-df = pd.read_csv("reddit_term_similarity_3000.csv")
-df = df.sort_values(['i','j'])
-
-n = max(df.i.max(),df.j.max())
-
-def zero_pad(grp):
- p = grp.shape[0]
- grp = grp.sort_values('j')
- return np.concatenate([np.zeros(n-p),np.zeros(1),np.array(grp.value)])
-
-col_names = df.sort_values('j').loc[:,['subreddit_j']].drop_duplicates()
-first_name = list(set(df.subreddit_i) - set(df.subreddit_j))[0]
-col_names = [first_name] + list(col_names.subreddit_j)
-mat = df.groupby('i').apply(zero_pad)
-mat.loc[n] = np.concatenate([np.zeros(n),np.ones(1)])
-mat = np.stack(mat)
-
-# plot the matrix using the first and second eigenvalues
-mat = mat + np.tril(mat.transpose(),k=-1)
-
-tsne_model = TSNE(2,learning_rate=500,perplexity=40,n_iter=2000)
-tsne_fit_model = tsne_model.fit(mat)
-tsne_fit_whole = tsne_fit_model.fit_transform(mat)
-
-plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':col_names})
-
-plot_data.to_feather("tsne_subreddit_fit.feather")