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')
+
+ base_scale = alt.Scale(scheme={"name":'category10',
+ "extent":[0,100],
+ "count":10})
+
+ color = alt.condition(cluster_click_select ,
+ alt.Color(field='color',type='nominal',scale=base_scale),
+ 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)
+ chart = chart.properties(width=1275,height=800)
return chart
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)
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)
return chart
def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
+ isolate_color = 101
+
+ cluster_sizes = clusters.groupby('cluster').count()
+ singletons = set(cluster_sizes.loc[cluster_sizes.subreddit == 1].reset_index().cluster)
+
tsne_data = tsne_data.merge(clusters,on='subreddit')
centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
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))
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]
+ if (centroids.iloc[i].name == -1) or (i in singletons):
+ color_assignments[i] = isolate_color
else:
- raise Exception("Can't color this many neighbors with this many colors")
+ 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']]
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)
+ tsne_data = tsne_data.rename(columns={'_subreddit':'subreddit'})
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)
projects / cdsc_reddit.git / blobdiff