From: Nathan TeBlunthuis Date: Thu, 28 Jan 2021 04:08:07 +0000 (-0800) Subject: add cluster selection to visualization X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/dbe4c87f8b70e348705319bdf3d0a10b64d763b5 add cluster selection to visualization --- diff --git a/visualization/tsne_vis.py b/visualization/tsne_vis.py index a52d812..4b42e8e 100644 --- a/visualization/tsne_vis.py +++ b/visualization/tsne_vis.py @@ -9,16 +9,38 @@ 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.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10'))) + chart = chart.interactive() chart = chart.properties(width=1275,height=1000) @@ -35,7 +57,7 @@ def viewport_plot(plot_data): 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) @@ -52,7 +74,7 @@ def viewport_plot(plot_data): 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) @@ -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])) 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)) @@ -119,13 +155,13 @@ def build_visualization(tsne_data, clusters, output): 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") +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")