64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
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import pyarrow
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import altair as alt
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alt.data_transformers.disable_max_rows()
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alt.data_transformers.enable('data_server')
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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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df = pd.read_csv("reddit_term_similarity_3000.csv")
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df = df.sort_values(['i','j'])
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n = max(df.i.max(),df.j.max())
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def zero_pad(grp):
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p = grp.shape[0]
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grp = grp.sort_values('j')
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return np.concatenate([np.zeros(n-p),np.zeros(1),np.array(grp.value)])
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col_names = df.sort_values('j').loc[:,['subreddit_j']].drop_duplicates()
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first_name = list(set(df.subreddit_i) - set(df.subreddit_j))[0]
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col_names = [first_name] + list(col_names.subreddit_j)
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mat = df.groupby('i').apply(zero_pad)
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mat.loc[n] = np.concatenate([np.zeros(n),np.ones(1)])
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mat = np.stack(mat)
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# plot the matrix using the first and second eigenvalues
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mat = mat + np.tril(mat.transpose(),k=-1)
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tsne_model = TSNE(2,learning_rate=500,perplexity=40,n_iter=2000)
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tsne_fit_model = tsne_model.fit(mat)
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tsne_fit_whole = tsne_fit_model.fit_transform(mat)
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plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':col_names})
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plot_data.to_feather("tsne_subreddit_fit.feather")
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slider = alt.binding_range(min=1,max=100,step=1,name='zoom: ')
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selector = alt.selection_single(name='zoomselect',fields=['zoom'],bind='scales',init={'zoom':1})
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xrange = plot_data.x.max()-plot_data.x.min()
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yrange = plot_data.y.max()-plot_data.y.min()
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chart = alt.Chart(plot_data).mark_text().encode(
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alt.X('x',axis=alt.Axis(grid=False)),
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alt.Y('y',axis=alt.Axis(grid=False)),
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text='subreddit')
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#chart = chart.add_selection(selector)
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chart = chart.configure_view(
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continuousHeight=xrange/20,
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continuousWidth=yrange/20
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)
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amount_shown = lambda zoom: {'width':xrange/zoom,'height':yrange/zoom}
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alt.data_transformers.enable('default')
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chart = chart.properties(width=1000,height=1000)
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chart = chart.interactive()
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chart.save("test_tsne_whole.html")
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chart = chart.properties(width=10000,height=10000)
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chart.save("test_tsne_whole.svg")
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