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