add note to try other tf normalization strategies.

[cdsc_reddit.git] / fit_tsne.py

diff --git a/fit_tsne.py b/fit_tsne.py
deleted file mode 100644 (file)
index 06e949a..0000000
--- a/fit_tsne.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import pyarrow
-import pandas as pd
-from numpy import random
-import numpy as np
-from sklearn.manifold import TSNE
-
-df = pd.read_feather("reddit_term_similarity_3000.feather")
-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.ones(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)
-
-mat = mat + np.tril(mat.transpose(),k=-1)
-dist = 2*np.arccos(mat)/np.pi
-
-tsne_model = TSNE(2,learning_rate=500,perplexity=50,n_iter=10000,metric='precomputed',early_exaggeration=20,n_jobs=-1)
-
-tsne_fit_model = tsne_model.fit(dist)
-
-tsne_fit_whole = tsne_fit_model.fit_transform(dist)
-
-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")