X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/db5879d6c92a826c65b86a68675c503b20914cf8..55b75ea6fcf421e95f4fe6b180dcec6e64676619:/visualization/tsne_vis.py?ds=inline diff --git a/visualization/tsne_vis.py b/visualization/tsne_vis.py index a52d812..eb6a6be 100644 --- a/visualization/tsne_vis.py +++ b/visualization/tsne_vis.py @@ -9,18 +9,44 @@ 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') + + 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 @@ -35,7 +61,7 @@ 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 +78,7 @@ 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) @@ -62,6 +88,11 @@ def viewport_plot(plot_data): 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}) @@ -71,28 +102,44 @@ 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)) 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']] @@ -103,11 +150,20 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4): 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)