groceryheist/cdsc_reddit
1
0
Fork 0
Code

lsi support for weekly similarities

Browse Source
This commit is contained in:
Nathan TeBlunthuis 2021-08-11 22:48:33 -07:00
parent b7c39a3494
commit 541e125b28
7 changed files with 95 additions and 38 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
clustering/hdbscan_clustering.py
View File

@ -18,12 +18,12 @@ def test_select_hdbscan_clustering():
# cluster_selection_epsilons=[0,0.05,0.1,0.15],
# cluster_selection_methods=['eom','leaf'],
# lsi_dimensions='all')
inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI"
outpath = "test_hdbscan";
min_cluster_sizes=[2,3,4];
min_samples=[1,2,3];
cluster_selection_epsilons=[0,0.1,0.3,0.5];
cluster_selection_methods=['eom'];
cluster_selection_methods=[1];
lsi_dimensions='all'
gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
gs.run(20)
@ -120,7 +120,7 @@ def run_hdbscan_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], mi
map(int,min_cluster_sizes),
map(int,min_samples),
map(float,cluster_selection_epsilons),
map(float,cluster_selection_methods))
cluster_selection_methods)
obj.run()
obj.save(savefile)

6
clustering/hdbscan_clustering_lsi.py
View File

@ -67,7 +67,7 @@ class _hdbscan_lsi_grid_sweep(grid_sweep):
s += f"_lsi-{self.lsi_dim}"
return s
def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom'],lsi_dimensions='all'):
def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=[1],lsi_dimensions='all'):
"""Run hdbscan clustering once or more with different parameters.
Usage:
@ -90,8 +90,8 @@ def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2]
list(map(int,min_cluster_sizes)),
list(map(int,min_samples)),
list(map(float,cluster_selection_epsilons)),
cluster_selection_methods
)
cluster_selection_methods)
obj.run(10)
obj.save(savefile)

3
clustering/lsi_base.py
View File

@ -18,10 +18,11 @@ class lsi_grid_sweep(grid_sweep):
self.subsweep = subsweep
inpath = Path(inpath)
if lsi_dimensions == 'all':
lsi_paths = list(inpath.glob("*"))
lsi_paths = list(inpath.glob("*.feather"))
else:
lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions]
print(lsi_paths)
lsi_nums = [int(p.stem) for p in lsi_paths]
self.hasrun = False
self.subgrids = [self.subsweep(lsi_path, outpath, lsi_dim, *args, **kwargs) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]

11
clustering/pick_best_clustering.py Normal file → Executable file
View File

@ -1,11 +1,12 @@
#!/usr/bin/env python3
import fire
import pandas as pd
from pathlib import Path
import shutil
selection_data="/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv"
selection_data="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/clustering/comment_authors_compex_LSI/selection_data.csv"
outpath = 'test_best.feather'
min_clusters=50; max_isolates=5000; min_cluster_size=2
min_clusters=50; max_isolates=7500; min_cluster_size=2
# pick the best clustering according to silhouette score subject to contraints
def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min_cluster_size):
@ -18,11 +19,15 @@ def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min
df.loc[df.n_isolates_0,'n_isolates'] = 0
df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l))
best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)].iloc[df.shape[1]]
best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)]
best_cluster = best_cluster.iloc[0]
best_lsi_dimensions = best_cluster.lsi_dimensions
print(best_cluster.to_dict())
best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather")
shutil.copy(best_path,output)
print(f"lsi dimensions:{best_lsi_dimensions}")
if __name__ == "__main__":
fire.Fire(pick_best_clustering)

8
similarities/similarities_helper.py
View File

@ -97,6 +97,7 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
'relative_tf':ds.field('relative_tf').cast('float32'),
'tf_idf':ds.field('tf_idf').cast('float32')}
print(projection)
df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
@ -240,7 +241,6 @@ def test_lsi_sims():
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
# first compute the lsi of the matrix
# then take the column similarities
print("running LSI",flush=True)
if type(n_components) is int:
n_components = [n_components]
@ -249,10 +249,14 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
svd_components = n_components[0]
if lsi_model_load is not None:
if lsi_model_load is not None and Path(lsi_model_load).exists():
print("loading LSI")
mod = pickle.load(open(lsi_model_load ,'rb'))
lsi_model_save = lsi_model_load
else:
print("running LSI",flush=True)
svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
mod = svd.fit(tfidfmat.T)

