X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/13eb95b3b06bd51324e0d05c73a44b5e8e830295..554660275fe525733918aa0e25d0c4ea86dc5a41:/visualization/tsne_vis.py?ds=sidebyside diff --git a/visualization/tsne_vis.py b/visualization/tsne_vis.py index 915cd7e..c192d21 100644 --- a/visualization/tsne_vis.py +++ b/visualization/tsne_vis.py @@ -5,19 +5,42 @@ 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.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10'))) + chart = chart.interactive() chart = chart.properties(width=1275,height=1000) @@ -34,7 +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) @@ -51,7 +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) @@ -70,15 +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)) @@ -100,26 +137,31 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4): tsne_data = tsne_data.merge(colors,on='cluster') return(tsne_data) -term_data = pd.read_feather("tsne_subreddit_fit.feather") -clusters = pd.read_feather("term_3000_clusters.feather") +def build_visualization(tsne_data, clusters, output): + + tsne_data = pd.read_feather(tsne_data) + clusters = pd.read_feather(clusters) + + tsne_data = assign_cluster_colors(tsne_data,clusters,10,8) -tsne_data = assign_cluster_colors(term_data,clusters,10,8) + term_zoom_plot = zoom_plot(tsne_data) -term_zoom_plot = zoom_plot(tsne_data) + term_zoom_plot.save(output) -term_zoom_plot.save("subreddit_terms_tsne_3000.html") + term_viewport_plot = viewport_plot(tsne_data) -term_viewport_plot = viewport_plot(tsne_data) + term_viewport_plot.save(output.replace(".html","_viewport.html")) -term_viewport_plot.save("subreddit_terms_tsne_3000_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") +# 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")