X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/811a0d87c4d394c2c7849a613f6aec2d81e49138..07b0dff9bc0dae2ab6f7fb7334007a5269a512ad:/timeseries/choose_clusters.py

diff --git a/timeseries/choose_clusters.py b/timeseries/choose_clusters.py
deleted file mode 100644
index c801379..0000000
--- a/timeseries/choose_clusters.py
+++ /dev/null
@@ -1,96 +0,0 @@
-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)