X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/f8ff8b2d0f634d4671de090b3c1ceba12be958d6..1bf206d219a38ffb0290dde83cd061d4a848f1c7:/fit_tsne.py

diff --git a/fit_tsne.py b/fit_tsne.py
index 7de2ac0..28b0fd3 100644
--- a/fit_tsne.py
+++ b/fit_tsne.py
@@ -1,35 +1,34 @@
+import fire
 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'])
+similarities = "term_similarities_10000.feather"
 
-n = max(df.i.max(),df.j.max())
+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.
 
-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)])
+    '''
+    df = pd.read_feather(similarities)
 
-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)
+    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)
 
-mat = mat + np.tril(mat.transpose(),k=-1)
-dist = 2*np.arccos(mat)/np.pi
+    tsne_fit_whole = tsne_fit_model.fit_transform(dist)
 
-tsne_model = TSNE(2,learning_rate=750,perplexity=50,n_iter=10000,metric='precomputed',early_exaggeration=20,n_jobs=-1)
+    plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':df.subreddit})
 
-tsne_fit_model = tsne_model.fit(dist)
+    plot_data.to_feather(output)
 
-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")
+if __name__ == "__main__":
+    fire.Fire(fit_tsne)