22
similarities/tfidf.py
View File

@ -4,7 +4,7 @@ from pyspark.sql import functions as f
from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
spark = SparkSession.builder.getOrCreate()
spark = SparkSession.builder.getOrCreate()y
df = spark.read.parquet(inpath)
@ -26,11 +26,12 @@ def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
topN=None,
included_subreddits=None):
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
return tfidf(inpath,
outpath,
topN,
'author',
@ -38,11 +39,12 @@ def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comm
included_subreddits=included_subreddits
)
def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
topN=None,
included_subreddits=None):
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
return tfidf(inpath,
outpath,
topN,
'term',
@ -50,11 +52,12 @@ def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/commen
included_subreddits=included_subreddits
)
def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
topN=None,
included_subreddits=None):
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
return tfidf_weekly(inpath,
outpath,
topN,
'author',
@ -62,12 +65,13 @@ def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfi
included_subreddits=included_subreddits
)
def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
topN=None,
included_subreddits=None):
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
return tfidf_weekly(inpath,
outpath,
topN,
'term',

75
similarities/weekly_cosine_similarities.py Normal file → Executable file
View File

@ -1,3 +1,4 @@
#!/usr/bin/env python3
from pyspark.sql import functions as f
from pyspark.sql import SparkSession
from pyspark.sql import Window
@ -8,17 +9,18 @@ import pandas as pd
import fire
from itertools import islice, chain
from pathlib import Path
from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities
from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
from scipy.sparse import csr_matrix
from multiprocessing import Pool, cpu_count
from functools import partial
# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet"
# tfidf_path = infile
# min_df=None
# max_df = None
# topN=100
# term_colname='author'
infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet"
tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
min_df=None
included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
max_df = None
topN=100
term_colname='author'
# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
# included_subreddits=None
@ -34,7 +36,7 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
max_df=max_df,
included_subreddits=included_subreddits,
topN=topN,
week=week.isoformat(),
week=week,
rescale_idf=False)
tfidf_colname='tf_idf'
@ -42,7 +44,7 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
print('computing similarities')
sims = simfunc(mat.T)
sims = simfunc(mat)
del mat
sims = pd.DataFrame(sims)
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
@ -53,14 +55,28 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
def pull_weeks(batch):
return set(batch.to_pandas()['week'])
# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs):
term_colname= kwargs.get('term_colname')
#lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
# simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model)
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500, simfunc=column_similarities):
print(outfile)
# do this step in parallel if we have the memory for it.
# should be doable with pool.map
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet(tfidf_path)
# load subreddits + topN
subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
subreddit_names = subreddit_names.sort_values("subreddit_id")
nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
@ -68,7 +84,7 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
spark.stop()
print(f"computing weekly similarities")
week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
pool = Pool(cpu_count())
@ -77,8 +93,8 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500):
return cosine_similarities_weekly(infile,
outfile,
'author',
min_df,
@ -86,8 +102,8 @@ def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_s
included_subreddits,
topN)
def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
return cosine_similarities_weekly(infile,
outfile,
'term',
min_df,
@ -95,6 +111,33 @@ def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_
included_subreddits,
topN)
def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=None,n_components=100,lsi_model=None):
return cosine_similarities_weekly_lsi(infile,
outfile,
'author',
min_df,
max_df,
included_subreddits,
topN,
n_components=n_components,
lsi_model=lsi_model)
def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None):
return cosine_similarities_weekly_lsi(infile,
outfile,
'term',
min_df,
max_df,
included_subreddits,
topN,
n_components=n_components,
lsi_model=lsi_model)
if __name__ == "__main__":
fire.Fire({'authors':author_cosine_similarities_weekly,
'terms':term_cosine_similarities_weekly})
'terms':term_cosine_similarities_weekly,
'authors-lsi':author_cosine_similarities_weekly_lsi,
'terms-lsi':term_cosine_similarities_weekly
})