Refactor and reorganze.

[cdsc_reddit.git] / clustering / fit_tsne.py

diff --git a/clustering/fit_tsne.py b/clustering/fit_tsne.py
new file mode 100644 (file)
index 0000000..28b0fd3
--- /dev/null
+++ b/clustering/fit_tsne.py
@@ -0,0 +1,34 @@
+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)