X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/e6294b5b90135a5163441c8dc62252dd6a188412..07b0dff9bc0dae2ab6f7fb7334007a5269a512ad:/clustering/fit_tsne.py

diff --git a/clustering/fit_tsne.py b/clustering/fit_tsne.py
deleted file mode 100644
index 28b0fd3..0000000
--- a/clustering/fit_tsne.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import fire
-import pyarrow
-import pandas as pd
-from numpy import random
-import numpy as np
-from sklearn.manifold import TSNE
-
-similarities = "term_similarities_10000.feather"
-
-def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=10000, early_exaggeration=20):
-    '''
-    similarities: feather file with a dataframe of similarity scores
-    learning_rate: parameter controlling how fast the model converges. Too low and you get outliers. Too high and you get a ball.
-    perplexity: number of neighbors to use. the default of 50 is often good.
-
-    '''
-    df = pd.read_feather(similarities)
-
-    n = df.shape[0]
-    mat = np.array(df.drop('subreddit',1),dtype=np.float64)
-    mat[range(n),range(n)] = 1
-    mat[mat > 1] = 1
-    dist = 2*np.arccos(mat)/np.pi
-    tsne_model = TSNE(2,learning_rate=750,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':df.subreddit})
-
-    plot_data.to_feather(output)
-
-if __name__ == "__main__":
-    fire.Fire(fit_tsne)