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add cluster selection to visualization

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Nathan TeBlunthuis 2021-01-27 20:08:07 -08:00
parent e6294b5b90
commit dbe4c87f8b
1 changed files with 51 additions and 15 deletions
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64
visualization/tsne_vis.py
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@ -9,16 +9,38 @@ import fire
import numpy as np import numpy as np
def base_plot(plot_data): 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( base = alt.Chart(plot_data).mark_text().encode(
alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))), 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))), alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
color=color,
text='subreddit') text='subreddit')
base = base.add_selection(cluster_click_select)
return base return base
def zoom_plot(plot_data): def zoom_plot(plot_data):
chart = base_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.interactive()
chart = chart.properties(width=1275,height=1000) chart = chart.properties(width=1275,height=1000)
@ -52,7 +74,7 @@ def viewport_plot(plot_data):
alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectory2)) 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) 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])) distances = np.empty(shape=(centroids.shape[0],centroids.shape[0]))
groups = tsne_data.groupby('cluster') 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)))) points = np.array(tsne_data.loc[:,['x','y']])
distances[:,centroid.Index] = c_dists 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) # nbrs = NearestNeighbors(n_neighbors=n_neighbors).fit(centroids)
# distances, indices = nbrs.kneighbors() # distances, indices = nbrs.kneighbors()
nbrs = NearestNeighbors(n_neighbors=n_neighbors,metric='precomputed').fit(distances) nearest = distances.argpartition(n_neighbors,0)
distances, indices = nbrs.kneighbors() 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)) color_assignments = np.repeat(-1,len(centroids))
@ -119,13 +155,13 @@ def build_visualization(tsne_data, clusters, output):
if __name__ == "__main__": if __name__ == "__main__":
fire.Fire(build_visualization) fire.Fire(build_visualization)
# commenter_data = pd.read_feather("tsne_author_fit.feather") commenter_data = pd.read_feather("tsne_author_fit.feather")
# clusters = pd.read_feather('author_3000_clusters.feather') clusters = pd.read_feather('author_3000_clusters.feather')
# commenter_data = assign_cluster_colors(commenter_data,clusters,10,8) commenter_data = assign_cluster_colors(commenter_data,clusters,10,8)
# commenter_zoom_plot = zoom_plot(commenter_data) commenter_zoom_plot = zoom_plot(commenter_data)
# commenter_viewport_plot = viewport_plot(commenter_data) commenter_viewport_plot = viewport_plot(commenter_data)
# commenter_zoom_plot.save("subreddit_commenters_tsne_3000.html") commenter_zoom_plot.save("subreddit_commenters_tsne_3000.html")
# commenter_viewport_plot.save("subreddit_commenters_tsne_3000_viewport.html") commenter_viewport_plot.save("subreddit_commenters_tsne_3000_viewport.html")
# chart = chart.properties(width=10000,height=10000) # chart = chart.properties(width=10000,height=10000)
# chart.save("test_tsne_whole.svg") # chart.save("test_tsne_whole.svg")