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add code for pulling activity time series from parquet.

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Nate E TeBlunthuis 2021-03-24 16:08:57 -07:00
parent 06430903f0
commit 36cb0a5546
2 changed files with 133 additions and 0 deletions
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96
timeseries/choose_clusters.py Normal file
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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)

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timeseries/cluster_timeseries.py Normal file
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import pandas as pd
import numpy as np
from pyspark.sql import functions as f
from pyspark.sql import SparkSession
from choose_clusters import load_clusters, load_densities
import fire
from pathlib import Path
def main(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
author_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather",
term_densities_path="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
output="data/subreddit_timeseries.parquet"):
clusters = load_clusters(term_clusters_path, author_clusters_path)
densities = load_densities(term_densities_path, author_densities_path)
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
df = df.withColumn('week', f.date_trunc('week', f.col("CreatedAt")))
# time of unique authors by series by week
ts = df.select(['subreddit','week','author']).distinct().groupby(['subreddit','week']).count()
ts = ts.repartition('subreddit')
spk_clusters = spark.createDataFrame(clusters)
ts = ts.join(spk_clusters, on='subreddit', how='inner')
spk_densities = spark.createDataFrame(densities)
ts = ts.join(spk_densities, on='subreddit', how='inner')
ts.write.parquet(output, mode='overwrite')
if __name__ == "__main__":
fire.Fire(main)