X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/2cc897543a7c1ff9dee0594385d3b72b275105ee..07b0dff9bc0dae2ab6f7fb7334007a5269a512ad:/visualization/tsne_vis.py

diff --git a/visualization/tsne_vis.py b/visualization/tsne_vis.py
deleted file mode 100644
index 915cd7e..0000000
--- a/visualization/tsne_vis.py
+++ /dev/null
@@ -1,125 +0,0 @@
-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 numpy as np
-
-def base_plot(plot_data):
-    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))),
-        text='subreddit')
-
-    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)
-
-    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.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
-    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')
-    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
-
-    # 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()
-
-    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)
-
-term_data = pd.read_feather("tsne_subreddit_fit.feather")
-clusters = pd.read_feather("term_3000_clusters.feather")
-
-tsne_data = assign_cluster_colors(term_data,clusters,10,8)
-
-term_zoom_plot = zoom_plot(tsne_data)
-
-term_zoom_plot.save("subreddit_terms_tsne_3000.html")
-
-term_viewport_plot = viewport_plot(tsne_data)
-
-term_viewport_plot.save("subreddit_terms_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")