35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import fire
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import pyarrow
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import pandas as pd
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from numpy import random
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import numpy as np
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from sklearn.manifold import TSNE
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similarities = "term_similarities_10000.feather"
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def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=10000, early_exaggeration=20):
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'''
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similarities: feather file with a dataframe of similarity scores
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learning_rate: parameter controlling how fast the model converges. Too low and you get outliers. Too high and you get a ball.
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perplexity: number of neighbors to use. the default of 50 is often good.
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'''
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df = pd.read_feather(similarities)
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n = df.shape[0]
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mat = np.array(df.drop('subreddit',1),dtype=np.float64)
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mat[range(n),range(n)] = 1
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mat[mat > 1] = 1
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dist = 2*np.arccos(mat)/np.pi
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tsne_model = TSNE(2,learning_rate=750,perplexity=50,n_iter=10000,metric='precomputed',early_exaggeration=20,n_jobs=-1)
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tsne_fit_model = tsne_model.fit(dist)
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tsne_fit_whole = tsne_fit_model.fit_transform(dist)
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plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':df.subreddit})
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plot_data.to_feather(output)
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if __name__ == "__main__":
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fire.Fire(fit_tsne)
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