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[cdsc_reddit.git] / timeseries / choose_clusters.py
1 from pyarrow import dataset as ds
2 import numpy as np
3 import pandas as pd
4 import plotnine as pn
5 random = np.random.RandomState(1968)
6
7 def load_densities(term_density_file="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
8                    author_density_file="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather"):
9
10     term_density = pd.read_feather(term_density_file)
11     author_density = pd.read_feather(author_density_file)
12
13     term_density.rename({'overlap_density':'term_density','index':'subreddit'},axis='columns',inplace=True)
14     author_density.rename({'overlap_density':'author_density','index':'subreddit'},axis='columns',inplace=True)
15
16     density = term_density.merge(author_density,on='subreddit',how='inner')
17
18     return density
19
20 def load_clusters(term_clusters_file="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
21                   author_clusters_file="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather"):
22     term_clusters = pd.read_feather(term_clusters_file)
23     author_clusters = pd.read_feather(author_clusters_file)
24
25     # rename, join and return
26     term_clusters.rename({'cluster':'term_cluster'},axis='columns',inplace=True)
27     author_clusters.rename({'cluster':'author_cluster'},axis='columns',inplace=True)
28
29     clusters = term_clusters.merge(author_clusters,on='subreddit',how='inner')
30
31     return clusters
32
33 if __name__ == '__main__':
34
35     df = load_densities()
36     cl = load_clusters()
37
38     df['td_rank'] = df.term_density.rank()
39     df['ad_rank'] = df.author_density.rank()
40
41     df['td_percentile'] = df.td_rank / df.shape[0]
42     df['ad_percentile'] = df.ad_rank / df.shape[0]
43
44     df = df.merge(cl, on='subreddit',how='inner')
45
46     term_cluster_density = df.groupby('term_cluster').agg({'td_rank':['mean','min','max'],
47                                                          'ad_rank':['mean','min','max'],
48                                                          'td_percentile':['mean','min','max'],
49                                                            'ad_percentile':['mean','min','max'],
50                                                            'subreddit':['count']})
51                                                          
52
53     author_cluster_density = df.groupby('author_cluster').agg({'td_rank':['mean','min','max'],
54                                                          'ad_rank':['mean','min','max'],
55                                                          'td_percentile':['mean','min','max'],
56                                                            'ad_percentile':['mean','min','max'],
57                                                            'subreddit':['count']})
58                                                          
59     # which clusters have the most term_density?
60     term_cluster_density.iloc[term_cluster_density.td_rank['mean'].sort_values().index]
61
62     # which clusters have the most author_density?
63     term_cluster_density.iloc[term_cluster_density.ad_rank['mean'].sort_values(ascending=False).index].loc[term_cluster_density.subreddit['count'] >= 5][0:20]
64
65     high_density_term_clusters = term_cluster_density.loc[(term_cluster_density.td_percentile['mean'] > 0.75) & (term_cluster_density.subreddit['count'] > 5)]
66
67     # let's just use term density instead of author density for now. We can do a second batch with author density next.
68     chosen_clusters = high_density_term_clusters.sample(3,random_state=random)
69
70     cluster_info = df.loc[df.term_cluster.isin(chosen_clusters.index.values)]
71
72     chosen_subreddits = cluster_info.subreddit.values
73
74     dataset = ds.dataset("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet",format='parquet')
75     comments = dataset.to_table(filter=ds.field("subreddit").isin(chosen_subreddits),columns=['id','subreddit','author','CreatedAt'])
76
77     comments = comments.to_pandas()
78
79     comments['week'] = comments.CreatedAt.dt.date - pd.to_timedelta(comments['CreatedAt'].dt.dayofweek, unit='d')
80
81     author_timeseries = comments.loc[:,['subreddit','author','week']].drop_duplicates().groupby(['subreddit','week']).count().reset_index()
82
83     for clid in chosen_clusters.index.values:
84
85         ts = pd.read_feather(f"data/ts_term_cluster_{clid}.feather")
86
87         pn.options.figure_size = (11.7,8.27)
88         p = pn.ggplot(ts)
89         p = p + pn.geom_line(pn.aes('week','value',group='subreddit'))
90         p = p + pn.facet_wrap('~ subreddit')
91         p.save(f"plots/ts_term_cluster_{clid}.png")
92         
93
94         fig, ax = pyplot.subplots(figsize=(11.7,8.27))
95         g = sns.FacetGrid(ts,row='subreddit')
96         g.map_dataframe(sns.scatterplot,'week','value',data=ts,ax=ax)

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