 # clusters = pd.read_feather('author_3000_clusters.feather')
 168 # commenter_data = assign_cluster_colors(commenter_data,clusters,10,8)
 169 # commenter_zoom_plot = zoom_plot(commenter_data)
 170 # commenter_viewport_plot = viewport_plot(commenter_data)
 171 # commenter_zoom_plot.save("subreddit_commenters_tsne_3000.html")
 172 # commenter_viewport_plot.save("subreddit_commenters_tsne_3000_viewport.html")
 173
 174 # chart = chart.properties(width=10000,height=10000)
 175 # chart.save("test_tsne_whole.svg")