-import pyarrow
-import altair as alt
-alt.data_transformers.disable_max_rows()
-alt.data_transformers.enable('default')
-from sklearn.neighbors import NearestNeighbors
-import pandas as pd
-from numpy import random
-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.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")
projects / cdsc_reddit.git / blobdiff