97 lines
4.6 KiB
Python
97 lines
4.6 KiB
Python
from pyarrow import dataset as ds
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import numpy as np
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import pandas as pd
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import plotnine as pn
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random = np.random.RandomState(1968)
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def load_densities(term_density_file="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
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author_density_file="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather"):
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term_density = pd.read_feather(term_density_file)
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author_density = pd.read_feather(author_density_file)
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term_density.rename({'overlap_density':'term_density','index':'subreddit'},axis='columns',inplace=True)
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author_density.rename({'overlap_density':'author_density','index':'subreddit'},axis='columns',inplace=True)
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density = term_density.merge(author_density,on='subreddit',how='inner')
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return density
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def load_clusters(term_clusters_file="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
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author_clusters_file="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather"):
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term_clusters = pd.read_feather(term_clusters_file)
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author_clusters = pd.read_feather(author_clusters_file)
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# rename, join and return
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term_clusters.rename({'cluster':'term_cluster'},axis='columns',inplace=True)
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author_clusters.rename({'cluster':'author_cluster'},axis='columns',inplace=True)
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clusters = term_clusters.merge(author_clusters,on='subreddit',how='inner')
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return clusters
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if __name__ == '__main__':
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df = load_densities()
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cl = load_clusters()
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df['td_rank'] = df.term_density.rank()
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df['ad_rank'] = df.author_density.rank()
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df['td_percentile'] = df.td_rank / df.shape[0]
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df['ad_percentile'] = df.ad_rank / df.shape[0]
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df = df.merge(cl, on='subreddit',how='inner')
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term_cluster_density = df.groupby('term_cluster').agg({'td_rank':['mean','min','max'],
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'ad_rank':['mean','min','max'],
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'td_percentile':['mean','min','max'],
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'ad_percentile':['mean','min','max'],
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'subreddit':['count']})
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author_cluster_density = df.groupby('author_cluster').agg({'td_rank':['mean','min','max'],
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'ad_rank':['mean','min','max'],
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'td_percentile':['mean','min','max'],
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'ad_percentile':['mean','min','max'],
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'subreddit':['count']})
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# which clusters have the most term_density?
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term_cluster_density.iloc[term_cluster_density.td_rank['mean'].sort_values().index]
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# which clusters have the most author_density?
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term_cluster_density.iloc[term_cluster_density.ad_rank['mean'].sort_values(ascending=False).index].loc[term_cluster_density.subreddit['count'] >= 5][0:20]
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high_density_term_clusters = term_cluster_density.loc[(term_cluster_density.td_percentile['mean'] > 0.75) & (term_cluster_density.subreddit['count'] > 5)]
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# let's just use term density instead of author density for now. We can do a second batch with author density next.
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chosen_clusters = high_density_term_clusters.sample(3,random_state=random)
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cluster_info = df.loc[df.term_cluster.isin(chosen_clusters.index.values)]
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chosen_subreddits = cluster_info.subreddit.values
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dataset = ds.dataset("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet",format='parquet')
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comments = dataset.to_table(filter=ds.field("subreddit").isin(chosen_subreddits),columns=['id','subreddit','author','CreatedAt'])
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comments = comments.to_pandas()
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comments['week'] = comments.CreatedAt.dt.date - pd.to_timedelta(comments['CreatedAt'].dt.dayofweek, unit='d')
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author_timeseries = comments.loc[:,['subreddit','author','week']].drop_duplicates().groupby(['subreddit','week']).count().reset_index()
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for clid in chosen_clusters.index.values:
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ts = pd.read_feather(f"data/ts_term_cluster_{clid}.feather")
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pn.options.figure_size = (11.7,8.27)
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p = pn.ggplot(ts)
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p = p + pn.geom_line(pn.aes('week','value',group='subreddit'))
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p = p + pn.facet_wrap('~ subreddit')
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p.save(f"plots/ts_term_cluster_{clid}.png")
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fig, ax = pyplot.subplots(figsize=(11.7,8.27))
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g = sns.FacetGrid(ts,row='subreddit')
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g.map_dataframe(sns.scatterplot,'week','value',data=ts,ax=ax)
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